Dr. Prasanna Ghimire is a Consultant Radiologist, clinician-researcher, and Associate Professor at Kathmandu University, based at Nepalgunj Medical College & Teaching Hospital, Kohalpur, Nepal.
With 20+ years of clinical and academic practice, he has authored 70+ peer-reviewed publications in journals including PLOS ONE, World Journal of Gastroenterology, Radiology Case Reports, BMC Infectious Diseases, and Current Oncology, achieving an h-index of 11 and an i10-index of 13 (Google Scholar, 979 citations).
He serves as Editor of the Nepalese Journal of Radiology, reviewer for 9 international journals, IRC Member at NGMC, and Advisor to the Nepal Medical Association. He has mentored 7+ postgraduate radiodiagnosis students.
Dr. Prasanna Ghimire is an Associate Professor of Radiology at Kathmandu University, based at Nepalgunj Medical College and Teaching Hospital (NGMC-TH). With over two decades of academic and clinical practice, he has built one of the most interdisciplinary profiles in Nepalese medicine — spanning advanced diagnostic imaging, environmental and occupational health, and rural development.
A government scholarship recipient, Dr. Ghimire completed fully-funded specialist training in Radiodiagnosis and Nuclear Medicine at Wuhan University, China (2008–2011). He undertook specialist musculoskeletal radiology training at Samsung Medical Center, Seoul in 2016, where he received an International Grant Award and Silver Medal at the 47th Korean Society of Ultrasound in Medicine Congress.
His research output spans 70+ peer-reviewed publications in international journals including PLOS ONE, World Journal of Gastroenterology, Radiology Case Reports, BMC Infectious Diseases, and Current Oncology. He serves as Editor of the Nepalese Journal of Radiology and reviewer for nine international journals. He is an active member of the Institutional Review Committee at NGMC and the undergraduate medical Subject Committee at Kathmandu University.
Beyond the hospital, Dr. Ghimire has devoted over 20 years to community health advisory roles with women's rehabilitation and rural development organizations. His blog Prasun's Words reflects wide-ranging intellectual curiosity — from soil pollution policy to rural economics.
Permanent Registration, Nepal Medical Council (2004)
Specialty Registration – Radiodiagnosis & Nuclear Medicine, NMC (2011)
Associate Professor, Kathmandu University (at NGMC-TH) — 2019–present
Editor, Nepalese Journal of Radiology (2018–2025)
IRC Member, NGMC (2023–present)
Undergraduate Medical Subject Committee, Kathmandu University (2023–present)
Thesis Supervisor, 7+ postgraduate radiodiagnosis students
Advisor, Nepal Medical Association – Bheri Branch (2024–present)
Interviewer, Public Service Commission, Nepal
External Examiner, Radiodiagnosis – Kathmandu University
External Expert, Karnali Academy & Rapti Academy of Health Sciences
Over two decades of sustained community health advisory engagement — working with women's rehabilitation organisations, rural development centres, and community health groups across Nepal.
My notes on topics in healthcare management, rural development, and environmental and occupational health. Open a topic to read it.
Public health, hospitals, finance, quality and research
Public health is the science and art of preventing disease, prolonging life and promoting health through the organized efforts of society (Winslow, 1920). Its unit of concern is the population or community rather than the individual patient, and its tools are prevention, promotion and policy rather than only treatment. The modern understanding adds equity, intersectoral action and attention to social determinants.
Assessment: monitoring health status, diagnosing community health problems, surveillance and research.
Policy development: mobilizing partnerships, developing plans and policies, enforcing laws and regulations.
Assurance: ensuring a competent workforce, access to services, quality, and evaluation of effectiveness.
The Lalonde Report (1974) divided the determinants of health into four fields: human biology, environment, lifestyle, and health care organization. The Dahlgren and Whitehead rainbow model places the individual and fixed factors (age, sex, genes) at the centre, surrounded by layers of lifestyle, social and community networks, living and working conditions, and finally general socio-economic, cultural and environmental conditions. The practical lesson is that most of what produces health lies outside the health sector.
| Level | Aim | Examples |
|---|---|---|
| Primordial | Prevent emergence of risk factors | Tobacco-control law, food policy, urban planning for physical activity |
| Primary | Prevent onset of disease | Immunization, health education, sanitation, chlorination, contraception |
| Secondary | Early detection and prompt treatment | Screening (cervical VIA, BP), case finding, DOTS for new TB cases |
| Tertiary | Limit disability and rehabilitate | Physiotherapy after stroke, foot care in diabetes, vocational rehabilitation |
The Declaration of Alma-Ata (1978) defined PHC as essential health care made universally accessible to individuals and families through their full participation and at a cost the community and country can afford. Four guiding principles are equity (equitable distribution), community participation, intersectoral coordination, and appropriate technology. The eight elements are remembered as ELEMENTS: Education on prevailing health problems, Locally endemic disease control, Expanded immunization, Maternal and child health including family planning, Essential drugs, Nutrition and food supply, Treatment of common diseases and injuries, and Safe water and sanitation. The Ottawa Charter (1986) set the health promotion agenda: build healthy public policy, create supportive environments, strengthen community action, develop personal skills, and reorient health services. The Astana Declaration (2018) reaffirmed PHC as the foundation of universal health coverage and the Sustainable Development Goals (SDG 3).
A health program is an organized set of activities directed at specific objectives within a time frame and budget. Management follows a cycle: situation analysis, priority setting, objective setting, strategy selection, planning and budgeting, implementation (organizing, staffing, directing, coordinating), monitoring and supervision, evaluation, and then feedback into the next planning round. The cycle is continuous; weaknesses found in evaluation are the starting point for the next situation analysis.
Situation analysis describes the health problem, the population, existing services and resources. Information comes from census, routine HMIS data, surveys (such as the Nepal Demographic and Health Survey), facility assessments and community consultation. Tools include the problem tree (causes and effects of a core problem), SWOT (internal strengths and weaknesses, external opportunities and threats), PESTEL (political, economic, social, technological, environmental, legal), and stakeholder analysis (interest versus influence grid).
Problems are ranked by magnitude (how many affected), severity (case fatality, disability), preventability or availability of effective interventions, community concern, and cost and feasibility. The Hanlon method gives a numerical score: Priority score = (A + B) x C x D, where A is size of the problem, B is seriousness, C is effectiveness of the intervention, and D is PEARL feasibility (Propriety, Economics, Acceptability, Resources, Legality). Other methods are the nominal group technique and the Delphi technique.
Goals are broad and long-term; objectives are specific, measurable statements. Good objectives are SMART: Specific, Measurable, Achievable, Relevant, Time-bound. Example: to raise facility delivery in the district from 62 percent to 80 percent within three years.
| Logframe level | Narrative summary | Indicators | Means of verification | Assumptions |
|---|---|---|---|---|
| Goal / Impact | Long-term change in health status | Maternal mortality ratio | Census, DHS, surveys | Policy remains supportive |
| Purpose / Outcome | Change from services used | Skilled birth attendance coverage | HMIS, household survey | Community accepts services |
| Outputs | Direct products of activities | Birthing centres functional, staff trained | Program reports | Staff retained |
| Activities / Inputs | Tasks and resources | Training held, budget spent | Financial and activity records | Funds released on time |
Work plan and Gantt chart: lists activities, responsible persons, timeline and resources; the Gantt chart shows activities against time.
Budgeting: line-item, program (activity-based) and performance budgets; costing must include recurrent and capital items.
Organizing and staffing: clear job descriptions, span of control, delegation, coordination mechanisms with local governments and partners.
Supportive supervision: a facilitative, problem-solving visit that uses a checklist, observes practice, gives on-the-spot coaching and feedback, as opposed to fault-finding inspection.
Logistics: forecasting, procurement, storage, distribution and a logistics management information system (LMIS); stock-outs undermine programs faster than almost any other failure.
Community participation: progresses from passive information-giving to consultation, joint decision-making and community ownership; Female Community Health Volunteers (FCHVs) are Nepal's flagship example.
Monitoring is the routine, continuous tracking of inputs, activities and outputs to see whether implementation follows the plan. Evaluation is the periodic, systematic assessment of relevance, effectiveness, efficiency, impact and sustainability. Indicators are expressed along the results chain: input, process, output, outcome, impact. Good indicators are valid, reliable, sensitive, specific and feasible (the same SMART logic).
| Type of evaluation | Question asked |
|---|---|
| Formative / needs | Is the design appropriate and feasible before launch? |
| Process | Are activities being carried out as planned, with what coverage and quality? |
| Outcome | Did the target group change in knowledge, behaviour or service use? |
| Impact | Did health status change and can it be attributed to the program? |
| Economic | Was the program worth its cost (cost-effectiveness, cost-benefit)? |
In Nepal, the electronic HMIS on DHIS2 collects monthly facility data and feeds review meetings at municipal, provincial and federal levels. Data quality dimensions are completeness, timeliness, accuracy and consistency. Periodic surveys (NDHS, Nepal Multiple Indicator Cluster Survey, Service Provision Assessment) complement routine data.
| Program | Key strategies and points to remember |
|---|---|
| Safe Motherhood and newborn health | Aama programme (cash incentive for institutional delivery and four ANC visits), skilled birth attendants, birthing centres, basic and comprehensive emergency obstetric and newborn care, Safe Motherhood and Reproductive Health Rights Act 2075 |
| Expanded Programme on Immunization | Started 1979; routine schedule includes BCG, pentavalent, OPV/IPV, PCV, rotavirus, measles-rubella, JE, TCV; fully immunized child concept; cold chain; Reaching Every District approach |
| Child health | Community-Based Integrated Management of Neonatal and Childhood Illness (CB-IMNCI), Vitamin A supplementation, deworming, ORS and zinc |
| Nutrition | Multi-sector Nutrition Plan; growth monitoring, infant and young child feeding, micronutrient supplementation, community management of acute malnutrition |
| Family planning | Short-acting, long-acting reversible methods and permanent methods; demand generation and outreach; unmet need is the key indicator |
| Communicable diseases | National TB Program (DOTS, case finding, treatment success), malaria and vector-borne disease elimination strategy, HIV/AIDS (testing, ART, PMTCT), zoonoses and outbreak response |
| Non-communicable diseases | Multisectoral action plan, package of essential NCD interventions (PEN), tobacco and alcohol control, mental health services integration |
| FCHV program | Launched 1988; volunteers deliver community-level promotion, distribution and referral; recognized for the fall in child mortality |
Economics studies how societies allocate scarce resources among competing uses. Scarcity forces choice, and every choice has an opportunity cost: the value of the next best alternative forgone. Health economics applies these ideas to health, health care and the factors that affect them. It asks three questions: what is produced (which services), how is it produced (efficiently), and for whom (equitably).
Microeconomics deals with individual markets, consumers and providers; macroeconomics with aggregates such as national health expenditure and growth.
Positive economics describes what is; normative economics says what ought to be.
Efficiency: technical efficiency (maximum output from given inputs), allocative efficiency (the right mix of outputs for society's preferences), and Pareto efficiency (no one can gain without another losing).
Equity: horizontal (equal treatment of equals) and vertical (unequal treatment of unequals according to need).
Kenneth Arrow argued that health care departs from the competitive model because of uncertainty in demand, information asymmetry between provider and patient, externalities, barriers to entry through professional licensing, and the presence of trust and ethical codes. Consequences include market failure and the need for government regulation or insurance.
Information asymmetry leads to supplier-induced demand, where the provider influences the quantity consumed.
Externalities: vaccination and infection control benefit others (positive); pollution and tobacco smoke harm others (negative).
Public goods (health information, vector control) are non-rival and non-excludable and are under-supplied by markets.
Merit goods are goods society wants consumed regardless of ability to pay, such as immunization.
Moral hazard (insured people use more care or take less precaution) and adverse selection (high-risk people are more likely to buy insurance) distort insurance markets.
The law of demand says that, other things equal, quantity demanded falls as price rises. Demand for health care is derived from the demand for health. Grossman's model (1972) treats health as a durable capital stock that depreciates with age and is produced using medical care, time and education; people demand health both as a consumption good and as an investment. Determinants include price, income, education, age, need, insurance, travel distance and time cost, and quality.
Price elasticity of demand (PED) = percentage change in quantity demanded divided by percentage change in price. Demand is inelastic when |PED| is below 1, which is typical for necessary care. The RAND Health Insurance Experiment found that cost-sharing reduced use of both effective and ineffective services, with a PED of about -0.2. Income elasticity is positive for most health care, and above 1 for some luxury services. Cross elasticity describes substitutes and complements.
Supply depends on input prices, technology, number of providers and regulation. A health production function links inputs (staff, equipment, drugs) to outputs (visits, deliveries) and ultimately outcomes. Costs are classified as follows.
| Cost concept | Meaning |
|---|---|
| Fixed cost | Does not change with volume in the short run (building, salaried staff) |
| Variable cost | Changes with volume (drugs, consumables) |
| Total cost | Fixed + variable |
| Average cost | Total cost divided by output |
| Marginal cost | Additional cost of one more unit of output |
| Opportunity cost | Value of the best alternative use of the resource |
| Sunk cost | Already spent and unrecoverable; should be ignored in new decisions |
Economies of scale arise when average cost falls as output expands, for example in large laboratories or in bulk procurement. Diseconomies appear when the organization becomes too large to manage.
Economic evaluation compares costs and consequences of alternatives. A full evaluation compares at least two options and looks at both cost and effect. The four main techniques are summarized below.
| Technique | Costs measured in | Outcomes measured in | Result |
|---|---|---|---|
| Cost-minimization (CMA) | Money | Assumed identical | Cheapest option |
| Cost-effectiveness (CEA) | Money | Natural units (cases averted, life-years gained) | Cost per unit of effect |
| Cost-utility (CUA) | Money | QALYs or DALYs | Cost per QALY gained or DALY averted |
| Cost-benefit (CBA) | Money | Money | Net benefit, benefit-cost ratio |
ICER = (C1 - C0) / (E1 - E0), the extra cost per extra unit of effect of the new option compared with the old.
QALY = years lived x quality weight (0 = death, 1 = full health).
DALY = YLL + YLD, where YLL = years of life lost to premature death and YLD = years lived with disability (prevalence x disability weight).
Present value = Future value / (1 + r)^t; discounting reflects time preference (costs and benefits are commonly discounted at 3 percent in many guidelines).
Net present value = sum of discounted benefits minus sum of discounted costs; benefit-cost ratio = PV benefits / PV costs (accept when above 1).
Sensitivity analysis (one-way, multi-way, probabilistic) tests how results change when uncertain assumptions are varied. Interventions below a cost-effectiveness threshold are considered good value; the older WHO-CHOICE rule of one to three times GDP per capita per DALY averted has been criticized, and many countries now use locally derived thresholds.
Sources of funds: general taxation, social health insurance, private insurance, out-of-pocket payment, external aid. Out-of-pocket spending is regressive and causes catastrophic expenditure and impoverishment.
National Health Accounts track total health expenditure by source, financing agent, provider and function. A common measure is total health expenditure as a percentage of GDP and per capita.
Inequality is measured by the Lorenz curve and Gini coefficient, and the concentration index for health variables.
The Preston curve shows that life expectancy rises with income per capita but with diminishing returns. The Commission on Macroeconomics and Health (2001) argued that investing in health promotes economic growth.
Nepal funds health mainly through government budget, household out-of-pocket payments (over half of current health expenditure in recent National Health Accounts) and external development partners. Policy responses include free essential health services, the Aama programme, the Social Health Security Program, and the Poor Citizen Treatment Fund. Challenges are fragmented financing, limited fiscal space, and weak purchasing. Verify the latest percentages from the most recent National Health Accounts report before quoting them in an exam.
A health system includes all organizations, people and actions whose primary intent is to promote, restore or maintain health (WHO, 2000). The three overall goals are improved health (level and equity), responsiveness, and fair financial contribution. WHO's framework describes six building blocks: service delivery, health workforce, health information systems, access to essential medicines and technologies, financing, and leadership and governance. Their outputs lead to access, coverage, quality and safety, and finally to the goals of improved health, responsiveness, financial risk protection and efficiency.
Functions per World Health Report 2000: stewardship, financing (collection, pooling, purchasing), resource generation, and service provision.
Hsiao's control knobs: financing, payment, organization, regulation and behaviour.
Universal Health Coverage (UHC) has three dimensions, often shown as a cube: population covered, services covered, and proportion of cost covered.
| Model | Financing | Features | Examples |
|---|---|---|---|
| Beveridge (national health service) | General taxation | Government owns and runs providers, universal coverage | United Kingdom, Sweden |
| Bismarck (social insurance) | Employer and employee contributions to sickness funds | Multiple non-profit funds, private providers | Germany, Japan |
| National Health Insurance | Single public insurer funded by taxes or premiums | Private providers, public payer | Canada, Korea, Taiwan |
| Out-of-pocket / market | Direct payment by users | Large private sector, little protection | Many low-income settings |
Roemer's matrix classifies systems by economic level and political ideology (entrepreneurial, welfare-oriented, comprehensive, socialist). Real systems are hybrids; Nepal combines public provision, a large private sector, donor funding, and a new social health insurance scheme.
Reforms aim to improve equity, efficiency and quality. Typical reform directions are decentralization (devolution, deconcentration, delegation, privatization), public-private partnership, contracting out, results-based or performance-based financing, essential service package definition, and strengthening of primary care. Decentralization is judged on accountability, local capacity and financing flows; transfer of functions without funds and skills creates new problems.
A policy is a statement of intent about goals and the means to achieve them, made by government or organizations. Health policy covers decisions that affect health institutions, services and financing. The policy cycle includes problem identification and agenda setting, policy formulation, adoption or legitimation, implementation, and evaluation, with feedback at each stage.
Kingdon's multiple streams: problems, policies and politics stream converge when a policy window opens, with a policy entrepreneur pushing the idea.
Walt and Gilson's policy triangle: content, process, context and actors; stresses that most policy analysis neglects actors.
Stakeholder analysis: identify stakeholders, interests, influence and positions, then plan engagement.
Implementation gap: policies fail through weak capacity, unclear roles, lack of budget and resistance; top-down and bottom-up approaches differ in how they treat front-line discretion.
Evidence-informed policy: research, routine data and local knowledge all feed decisions, but political feasibility often decides.
The Constitution of Nepal (2015) guarantees the right to basic health services free of cost as a fundamental right, the right to access to emergency health care, and equal access to health care. It created a federal structure with three tiers: federal, seven provinces, and 753 local governments, each with defined powers over health. Local governments run basic health services and public health, provinces manage hospitals and provincial programs, and the federal level handles policy, standards, specialized hospitals and international coordination.
| Instrument | Key point |
|---|---|
| National Health Policy 2019 | Aligned with the constitution and SDGs; promotes universal access, quality, multisectoral action and health insurance |
| Nepal Health Sector Strategy 2015-2020 and the successor strategic plan 2023-2030 | Strategic direction for service delivery, systems strengthening and equity across the health sector |
| Public Health Service Act 2018 | Operationalizes rights to basic health and emergency services, regulates public health services |
| Safe Motherhood and Reproductive Health Rights Act 2018 | Rights to safe motherhood, safe abortion and reproductive health |
| Social Health Security Act 2017 | Legal basis for the national health insurance scheme and Health Insurance Board |
| Local Government Operation Act 2017 | Roles of municipalities and rural municipalities in health |
| Health Service Act 1997 (2053) and Regulations | Organization and service conditions of health workers |
Structures: Ministry of Health and Population, Department of Health Services, divisions and centres, provincial health directorates, hospitals and local health facilities including health posts, primary health care centres and community health units. Challenges include coordination between three tiers, workforce distribution, medicine supply, quality and out-of-pocket spending.
The WHO describes a hospital as an integral part of a social and medical organization whose function is to provide complete health care, curative and preventive, to the population, and which also serves as a centre for training of health workers and for biosocial research. Functions are patient care (outpatient, inpatient, emergency, diagnostic), prevention and promotion, education and training, and research.
By ownership: government, private, community, trust, teaching.
By size (beds): small (under 50 or 100), medium, large, tertiary and referral.
By level: primary (district), secondary, tertiary and specialized hospitals.
By service: general, specialty (eye, maternity, psychiatric, cancer), and teaching hospitals.
Planning starts with a needs assessment of the catchment population, disease burden, existing services, and resources. The site should be accessible, with adequate water, power, drainage and room for expansion. Design considers functional relationships: emergency near radiology and operating theatre, wards close to supporting services, infection control zoning, separation of clean and dirty traffic, and universal accessibility.
A common estimate of beds needed: Beds = (Annual admissions x Average length of stay) / (365 x Desired bed occupancy rate). Example: 6,000 admissions, ALOS 4 days, target occupancy 80 percent gives 24,000 / 292 = about 82 beds.
A hospital is a professional bureaucracy with dual lines of authority: administrative and clinical. The governing board or development committee sets policy, the medical director or chief executive manages operations, and department heads supervise clinical and support services. Typical divisions are medical, nursing, administration and finance, and support services. Effective management uses clear job descriptions, standard operating procedures, committees (infection control, drugs and therapeutics, quality, medical records, mortality review) and regular performance review.
| Service | Key management points |
|---|---|
| Outpatient department (OPD) | Registration, triage, appointment systems, waiting time, patient flow, privacy; first point of contact |
| Emergency | Triage, resuscitation capacity, 24-hour staffing, links to ICU and OT, disaster readiness |
| Inpatient wards | Nurse staffing ratios, bed allocation, discharge planning, patient safety and infection control |
| Operating theatre | Scheduling, sterility zones, WHO surgical safety checklist, turnover time |
| Intensive care | High nurse ratio, protocols, ventilator and monitor maintenance, infection surveillance |
| Laboratory and radiology | Quality control, turnaround time, safety, equipment calibration |
| Pharmacy | Essential medicine list, formulary, storage, dispensing, rational drug use |
| CSSD | Cleaning, packing, sterilization and traceability of instruments |
| Medical records | Unique patient identification, confidentiality, coding with ICD, retention rules |
| Dietary, laundry, housekeeping, mortuary | Hygiene standards, therapeutic diets, linen cycle, dignity and legal documentation |
| Indicator | Formula |
|---|---|
| Bed occupancy rate (BOR) | Inpatient days in period / (Available beds x days in period) x 100 |
| Average length of stay (ALOS) | Total inpatient days / Total discharges (including deaths) |
| Bed turnover rate (BTR) | Discharges / Average available beds |
| Turnover interval (TOI) | (Available bed days - Inpatient days) / Discharges |
| Hospital death rate | Deaths in hospital / Discharges x 100 |
| Net death rate | Deaths after 48 hours / Discharges x 100 |
| Hospital infection rate | New hospital-acquired infections / Discharges x 100 |
| Cesarean section rate | Cesarean deliveries / Total deliveries x 100 |
An efficient hospital typically runs a BOR of about 80 to 85 percent; very high occupancy suggests overcrowding and risk, and very low occupancy signals under-use. BOR, ALOS and BTR are linked: BOR = (BTR x ALOS) / days in period.
Inventory control: ABC analysis (by value), VED analysis (vital, essential, desirable), EOQ (economic order quantity), reorder level and buffer stock; first-expiry-first-out for drugs.
Equipment management: needs assessment, procurement, installation, user training, preventive maintenance, calibration, condemnation and replacement.
Health care waste: segregate at source using colour codes (for example yellow for infectious, red for contaminated plastics, blue for sharps and glass according to local guidelines), safe collection, autoclave or incineration, sharps pit, and safe final disposal.
Infection prevention: hand hygiene (WHO five moments), standard and transmission-based precautions, personal protective equipment, sterilization, surveillance of hospital-acquired infections such as catheter-associated UTI, surgical site infection and ventilator-associated pneumonia.
Patient safety programs use incident reporting, root-cause analysis, checklists, medication safety practices (right patient, drug, dose, route, time) and falls and pressure-ulcer prevention. A hospital information system integrates registration, billing, clinical records, laboratory, pharmacy and stores. A hospital disaster plan defines command structure, triage areas, surge capacity, communication, evacuation, and regular drills; the plan must address both internal disasters (fire, power failure) and external mass-casualty incidents.
Health financing is the system by which money is raised, pooled and used to buy services. Its three core functions are revenue collection, pooling of funds, and purchasing of services. The objectives are financial protection, equity, efficiency, and sustainability on the path to universal health coverage.
Revenue sources: general taxes (direct and indirect), earmarked or sin taxes (tobacco, alcohol, sugar), payroll contributions to social insurance, voluntary private insurance premiums, user fees and out-of-pocket payment, external grants and loans, and innovative sources such as solidarity levies.
Pooling: combining prepaid funds across large populations so that the healthy subsidize the sick and the rich subsidize the poor. Fragmented small pools raise costs and reduce redistribution.
Purchasing: strategic purchasing links payment to need, performance and quality, in contrast to passive purchasing by historical budgets.
Out-of-pocket payment at the point of use is the least equitable way to finance health. Expenditure is called catastrophic when it exceeds a threshold of household income or capacity to pay (commonly 10 percent of total consumption or 40 percent of non-food spending). Impoverishment occurs when payment pushes a household below the poverty line. Financial protection is a core SDG 3.8 indicator. Targeting subsidies to the poor, exempting basic services, and pre-payment all increase protection.
Insurance transfers financial risk from an individual to a pool in return for a premium. Insurable risks are random, measurable and large enough to matter. The law of large numbers lets insurers predict claims and set premiums; actuarial calculation uses expected claims plus administrative cost and margin.
| Term | Meaning |
|---|---|
| Premium | Regular amount paid for coverage |
| Deductible | Amount the insured pays before insurance begins to pay |
| Co-payment | Fixed amount paid per service |
| Co-insurance | Percentage of cost shared by the insured |
| Ceiling or cap | Maximum benefit payable in a period |
| Gatekeeping | Referral from primary care required before specialist care |
| Risk pooling | Sharing of risk among many members |
| Adverse selection | Higher-risk people join more readily; leads to premium spiral |
| Moral hazard | Insured behave in ways that increase claims |
| Cream skimming | Insurers prefer low-risk members |
Social (national) health insurance: compulsory, income-related contributions, benefits by need, government regulated; may be extended to the informal sector with subsidies.
Private voluntary insurance: risk-rated premiums, choice of plan, covers those who can pay; tends to leave out high-risk and poor people.
Community-based health insurance (CBHI): voluntary schemes run by communities or cooperatives; small risk pools, vulnerable to adverse selection and low sustainability.
Tax-funded universal systems: services free or nearly free at the point of use.
| Method | How it works | Advantages | Disadvantages |
|---|---|---|---|
| Fee for service | Payment per item | Productivity, access | Over-servicing, cost escalation |
| Capitation | Fixed amount per enrolled person | Cost control, prevention incentive | Under-provision, risk selection |
| Salary | Fixed pay per period | Predictable, simple | Low productivity incentive |
| Case-based / DRG | Fixed price per case group | Efficiency, transparency | Early discharge, upcoding |
| Per diem | Payment per bed-day | Simple | Encourages longer stays |
| Global budget | Lump sum for period | Strong cost control | Rationing, waiting lists |
| Pay for performance | Bonus for meeting targets | Quality focus | Gaming, measurement cost |
Nepal's Social Health Security Act (2017) established the Health Insurance Board and a national insurance program. Enrolment is by household, with a premium for a family of five and an additional amount per extra member, subsidies for poor and vulnerable groups, and a benefit ceiling per family per year (check the current premium and ceiling in the latest Board notice, since these have been revised). Care is accessed through the family's designated first service point, with referral to higher-level hospitals. Other financing mechanisms include the Aama programme (free delivery care and transport incentive), free basic health services, the Poor Citizen Treatment Fund for specified diseases, and fiscal grants to provinces and local governments.
Challenges: low enrolment and renewal, claim and reimbursement delays, adverse selection, limited provider capacity, weak purchasing capability, fragmentation of schemes, and continued reliance on out-of-pocket payment. Strategies: expand coverage with subsidies, improve information systems, introduce case-based payment, strengthen fraud control, and integrate vertical programs.
Human resource management (HRM) covers the whole set of activities for acquiring, maintaining and using people: planning, recruitment, selection, compensation, appraisal and employee relations. Human resource development (HRD) is the planned process of improving knowledge, skills, attitudes and performance of people and organizations through training, education, career development and organizational change. In health, the workforce is the main input, and the WHO suggested a threshold of 4.45 doctors, nurses and midwives per 1,000 population for progress toward the SDGs.
HR planning matches the supply of staff with the demand. Steps: analyse organizational goals, forecast demand (workload indicators of staffing need, service targets, ratios), analyse the current inventory and expected supply (attrition, retirement), identify gaps, and design recruitment, training or redeployment strategies. The WHO workforce-life-cycle approach considers entry, retention and exit of workers.
Job analysis: produces job description (duties) and job specification (qualifications).
Recruitment: sources internal and external; process of attracting suitable candidates.
Selection: screening, written tests, interviews (structured preferred), reference checks; validity and reliability are the key quality criteria.
Induction/orientation: familiarizes new staff with the organization, rules and culture, which improves retention.
Training needs assessment looks at organizational, task and individual levels, and identifies the gap between required and actual performance. Methods include on-the-job (coaching, mentoring, job rotation, supervised practice) and off-the-job (workshops, simulation, e-learning). Kirkpatrick's four levels evaluate training: reaction, learning, behaviour, results. Continuing professional development, licensing renewal with councils such as the Nepal Medical Council and Nepal Nursing Council, and structured career paths improve retention.
Performance appraisal assesses employee performance against standards to guide pay, promotion and development. Methods: graphic rating scales, behaviourally anchored rating scales (BARS), management by objectives (MBO), 360-degree feedback, and key performance indicators. Common errors include halo effect, leniency, central tendency and recency bias. Appraisal should be followed by feedback and a development plan.
| Theory | Core idea |
|---|---|
| Maslow's hierarchy | Needs ascend: physiological, safety, belonging, esteem, self-actualization |
| Herzberg two-factor | Hygiene factors prevent dissatisfaction (salary, conditions); motivators create satisfaction (achievement, recognition, growth) |
| McGregor Theory X and Y | Managers assume workers avoid work (X) or seek responsibility (Y) |
| Vroom expectancy | Motivation = expectancy x instrumentality x valence |
| Adams equity | People compare their input-output ratio with others |
| McClelland | Needs for achievement, affiliation and power |
Leadership theories progress from traits to behaviour (Ohio State, Blake and Mouton managerial grid), contingency (Fiedler, Hersey and Blanchard situational leadership), and transformational leadership. In health care, leaders also need clinical credibility, team-building skills and ethical judgement.
OD is a planned, system-wide, long-range effort, supported by top management, to improve an organization's effectiveness and health through planned interventions using behavioural science knowledge. It uses action research: diagnosis, data feedback, planning, intervention and evaluation, repeated cyclically.
Lewin's change model: unfreeze (create readiness), change (move), refreeze (stabilize).
Kotter's eight steps: create urgency, build a guiding coalition, form a vision, communicate, empower action, create short-term wins, consolidate gains, anchor changes in culture.
Resistance to change: causes include fear of loss, habit, poor communication and lack of trust; overcome through participation, communication, support and incentives.
OD interventions: team building, survey feedback, process consultation, role analysis, conflict resolution, sensitivity training, job redesign and strategic planning.
Organizational culture is the shared values, norms and assumptions; leaders shape it, and it determines how change is accepted.
Conflict management: avoiding, accommodating, competing, compromising, collaborating (Thomas-Kilmann styles).
Health workers are governed by the Health Service Act and Regulations, and federalism has moved many recruitment and management decisions to provinces and local governments. Issues include uneven distribution (shortage in remote areas), migration, vacancies, limited career paths and weak motivation. Strategies: rural retention incentives, scholarship with bonded service, skill-mix and task shifting, performance management, and a human resources for health strategic plan.
Data are raw facts, information is processed data that has meaning, and knowledge is information applied in context to support decisions. A health information system (HIS) collects, stores, processes and reports data needed for managing health services and policy. WHO's HIS building block asks for timely, reliable data on determinants, health status and system performance. A health management information system (HMIS) is the routine reporting component from facilities.
Components: data sources, collection tools, processing, storage, analysis, presentation and use.
Data sources: routine facility data, civil registration and vital statistics, census, surveys, surveillance, administrative and financial records.
Data quality: accuracy, completeness, timeliness, consistency, validity, confidentiality.
Information cycle: collection, compilation, analysis, interpretation, feedback, use for decision.
| Term | Meaning |
|---|---|
| EMR (electronic medical record) | Digital version of a patient's chart within one organization |
| EHR (electronic health record) | Longitudinal record shared across providers and settings |
| PHR (personal health record) | Record managed by the patient |
| CPOE | Computerized provider order entry for drugs and tests |
| CDSS | Clinical decision support system giving alerts and guidance |
Benefits include legibility, availability, fewer medication errors, data for audit and research, and better continuity. Problems include cost, training, workflow disruption, poor interoperability, security risk and alert fatigue.
ICD-10 / ICD-11: classification of diseases and causes of death for statistics.
SNOMED CT: clinical terminology; LOINC: laboratory and observation codes; ATC: drug classification.
HL7 and FHIR: standards for exchanging clinical and administrative data; DICOM: medical images; PACS stores and distributes images.
Interoperability levels: foundational, structural, semantic and organizational.
eHealth is the use of information and communication technologies for health. mHealth is delivery of health services via mobile devices, including SMS reminders, apps and community worker tools. Telemedicine provides clinical services at a distance and takes forms such as store-and-forward, real-time video consultation, and remote monitoring. Telemedicine reduces travel and extends specialist access to remote areas but needs connectivity, legal clarity, standards for consent and records, and reimbursement. Other applications are tele-radiology, tele-pathology, and AI-assisted image interpretation.
The system development life cycle runs through feasibility study, requirement analysis, design, development, testing, implementation and maintenance. Agile methods deliver in iterative cycles with continuous user feedback. Key success factors are user involvement, leadership support, training, data governance, sustainable funding and local ownership. Data warehouses, dashboards and business intelligence tools help turn data into decisions; geographic information systems map disease and services.
Information security rests on confidentiality, integrity and availability (the CIA triad). Controls include role-based access, authentication, encryption, audit logs, backups and staff training. Ethical principles are informed consent, minimum necessary use, anonymization for research and clear ownership of data. HIPAA and GDPR are international references; Nepal's Individual Privacy Act (2018) and related laws provide the national framework, and facilities should have a policy on data sharing and breach response.
Nepal uses DHIS2 for the electronic HMIS and for surveillance reporting, an electronic logistics system, and an increasing number of hospital information systems and telemedicine services; the national eHealth strategy and Digital Nepal Framework guide development. Challenges include fragmented systems, limited internet in remote areas, limited human capacity, data quality and lack of unified patient identifiers. Strategies: interoperability standards, a national architecture, capacity building, and use of data in review meetings.
Quality in health care is the degree to which services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge (Institute of Medicine). The IOM's six aims, often called STEEEP, say that care should be safe, timely, effective, efficient, equitable and patient-centred. Other dimensions include accessibility, acceptability, appropriateness, continuity and competence.
Avedis Donabedian proposed assessing quality through structure (facilities, staff, equipment, organization), process (what is done in giving and receiving care), and outcome (changes in health status, satisfaction). Good structure raises the chance of good process, which in turn improves outcomes.
Quality control: inspection to detect defects against a standard.
Quality assurance: planned and systematic activities to provide confidence that requirements will be met, including standards, audit and accreditation.
Quality improvement: continuous, problem-solving effort to raise performance using data and teams.
Total quality management (TQM): an organization-wide philosophy based on customer focus, leadership, employee involvement, process approach, evidence-based decisions and continuous improvement.
| Guru | Key contribution |
|---|---|
| W. Edwards Deming | 14 points; PDCA (Plan-Do-Check-Act) cycle; system causes account for most variation |
| Joseph Juran | Quality trilogy: planning, control, improvement; fitness for use; Pareto principle |
| Philip Crosby | Zero defects; quality is conformance to requirements; cost of poor quality |
| Kaoru Ishikawa | Cause-and-effect (fishbone) diagram; quality circles |
| Armand Feigenbaum | Total quality control |
| Donabedian | Structure-process-outcome in health care |
Seven basic tools: check sheet, histogram, Pareto chart, cause-and-effect diagram, scatter diagram, run/control chart, flowchart.
PDCA / PDSA: Plan a change, Do it on a small scale, Check results, Act to adopt, adapt or abandon.
Six Sigma (DMAIC): Define, Measure, Analyse, Improve, Control; aims at 3.4 defects per million opportunities.
Lean: removes waste (waiting, over-processing, motion, defects, inventory, transport, overproduction) and improves flow.
Root cause analysis and FMEA: retrospective analysis of adverse events and prospective analysis of failure modes, effects and risk priority.
Benchmarking and clinical audit: compare performance with best practice and with standards, then re-audit after change.
Standards state the expected level of structure, process or outcome. Indicators measure compliance, for example surgical site infection rate, hand-hygiene compliance, door-to-needle time, readmission rate, medication error rate, waiting time and patient satisfaction. Accreditation is external, voluntary or mandatory recognition that an organization meets published standards; well-known systems are the Joint Commission International, NABH in India, and ISO 9001 certification for management systems. In Nepal, minimum service standards and facility licensing guide hospital quality, and the Ministry sets quality assurance directives for public and private institutions.
Patient safety aims to prevent harm. Key concepts are adverse event, near miss, sentinel event, error (active and latent failures; Reason's Swiss cheese model), and just culture. WHO's patient safety solutions include the surgical safety checklist, safe medication practices, hand hygiene, patient identification, and safe injection. Patient satisfaction is measured by exit interviews, suggestion boxes, complaint analysis and structured surveys; it reflects expectations as well as care.
A hospital quality system includes a quality committee, policy and manual, clinical guidelines and protocols, an incident reporting system, regular audits, infection control, credentialing and privileging of clinicians, staff training, and management review. Leadership commitment, a blame-free reporting culture and data feedback to front-line teams are the strongest predictors of success.
A project is a temporary endeavour with a defined beginning and end, undertaken to create a unique product, service or result within constraints of time, cost, scope and quality. Characteristics: specific objective, unique outcome, limited duration, defined budget, interdependent tasks, a project team, and risk and uncertainty. A program is a group of related projects managed together; operations are ongoing and repetitive.
Constraints form the project triangle: scope, time and cost, with quality at the centre. A change in one affects the others.
| Phase | Main activities |
|---|---|
| Initiation / identification | Problem analysis, concept note, feasibility, stakeholder identification, go or no-go decision |
| Planning | Objectives, scope, work breakdown structure, schedule, budget, risk and communication plans, logframe |
| Execution | Mobilizing team and resources, procurement, implementation, coordination |
| Monitoring and control | Progress tracking, variance analysis, corrective action, change control |
| Closure | Handover, final evaluation, audit, lessons learned, release of resources |
Projects arise from needs assessment, policy priorities, donor calls or community demand. Appraisal checks feasibility from several angles.
Technical: appropriateness of technology, location, capacity and inputs.
Financial: returns and cash flow; economic: social costs and benefits; social: acceptability, equity, gender and inclusion.
Environmental and legal: impact, permits and compliance.
Managerial and institutional: capacity of implementing organization and sustainability.
Financial appraisal tools: Payback period (time to recover the investment), Net present value (accept when above zero), Internal rate of return (discount rate making NPV zero; accept if above the cost of capital), and Benefit-cost ratio (accept if above 1). Discounted methods are preferred to payback because they recognize time value of money.
Logical framework approach: problem tree, objective tree, and the 4x4 logframe matrix with indicators, means of verification and assumptions.
Work breakdown structure (WBS): hierarchical decomposition of scope into manageable work packages.
Gantt chart: bar chart of activities against time.
Network analysis: Critical Path Method (CPM) uses deterministic durations; PERT uses three estimates. Expected time te = (o + 4m + p) / 6, where o is optimistic, m is most likely and p is pessimistic; standard deviation = (p - o) / 6; variance = square of that. The critical path is the longest path and determines project duration; slack or float is the delay an activity can have without delaying the project.
Resource levelling and crashing: adding resources to shorten critical activities at extra cost.
Forward pass gives earliest start (ES) and finish (EF = ES + duration); backward pass gives latest finish (LF) and start (LS = LF - duration); Slack = LS - ES. Activities with zero slack are critical.
Budget: activity-based costing of human resources, equipment, travel, training, and overhead; includes contingency and a cash-flow schedule.
Procurement: needs, specifications, tender or quotation, evaluation, contract award, delivery and payment. In Nepal, the Public Procurement Act (2007) and Regulations (2007) govern public purchasing, with provisions for transparency and competition.
Risk management: identify, assess (probability and impact), respond (avoid, reduce, transfer, accept) and monitor risks through a risk register.
Communication and stakeholder management: regular reports, meetings, escalation lines and feedback.
Monitoring uses progress reports, site visits, financial statements and indicator tracking, and compares plan with actual. Earned value analysis integrates scope, time and cost: Schedule variance = EV - PV; Cost variance = EV - AC; SPI = EV / PV; CPI = EV / AC. Values below 1 indicate delay or overspending. Evaluation at midterm and end looks at relevance, effectiveness, efficiency, impact and sustainability. Closure includes handover to the community or government, final accounts, audit and documentation of lessons.
A project manager needs planning, negotiation, team-building and conflict-resolution skills. Teams pass through forming, storming, norming and performing (Tuckman). A project proposal normally includes title, background and rationale, problem statement, goal and objectives, activities, methodology, expected results, work plan, monitoring and evaluation, budget, risk and sustainability, and annexes.
| Subject | Formula or key fact |
|---|---|
| HEM 512 | ICER = (C1 - C0) / (E1 - E0); DALY = YLL + YLD; PV = FV / (1 + r)^t; PED = %change in Q / %change in P |
| HOM 511 | BOR = inpatient days / (beds x days) x 100; ALOS = inpatient days / discharges; BTR = discharges / beds; TOI = (bed days - inpatient days) / discharges; Beds = (admissions x ALOS) / (365 x target BOR) |
| HEM 592 | PERT te = (o + 4m + p) / 6; SD = (p - o) / 6; Slack = LS - ES; SPI = EV / PV; CPI = EV / AC; accept when NPV > 0, BCR > 1, IRR > cost of capital |
| HEM 511 | Hanlon score = (A + B) x C x D; SMART objectives; logframe 4 x 4; levels of prevention (primordial, primary, secondary, tertiary) |
| HOM 551 | PDCA; DMAIC; Donabedian structure-process-outcome; STEEEP aims; Six Sigma 3.4 defects per million |
| MGT 561 | Lewin: unfreeze-change-refreeze; Kotter 8 steps; Kirkpatrick 4 levels; Vroom: V = E x I x Valence |
| HEM 541 | CIA triad: confidentiality, integrity, availability; EMR within one organization, EHR across providers; HL7/FHIR, DICOM, ICD, SNOMED CT |
| MGT 611 | Financing functions: collection, pooling, purchasing; catastrophic expenditure threshold 10 percent of consumption or 40 percent of non-food spending |
| HEM 543 | WHO six building blocks; UHC cube; Kingdon multiple streams; Walt and Gilson policy triangle; Nepal's three tiers of government |
Accuracy note: Nepal-specific figures (insurance premiums and ceilings, health expenditure shares, programme coverage, strategy dates) change over time. Check them against your lecture slides, the current Acts and the latest ministry reports before writing them in an exam.
The term derives from the French rechercher, “to search again”: repeated, systematic searching to approach the truth. Research may be defined as a systematic, organised and ethical process of inquiry to discover, validate or revise facts, theories or applications.
| Author | Definition (paraphrased) |
|---|---|
| Kerlinger (1970) | Systematic, controlled, empirical and critical investigation of hypothetical propositions about presumed relations among phenomena |
| Leedy (1989) | A procedure for finding, with demonstrable facts, the answer to a question or the resolution of a problem |
| Redman and Mory | A systematised effort to gain new knowledge |
| Sekaran | Organised, systematic, data-based, critical, scientific enquiry into a specific problem |
| Best | Systematic, objective analysis and recording of observations that may lead to theory |
| Kothari (1985) | A search for knowledge through objective and systematic methods of finding solutions to a problem |
Characteristics. Good research is systematic, logical, empirical, replicable, free from personal bias, valid and reliable, problem-directed, theoretically grounded and ethical. Aims include generating knowledge, solving a problem in the work setting, building a base for theory, filling a gap in knowledge and testing a causal hypothesis. In health care management it supplies evidence for managerial decisions, policy design and evaluation, clinical governance, quality assurance and resource allocation.
Research and related activities. Routine clinical audit checks whether existing standards are met within one institution, and administrative data collection tracks utilisation without an a priori hypothesis; neither has the knowledge-generating intent of research, which requires a formulated question, a planned design, systematic data collection and analysis, and reporting that contributes verified knowledge.
Steps of the scientific method: sensing the problem, problem identification, theoretical framework, hypothesis, design, data collection, analysis, interpretation and generalisation.
| Basis | Types |
|---|---|
| Purpose | Basic, applied, evaluation, research and development, action |
| Method | Historical, descriptive, causal, experimental |
| Approach | Quantitative, qualitative, mixed methods |
| Feature | Basic (pure) | Applied |
|---|---|---|
| Objective | Develop theory; expand knowledge | Solve an immediate problem |
| Focus | The researcher’s interest | A problem faced by society or an organisation |
| Time limit | Generally none | Fixed |
| Generalisation | Results generalise to similar settings | Largely context-bound |
| Practical use | Not immediate | Immediate |
Illustration: how a bacterium acquires drug resistance (basic) versus reducing infection rates in one hospital (applied).
| Research methods | Research methodology | |
|---|---|---|
| Concerned with | The how | The why |
| Meaning | Tools and techniques for collecting and analysing data | The overall strategy with its philosophical and logical rationale |
| Scope | Narrow, practical | Broad, theoretical |
| Examples | Interview, survey, questionnaire, sampling, t-test | Positivist quantitative methodology; interpretivist qualitative methodology |
Kothari (1985) describes methodology as a way of systematically solving the research problem. It explains why the study was undertaken, how the problem and hypothesis were formulated, why a technique was chosen and why others were not. Methodology therefore sits above design, which sits above methods: paradigm (worldview) > methodology or approach (strategy) > design (plan) > methods and tools (techniques). A focus group discussion, for example, is a method, not a design.
| Quantitative | Qualitative | Mixed methods | |
|---|---|---|---|
| Paradigm | Positivist / post-positivist | Interpretivist / constructivist | Pragmatist |
| Data | Numbers; closed-ended instruments | Words, images, observations | Both |
| Logic | Deductive (tests theory) | Inductive (builds theory) | Both |
| Main methods | Survey, correlational, experimental | Ethnography, focus groups, in-depth interviews, case study | Sequential or concurrent combination |
| Aim | Measure, test, generalise | Depth, meaning, explanation | The what plus the why |
| Sample | Representative, usually probability | Purposive, small, to saturation | Both |
The choice of approach follows the research question, the nature of the phenomenon, available resources and the researcher’s paradigm. Designs for mixed methods are treated in Unit 2. In Nepal, research on health, fertility and health-seeking behaviour moved from a mainly quantitative focus in the 1980s–90s to mixed methods in 2000–10, because numbers alone could not explain behaviour.
A paradigm is a framework of commonly accepted views indicating what direction research should take and how. The term entered the philosophy of science through Kuhn (The Structure of Scientific Revolutions, 1962). It is usually described along three dimensions (Guba and Lincoln): ontology (what is reality?), epistemology (what is knowledge and how is it gained?) and methodology (how do we find out?); axiology, the role of values, is often added.
| Positivism / post-positivism | Interpretivism / constructivism | Critical (transformative) | Pragmatism | |
|---|---|---|---|---|
| Ontology | One objective reality (post-positivism: imperfectly known) | Multiple, socially constructed realities | Realities shaped by history and power | Whatever works for the question |
| Epistemology | Objective; researcher independent | Subjective; co-constructed meaning | Value-laden; aims at change | Practical, problem-centred |
| Usual approach | Quantitative | Qualitative | Qualitative or mixed | Mixed methods |
| Positivism (quantitative) | Interpretivism (qualitative) | |
|---|---|---|
| Reality | Single, measurable | Multiple |
| Design | Fixed beforehand; hypothesis first | Flexible; emerges with the data |
| Researcher | Objective observer | Immersed participant |
| Theory | Tests theory | Generates theory |
| Quality criteria | Validity, reliability, objectivity | Trustworthiness: credibility, transferability, dependability, confirmability |
The research process is iterative rather than linear: formulate the problem; review the literature; state aims, objectives and hypotheses; choose the design; decide the sample design and tools; collect data; process and analyse data and test hypotheses; interpret, generalise and report. The literature and the findings continually refine the question.
Criteria of good research: clear objectives; relevance; a systematic procedure; a sound theoretical base; valid and reliable data; logical consistency; ethical compliance; transparency; feasibility; and actionable outcomes. In summary, good research is scientifically sound, socially relevant and ethically strong.
Ethical responsibilities of the researcher are owed to the self (honest conduct), to respondents (explaining purpose and use of data), to participating organisations (informing them of purpose, data use and publication) and to the employer or institution (following accepted conventions). Common violations include pressuring respondents for confidential information, violating privacy, fabricating or altering data, reporting only favourable data, claiming others’ work, releasing results prematurely, duplicate publication, and ghost, guest or gift authorship.
A research design is “the plan, structure and strategy of investigation conceived so as to obtain answers to research questions and to control variance” (Kerlinger, 1986); Zikmund (2007) calls it a master plan specifying the methods and procedures for collecting and analysing the needed information. It states what the study is about and why, where it will be done, what data are needed, over what period, with what sampling design, which collection techniques, how the data will be analysed and how the report will be written. Every decision must be justified by the objectives and be consistent with the research philosophy. A good design is flexible, appropriate and efficient, minimises bias, maximises reliability and yields maximum information.
Elements: research question or hypothesis; variables (independent = exposure or manipulated, dependent = outcome); data collection methods; sampling method; data analysis plan; time horizon (cross-sectional or longitudinal); ethics. Kothari’s components are sampling, observational, statistical and operational design.
| Question | Design |
|---|---|
| Does the researcher assign the exposure? | Yes: experimental (RCT, quasi-experiment) |
| No; is there a comparison group? | No: descriptive (case report, case series, cross-sectional survey) |
| Comparison group present | Analytical: selected by outcome = case-control; by exposure and followed = cohort; both measured at once = analytical cross-sectional |
Designs are described as “evolving” because health care management is a young research area in Nepal and new influences are reshaping practice: mixed methods, mobile data collection (for example KoBo Collect), ethical scrutiny of data-intensive research, new technology (sensors, geospatial mapping), interdisciplinary collaboration and open access. Classifications of design are dimensions, not competing categories: one study may be analytical, observational, prospective, a cohort and empirical at the same time.
| Descriptive | Analytical | |
|---|---|---|
| Question | What, who, where, when, how many | Why and how |
| Purpose | Describe characteristics and trends | Test hypotheses; explain causes |
| Comparison group | Not required | Required |
| Methods | Case report, case series, prevalence survey, ecological study | Cohort, case-control, experiments |
| Limitation | Cannot establish causality | Needs more rigour and resources |
A case report describes one patient and a case series a group; both generate hypotheses. An ecological study compares whole groups (for example districts) from routine data and is exposed to the ecological fallacy, the false inference that a group-level relationship holds for individuals. A cross-sectional survey measures exposure and outcome together and gives prevalence. Descriptive research is often called ex post facto: the researcher cannot control the variables and reports what has occurred. Descriptive research generates hypotheses; analytical research tests them.
| Observational | Experimental | |
|---|---|---|
| Researcher | Observes; does not intervene | Manipulates the independent variable |
| Exposure | Naturally determined | Assigned by the researcher |
| Causality | Cannot easily be established | Can be established |
| Bias | Confounding likely | Reduced by randomisation and blinding |
| Examples | Cross-sectional, case-control, cohort | RCT, quasi-experiment |
| Limitation | No control over variables; suits harmful exposures | May not generalise; harmful exposures cannot be assigned |
When an intervention is delivered without randomisation, the study is quasi-experimental (for example before-and-after or interrupted time series). It is practical in health systems but remains open to confounding.
| Prospective | Retrospective | Cross-sectional | |
|---|---|---|---|
| Direction | Forward: exposure to outcome | Backward: outcome to exposure | Single point in time |
| Data | New data as events occur | Existing records or recall | Exposure and outcome together |
| Bias | More control; less bias | Recall and record bias | Selection bias; no time sequence |
| Typical design | Cohort | Case-control; record review | Prevalence survey |
| Measure | Incidence; relative risk | Odds ratio | Prevalence |
| Weakness | Costly and slow | Weaker evidence | Cannot show causality |
Cohort studies may be prospective, retrospective (historical, reconstructed from records) or ambispective; a case-control study is retrospective by nature. Cross-sectional studies of long-duration disease may over-represent survivors (prevalence–incidence bias).
Case-control. Cases (with the outcome) and controls (without it) are selected and past exposure is compared; the measure is the odds ratio. It suits rare diseases, multiple exposures and outbreak investigation, and is quick and inexpensive. Its limitations are recall bias, selection bias in choosing controls and the absence of incidence. Nested case-control. A case-control study within a defined prospective cohort: members who develop the disease become cases, and controls are sampled from members still at risk when each case occurs.
| Case-control | Nested case-control | |
|---|---|---|
| Source population | Not always clearly defined | A defined cohort |
| Exposure data | Collected after the outcome; recall bias | Often recorded before the outcome |
| Efficiency | Quick and cheap | Cheaper than analysing the whole cohort; suits costly biomarkers |
| Selection bias | Control choice can bias | Reduced; same cohort |
Cohort designs suit rare exposures; case-control designs suit rare diseases.
| Conceptual | Empirical | |
|---|---|---|
| Focus | New ideas, theories, frameworks | Testing hypotheses with evidence |
| Method | Reasoning; literature analysis | Experiment, observation, data collection |
| Outcome | New or reinterpreted concepts | Evidence-based conclusions |
Conceptual research lays the groundwork that empirical research tests. Most health and management research is empirical.
Descriptive research portrays characteristics of a person, group or situation without manipulating variables, using surveys, case studies, observation or content analysis; its design is structured and uses probability sampling where generalisation is intended. Diagnostic research identifies how often a problem occurs and its underlying causes. Both require clear definitions, valid and reliable measures, ethical safeguards and an analysis plan. Advantages are a detailed baseline picture and relatively low cost; limitations are the absence of causal inference and limited generalisability.
Exploratory (formulative) design is flexible and unstructured, used for poorly defined problems. Its purpose is to gain insight, identify variables and generate hypotheses. Methods include literature review, experience survey, interviews, focus groups and case analysis. It is inexpensive and quick but not conclusive, and is most valuable at the start of an inquiry.
Hypothesis-testing research examines a stated proposition using analytical designs (experiments, cohort, case-control) and controls extraneous variables to support causal inference. The sequence is: state H0 and Ha; choose the design; collect data; select the test; compute the statistic and p-value; reject or fail to reject H0; interpret with confidence intervals and limitations (see Units 5 and 6).
Mixed methods systematically integrates quantitative and qualitative data in one study through purposeful mixing and triangulation.
| Design | Sequence | Purpose |
|---|---|---|
| Convergent parallel | QUAN and QUAL concurrently; analysed separately, then merged | Compare and corroborate |
| Explanatory sequential | QUAN, then QUAL | Qualitative data explain the numbers |
| Exploratory sequential | QUAL, then QUAN | Qualitative work builds the instrument that is then tested |
| Embedded | One strand nested in a larger design | For example, interviews within a trial |
Integration may be by connecting (one strand selects participants for the other), building (themes become questionnaire items) or merging (a joint display comparing results). Limitations are cost, the need for dual expertise and conflicting findings. Nepal-based examples include barriers to health care in traditional societies (qualitative) paired with approaches to improve public health (quantitative), and salary and working hours (quantitative) paired with job satisfaction (qualitative) among nurses in private hospitals of Kathmandu Valley.
| Threat | Meaning | Control |
|---|---|---|
| Chance | Random error; shrinks with sample size | Larger sample; confidence intervals |
| Selection bias | Subjects differ systematically from the target population | Random sampling; follow-up effort |
| Information bias | Exposure or outcome measured with error | Blinding; standard tools |
| Confounding | A third factor linked to exposure and outcome, not on the causal path | Randomisation, restriction, matching; stratification, regression |
Causal inference follows the criteria of Bradford Hill (strength, consistency, specificity, temporality, biological gradient, plausibility, coherence, experiment, analogy); temporality is the only essential criterion. Evidence is ranked from systematic reviews of randomised trials, through RCTs, cohort, case-control and cross-sectional studies, to case series and expert opinion.
A randomised controlled trial (RCT) estimates the effect of an intervention (treatment, training, policy, medicine) by comparing a group that receives it with a control group (usual care, placebo, another intervention or none). Participants are assigned at random, so the groups are comparable at baseline and a difference at the end can be attributed to the intervention. Its key features are randomisation, a control group, blinding, a prospective design and intention-to-treat analysis. It is the standard for evaluating health services and interventions because it reduces selection bias, balances known and unknown confounders and has high internal validity; its weakness is external validity, which strict eligibility criteria, population and setting may limit.
Structure. Target population > eligibility screening and informed consent > baseline assessment > concealed random allocation > experimental or control arm > follow-up > blinded outcome measurement > intention-to-treat comparison.
| Design | Description |
|---|---|
| Parallel | Each group receives a different intervention throughout |
| Crossover | Each participant receives the interventions in sequence with a washout period |
| Factorial | Two or more interventions tested alone and in combination (for example 2 × 2) |
| Cluster | Groups (schools, clinics, villages) are randomised rather than individuals |
| Year | Event |
|---|---|
| 1537 | Ambroise Paré compared wound treatments in soldiers (not randomised) |
| 1747 | James Lind compared scurvy treatments at sea; citrus worked (controlled, not randomised) |
| 1863 | Austin Flint used a dummy remedy against active treatment for rheumatism (early placebo control) |
| 1920s–30s | R. A. Fisher introduced randomisation in agricultural experiments |
| 1948 | British Medical Research Council streptomycin trial for tuberculosis (Bradford Hill): the first modern RCT, with concealed random allocation but no placebo |
| 1960s onward | RCTs become standard in medicine, psychology, economics and policy |
| 2019 | Nobel Prize to Banerjee, Duflo and Kremer for experimental approaches to poverty |
Design requires a specific, answerable question; a defined intervention (content, duration, intensity); a unit of randomisation (individual, household, school, village); primary and secondary outcomes; a control condition; an implementation plan; and monitoring of fidelity, adherence and spillover. The conduct follows the protocol: (1) protocol with objectives, intervention, outcomes, sample size and randomisation plan; (2) ethical approval and trial registration; (3) recruitment by inclusion and exclusion criteria with written consent and a baseline survey; (4) random sequence generation and allocation concealment; (5) delivery of the intervention with blinding where possible and prevention of contamination; (6) collection of pre-specified outcomes and monitoring of adherence and adverse events; (7) endline survey and intention-to-treat analysis, with interim analysis only if planned; (8) reporting by CONSORT and dissemination to stakeholders.
| Arm | Receives |
|---|---|
| Experimental | The new intervention |
| Placebo control | An inactive substance identical in appearance |
| Active control | The existing standard treatment |
| No-treatment control | Nothing; outcomes monitored |
| Multi-arm | Different doses or combinations, or new versus standard versus placebo |
Randomisation allocates participants to arms by chance (computer-generated numbers, coin toss), avoiding selection bias, balancing confounders and strengthening internal validity.
| Method | Use |
|---|---|
| Simple | Pure chance; best in large samples; may give unequal groups in small trials |
| Block | Equal numbers in each arm at regular intervals (blocks of 4, 6, 8) |
| Stratified | Group by key characteristics (age, sex, severity), then randomise within strata |
| Cluster | Randomise groups; useful in public health and to limit contamination |
| Allocation concealment | Blinding (masking) | |
|---|---|---|
| When | Before and at allocation | After allocation, throughout the trial |
| Hides | The upcoming assignment sequence | Each participant’s arm |
| From | Enrolling staff and participants | Participants, providers, assessors, analysts |
| Prevents | Selection (allocation) bias | Performance and detection bias |
| Methods | Central randomisation; sealed opaque numbered envelopes | Identical placebo; coded packs; blinded assessors |
| Always possible? | Yes | No (for example surgery) |
Levels of blinding: single (participants or assessors), double (participants and investigators; the usual standard) and triple (including statisticians). Where blinding is impossible, use objective outcomes and independent assessors.
CONSORT (Consolidated Standards of Reporting Trials) was first published in 1996 and revised in 2001 and 2010. The 2010 checklist has 25 items; the 2025 update has 30. It is a reporting guideline, not a tool for assessing trial quality, and has two core components: the checklist and the flow diagram. The flow diagram has four stages: enrolment (assessed for eligibility, excluded, randomised), allocation, follow-up (lost, discontinued) and analysis (analysed, excluded). Related guidelines: STROBE (observational studies), PRISMA (systematic reviews), STARD (diagnostic accuracy) and SPIRIT (trial protocols).
| Section | Content |
|---|---|
| Title and abstract | Identification as a randomised trial; structured abstract |
| Introduction | Background, rationale, objectives, hypotheses |
| Methods | Design and allocation ratio; eligibility; interventions; outcomes; sample size; interim analyses; randomisation (sequence, concealment, implementation); blinding; statistics |
| Results | Participant flow; recruitment dates; baseline data; numbers analysed; effect sizes with confidence intervals; harms |
| Discussion | Limitations; generalisability; balanced interpretation |
| Other | Registration; protocol access; funding |
Bias is a systematic error that distorts the estimated effect.
| Bias | What happens | Minimisation |
|---|---|---|
| Selection | Groups not comparable or representative | Random sampling and allocation; clear criteria |
| Allocation | Assignment predicted or influenced | Concealment; central randomisation |
| Performance | Differences in care apart from the intervention | Blind participants and providers; standard protocol |
| Detection | Outcomes assessed differently between groups | Objective measures; blinded assessors |
| Attrition | Unequal losses between groups | Reduce loss; record reasons; intention-to-treat |
| Reporting | Selective reporting of outcomes | Register trial; pre-specify outcomes |
| Confounding | Third variable linked to exposure and outcome | Randomisation, stratification, restriction, adjustment |
| Observer / response / recall | Expectation, social desirability, poor memory | Blinding, training, neutral wording, records |
| Publication / funding | Positive results published more; sponsor influence | Registries; disclosure of conflicts |
| Intention-to-treat (ITT) | Per-protocol (PP) | |
|---|---|---|
| Analysed | Everyone, as randomised | Only those who complied |
| Principle | Once randomised, always analysed | Completers only |
| Preserves randomisation | Yes | No |
| Answers | Real-world effectiveness | Efficacy under ideal compliance |
Example. Of 100 randomised to a drug, 20 never took it; 56 of 80 takers and 6 of 20 non-takers were cured; 50 of 100 controls were cured. ITT gives 62% versus 50%; per-protocol gives 70% versus 50%, which is biased because compliers differ from non-compliers.
In Nepal, clinical trials require NHRC ethical approval; the 2022 guideline requires registration in a WHO-accredited registry and permission from the drug regulator (DDA) before the trial begins. Limits of RCTs in health management include ethical limits on withholding services, restricted external validity, contamination between wards or staff, cost and the frequent impossibility of blinding.
| Phase | Purpose | Typical subjects |
|---|---|---|
| Preclinical | Laboratory and animal studies | Not human |
| I | Safety, dosage, side effects | 20–100 volunteers or patients |
| II | Preliminary efficacy; optimal dose | Hundreds of patients |
| III | Confirm efficacy against standard care | Hundreds to thousands; often multi-centre RCT |
| IV | Post-marketing surveillance; rare and long-term effects | Real-world populations |
The path runs from a broad area to a generic topic, a research problem (a gap between the actual and the desired state), research questions tested against the FINER criteria, an aim, SMART objectives and, for analytical studies, hypotheses. A good topic is feasible, of genuine interest and within the researcher’s knowledge, expresses a relation between variables, is clearly stated with testable variables, is manageable in time and cost, has attainable data and is relevant to the organisation or society.
Where ideas emerge: hospital observation, management and administrative challenges, literature and policy review, discussion with supervisors and experts, service-delivery gaps and monitoring data. Brainstorming lists variables, populations and outcomes, asks who, what, where, why and how, and weighs feasibility, relevance and impact. Focusing specifies the population, setting and variables and makes the question measurable and aligned with the methodology.
A research problem is a difficulty for which a solution is sought; Sekaran (1992) defines it as a gap between the actual and the desired ideal state (for example, high staff turnover reducing profitability). Sources include complaints, observation, deviation from plan, reading and records. To define a problem: state it generally, understand its nature, survey the literature, consult experts and rephrase it analytically.
| Framework | Use | Components |
|---|---|---|
| PICO / PICOT | Quantitative, clinical | Population, Intervention, Comparison, Outcome, Time |
| PECO | Observational | Population, Exposure, Comparison, Outcome |
| SPIDER | Qualitative | Sample, Phenomenon of Interest, Design, Evaluation, Research type |
| FINER | Testing a question | Feasible, Interesting, Novel, Ethical, Relevant |
A literature review summarises and analyses current knowledge on a topic. It refines the question and objectives, avoids duplication, identifies methods, tools and variables, reveals gaps and builds the theoretical framework.
| Level | Meaning | Examples |
|---|---|---|
| Primary | Original work or data | Original studies, theses, government reports |
| Secondary | Interpretation of primary work | Books, review articles |
| Tertiary | Tools for locating the others | Indexes, abstracts, bibliographies |
Steps: (1) define the problem and scope; (2) identify concepts and keywords, synonyms and MeSH terms; (3) select databases; (4) build the search using Boolean operators (AND narrows, OR widens, NOT excludes), truncation, phrases and filters; (5) screen titles, then abstracts, then full texts against inclusion and exclusion criteria; (6) appraise design, methods, data quality and limitations; (7) organise by theme, variable or method with a reference manager (Zotero, Mendeley, EndNote); (8) identify the gap; (9) write the review and document the search (database, string, hits, date). Snowballing from reference lists finds studies the search missed. A search strategy is the planned, transparent and reproducible form of these steps.
Grey literature is material produced outside commercial publishing (government and WHO reports, policy documents, theses, conference papers, MoHP annual reports, unpublished trial data). It is a concept separate from primary literature and may be primary or secondary. It reduces publication bias and supplies local data, but quality varies and indexing is poor, so authenticity, credibility, relevance and date must be checked and full retrieval details cited.
| Systematic | Narrative | Scoping | |
|---|---|---|---|
| Purpose | Answer a specific question | General overview | Map the field; find gaps |
| Question | Narrow, well defined | Broad, flexible | Broad but structured |
| Protocol | Predefined, often registered | None | Recommended |
| Search | Comprehensive, reproducible | Selective, subjective | Comprehensive, transparent |
| Quality appraisal | Mandatory | Not required | Usually not required |
| Synthesis | Meta-analysis or structured narrative | Descriptive | Thematic mapping |
| Guideline | PRISMA | None | PRISMA-ScR |
| Resources | High | Low | Moderate |
The PRISMA flow has four phases: identification, screening, eligibility and inclusion (for example, 1,250 records, 900 after duplicates, 780 excluded at screening, 120 full texts assessed, 85 excluded with reasons, 35 included). A systematic review answers a question; a scoping review explores a field.
| Category | Examples | Best for |
|---|---|---|
| Multidisciplinary | Scopus, Web of Science, Google Scholar, ProQuest | Broad or emerging topics |
| Health sciences | PubMed/MEDLINE, Embase, CINAHL, Cochrane Library | Biomedical and nursing evidence; systematic reviews |
| Social sciences | PsycINFO, Sociological Abstracts, ERIC | Psychology, sociology, education |
| Grey literature | OpenGrey, ProQuest Dissertations | Reducing publication bias |
| Local | NepJOL; university repositories | Nepal-specific studies |
| Organisational | WHO, World Bank, ministry portals | Policy documents and statistics |
| Variable | Meaning | Example |
|---|---|---|
| Independent | The cause or exposure | Training |
| Dependent | The outcome | Productivity |
| Moderating | Changes the strength or direction of the relationship | Age over or under 50 |
| Intervening (mediating) | Lies on the causal pathway | Training > motivation > productivity |
| Confounding | Linked to exposure and outcome, not on the pathway | Age, socioeconomic status |
| Controlled | Held constant | Setting, duration |
| Question type | Form |
|---|---|
| Descriptive | What are the characteristics of X? |
| Comparative | What are the differences between X and Y? |
| Correlational | What is the relationship between X and Y? |
| Explanatory | Does X affect Y? |
| Evaluation | How well does Y work? |
A fully developed question is clear and specific (problem, population, context), researchable and measurable, focused and feasible, relevant, novel, ethical and aligned with the design. The aim is a single broad statement of intent; objectives are specific, measurable steps (one general and several specific), written with action verbs such as estimate, compare, assess or identify and not with vague verbs such as know or understand. Objectives should be SMART: specific, measurable, achievable, relevant and time-bound. Critical challenges include questions that are too broad or too narrow, unmeasurable variables, questions already answered, infeasibility (time, access, resources), ethical constraints and misalignment with the aim.
Sequence: target population > accessible population > sampling frame > sample > statistic that estimates the parameter.
A good procedure yields a truly representative sample, small sampling error, viability within the budget, control of systematic bias and results applicable to the population with reasonable confidence. The steps are: define the population; specify the frame; define the sampling unit; choose the method; weigh budget; determine sample size; write the operational plan; select the sample.
Every unit has a known, non-zero chance of selection by a random mechanism, so sampling error can be measured. Only simple random sampling gives every unit an equal chance.
| Method | How it works | Merit | Demerit |
|---|---|---|---|
| Simple random | Lottery or random-number table | Unbiased; simple | Needs a complete list; may miss subgroups |
| Systematic | Every kth unit after a random start; k = N/n | Easy, cheap, evenly spread | Biased if the list has hidden periodicity |
| Stratified | Divide into homogeneous strata and sample each (proportionate or disproportionate) | More precise; represents subgroups | Needs prior information |
| Cluster / multistage | Randomly select clusters, then units within them | Practical for large, spread populations | Less precise than simple random for the same n |
Examples. Stratified: a population of 100 (20 managers, 30 officers, 50 assistants) with a sample of 10 gives 2, 3 and 5 by proportionate allocation. Systematic: 1,200 records and a sample of 120 give k = 10; a random start of 7 selects 7, 17, 27 and so on. Strata are homogeneous within and all are sampled (raising precision); clusters are heterogeneous within and only some are sampled (lowering cost).
| Method | How it works | Use |
|---|---|---|
| Convenience | Nearest, available, willing units | Pre-testing; quick work; not fully valid |
| Purposive | Researcher selects information-rich units | Key informants; qualitative studies |
| Quota | Fixed numbers per category, filled non-randomly | Quick surveys with known proportions |
| Snowball | Eligible people refer others | Hard-to-reach groups |
| Sample survey | Census | |
|---|---|---|
| Coverage | Selected units | All units |
| Cost and time | Low | Very high |
| Sampling error | Present | None |
| Non-sampling error | Present | Present, often large |
| Example | Opinion polls; health surveys | National population census |
Sampling saves time and money, allows more careful measurement by trained staff, is the only option for infinite populations or destructive testing (a blood sample), and permits estimation of sampling error.
| Sampling error | Non-sampling error | |
|---|---|---|
| Nature | Random variation around the true value | Systematic error in collecting or processing data |
| Larger sample | Decreases | Does not decrease; occurs in a census too |
| Measurable | Yes (standard error, confidence interval) | Hard to quantify |
| Examples | Chance selection of unrepresentative units | Poor frame, non-response, interviewer error, coding error |
| Probability | Non-probability | |
|---|---|---|
| Selection | Random | Non-random |
| Frame needed | Yes | No |
| Bias | Less researcher bias | Higher risk |
| Generalisation | Possible | Difficult |
| Cost and time | More | Less |
| Use | Quantitative, analytical, epidemiological | Exploratory, qualitative, pilot, hidden populations |
An inappropriate sample size yields invalid conclusions: too small a sample gives weak results and a high Type II error; too large a sample wastes resources and exposes participants unnecessarily. Calculation is therefore a scientific and an ethical requirement. Determinants are the homogeneity of the universe, variability (take p = 0.5 when unknown), the number of subgroups, the nature of the study, the type of sampling, the required precision and confidence (halving the margin of error needs four times the sample), and resources. Z values: 90% = 1.645; 95% = 1.96; 99% = 2.576; power 80% = 0.84; 90% = 1.28. Qualitative studies use saturation, not formulae.
| Purpose | Formula |
|---|---|
| Single proportion (Cochran) | n = Z²p(1 − p) / d² |
| Single mean | n = Z²σ² / d² |
| Finite population | nadj = n / (1 + (n − 1)/N) |
| Non-response | nfinal = n / (1 − non-response rate) |
| Cluster design | DEFF = 1 + (m − 1)ρ; ncluster = n × DEFF |
| Two means (per group) | n = 2σ²(Zα/2 + Zβ)² / difference² |
| Two proportions (per group) | n = (Zα/2 + Zβ)²(p₁q₁ + p₂q₂) / (p₁ − p₂)² |
Worked examples. With Z = 1.96, p = 0.5 and d = 0.05, n = 384.16, taken as 385. For an expected prevalence of 30% with d = 5% and 95% confidence, n = 3.8416 × 0.30 × 0.70 / 0.0025 = 322.7, so 323; with 10% non-response, 323 / 0.9 ≈ 359. To compare 40% with 25% at 95% confidence and 80% power, n = 7.84 × (0.24 + 0.1875) / 0.0225 ≈ 149 per group. For a mean with SD 12 mmHg and d = 2, n = 3.8416 × 144 / 4 ≈ 139. Software such as OpenEpi or G*Power should be used for complex designs, with all assumptions stated.
Statistical power is the probability that a test correctly rejects a false null hypothesis, 1 − β; the minimum acceptable is 80%. Power increases with larger samples, a higher α, a larger effect size and lower variability.
A hypothesis is a testable statement about population parameters. The null hypothesis (H0) states no difference, effect or relationship; the alternative (H1) states that one exists. The significance level α, usually 0.05, is the maximum tolerated probability of rejecting a true null. If p ≤ α, reject H0; otherwise fail to reject it (never “accept” H0).
| Decision | H0 true | H0 false |
|---|---|---|
| Reject H0 | Type I error (α; false positive) | Correct (power = 1 − β) |
| Fail to reject H0 | Correct (1 − α) | Type II error (β; false negative) |
| Type I | Type II | |
|---|---|---|
| Example | Concluding a drug works when it does not | Concluding a method has no impact when it does |
| Controlled by | Fixing α in advance | Sample size and power |
Lowering α raises β; only a larger sample reduces both. Parametric tests (t, z, F, ANOVA) assume a known, usually normal, distribution and interval or ratio data and are more powerful; non-parametric tests (chi-square, Mann-Whitney U, Kruskal-Wallis, Wilcoxon) are distribution-free and suit nominal or ordinal data and small samples.
| Statistical | Social | Clinical | |
|---|---|---|---|
| Focus | Probability | Social impact | Health impact |
| Basis | p ≤ α | Context, equity, policy | Real change in outcome or quality of life |
| Question | Is it real, not chance? | Does it matter to society? | Does it help patients? |
Statistical significance depends on sample size: a negligible effect becomes “significant” in a very large sample (a 1 mmHg fall in blood pressure may have p < 0.05 without benefit). Clinical significance is judged by effect size, absolute risk reduction, number needed to treat and clinical judgement. Results should be reported as an effect size with a confidence interval, not as a p-value alone.
Quantitative data are numeric and analysed statistically. They are numerical (discrete: countable, such as patients admitted; continuous: may take decimals, such as blood pressure) or categorical, measured on four scales (mnemonic NOIR).
| Scale | Property | Examples | Suitable statistics |
|---|---|---|---|
| Nominal | Labels only | Sex, blood group | Frequency, percentage, mode, chi-square |
| Ordinal | Rank order, unequal gaps | Cancer stage, satisfaction category | Median, percentile, rank tests |
| Interval | Equal intervals, arbitrary zero | Temperature in °C | Mean, SD, t-test, ANOVA |
| Ratio | Equal intervals and true zero | Height, weight, income | All, including coefficient of variation |
A single Likert item is ordinal; a summed Likert scale is often treated as interval, which should be stated. A higher scale can be collapsed into a lower one (age to age groups) but not the reverse. Qualitative data are non-numeric and context-rich (transcripts, field notes, audio, images, documents, artefacts) and answer why, how and with what meaning. Rigour requires an explicit framework, clear description of context and sampling, triangulation, analysis by a second researcher, enough raw data to support interpretation and reflexivity (reflection on the researcher’s role and bias). Software includes NVivo, ATLAS.ti and Dedoose.
Primary data are collected first-hand for the problem (observation, interview, questionnaire, experiment, measurement, focus group): specific, controlled and current, but costly and slow. Secondary data were collected earlier by others (HMIS, census, NDHS, hospital records, reports): cheap and quick, but possibly unreliable, outdated or inconsistent in units. Secondary sources are appraised with PROMPT (Presentation, Relevance, Objectivity, Method, Provenance, Timeliness), returning to the original source for accuracy. Observation may be participant (four roles: complete participant, participant as observer, observer as participant, complete observer) or non-participant, using diary, checklist, rating scale, camera or recorder.
| Method | Features | Disadvantage |
|---|---|---|
| Structured | Fixed questions and order; mainly quantitative | Rigid; limited depth |
| Semi-structured | Theme guide with flexible probing; most common qualitative form | Needs skill and time |
| Unstructured | Informal, respondent-led | Hard to compare; interviewer bias |
| Focus group | Moderator guides 6–10 homogeneous participants | Dominant voices; sensitive topics |
| Face-to-face | Probing; high response | Costly |
| Telephone | Cheap and fast | No visual cues; short topics only |
| Online | Convenient | Digital divide; limited rapport |
Computer-assisted modes are CAPI (face-to-face), CATI (telephone) and CASI (self-administered, suited to sensitive questions). Approaching respondents: obtain informed consent, ensure voluntariness, explain purpose and use of data, introduce oneself with identification, and use polite, non-coercive language at a convenient time; advance letters, call-backs and reminders reduce non-response. Technique: prepare (objectives, guide, rapport); conduct (general to specific, open questions, active listening, neutral wording, no leading questions); probe (clarification, elaboration, silence); close (summarise, thank, explain next steps). Recording: written notes (quick, miss detail), audio (accurate; needs consent and transcription), video (captures non-verbal cues; intrusive); obtain consent before recording, back up files, transcribe and verify, and anonymise.
Questionnaire design: define the information need; decide the survey type; content of each question; open or closed format; wording; sequence; pre-coding; layout; pre-test; finalise. Avoid ambiguous, double-barrelled, leading, loaded and double-negative questions and questions beyond memory. Begin with an easy opener, keep a logical flow, place sensitive items in the body and demographics at the end.
Missing values are classed as missing completely at random (MCAR), at random (MAR) or not at random (MNAR, the most serious). When more than about 10–15% is missing, handle with care: deletion (listwise or pairwise) or imputation (mean or median, regression, or multiple imputation, preferred in health research because mean imputation understates variance); report the percentage missing, the method and the reason. Outliers arise from entry error, measurement error or true extremes; detect them by box plot, histogram, scatter plot, z-score beyond 3 or the 1.5 × IQR rule, verify against the source and never delete silently.
Analysis may be univariate, bivariate or multivariate. Descriptive statistics comprise frequencies and percentages, central tendency (mean, median, mode) and dispersion (range, SD, variance, IQR); use mean and SD for roughly normal data and median and IQR for skewed data (length of stay, costs). Inferential statistics estimate and test hypotheses about populations.
| Outcome | Comparison | Parametric | Non-parametric |
|---|---|---|---|
| Numeric | Two independent groups | Independent t-test | Mann-Whitney U |
| Numeric | Two paired measurements | Paired t-test | Wilcoxon signed-rank |
| Numeric | Three or more groups | One-way ANOVA | Kruskal-Wallis |
| Numeric | Numeric predictor | Pearson correlation; linear regression | Spearman |
| Categorical | Categorical predictor | Chi-square (Fisher’s exact if counts are small); McNemar for paired data | |
| Binary | Several predictors | Logistic regression | |
ANOVA assumes a numeric outcome, independent observations, approximate normality within groups (Shapiro-Wilk) and equal variances (Levene’s test). Qualitative analysis is inductive (themes emerge from data) or deductive (theory first), commonly combined. Thematic analysis (Braun and Clarke) proceeds through familiarisation, coding, generating themes, reviewing themes, defining and naming themes, and writing up with illustrative quotations. Other approaches are content, narrative and discourse analysis, grounded theory and framework analysis.
Descriptive: n and percent for categories; mean and SD (normal) or median and IQR (skewed). Measures of association (2 × 2 table with cells a, b / c, d): relative risk RR = [a/(a + b)] / [c/(c + d)] (cohort, RCT); prevalence ratio (cross-sectional); odds ratio OR = ad/bc (case-control; approximates RR when disease is rare). A ratio of 1 indicates no association, above 1 a risk factor and below 1 a protective factor. Correlation: Pearson r (linear) and Spearman ρ (ranks). Regression: linear (numeric outcome), logistic (binary outcome; adjusted odds ratios) and multiple (adjusting for confounders).
The p-value is the probability of results at least as extreme as those observed if H0 is true; it is not the probability that H0 is true and not an effect size. A 95% confidence interval gives plausible values for the parameter; if a ratio’s interval includes 1 (or a difference’s includes 0) the result is not significant at 5%.
| Qualitative approach | Core idea |
|---|---|
| Thematic | Identify, analyse and report patterns |
| Content | Classify and summarise categories in text |
| Framework (Ritchie and Spencer) | Matrix-based; familiarisation, thematic framework, indexing, charting, mapping and interpretation |
| Grounded theory (Glaser and Strauss) | Builds theory by constant comparison (open, axial, selective coding) to saturation |
Trustworthiness (Lincoln and Guba) is secured by credibility (triangulation, member checking), transferability (thick description), dependability (audit trail) and confirmability (reflexivity).
| Purpose | Display |
|---|---|
| Key findings in a sentence | Text |
| Exact values | Table |
| Categories compared | Bar chart (pie only for few parts of a whole) |
| Trend over time | Line graph |
| Distribution of a numeric variable | Histogram |
| Two numeric variables | Scatter plot |
| Spread and outliers | Box plot |
Label axes and units, convey one message per figure and link each result to an objective. Report the estimate with its 95% confidence interval and the exact p-value. Tables are numbered and titled above; figures below. Quotations should be short, verbatim and anonymised (for example “Female nurse, 35 years, FGD 2”), balancing typical and divergent views.
SPSS: Variable View (name, type, label, values, missing codes, measure) > Data View (one row per respondent) > clean (Frequencies, Explore) > transform (Recode, Compute) > analyse (Crosstabs for chi-square; Compare Means for t-test and ANOVA; Correlate; Regression) > output (exact p and 95% CI; save data, syntax and output).
ATLAS.ti: create a project > add documents (transcripts, notes, PDFs, images, audio) > code quotations (open or deductive) > merge and refine codes into a codebook > write memos > group codes into themes > explore (code manager, co-occurrence, networks) > query by document group > export reports and quotations.
Spread and the normal curve. For 4, 6, 6, 8, 11: mean 7, median 6, variance 28/4 = 7, SD 2.65. In a normal distribution about 68%, 95% and 99.7% of values lie within 1, 1.96 and 3 SD of the mean; z = (x − mean)/SD.
Standard error and confidence interval. SE of a mean = SD/√n; of a proportion = √(pq/n). If 45 of 150 women are anaemic, p = 0.30, SE = 0.037 and the 95% interval is 22.7% to 37.3%.
Hypothesis test. State H0 and H1; set α and tails; choose the test and check assumptions; compute the statistic; find the critical value or p; decide; interpret with effect size and interval. Two-tailed critical Z: ±1.645 (α 0.10), ±1.96 (0.05), ±2.576 (0.01). Example: mean waiting time claimed 30 minutes; sample of 64 gives mean 33, SD 12; SE = 1.5, Z = 2.0 > 1.96, so H0 is rejected (p ≈ 0.046); the interval 30.06 to 35.94 excludes 30.
| Test | Statistic | df |
|---|---|---|
| One-proportion Z | (p − P0)/√(P0Q0/n) | — |
| One-sample t | (x̄ − μ0)/(s/√n) | n − 1 |
| Independent t | (x̄1 − x̄2)/[Sp√(1/n1 + 1/n2)] | n1 + n2 − 2 |
| Paired t | d̄/(sd/√n) | n − 1 |
| One-way ANOVA | F = MSB/MSW | k − 1; N − k |
| Chi-square | Σ(O − E)²/E | (r − 1)(c − 1) |
ANOVA example. Haemoglobin under supplements A (5, 6, 7, 6), B (8, 9, 7, 8) and C (10, 11, 9, 10): SSB = 32 (2 df), SSW = 6 (9 df), F = 16/0.667 = 24.0, above the critical 4.26, so H0 is rejected; a post hoc test (Tukey HSD = 1.61) shows every pair differs. Repeated t-tests would inflate the false-positive rate (to about 14% for three pairs).
Chi-square example. Of 100 cases and 100 controls, 60 and 30 were exposed. Expected counts are 45, 45, 55 and 55, so χ² = 5 + 5 + 4.09 + 4.09 = 18.18 on 1 df (critical 3.84; p < 0.001): exposure and case status are associated. Expected counts should be at least 5 in at least 80% of cells; otherwise use Fisher’s exact test.
Simple regression. For training years x = 1–5 and satisfaction y = 2, 4, 5, 4, 5: b = 0.6, a = 2.2, R² = 0.60, but t = 2.12 on 3 df is below the critical 3.182, so the slope is not significant: a high R² with five observations does not establish a relationship. Residual plots should show random scatter about zero; assumptions are linearity, independent errors, normal residuals and constant variance.
Ethics comprises the norms of conduct, the discipline that studies standards of conduct and a way of deciding how to act. It draws on the Hippocratic tradition of non-maleficence (the phrase “first, do no harm” is not literally in the Oath, which promises to abstain from doing harm). Integrity is consistent behaviour in line with principles; honesty is truthfulness and fairness even when a finding is inconvenient; loyalty is allegiance to a person, group or institution and must never override honesty in reporting. “Pure” research is not exempt from ethics, as the history of research abuse shows.
| Event | Lesson |
|---|---|
| Tuskegee syphilis study (1932–72): 399 poor, mostly illiterate African American men with syphilis (and 201 controls) were left untreated and deceived, even after penicillin | No informed consent; denial of treatment; exploitation of the poor |
| Nazi experiments (1939–45) | Led to the Nuremberg trials |
| Nuremberg Code (1947): ten principles; voluntary informed consent essential | First international research ethics code |
| Willowbrook (1956–70): institutionalised children deliberately infected with hepatitis | Coercion of parents and vulnerable children |
| Declaration of Helsinki (1964), World Medical Association | Subject’s welfare before science and society; independent committee review added in 1975 |
| Beecher (1966): 22 unethical studies listed in NEJM | Journals should examine consent |
| Belmont Report (1979) | Three principles; basis of regulation |
| CIOMS guidelines (from 1982) | Cultural context and external review in developing countries |
Participants are protected by two pillars: informed consent and independent ethics review.
| Belmont principle | Meaning | Applied as |
|---|---|---|
| Respect for persons (autonomy) | Treat people as autonomous; protect those with diminished autonomy | Informed consent; voluntariness |
| Beneficence | Do good; avoid harm | Maximise benefit, minimise risk |
| Justice | Fairness | Fair selection; no exploitation; benefits shared with those who bear the burden |
Non-maleficence is contained within beneficence, so Belmont names three principles; the Beauchamp and Childress model names four (autonomy, beneficence, non-maleficence, justice). Vulnerable populations include children, the elderly, the mentally ill, the poor and uneducated, pregnant women, prisoners, and employees, students or soldiers, who are open to coercion by authority. Recruiting one’s own students or employees is coercion-prone; recruitment should be open to a wider population.
A research problem should be essential (not trivial or duplicative), worth the burden placed on participants, and not designed to exploit or stigmatise a group. The NHRC guidelines rest on four core principles (respect for autonomy; beneficence and non-maleficence; justice; respect for the environment) and thirteen general principles, including essentiality, voluntariness, non-exploitation, social responsibility, privacy and confidentiality, risk minimisation, benefit maximisation, professional competence, transparency and accountability, and dissemination of findings. Externally sponsored research requires a Nepali principal investigator who carries legal, technical and ethical responsibility in Nepal; transfer of biological samples abroad requires an agreement.
Plagiarism is presenting another’s words, data or ideas as one’s own, including self-plagiarism and patchwriting. It is avoided by citing sources, quoting exact words with quotation marks, paraphrasing properly with citation, keeping note records and using similarity-check software. Related misconduct includes fabrication, falsification, authorship misconduct (ghost, guest, gift), undeclared conflicts of interest, salami slicing and duplicate publication.
Informed consent is a process, not merely a form, by which a person voluntarily confirms willingness to participate after being told all relevant aspects. It requires decision-making capacity and a signed, dated form; where a person cannot sign, a thumbprint with an independent literate witness is used. Valid consent requires adequate information, comprehension, voluntariness and competence.
| Age (NHRC 2022) | Requirement |
|---|---|
| 18 and above | Written consent by the person |
| 12 to under 18 | Written consent of the legally authorised representative plus written assent of the child |
| 7 to under 12 | Representative’s consent plus verbal assent (recorded on audio or video) |
| Under 7 | Representative’s consent; no assent required |
A child’s refusal must be respected; adults lacking capacity need a legally authorised representative. A consent form states purpose, procedures, risks and discomforts, benefits, payment, voluntary participation and the right to withdraw without penalty, compensation for injury, confidentiality, alternatives, sample storage, contacts and signature. Incentives should compensate time and expense without becoming an undue inducement and must not depend on remaining in the study. Sensitive information (sexual behaviour, illegal acts, HIV, mental health, violence) calls for private settings, trained interviewers, the option to skip questions, referral for support and protection from legal or social harm. Confidentiality is protected by anonymous collection where possible, coding and removal of identifiers, restricted access, encryption and separate storage of consent forms; it may be breached only in strict exceptions (for example child abuse or imminent harm) after consulting the ethics committee. Bias is limited by randomisation, blinding, training, protocol compliance, restriction and stratification, and by reporting unexpected findings.
History. The Nepal Medical Research Committee was set up on 15 April 1982 under the Ministry of Health as the first regulatory body. The NHRC was established on 12 April 1991 by an Act of Parliament as an autonomous body; the National Health Research Policy followed in 2003. Responsibilities: to promote and coordinate health research, undertake or commission research on current and future problems, provide consultancy and information, and advise government for evidence-based policy and planning.
NHRC Act 1991. Section 3 establishes the Council. Under Section 11, any individual or organisation wishing to conduct health research must obtain the Council’s permission, which may set conditions. Under Section 12 the Council may issue directions and may warn or restrict research conducted without approval or in breach of directions, after giving a fair chance to defend.
Executive committee (Act, Section 7). Seven persons nominated by government from among physicians of modern and ayurvedic medicine with health research experience (including a chairperson and vice-chairperson); representatives of the Ministry of Finance, the National Planning Commission and the Ministry of Health; the head of the Research Committee of the Institute of Medicine; the Chairperson of the Nepal Medical Council; and a member secretary designated by government. Sections: Ethical Review, Monitoring and Evaluation; Research; Capacity Building; Information and Library; Journal of NHRC.
Ethical review pathway. Proposals are developed with consent forms and tools; most institutional studies go to the Institutional Review Committee (IRC) of the institution; national-level studies, externally funded studies and clinical trials go to the NHRC Ethical Review Board (ERB); approval is followed by data collection with progress reports, adverse-event reports and amendments, and a final report. The ERB has been FERCAP-accredited since November 2019 and uses a digital application system; about 56 IRCs are accredited according to the lecture (recent sources give about 60 to 63). Clinical trials must be registered in a WHO-accredited registry (a national registry is being built under a December 2024 NHRC–WHO agreement).
Journal of NHRC (JNHRC) is the Council’s peer-reviewed, open-access journal, started in 2002 and indexed in MEDLINE and PubMed since 2010; an ethical approval letter is compulsory and trials require a registry number. The National Ethical Guidelines for Health Research in Nepal have been revised several times (2001, 2011, 2019 and the current 2022 edition). Legislation determines what is mandatory, deters violators and provides access to justice, but ethics remains necessary because not everything can be legislated.
A research proposal is a persuasive document proposing a study: question, objectives, literature review, methodology, expected results and impact. Its audience is funding agencies, academic institutions and ethics committees. It is written in the future tense and ends at the analysis plan; the report uses the past tense and adds results, discussion, conclusions and recommendations.
The problem statement is concise and precise: what the problem is and why it matters, rooted in the gap between the actual and the desired state. It proceeds like a funnel: the ideal state, the reality (the failure or burden), evidence of its magnitude, the gap in knowledge and how this study addresses it. The rationale explains why the study is needed and who will use the results. Ideas arise from the literature, discussion and brainstorming, district meetings and seminars, observation, monitoring data and daily experience.
One to three research questions show the relationship among variables. Hypotheses are testable, derive from theory or observation, state a null and an alternative, and are directional or non-directional. Objectives comprise one general and several specific objectives with action verbs, following SMART criteria. Example (outpatient satisfaction in a district hospital): H0, there is no association between waiting time and satisfaction; H1, longer waiting is associated with lower satisfaction.
The methodology chapter states and justifies: the study design (cross-sectional or longitudinal; descriptive or analytical) and approach; study area and population; inclusion and exclusion criteria; study unit and sampling unit; sampling frame and technique; sample size calculation (or saturation) with formula, assumptions and non-response allowance; variables with operational definitions; and the conceptual framework. An operational definition states how a variable will be measured so that others can replicate it (for example, “long waiting time” = more than 60 minutes from registration to start of consultation, recorded on a tracking slip).
| Technique | Tool |
|---|---|
| Structured interview or survey | Interview schedule or questionnaire |
| In-depth interview | Open-ended interview guide |
| Focus group discussion | FGD guide and moderator checklist |
| Direct observation | Observation checklist |
When a standard tool is adopted, state the permission obtained, its validity and reliability, and any translation and back-translation. Building a tool proceeds from the objectives and conceptual framework: review existing validated tools; draft items, formats and instructions; use simple, specific wording; obtain expert review; translate and back-translate if needed; pre-test; revise; and train and supervise data collectors. A pre-test tries the instrument on a small group from the target population (about 5–10% of the planned size) outside the study area to check wording, skip patterns and timing; a pilot study is a rehearsal of the whole procedure including recruitment, logistics, entry and variance estimates.
Implementation specifies who collects data, enumerator training, supervision, timeline, quality checks (daily review of forms, double entry of a sample) and handling of non-response. The analysis plan covers entry, cleaning and a table giving each variable’s scale and the descriptive and inferential test for each objective. Ethical considerations include IRC or NHRC approval, informed consent, confidentiality and benefit to participants. A work plan (Gantt chart by month) and an itemised budget (personnel, field costs, equipment and software, ethical review, dissemination, and a contingency of 5–10%) complete the proposal.
Validity is whether an instrument measures what it intends to measure.
| Type | Meaning | Method |
|---|---|---|
| Face | Appears relevant | Informal review |
| Content | Items cover the whole construct | Expert panel; content validity index (about 0.8 or higher) |
| Criterion | Agrees with a gold standard | Concurrent or predictive comparison |
| Construct | Measures the abstract construct | Factor analysis; known groups; convergent and discriminant validity |
Reliability is consistency of results.
| Type | Meaning | Statistic |
|---|---|---|
| Internal consistency | Items measure the same construct | Cronbach’s alpha: ≥ 0.70 acceptable, ≥ 0.80 good; above about 0.90 may indicate redundancy |
| Test-retest | Stability over time | Correlation or intraclass correlation |
| Inter-rater | Agreement between observers | Cohen’s kappa |
| Split-half | Two halves agree | Correlation with Spearman-Brown correction |
Cohen’s kappa = (Po − Pe)/(1 − Pe), where Po is observed and Pe chance-expected agreement. Landis and Koch bands: 0.01–0.20 slight; 0.21–0.40 fair; 0.41–0.60 moderate; 0.61–0.80 substantial; 0.81–1.00 almost perfect. Worked example: two raters classify 100 charts (both complete 70, both incomplete 15, disagreements 10 and 5): Po = 0.85, Pe = 0.80 × 0.75 + 0.20 × 0.25 = 0.65, kappa = 0.20/0.35 = 0.57, moderate agreement. A reliable instrument may be invalid (a scale that always reads 2 kg too heavy), but an unreliable one cannot be valid: reliability is necessary, not sufficient. Qualitative rigour is assessed by credibility, transferability, dependability and confirmability; in mixed methods, report validity and reliability for each strand (Zohrabi, 2013; Bolarinwa, 2015).
Steps: logical analysis of the subject matter; final outline; rough draft; rewriting and polishing (the hardest step); final references; final draft in concise, objective, simple language.
| Part | Contents |
|---|---|
| Preliminaries | Title and approval pages; acknowledgement; abstract (about 250–300 words, written last); contents; lists of tables and figures; abbreviations |
| Introduction | Background tapering to the problem; problem statement; rationale; questions and hypotheses; objectives; operational definitions |
| Literature review | Search strategy; review organised by objective and variable, ending with the gap; conceptual framework |
| Methodology | Design, area, population, sampling, sample size, tools, pre-test, validity and reliability, data collection, analysis plan, ethics |
| Results (report) | Findings by objective, in tables, figures and quotations, without interpretation |
| Discussion (report) | Comparison with the literature; explanation; strengths and limitations |
| Conclusions and recommendations (report) | Conclusions answer the objectives; recommendations are actionable and say who should act |
| Supplementary | References; appendices (tools, consent form, work plan, budget) |
The IMRaD structure (Introduction, Methods, Results and Discussion) underlies scientific articles. Findings state facts; conclusions weave them together. Use short sentences, sub-headings, uniform format and objective language, and reserve “significant” for statistical significance.
A citation acknowledges a source in the text; the reference list gives full details of cited sources; a bibliography may also include works consulted but not cited. One style is used consistently.
| Vancouver | APA (author–date) | |
|---|---|---|
| In text | Number | (Creswell, 2018) |
| List order | Order of first citation | Alphabetical |
| Journal article | Author AA, Author BB. Title. Abbreviated Journal. Year;Volume(Issue):pages. | Author, A. A., & Author, B. B. (Year). Title. Journal, volume(issue), pages. |
| Common in | Medicine | Social and behavioural sciences |
Grey literature is cited fully (organisation, title, year, URL, access date). Reference managers (Zotero, Mendeley, EndNote) help; every in-text citation must appear in the list and vice versa. An abbreviation shortens a word (J Nepal Health Res Counc); an acronym is formed from initials (WHO).
| Pair | Key difference | Unit |
|---|---|---|
| Methods vs methodology | How (tools) vs why (logic and philosophy) | 1 |
| Basic vs applied | Theory and curiosity vs an immediate practical problem | 1 |
| Positivism vs interpretivism | One objective reality, deductive vs multiple constructed realities, inductive | 1 |
| Descriptive vs analytical | What, no comparison group vs why, comparison group | 2 |
| Observational vs experimental | Researcher does not assign exposure vs does | 2 |
| Case-control vs cohort | Starts from outcome, odds ratio vs starts from exposure, relative risk | 2 |
| Case-control vs nested case-control | Vague source population vs defined cohort with prospective exposure data | 2 |
| Allocation concealment vs blinding | Before allocation (selection bias) vs after (performance and detection bias) | 3 |
| ITT vs per-protocol | All randomised vs compliers only | 3 |
| Systematic vs scoping vs narrative | Question and protocol vs map of the field vs flexible overview | 4 |
| Stratified vs cluster | Sample every homogeneous stratum vs sample whole heterogeneous clusters | 5 |
| Probability vs non-probability | Known chance, generalisable vs unknown chance, exploratory | 5 |
| Sampling vs non-sampling error | Falls with n vs does not | 5 |
| Type I vs Type II error | α, false positive vs β, false negative | 5 |
| Statistical vs clinical significance | p-value vs patient benefit | 5 |
| Primary vs secondary data | First-hand vs existing | 6 |
| Parametric vs non-parametric | Normal, interval or ratio data vs distribution-free, nominal or ordinal | 6 |
| Reliability vs validity | Consistency vs accuracy | 8 |
| Pre-test vs pilot | Wording and clarity of the tool vs rehearsal of the full procedure | 8 |
| Citation vs bibliography | In-text acknowledgement vs full list at the end | 8 |
Symbols: x̄ sample mean; s sample SD; n sample size; N population size; p sample proportion, q = 1 − p; σ population SD; μ population mean; α significance level; β Type II error; df degrees of freedom.
| Item | Formula |
|---|---|
| Mean, variance, SD, CV | x̄ = Σx/n; s² = Σ(x − x̄)²/(n − 1); s = √s²; CV = s/x̄ × 100 |
| Z score | z = (x − μ)/σ |
| Standard error of a mean; of a proportion | SD/√n; √(pq/n) |
| 95% CI of a mean; of a proportion | x̄ ± 1.96 SE (t × SE for small samples); p ± 1.96√(pq/n) |
| Sample size, proportion; mean | n = Z²pq/d²; n = Z²σ²/d² |
| Sample size per group, two proportions; two means | (Zα/2 + Zβ)²(p₁q₁ + p₂q₂)/(p₁ − p₂)²; 2σ²(Zα/2 + Zβ)²/δ² |
| Finite population; non-response; design effect | n/(1 + (n − 1)/N); n/(1 − non-response rate); DEFF = 1 + (m − 1)ρ |
| Sampling interval | k = N/n |
| Relative risk; odds ratio | [a/(a + b)]/[c/(c + d)]; ad/bc |
| Sensitivity; specificity; PPV; NPV | TP/(TP + FN); TN/(TN + FP); TP/(TP + FP); TN/(TN + FN) |
| t, one-sample; independent; paired | (x̄ − μ₀)/(s/√n); (x̄₁ − x̄₂)/[Sp√(1/n₁ + 1/n₂)]; d̄/(sd/√n) |
| Chi-square; McNemar | Σ(O − E)²/E, df = (r − 1)(c − 1); (b − c)²/(b + c), df = 1 |
| Pearson r; regression slope and intercept | Σ(x − x̄)(y − ȳ)/√[Σ(x − x̄)²Σ(y − ȳ)²]; b = Σ(x − x̄)(y − ȳ)/Σ(x − x̄)², a = ȳ − bx̄ |
| Cohen’s kappa; Cronbach’s alpha; Spearman-Brown | (Po − Pe)/(1 − Pe); [k/(k − 1)](1 − Σsi²/sT²); 2r/(1 + r) |
| Guideline | Study type |
|---|---|
| CONSORT (25 items in 2010; 30 in 2025) | Randomised trials; checklist and flow diagram |
| SPIRIT | Trial protocols |
| STROBE | Observational studies |
| PRISMA; PRISMA-ScR | Systematic reviews; scoping reviews |
| STARD | Diagnostic accuracy studies |
| COREQ; SRQR | Qualitative research |
Key dates. Nuremberg Code 1947; Declaration of Helsinki 1964 (independent review added 1975); Belmont Report 1979; Nepal Medical Research Committee 1982; NHRC Act 1991; National Ethical Guidelines for Health Research in Nepal 2001, 2011, 2019 and 2022 (current).
Note on figures. Where source versions disagreed, the better-supported figure is used: CONSORT 25 (2010) and 30 (2025); assent ages 7 to under 12 (verbal) and 12 to under 18 (written); Journal of NHRC started in 2002 and indexed in PubMed since 2010; independent committee review entered the Helsinki Declaration in 1975; Belmont lists three principles.
Each topic below links to its section in the notes and carries tags. Use your browser’s find function (Ctrl+F or Cmd+F) or the site search with a tag such as #odds-ratio to locate every topic that matters for it.
| Topic | Tags |
|---|---|
| 1.1 Concept and types of research | #research-definition #kerlinger #kothari #basic-vs-applied #evaluation-research #action-research #scientific-method |
| 1.2 Methods versus methodology | #methods #methodology #how-vs-why |
| 1.3 Research approaches | #quantitative #qualitative #mixed-methods #deductive #inductive |
| 1.4 Research paradigms | #paradigm #ontology #epistemology #positivism #interpretivism #pragmatism #kuhn |
| 1.5 Research process and criteria of good research | #research-process #good-research #researcher-ethics #violations |
| Topic | Tags |
|---|---|
| 2.1 Elements and types of research design | #research-design #design-elements #choosing-a-design #evolving-designs |
| 2.2 Descriptive vs analytical | #descriptive #analytical #case-report #case-series #ecological-study #ecological-fallacy |
| 2.3 Observational vs experimental | #observational #experimental #quasi-experimental |
| 2.4 Prospective, retrospective, cross-sectional | #prospective #retrospective #cross-sectional #cohort #prevalence |
| 2.5 Case-control vs nested case-control | #case-control #nested-case-control #odds-ratio #recall-bias |
| 2.6 Conceptual vs empirical | #conceptual #empirical |
| 2.7 Descriptive and diagnostic design | #diagnostic-research #descriptive-design |
| 2.8 Exploratory design | #exploratory #formulative #hypothesis-generation |
| 2.9 Hypothesis-testing design | #hypothesis-testing #null-hypothesis #alternative-hypothesis |
| 2.10 Mixed-methods designs | #convergent-parallel #explanatory-sequential #exploratory-sequential #embedded #triangulation |
| Supplement: bias and causation | #chance #selection-bias #information-bias #confounding #bradford-hill #hierarchy-of-evidence |
| Topic | Tags |
|---|---|
| 3.1 Concept, history and conduct | #rct #gold-standard #parallel #crossover #factorial #cluster-trial #lind #streptomycin-1948 |
| 3.2 Arms, randomisation, blinding | #trial-arms #placebo #randomisation #block #stratified-randomisation #allocation-concealment #blinding |
| 3.3 CONSORT | #consort #flow-diagram #reporting-guideline #spirit #strobe #prisma |
| 3.4 Bias and its control | #selection-bias #performance-bias #detection-bias #attrition-bias #reporting-bias #intention-to-treat #per-protocol |
| 3.5 Ethics in RCTs | #equipoise #consent #placebo-ethics #dsmb #trial-registration |
| Supplement: trial phases | #phase-1 #phase-2 #phase-3 #phase-4 |
| Topic | Tags |
|---|---|
| 4.1 Topic, problem and focusing the question | #research-topic #research-problem #brainstorming #pico #peco #spider #finer |
| 4.2 Literature search and grey literature | #literature-review #search-strategy #boolean #grey-literature #snowballing #primary-secondary-tertiary |
| 4.3 Systematic, narrative, scoping reviews | #systematic-review #narrative-review #scoping-review #prisma #prisma-scr |
| 4.4 Databases | #pubmed #scopus #nepjol #cochrane #google-scholar |
| 4.5 Variables | #independent-variable #dependent-variable #moderating #mediating #confounder |
| 4.6 Research questions, aims, objectives | #research-question #aim #objectives #smart #question-challenges |
| 4.7 Assignment | #search-table |
| Topic | Tags |
|---|---|
| 5.1 Unit, frame and methods | #population #sampling-unit #sampling-frame #parameter #statistic |
| 5.2 Criteria and types of sampling | #simple-random #systematic #stratified #cluster-sampling #convenience #purposive #quota #snowball |
| 5.3 Sample survey vs census | #census #sample-survey #sampling-error #non-sampling-error |
| 5.4 Probability vs non-probability | #probability-sampling #non-probability-sampling |
| 5.5 Sample size and power | #sample-size #cochran #design-effect #non-response #power |
| 5.6 Type I and Type II error | #type-1-error #type-2-error #alpha #beta #parametric #non-parametric |
| 5.7 Significance | #statistical-significance #clinical-significance #social-significance #effect-size |
| Topic | Tags |
|---|---|
| 6.1 Types of data and sources | #noir #nominal #ordinal #interval #ratio #primary-data #secondary-data #prompt #observation |
| 6.2 Interviews and questionnaires | #structured-interview #semi-structured #fgd #capi #questionnaire-design #recording |
| 6.3 Data preparation | #editing #coding #codebook #cleaning #tabulation #missing-values #outliers #imputation |
| 6.4 Analysis and presentation | #descriptive-statistics #inferential #choosing-a-test #anova #thematic-analysis #content-analysis |
| 6.5 Interpretation | #p-value #confidence-interval #relative-risk #odds-ratio #regression #framework-analysis #grounded-theory #trustworthiness |
| 6.6 Communicating and displaying data | #tables #graphs #quotations #reporting-results |
| 6.7 SPSS and ATLAS.ti | #spss #atlas-ti #workflow |
| Supplement: worked statistics | #standard-error #z-test #t-test #chi-square #regression-example #worked-example |
| Topic | Tags |
|---|---|
| 7.1 Ethics and profession | #ethics #integrity #honesty #loyalty |
| 7.2 History, codes and principles | #tuskegee #nuremberg #helsinki #belmont #cioms #vulnerable-groups |
| 7.3 Research problem and plagiarism | #plagiarism #misconduct #nhrc-principles #fabrication #falsification |
| 7.4 Collecting information | #informed-consent #assent #incentives #sensitive-information #confidentiality |
| 7.5 Writing and dissemination | #authorship #icmje #salami-slicing #conflict-of-interest |
| 7.6 NHRC | #nhrc #nhrc-act-1991 #section-11 #erb #irc #jnhrc #ethical-guidelines |
| Topic | Tags |
|---|---|
| 8.1 Problem statement and hypotheses | #problem-statement #rationale #hypothesis #proposal |
| 8.2 Design, sampling, instrumentation | #methodology-chapter #operational-definition #inclusion-exclusion |
| 8.3 Tools and implementation | #study-tools #pre-test #pilot-study #work-plan #budget #gantt |
| 8.4 Validity and reliability | #validity #reliability #cronbach-alpha #kappa #content-validity-index |
| 8.5 Report outline | #report-writing #imrad #abstract |
| 8.6 Citation and bibliography | #citation #vancouver #apa #reference-manager |
| 8.7 Precautions in report writing | #report-precautions #reporting-guidelines |
| Topic | Tags |
|---|---|
| A. Master comparison table | #comparison #revision |
| B. Formulae | #formulae #sample-size-formula |
| C. Reporting guidelines and dates | #consort #strobe #prisma #stard #key-dates |
Development, economy, community and planning
A rural area is a region with low population density, limited industrialization and heavy dependence on agriculture and other primary activities such as farming, livestock, forestry and fishing.
Characteristics:
In Nepal, rural areas still cover most of the country’s land and are home to a significant share of the population, though urbanization is accelerating.
Rural development is a comprehensive, multi-dimensional process of improving the economic, social, cultural and environmental well-being of rural people in a sustainable and inclusive manner.
Economic: agricultural productivity, non-farm enterprises, employment, access to markets and finance, poverty reduction.
Social: education, health, nutrition, sanitation, gender equality and inclusion, empowerment of women, Dalits, Janajatis and persons with disability.
Infrastructural: rural roads, electrification and renewable energy, drinking water, communication, housing.
Agricultural and natural resources: sustainable agriculture, irrigation, community forestry, climate adaptation, disaster risk reduction.
Institutional and governance: strengthening local government, participatory planning, capacity building, service delivery.
Environmental: sustainable resource use, conservation, climate resilience, green and low-carbon development.
Pre-1951 (Rana period): very limited development; feudal tenure (Birta, Jagir, Raikar); development mainly for the ruling class.
1951–1970: planned development begins; development institutions; land reform; focus on basic infrastructure and agriculture.
1970s–1980s: Integrated Rural Development Projects; multi-sectoral emphasis; Village Development Committees.
1990s: decentralization and local self-governance; community forestry and participatory approaches; growing role of NGOs.
2000s: poverty alleviation as the central agenda; rural roads, electricity and water; livelihood programmes.
Post-2015 (federal era): stronger local governments; local economic development, climate resilience, rural tourism, digital inclusion.
Rural reconstruction: rebuilding rural areas affected by underdevelopment, conflict or disaster.
Community development: bottom-up process of identifying problems and mobilizing resources.
Sectoral development: focus on one sector such as agriculture, health or infrastructure.
Area development: concentrated effort in a specific area (e.g. IRDPs).
Target group approach: programmes for small farmers, landless, women, Dalits.
Integrated rural development: coordinated multi-sectoral approach.
Sustainable development: meeting present needs without compromising future generations.
Participatory development: local involvement in planning, implementation, monitoring and evaluation.
Population issues: high pressure on limited agricultural land, high dependency ratio, youth out-migration and a changing demographic structure.
Poverty: rural poverty is multi-dimensional, covering income poverty, lack of education, health and sanitation, and vulnerability to shocks. Measurement tools include the national poverty line and the Multidimensional Poverty Index (MPI).
Education: low enrolment in higher grades, high dropout, poor learning outcomes, gender disparity, shortage of qualified teachers in remote areas.
Health: limited access to quality services, higher maternal and child mortality in remote areas, malnutrition, and a rising burden of non-communicable diseases.
Access to improved water and sanitation has risen significantly, but quality and functionality remain issues in many rural areas. Housing is still inadequate for a large section of the rural poor.
| Basis | Economic Growth | Economic Development |
|---|---|---|
| Meaning | Increase in national output | Growth plus structural and qualitative change |
| Nature | Quantitative | Quantitative and qualitative |
| Time horizon | Short-term | Long-term |
| Measurement | GDP, GNP, per capita income | HDI, PQLI, inequality measures, happiness indices |
| Scope | Narrow | Broad |
| Possibility | Possible without development | Not possible without growth |
Objectives of development: raising living standards; reducing poverty and inequality; expanding human choices and capabilities; structural transformation; environmental sustainability; social justice.
Problems of developing economies:
Schultz – transformation of traditional agriculture: traditional agriculture is poor but efficient; farmers allocate resources rationally. Transformation needs investment in human capital, agricultural research and affordable modern inputs.
Lewis – unlimited supply of labour: surplus labour in the subsistence sector moves to the modern sector at a constant wage until the surplus is exhausted.
Balanced vs unbalanced growth: Rosenstein-Rodan and Nurkse favour simultaneous investment in many industries to create mutual demand; Hirschman favours deliberate imbalance through leading sectors with strong linkages.
Modernization theory: development as a linear move from traditional to modern society.
Dependency theory: underdevelopment results from the exploitative relationship between the core and the periphery.
Human development approach: popularized by UNDP and Amartya Sen; expands people’s capabilities and freedoms rather than only income.
Concept and characteristics; planning in capitalist, socialist and mixed economies; types of planning; growth pole and growth centre; capital-output ratio; planning in SAARC countries and Nepal.
Project planning; project cycle; screening; implementation, monitoring and evaluation of rural development projects.
Expanded outline: demand, supply and market equilibrium; production theory and function; the law of variable proportions and its three stages; cost concepts and cost minimization; capital formation in rural areas; the vicious circle of poverty and ways to break it; Mellor, Lewis and Ranis-Fei models; food security in Nepal; impact of economic liberalization on the rural economy.
Demand, supply and price determination; welfare economics; scope of rural economics; production theory; Cobb-Douglas production function; linear programming.
Concept, assumptions and applicability in agriculture; farm management; risk and uncertainty; crop and cattle insurance; minimum support prices.
Meaning, process and conditions; vicious and virtuous cycles; capital formation in agriculture; Rostow, Mellor, Lewis and Ranis-Fei models with critiques.
Concept and dimensions; food, input and marketing subsidies; liberalization, privatization, globalization and their impact on agriculture and rural development.
Knowledge on contemporary issues, models, techniques, problems, prospects, policies and strategies of rural-urban integration.
Expanded outline: rural-urban linkage and interdependency; economic, social, spatial and political linkages; dispersed and agglomerated settlements; continuum vs dichotomy; theories of urban origin and growth; Central Place Theory (Christaller); growth pole and growth centre theory; migration theories including the Todaro model; strategies for strengthening positive linkages.
Urban and rural settings; hinterland linkages; dispersed and agglomerated settlements; dichotomy, continuum and hierarchy.
Theories of evolution of early towns; changing town-country relationship; change in rural-urban relation in Nepal.
Types of rural-urban linkages; constraints in developing countries.
Central place theory (Christaller, Lösch); gateway model; Von Thünen; core-periphery; growth pole/centre; Todaro migration model.
Growth centre identification; scalogram; infrastructure index; centrality; hinterland measures; spatial interaction, flow model, facility location and accessibility.
Spatial transformation model; spatial diffusion of innovation (Hägerstrand); micro-enterprises; informal sector and local resource use.
Integrated rural-urban linkage planning; problems and prospects; public-private partnership; public institutions and strategies.
One of the most important methodological courses in the programme, giving the statistical tools for research, data analysis and evidence-based decisions in rural development.
Measures of central tendency identify the central or typical value of a dataset. They describe a population or sample, compare groups, show average conditions (income, landholding, years of schooling) and support policy decisions.
Sum of all observations divided by their number: x̄ = Σxᵢ / n.
Merits: easy to calculate, uses all data, algebraically treatable.
Demerits: affected by extreme values; unsuitable for highly skewed data.
Application: average household income, landholding size, productivity, years of education.
The middle value when data are ordered: the middle observation for odd n, the average of the two middle values for even n.
Merits: not affected by extremes; suits skewed data and open-ended classes.
Demerits: ignores most data points; less suited to further mathematical treatment.
Application: income and land distribution in rural areas.
The most frequent value. It may be unimodal, bimodal or multimodal.
Merits: easy to understand; usable with nominal data; unaffected by extremes.
Demerits: may not exist or may be multiple; not suited to further analysis.
Application: most common occupation, preferred crop or family size.
Average variation of data points from the mean: s = √[Σ(xᵢ − x̄)² / (n − 1)]. A small SD means data cluster near the mean; a large SD means wide spread. It is the most reliable measure of dispersion and is essential for further tests.
Why it matters: growth alone does not guarantee poverty reduction, so inequality in income, land, education and opportunity must be measured.
Lorenz curve: plots cumulative share of population against cumulative share of income or wealth. The 45-degree line is perfect equality; the larger the gap from it, the greater the inequality.
Gini coefficient: derived from the Lorenz curve, from 0 (perfect equality) to 1 (perfect inequality). Widely used for income and land inequality in rural Nepal.
Probability measures the likelihood of an event from 0 (impossible) to 1 (certain). Key laws: addition law, multiplication law, conditional probability and Bayes’ theorem.
Random variable: discrete (countable, e.g. number of livestock) or continuous (e.g. income, land size).
Binomial distribution: fixed number of independent trials, two outcomes, constant probability of success.
Normal distribution: bell-shaped and symmetric, defined by mean and standard deviation; many tests assume normality.
Central limit theorem: the sampling distribution of the mean approaches normal as sample size grows, whatever the population’s shape.
Meaning: selecting a representative subset to draw conclusions about a population, since studying everyone is often impractical, slow and costly.
Terms: population, sample, parameter (of a population), statistic (of a sample).
Probability sampling: simple random, stratified, systematic, cluster, multi-stage.
Non-probability sampling: purposive, convenience, quota, snowball.
Errors: sampling error arises from chance variation in selection; non-sampling error from data collection, measurement, non-response and processing.
Standard error: the precision of a sample statistic; smaller means more precise.
Correlation measures the strength and direction of a linear relationship. Pearson’s r is for continuous data; Spearman’s rank correlation for ordinal data or non-linear relationships. r runs from −1 to +1.
Regression examines the relationship between a dependent variable and one or more independent variables. Simple linear: Y = a + bX. Multiple regression uses two or more predictors. R² is the share of variation explained.
ANOVA tests the overall significance of a regression model. Dummy variables (0 and 1) bring categorical variables into regression.
Estimation: point estimation gives a single value; interval estimation gives a confidence interval.
Hypothesis testing steps:
Common tests: Z-test, t-test, F-test, Chi-square test.
Sustainable development meets present needs without compromising future generations (Brundtland Commission, 1987). Its three pillars are economic, social and environmental sustainability.
Major themes: global environmental issues and rural areas; the poverty–environment relationship; climate change and livelihoods; sustainable agriculture; renewable energy and biomass; biodiversity conservation; ecological zones of Nepal; community-based resource management; role of GOs, NGOs, INGOs and CBOs; challenges and prospects in Nepal.
Concepts and foundations of sustainable development.
Indicators and trends of sustainable development.
Disaster and sustainable development; poverty and sustainability; biomass-based energy; green revolution and environmental degradation.
Ecological zones; forest resources; human settlement; land and water resources; biodiversity; intellectual property rights.
Sustainability of development in rural Nepal; capacity building; capital mobilization; conservation of local resources; role of GOs, NGOs, INGOs and CBOs.
Evolution of rural development policies in Nepal, national plans, agricultural policies, decentralization policies, poverty alleviation strategies, and current policy frameworks under federalism.
Concept of governance and good governance, its principles, the local governance system in Nepal, federalism and rural development, and challenges of local governance.
Concept and types of infrastructure, its role in rural development, the status of rural infrastructure in Nepal, financing mechanisms, and the impact of infrastructure on poverty and growth.
The foundation for conducting rigorous research in rural development.
Meaning: systematic, scientific study of human society, relationships, behaviour and institutions.
Objectives: describe, explain, predict, influence social processes and deepen understanding of behaviour.
Nature: empirical, theoretical, cumulative, interdisciplinary, dynamic.
Types: basic, applied, exploratory, descriptive, explanatory, evaluative.
Need in rural development: identify real problems; design effective policies; evaluate interventions; promote evidence-based decisions; empower communities through participatory research.
Steps:
Concepts: abstract ideas representing social phenomena (poverty, empowerment, social capital).
Variables: independent (cause), dependent (effect), and control, intervening or moderating variables.
Operationalization: converting abstract concepts into measurable indicators.
Levels of measurement: nominal, ordinal, interval, ratio.
Reliability: consistency of an instrument (test-retest, inter-rater, Cronbach’s alpha).
Validity: measuring what is intended (content, construct, criterion).
Correlation vs causality: correlation shows association; causality needs temporal order, covariation and elimination of alternative explanations.
Approaches: inductive (observation → pattern → theory, qualitative); deductive (theory → hypothesis → observation, quantitative).
Meaning: the overall plan or blueprint that guides the research process.
Types: exploratory, descriptive, explanatory/causal, experimental, ex-post facto, action research, survey, case study, historical, field, evaluation.
Time dimension: cross-sectional; longitudinal or time-series.
Components: title; introduction and background; statement of the problem; rationale; objectives; research questions or hypotheses; literature review; conceptual or theoretical framework; methodology (design, sampling, tools, analysis); limitations; references; work plan and budget.
Methods in rural development research: household survey (questionnaire or schedule), key informant interview, focus group discussion, participant observation, PRA tools, case study, secondary data.
Editing, coding and data entry; univariate, bivariate and multivariate analysis; qualitative analysis (thematic and content analysis).
A good report is clear, logical, objective and well structured: preliminary pages, main body and supplementary pages.
Also covered: skill development and vocational training for rural youth.
Technology is the systematic application of scientific knowledge for practical purposes.
Appropriate technology suits local environmental, cultural, economic and social conditions: low cost, simple to use and maintain, uses local materials and skills, environmentally friendly, culturally acceptable, employment generating.
Role in rural transformation: raises productivity, improves quality of life, creates local employment, reduces drudgery (especially for women), promotes self-reliance, supports sustainability.
Technology transfer needs assessment, adaptation, training and continuous support.
Energy is a critical input for development and correlates strongly with HDI.
Energy in Nepal: heavy dependence on traditional biomass (firewood, dung, residues); large but underused hydropower; growing solar and renewables; challenges of access, reliability and affordability in rural areas.
Renewable technologies for rural areas: solar (home systems, cookers, dryers, water pumps), micro and pico hydropower, improved water mills, biogas, improved cook stoves, wind and geothermal (limited potential).
Policy: Nepal promotes these through subsidies, soft loans and technical support, mainly via the Alternative Energy Promotion Centre (AEPC).
Rainwater harvesting; low-cost drip and sprinkler irrigation; fog-water collection; compost; improved agricultural tools.
Rural green roads; gravity ropeways; waterways.
Solid, liquid and gaseous fuels; improved cooking stoves; biogas plants.
Indigenous technologies: metals, construction materials, weaving, dyeing, paper, fermentation, oils, fishing devices, soap-making, low-cost housing and toilets.
Key themes: characteristics of rural markets; rural vs urban marketing; marketing mix (product, price, place, promotion, people); agricultural marketing and its problems in Nepal; entrepreneurship and types of enterprises; women entrepreneurship; the entrepreneurial process and business plan; legal and institutional support.
Characteristics of rural markets; rural vs urban marketing; marketing mix.
Structure and status of agricultural marketing in Nepal.
Buying behaviour; profile of Nepalese consumers; cottage and small industries.
Segmentation; product, place, price and promotion; branding, grading, labelling, packaging.
Types of entrepreneurship; role in rural development; women entrepreneurship; enabling factors; risks.
Establishing a new venture; IPR; licensing; insurance, liability and contracts.
Business opportunities; idea evaluation; SWOT, benefit-cost and profit-loss analysis; business plan.
Key themes: concept, nature and forms of rural tourism; positive and negative impacts; planning and management; marketing and value chain; ecotourism principles; Nepali models (Sirubari, Ghalegaun, Bandipur, homestays, TRPAP, ACAP).
Nature, components and scope; typology of tourists; evolution; impacts; global code of ethics.
Levels of planning; stakeholders; tourism policies in Nepal; SWOT; diversification.
Segmentation, targeting, positioning; marketing mix; enterprise development; value chain analysis.
Principles; advantages for communities; issues and challenges; sustainability tools.
Protected areas and heritage sites; ACAP; Bandipur, Sirubari, Ghalegaun; pro-poor tourism (TRPAP).
Key themes: concept and components of ecosystems; characteristics of highland ecosystems; ecological zones of Nepal; highland problems (fragility, isolation, limited livelihoods, out-migration); sustainable management; agricultural terracing and community-based management.
Concept, levels, types, components and models.
Biological and abiotic factors; climate, water, disasters and resource degradation.
Goods and services: food, health, business, tourism, recreation.
Features, niche, potentials, constraints and instabilities.
Landscape and resource change; migration; management and enrichment; agricultural terracing.
Community and community development; group dynamics; values, principles, strategies; social inclusion and mobilization; civil society.
Psycho-sociological theory; socio-democratic theory (A. Gramsci).
State-led, market-led and NGO-led strategies; people-centred approaches; state policies in Nepal.
History from pre-unification to post-2046 BS; Khedi, Parma, Bhajaita, Dhikuri, Guthi, Nogar, Rodi, Bheja.
SFDP, community forestry, PCRW, Grameen Bikas Bank, farmer-managed irrigation, community schools and health programmes.
Role of participation and communication; problems and challenges; case studies.
Concept and definitions of community and community development; group dynamics; values and principles of community development; processes, strategies and techniques of community development; social inclusion and social mobilization; civil society and community development.
Community:
Community Development:
In summary, understanding these concepts and their interplay helps in effectively planning and executing community development initiatives, ensuring they are inclusive, sustainable, and responsive to the needs of the rural population.
Psycho-sociological theory of community development; socio-democratic theory of community development (A. Gramsci).
Overview: The Psycho-Sociological Theory of Community Development focuses on the interplay between psychological and social factors in the development of communities. It recognizes that a community is not merely a collection of individuals but a complex web of relationships, shared values, norms, and social structures that significantly influence individual well-being and collective action.This theory emphasizes the role of individual and collective psychological processes in shaping community dynamics and development.
Key Components:
Application:
Overview: Antonio Gramsci's Socio-Democratic Theory of Community Development is rooted in his broader theoretical framework, which includes concepts like hegemony, cultural leadership, and the role of civil society in social change.
Key Concepts:
Relevance to Socio-Democratic Ideals: Gramsci's emphasis on challenging power imbalances, fostering critical consciousness, and empowering marginalized groups aligns with core socio-democratic values of social justice, equality, and participation. A socio-democratic approach to community development, drawing on Gramsci's insights, would likely focus on:
In essence, while not a direct theory of community development, Gramsci's work provides a powerful framework for analyzing power dynamics, understanding how consent is manufactured, and developing strategies for transformative community change rooted in principles of social justice and democratic participation.
Application:
Summary:
Understanding these theories provides valuable insights into how community development can be approached from different angles, addressing both psychological and socio-political dimensions to foster sustainable and equitable development.
State-led development strategy; market-led development strategy; NGO-led development strategy; people-centered development approaches; development as community transformation; state policies in community development with special focus on recent policies in Nepal.
Definition: State-led development refers to an approach where the government takes a central role in planning, implementing, and overseeing development initiatives. This strategy often involves top-down decision-making where policies and programs are designed and executed by government institutions.
Characteristics:
Examples:
Pros:
Cons:
Definition: Market-led development focuses on leveraging market mechanisms, private sector investment, and entrepreneurial activities to drive economic growth and development. This strategy emphasizes the role of the market in allocating resources and promoting innovation.
Characteristics:
Examples:
Pros:
Cons:
Definition: NGO-led development involves non-governmental organizations taking a leading role in planning and implementing development projects. NGOs often focus on grassroots-level initiatives and aim to address specific needs within communities.
Characteristics:
Examples:
Pros:
Cons:
Definition: People-centered development emphasizes the active involvement of community members in the development process. It focuses on empowering individuals and communities to take charge of their own development and ensure that development efforts are aligned with their needs and aspirations.
Characteristics:
Examples:
Pros:
Cons:
Definition: Development as community transformation views development as a process of profound and holistic change within communities. This approach emphasizes not just economic growth but also social, cultural, and political transformation.
Characteristics:
Examples:
Pros:
Cons:
Overview: State policies play a critical role in shaping community development by providing frameworks, resources, and support for various initiatives. In Nepal, recent policies have been focused on addressing diverse development needs, improving governance, and promoting inclusive growth.
Recent Policies in Nepal:
Pros:
Cons:
In summary, understanding these trends provides a comprehensive view of the various approaches and strategies in community development, highlighting their strengths, challenges, and implications for effective and sustainable development. The focus on recent policies in Nepal illustrates how state-led strategies can evolve to address contemporary development needs and priorities.
History of community development (CD): pre-unification, unification and Rana periods, planned development period (2007-2017BS), Panchyat period, after 2046BS – BOVO, PDDP, LGP, DLGSP; community-based organizations (CBOs) in Nepal: Khedi (in far-west), Parma, Bhajaita, Dhikuri, Guthi, Nogar, Rodi, Bheja).
1. Pre-Unification Period (Before 1768)
2. Unification and Rana Period (1768–1950)
3. Planned Development Period (2007-2017 BS) [1950-1960]
4. Panchayat Period (1961-1990)
5. Post-2046 BS (1990 onwards)
1. Khedi (Far-Western Nepal)
2. Parma
3. Bhajaita
4. Dhikuri
5. Guthi
6. Nogar
7. Rodi
8. Bheja
The history of community development in Nepal reflects a transition from centralized, top-down approaches to more participatory and decentralized strategies. The evolution includes a focus on planned development, decentralization, and local governance reforms. Community-based organizations (CBOs) have played a crucial role in addressing local needs and fostering development at the grassroots level.
Understanding these historical contexts and the roles of various CBOs provides valuable insights into the development dynamics in Nepal and helps in crafting effective and inclusive development strategies.
Small Farmer’s Development Programme (SFDF); Community Forestry; Production Credit of Rural Women (PCRW); Grameen Bikas Bank; Farmer Managed Community Irrigation System (FMCIS); community schools and colleges; community health programmes; recent trends in community-based programmes in Nepal.
Overview: The Small Farmers Development Programme (SFDP) was established to support small and marginal farmers by improving their productivity and income. It aims to enhance agricultural practices and provide necessary resources and support to small-scale farmers.
Key Features:
Strengths:
Challenges:
Overview: Community Forestry programs in Nepal focus on the sustainable management of forest resources by local communities. The goal is to enhance forest conservation while providing economic and social benefits to communities.
Key Features:
Strengths:
Challenges:
Overview: The Production Credit for Rural Women (PCRW) program provides financial support to rural women for income-generating activities. The aim is to empower women economically and socially.
Key Features:
Strengths:
Challenges:
Overview: Grameen Bikas Bank, inspired by the Grameen Bank model from Bangladesh, focuses on providing microfinance services to rural communities in Nepal. The goal is to support poverty alleviation through access to financial services.
Key Features:
Strengths:
Challenges:
Overview: The Farmer Managed Community Irrigation System (FMCIS) involves local farmers in the management and maintenance of irrigation systems. This approach aims to improve agricultural productivity by ensuring efficient water use. These systems represent grassroots democratic institutions where communities take responsibility for natural resource management and allocation.
Key Features:
Strengths:
Challenges:
Numerous Farmer Managed Irrigation Systems (FMIS) operate across Nepal, from the highlands and mid-hills to the Terai plains, supplying irrigation to about 70% of the country’s total irrigated land, which exceeds 1.2 million hectares. These systems are highly localized, with management practices that are adapted to the specific environmental and social conditions of each area, reflecting the unique needs and organizational structures of local communities.
FMIS are recognized as grassroots democratic institutions where communities collectively take charge of managing and distributing water resources, establishing their own rules and procedures through consensus27. This community-driven approach not only supports local agricultural productivity but also embodies traditional knowledge and practices that have evolved over generations.
The autonomy and adaptability of Nepal’s FMIS offer a strong foundation for the sustainable management and development of the country’s natural resources, particularly water, which is considered the nation’s most vital resource. These systems are viewed as national heritage and play a crucial role in ensuring food security and economic stability for rural populations.
Overview: Community schools and colleges aim to provide quality education at the local level, often focusing on underserved and rural areas. These institutions are managed and supported by local communities.
Key Features:
Strengths:
Challenges:
Overview: Community health programs focus on improving health outcomes at the local level through preventive and curative health services. These programs often involve local communities in health education and service delivery.
Key Features:
Strengths:
Challenges:
Overview: Recent trends in community-based programs in Nepal reflect a shift towards more inclusive, participatory, and sustainable approaches. These trends focus on addressing emerging needs and leveraging local resources and knowledge.
Key Trends:
Summary:
The analysis of various CBOs and programs in Nepal reveals a rich tapestry of initiatives aimed at improving livelihoods, empowering communities, and addressing local needs. From agricultural support and microfinance to education and health, these programs have made significant contributions to community development. However, challenges such as resource constraints, management capacity, and sustainability need continuous attention. Recent trends indicate a move towards more inclusive, technology-driven, and sustainable community-based approaches, reflecting the evolving landscape of development in Nepal.
Concept, definitions and role of people’s participation in community development; role of communication in people’s participation and community development; problems and challenges in people’s participation in community development; local and regional, national and international case studies.
Occupations and income sources; household assets; rural-urban income gaps; agricultural policy; globalization; governance reforms.
Farm size and productivity; land reform; green revolution; crop insurance; agrarian crisis; climate adaptation; terracing.
Dairying, poultry, fishery, horticulture; rural non-farm sector; wages; labour migration.
Biogas, solar and wind systems; alternative energy agencies; planning.
Multi-level and micro-level planning; sufficiency of allocation.
Rural Economic Structure
Rural occupations and income sources in Nepal; household asset situation; rising rural-urban income gaps; emerging trends in rural expenditure pattern; agriculture in the world; role of agriculture in the economic development of rural Nepal; agricultural development under the national development plans; national agricultural policy; implications of globalization for agriculture in Nepal; governance reforms for better policy formulation and implementation in the agricultural sector.
Rural Occupations and Income Sources in Nepal
Rural occupations in Nepal are diverse, largely influenced by geography, resources, and cultural practices. Agriculture dominates the rural economy, employing about 65% of the population. In addition to farming, rural occupations include animal husbandry, fishing, forestry, and cottage industries such as handicrafts, weaving, and pottery. Seasonal migration to urban areas or foreign countries is also a significant source of income for rural households.
Rural income sources can be divided into two categories:
Household Asset Situation in Rural Nepal
The household asset situation in rural Nepal varies widely, depending on factors like geography, social structure, and access to resources. Rural assets typically include land, livestock, agricultural tools, and housing. However, the distribution of land remains unequal, with many households owning small or marginal landholdings, while some are landless. The ownership of modern agricultural equipment is limited, with most farmers using traditional tools. Access to improved seeds, fertilizers, irrigation facilities, and credit services remains low, which constrains agricultural productivity and asset accumulation.
Remittances have helped many households improve their asset base, often leading to investments in housing, education, and non-agricultural businesses. However, the dependency on remittances also exposes households to risks associated with global economic changes and labor market fluctuations.
Rising Rural-Urban Income Gaps
The rural-urban income gap in Nepal has been widening due to several factors:
The average household income in rural regions is about 27% of that in urban centers like Kathmandu, with rural households in the mountains earning as little as one-fifth of the income of their urban counterparts
Emerging Trends in Rural Expenditure Patterns
Rural expenditure patterns are changing due to factors like increased remittance income, access to new markets, and rising aspirations:
Agriculture in the World
Globally, agriculture is the backbone of many developing economies, contributing significantly to GDP, employment, and food security. However, the agricultural sector faces numerous challenges, including climate change, land degradation, water scarcity, and fluctuating commodity prices. The adoption of technology, improved farming practices, and sustainable agriculture are crucial for addressing these challenges and ensuring global food security.
In developed countries, agriculture is highly mechanized and efficient, contributing a smaller share to GDP but still playing a key role in exports. In contrast, in many developing countries like Nepal, agriculture remains labor-intensive and a major source of livelihood despite low productivity and technological adoption.
Role of Agriculture in the Economic Development of Rural Nepal
Agriculture is the foundation of Nepal’s rural economy. It contributes about one-third of the country’s GDP and provides employment to the majority of the population. Its importance in rural economic development includes:
However, Nepal’s agriculture is characterized by low productivity, small landholdings, and limited mechanization. These challenges must be addressed to realize agriculture's full potential in rural economic development.
Agricultural Development Under National Development Plans
Agricultural development has been a priority in Nepal’s national development plans, with efforts focused on increasing productivity, modernizing farming techniques, and improving rural infrastructure. Key initiatives include:
Despite these initiatives, the sector still faces challenges, including low investment, inadequate infrastructure, and climate change impacts.
National Agricultural Policy
Nepal’s National Agricultural Policy (NAP) aims to transform the agricultural sector by promoting sustainable and inclusive development. Key objectives of the policy include:
The policy also emphasizes the need for better infrastructure, access to markets, and capacity building for farmers to enhance agricultural output and rural livelihoods.
Implications of Globalization for Agriculture in Nepal
Globalization presents both opportunities and challenges for Nepal’s agricultural sector:
Globalization also exposes Nepal’s farmers to price volatility in global markets, creating additional risks for smallholders.
Governance Reforms for Better Policy Formulation and Implementation in the Agricultural Sector
Effective governance reforms are essential for the successful formulation and implementation of agricultural policies. Key areas of reform include:
Governance reforms are vital to ensure that policies are effectively implemented, monitored, and adjusted to achieve sustainable agricultural development in rural Nepal.
Farm size and productivity; farm managerial functions; land reform; impact of green revolution in cropping pattern; crop insurance; performance of agriculture; diversity and uncertainties in agriculture; confronting challenges; agrarian Crisis and farmers; adaptation or mitigation measures against climate change impacts on agriculture; role of biotechnology in agriculture; dry land farming technologies; agricultural terracing – construction, use and management.
This unit covers key aspects related to the organizational framework of agriculture in Nepal, focusing on farm size, productivity, managerial functions, land reforms, crop insurance, and modern agricultural technologies. Each of these elements plays a role in shaping the agricultural landscape in Nepal, particularly in the context of rural development.
Nepal's rural economy is predominantly agricultural, with approximately 66% of the population engaged in farming and related activities. The agricultural sector plays a crucial role in the country's economy, contributing about 23.95% to the GDP as of 2022, down from 65% in 1975. This decline reflects broader economic shifts towards manufacturing and services, driven in part by remittance-fueled investments in urban areas
In Nepal, the average farm size is small due to fragmentation, and land holdings are typically less than 1 hectare. This impacts productivity in the following ways:
Most farms operate on a subsistence basis, with less than 10% of holdings producing surplus for market sale. This reliance on traditional farming practices results in low productivity levels, with about 25% of arable land remaining uncultivated due to inadequate modern farming technique
Farm management in Nepal involves decision-making regarding:
Effective farm management is often constrained by limited access to agricultural extension services, lack of education, and financial constraints in rural areas.
Land reform has been a major policy area in Nepal, aimed at addressing land ownership inequalities. Key aspects include:
The Green Revolution introduced high-yielding varieties (HYVs) of crops, fertilizers, and irrigation, significantly affecting cropping patterns in Nepal:
Crop insurance in Nepal is still in its infancy, though it is critical for mitigating risks related to weather, pests, and market fluctuations. Key points include:
Agricultural performance in Nepal has shown mixed results:
Nepal's diverse geography leads to a variety of agro-ecological zones, resulting in diversity in crops and farming systems, but also challenges due to uncertainties:
Farmers in Nepal face multiple challenges, including:
The agrarian crisis in Nepal is marked by:
Climate change poses significant threats to agriculture in Nepal, but there are ongoing adaptation and mitigation efforts:
Biotechnology has potential to transform Nepal’s agriculture:
In areas where irrigation is scarce, dry land farming technologies are vital for improving productivity:
Terracing is a common agricultural practice in Nepal’s hilly and mountainous regions:
The organizational aspects of agriculture in Nepal are shaped by small farm sizes, diverse cropping systems, and ongoing challenges in accessing technology and markets. Addressing these challenges through land reforms, better farm management, and the adoption of modern technologies is crucial for improving productivity and ensuring food security in rural Nepal. At the same time, mitigating the impacts of climate change and enhancing resilience in agriculture are critical to ensuring the sustainability of the sector.
Dairying, poultry, fishery, horticulture, sheep rearing; mixed farming systems; managing intensive livestock systems; rural non-farm sector; rural non-farm employment; wages in and earnings from rural labor market; implications of rural labour migration.
Alternative Occupations in Rural Areas in Nepal highlights the importance of diversifying rural income sources beyond subsistence agriculture. It explores key sectors that can enhance rural livelihoods, create employment, and reduce poverty, while also addressing rural migration. Here’s a detailed breakdown of these alternative occupations in the context of Nepal:
These alternative occupations and trends illustrate the need for diversified rural livelihoods in Nepal to improve economic resilience and reduce poverty.
—
Significance and status of biogas production and use; significance and status of solar and wind energy systems and their installation and use; role of alternative energy related agencies in rural power generation and distribution; non-conventional energy planning for rural development.
Conventional and Non-conventional Sources of Rural Energy in Nepal emphasizes the transition from traditional energy sources like firewood to cleaner and more sustainable options. This unit explores the significance, status, and challenges of biogas, solar, and wind energy systems in rural Nepal, along with the roles of alternative energy-related agencies and the importance of non-conventional energy planning for rural development.
The adoption of biogas, solar, and wind energy in rural Nepal has significant potential to transform rural livelihoods by providing clean, sustainable, and affordable energy. The efforts of agencies like AEPC, along with strong non-conventional energy planning, can help Nepal move towards energy self-sufficiency, improve rural living standards, and address the challenges of energy access in remote areas. However, there is still a need for greater investment, research, and policy support to fully harness the potential of non-conventional energy sources for rural development.
Multi-level planning; organization for planning: centre, regions, districts, village development committees (VDCs); importance of micro-level planning for rural development; sufficiency of programme and budget allocation for rural development, and focus on rural area development.
Rural development in Nepal has been a critical focus in the national development strategy, as the majority of the population lives in rural areas and depends on agriculture and related activities. The government of Nepal has adopted multi-level planning processes to ensure the efficient allocation of resources and implementation of development programs aimed at improving the socioeconomic conditions of rural communities.
Multi-level planning refers to the coordinated approach to development where different levels of government—central, regional, district, and local—participate in planning and implementation:
Planning in Nepal involves various organizational levels, ensuring that development efforts are well-coordinated and effectively reach rural areas:
Micro-level planning focuses on the smallest units of development, such as villages and communities. It ensures that development initiatives are tailored to the specific needs of local populations:
The sufficiency of budget allocation for rural development has been a critical issue in Nepal, as the effectiveness of rural development programs depends on adequate financial resources. Several factors influence the adequacy of budget allocation:
Rural development is a cornerstone of Nepal’s national development strategy, given that the majority of the population lives in rural areas. Key areas of focus include:
Nepal's rural development efforts are characterized by a multi-level planning approach that involves various governmental tiers, from the central government to village-level planning bodies. Micro-level planning is vital for addressing local needs and ensuring community participation in development projects. While budget allocation for rural development is a priority in national plans, constraints in financial resources and implementation capacity often hinder the effectiveness of these efforts. Strengthening local governance and ensuring sufficient resource allocation are key to achieving sustainable rural development in Nepal.
Project cycle; ZOPP; participatory project cycle management; situational analysis; logical framework.
Identification steps; size, location and technology; pre-feasibility; formulation techniques.
Technical, economic and financial feasibility; BCR, NPV, IRR; sensitivity analysis; social benefit-cost analysis.
Monitoring; MIS; PERT and CPM; evaluation; impact assessment; PPME.
History and role of microfinance in poverty alleviation; women empowerment and national policy.
Practical and strategic needs; gender sensitization and mainstreaming.
SHG stages, types and structures; micro-savings and micro-insurance; Grameen Bank, PCRW, SFDP experiences.
Features, objectives and types; importance in an agro-based economy.
Rochdale to present; Owen, Fourier, Delitzsch, Raiffeisen; Israel, Germany, Japan, SAARC.
Evolution; periodic plans; NCDB; NCF; acts, rules and directives.
Objectives, identity and seven principles.
Structure; vision and functions of the Cooperatives Department.
Status, role in rural women’s development; critical evaluation.
Environment, climate, waste, occupation and risk
Human ecology studies the interrelationship between people and their natural, social and built environments. Humans are both products and modifiers of the environment. Population, social organization, environment and technology interact, and health is the outcome of these interactions.
An ecosystem is a community of living organisms (biotic) interacting with the non-living environment (abiotic) through energy flow and nutrient cycles. Ecosystem services (provisioning, regulating, cultural, supporting) sustain health by supplying food, clean water, climate regulation and disease control.
The workplace is part of the environment. Occupational hazards (chemical, physical, biological, ergonomic, psychosocial) often spill into communities, as with industrial emissions or take-home contamination. Lead, asbestos and pesticides show how the same exposure links workers and the public, and both fields use the exposure–dose–response logic.
Environmentalism values nature for its own sake (ecocentric or biocentric view) and argues for protection regardless of human use. Utilitarianism values the environment by its usefulness to people and judges actions by the greatest good for the greatest number, which leads to cost-benefit reasoning. Policy tension between development and conservation is often resolved with the precautionary principle.
Biophysical sciences (toxicology, chemistry, microbiology, physics, ecology, engineering) identify hazards and exposure pathways. Social sciences (sociology, economics, anthropology, behavioural science, policy studies) explain behaviour, inequity and governance. Effective environmental health needs both.
A widely used framework is the WHO DPSEEA chain:
Health depends on the integrity of ecosystems. Ecosystem health, One Health and Planetary Health approaches treat human, animal and environmental health as interconnected, so protecting ecosystems is a health intervention.
The model has three interacting components: the person (abilities, characteristics), the environment (physical, social, cultural) and the occupation (tasks and roles). Well-being and performance depend on the fit between them, and a change in any component changes the fit. In environmental and occupational health it guides assessment of worker capacity, workplace design and task demands.
Sustainable development meets present needs without compromising future generations (Brundtland, 1987). A healthy environment is both a goal and a means of development, linked to SDG 3 (health), 6 (water and sanitation), 7 (clean energy), 11 (cities), 12 (consumption) and 13 (climate). Target 3.9 aims to reduce deaths and illness from hazardous chemicals and air, water and soil pollution.
EIA predicts the environmental effects of a proposed project before a decision is made. Main steps: screening, scoping, baseline study, impact prediction and evaluation, mitigation measures, report (EIS), review and public consultation, decision, and monitoring and follow-up. Health issues are often weakly covered and need explicit attention.
Health impact assessment (HIA) judges the potential effects of a policy, programme or project on the health of a population and how they are distributed. Steps: screening, scoping, appraisal, reporting and monitoring. Social impact assessment (SIA) examines social change such as livelihoods, displacement, culture and community cohesion. The two overlap through social determinants of health.
Four steps:
Risk management (deciding what to do) is a separate step. Risk depends on hazard and exposure. For non-cancer effects, hazard quotient = exposure ÷ reference dose; for cancer, risk = lifetime average daily dose × slope factor. Uncertainty must be reported (enHealth, 2012).
Exposure is contact between an agent and a person. The pathway runs from source to release, environmental medium, point of contact, route (inhalation, ingestion, dermal) and receptor.
Burden of disease measures the gap between current health and an ideal state. Indicators include mortality, morbidity, life expectancy and healthy life expectancy. The key summary measure is the DALY (disability-adjusted life year):
QALYs measure health gained by interventions.
The Global Burden of Disease (GBD) study estimates deaths and DALYs by cause, risk factor, age, sex and place. It shows the shift from communicable to non-communicable disease and ranks environmental and occupational risks such as air pollution, unsafe water and sanitation, lead and workplace exposures using comparative risk assessment.
The WHO method (Prüss-Üstün et al., 2003):
Results depend on a chosen counterfactual or minimum-risk exposure level.
Ambient and household air pollution are among the leading environmental risks for death worldwide, and South Asia is heavily affected. In Nepal, fine particles (PM2.5) are high in the Kathmandu Valley and the Terai, with peaks in winter and pre-monsoon from vehicles, brick kilns, waste and biomass burning, dust, forest fires and crop-residue smoke. WHO 2021 guidelines: PM2.5 5 µg/m³ (annual) and 15 µg/m³ (24-hour).
Use population-weighted exposure (e.g. PM2.5), exposure–response functions and baseline rates for heart disease, stroke, COPD, lung cancer and child respiratory infection. Compute the attributable fraction, then attributable deaths and DALYs, as in the GBD approach.
Faecal–oral routes are fluids, fingers, flies, fields, fomites and food. WaSH-related disease includes diarrhoea, trachoma, soil-transmitted helminths and child stunting.
Exposure is classified by service level (safely managed, basic, limited, unimproved, surface water) and compared with a counterfactual such as continuous piped water with sewer connection. Attributable fractions are applied to diarrhoeal deaths and DALYs.
Soil pollution is the presence of substances at levels that harm soil function, ecosystems or health.
Examples include land use policy and legislation, pesticide and fertilizer control laws, solid waste management law, and environmental protection law and rules. Gaps are weak soil monitoring, limited enforcement and little health linkage. SDG target 3.9 includes soil pollution.
Noise is unwanted or harmful sound, measured in decibels on the logarithmic dB(A) scale (a 10 dB rise is roughly a tenfold increase in sound energy). Main sources in Nepali cities: traffic and horns, construction, industry, generators, loudspeakers, festivals and entertainment.
Examples include noise provisions under environmental protection law, limits on pressure horns and loudspeakers, vehicle fitness rules, no-horn zones and land-use zoning. Enforcement and monitoring are weak.
Weather is short-term atmospheric condition; climate is the long-term average (typically 30 years). Climate change is a lasting shift driven by natural variability and, mainly now, human activity.
Nepal contributes very little to global emissions but is highly vulnerable. The Himalayan region is warming faster than the global average. Observed patterns: shifting and erratic monsoon rainfall, glacier retreat with glacial lake outburst flood risk, more floods and landslides, droughts, heat in the Terai, and forest fires.
Climate affects health directly (heat, floods) and indirectly (water, food, vectors, livelihoods). Women are often more affected because of roles in water and fuel collection, agriculture and caregiving, limited mobility and decision-making in disasters, and pregnancy-related risks. Men face outdoor occupational heat exposure and distress migration. Adaptation should be gender-responsive.
Fossil fuel use, land-use change and urban growth raise emissions, create urban heat islands and release co-pollutants. Environmental impacts include air and water quality decline, biodiversity loss and ecosystem change.
Mitigation reduces greenhouse gas emissions (renewable energy, clean cooking, public transport, forest protection). Adaptation adjusts to impacts (early warning, water storage, climate-smart agriculture). Co-benefits include cleaner air from clean energy, more physical activity from active transport and healthier diets.
WHO’s framework has ten components: leadership and governance; health workforce; vulnerability, capacity and adaptation assessment; integrated risk monitoring and early warning; health and climate research; climate-resilient technologies and infrastructure; management of environmental determinants; climate-informed health programmes; emergency preparedness; and climate and health financing.
Risk arises from the interaction of hazard, exposure and vulnerability. Vulnerability is the tendency of a person, community or system to be harmed, shaped by poverty, age, gender, health status, location and access to services.
Vulnerability is often expressed as potential impact minus adaptive capacity.
Identify and rank hazards by frequency and severity, analyze risk as likelihood × consequence (risk matrix), then map using GIS. Overlaying hazard and vulnerability maps produces a risk map showing priority areas.
Urban waste in Nepal is mostly organic, and generation rises with urbanization and income.
Open dumping and riverbank disposal remain problems alongside landfill sites such as Sisdol and Banchare Danda.
Waste pickers, sweepers and collectors face injuries (cuts, needle sticks), musculoskeletal disorders, respiratory problems from dust and bioaerosols, skin and gastrointestinal infections, hepatitis B, tetanus and road accidents. Controls: PPE, vaccination, safe tools, training, mechanization, formal employment and social protection.
Health effects: diarrhoea, respiratory disease, vector-borne disease, cancer risk from toxic emissions, and stress near dumpsites.
Hazardous waste is ignitable, corrosive, reactive or toxic. Sources: industry, health care, agriculture (pesticides), households (batteries, paints, solvents), e-waste. Generation is influenced by industrialization, consumption, technology, income, regulation and awareness.
Exposure occurs by ingestion, inhalation or skin contact. Effects include neurotoxicity (lead, mercury), cancer (benzene, asbestos), reproductive harm and organ damage. Informal e-waste recycling exposes workers and communities to metals and burning by-products.
Common practices are segregation, autoclaving, incineration, shredding, encapsulation and deep burial, with sharps pits. Informal e-waste recycling continues, and treatment, storage and disposal facilities for hazardous waste are limited.
The Solid Waste Management Act, health care waste guidelines, public health and environmental protection laws, and international conventions such as Basel (transboundary waste) and Stockholm (persistent pollutants).
The Health Care Waste Management Guidelines (2014) set out minimization, segregation at source in colour-coded containers, safe storage and transport, treatment (autoclave, microwave, incineration, chemical) and disposal, with a waste management committee in each facility. Challenges: resources, staff training and inconsistent practice.
Sewerage coverage in Nepali cities is limited, treatment capacity is small (e.g. Guheshwori in Kathmandu), and much wastewater enters rivers untreated.
Wastewater is linked to diarrhoeal disease, helminth infection in wastewater-irrigated farmers, typhoid and skin disease (Feachem et al., 1983). Risk groups: sanitation workers, farmers, children and downstream communities. WHO’s guidelines for wastewater use in agriculture use a multiple-barrier approach.
The sanitation chain runs from containment to emptying, transport, treatment and safe reuse or disposal. A broken link contaminates water supplies and spreads disease.
Excreted pathogens spread by the faecal–oral route through water, food, hands, flies and soil. They include viruses, bacteria, protozoa and helminths. Diarrhoea, typhoid, hepatitis A and E, cholera outbreaks and soil-transmitted helminths remain public health problems in Nepal. Bradley’s classification groups water-related disease as water-borne, water-washed, water-based and water-related insect-vector.
| Pathogen | Reservoir | Transmission | Environmental control |
|---|---|---|---|
| Enteroviruses | Humans | Faecal–oral, respiratory | Sanitation, hygiene, vaccination (polio) |
| Hepatitis A virus | Humans | Contaminated water and food | Safe water, sanitation, vaccine |
| Rotavirus | Humans | Faecal–oral, contaminated surfaces | Handwashing, sanitation, vaccine |
| Escherichia coli (pathogenic) | Humans, cattle | Contaminated food and water | Safe water, food hygiene |
| Salmonella typhi | Humans only (carriers) | Faecal–oral | Safe water, sanitation, food-handler hygiene, vaccine |
| Leptospira interrogans | Rodents, livestock | Urine-contaminated water or soil entering skin | Rodent control, footwear, drainage |
| Vibrio cholerae | Humans, aquatic environment | Contaminated water and food | Safe water, sanitation, oral rehydration, vaccine |
| Entamoeba histolytica | Humans | Cysts, faecal–oral | Sanitation, water filtration, hygiene |
| Giardia lamblia | Humans, animals | Cysts in water | Filtration, boiling, hygiene |
| Ascaris lumbricoides | Humans | Eggs in soil, swallowed | Sanitation, handwashing, deworming |
| Ancylostoma duodenale | Humans | Larvae penetrate skin from soil | Footwear, sanitation, deworming |
| Fasciola hepatica | Livestock, snail host | Eating contaminated aquatic plants | Avoid raw aquatic plants, snail and livestock control |
| Taenia solium | Humans, pigs | Undercooked pork; eggs (cysticercosis) | Meat inspection, cooking, sanitation, pig confinement |
| Schistosoma haematobium | Humans, snail host | Skin contact with infested freshwater | Sanitation, snail control; not endemic in Nepal |
Introduction:
Environmental degradation and its impact on human health have been a serious global issue for the last few decades. Environmental degradation is both anthropogenic as well as non-anthropogenic. The two main reasons include the Industrial Revolution and a population explosion. There are mainly three main types of environmental degradation which are land/soil degradation, water degradation, and air degradation. WHO estimates anthropogenic factors contribute to a significant percentage of all environmental degradation in recent years with environment-related deaths contributing almost 23 % of global deaths (Prüss-Üstün et al., 2006).
Relationship between environmental degradation and human health:
Human beings rely on the environment heavily for various resources as well as ecosystem-related services such as clean air, water, and food. There is an intricate relationship between human beings and the environment. A multitude of health-related problems occur due to environmental degradation because of disruption in essential services. Environmental degradation has immediate, short term and long-term effects on human health. Immediate effects are generally caused by air pollution affecting the respiratory and cardiovascular systems.
Current effects of environmental degradation on the human health:
Air pollution: Air pollution is a major environmental hazard and is attributed to in 2012, ambient air pollution was responsible for 3.7 million deaths, representing 6.7 % of the total deaths with About 88% of these deaths occurring in low and middle-income countries. (WHO, 2014). Increasing levels of air pollution, driven by factors like industrial emissions, vehicular exhaust, burning fossil fuel, and deforestation with an increase in greenhouse gases including carbon monoxide, carbon dioxide, sulfur dioxide, nitrogen oxides, and hydrocarbons have been linked to respiratory, cardiovascular, and neurological disorders. Inhalation of particulate matter and toxic gases can result in respiratory infections, asthma, lung cancer, and chronic illnesses. Recently studies in China, Italy, and the United States have shown an association between air pollution and the transmission of COVID-19 (Zhang et al., 2020).
Contaminated water and waterborne diseases; water scarcity
Environmental degradation often results in water contamination by various means including industrial waste, agriculture runoff, and poor and improper/ unhealthy sanitation practices. In 2022, at least 1.7 billion people used a drinking water source contaminated with feces globally. Consuming contaminated water may result in individuals suffering from various waterborne diseases such as diarrhea, cholera, typhoid, and polio which are estimated to cause approximately 505 000 diarrhoeal deaths each year (Shayo et al., 2023). Depletion of freshwater resources and high salinity of water pose significant problems, especially for women and children in low-income countries.
Loss of biodiversity and emerging zoonotic diseases:
Degradation of the existing habitat and increasing human encroachment activities in the wildlife, transmission of zoonotic diseases is facilitated. biodiversity loss appears to increase the risk of human exposure to both new and established zoonotic pathogens. Multiple human-mediated environmental changes and activities have been found to be key drivers of zoonotic disease emergence, promoting the conditions in which zoonoses can emerge e.g. Ebola virus, SARS, and most recently Covid-19 pandemic (Keesing & Ostfeld, 2021).
Food security and malnutrition:
Environmental degradation causes disruption in the agricultural system heralding in decreasing yield of crops, food shortages, and resultant escalation of food prices. This contributes to malnutrition, both overnutrition and undernutrition having long-term impacts on human health. In recent years, in developing countries, the rapid growth of food production has also a concern for having a negative impact on the environment due to the overuse of various fertilizers, pesticides, and insecticides (Subramaniam & Masron, 2021).
Future consequences of environmental degradation on Human Health
Climate change and extreme weather events:
Environmental degradation results in worsening climate change with a resultant increase in the frequency and intensity of extreme weather events such as hurricanes, floods, droughts, and heat waves. These results in disruptions to food and water security, worsening air quality, the rise of vector-borne diseases, exacerbations of chronic disease, mental health stressors, and worker injury risks. It is estimated to cause almost 60,000 deaths per annum globally (Leal Filho et al., 2022).
Environmental toxins and chronic diseases, cancers:
Continued environmental degradation makes human beings prone to continuous exposure to harmful substances such as heavy metals, pesticides, and persistent organic pollutants. According to the Global Alliance on Health and Pollution, exposure to toxic air, water, soil, and chemical pollution is the largest environmental cause of premature death, with an estimated 8.3 million deaths globally in 2017. Although much is known about the substances that result in environmental hazards impacting human health, many challenges exist due to various factors. Overt dependence on substances and energy sources associated with toxins, difficulty in assessing the actual environmental impact, and vested interests of stakeholders are the prime factors. Neurodegenerative disorders including Parkinson’s disease and amyotrophic lateral sclerosis have been linked to various toxins including rotenone and permethrin used as insecticides. Studies in low-income countries have demonstrated an increase in infectious diseases attributed to environmental pollutants and the resultant poor immune status of people (Sarah et al., 2022). According to an analysis published by the International Agency for Research on Cancer (IARC), 120 chemicals have been identified as “known” carcinogens; another 81 have been identified as “probable” carcinogens; and another 299 as “possibly'' carcinogenic. Well-known carcinogens are asbestos, nickel, cadmium, radon, vinyl chloride, benzidine, and benzene. Besides, an association between endocrine and reproductive disorders with environmental toxins has also been noted (Cohen & Jefferies, 2019).
Mental health impacts:
Escalation in environmental degradation has been associated with varying degrees of mental health issues in humans. Climate change, deforestation, and pollution have a negative impact on mental health as demonstrated in various studies. It is generally noted that individuals of low socioeconomic status or indigenous populations are in psychosocial distress (Wigand et al., 2022).
Displacement, migration, socioeconomic and health impacts:
Although migration can be an adaptive strategy to climate changes, migration due to environmental changes is complex and its impact differs variably on a multitude of factors having direct or indirect effects. The migrating or host communities may be subjected to the long-term effects of migration in geomorphological and population health contexts (Schwerdtle et al., 2018).
Multidimensional and disproportionate impact on vulnerable populations:
Climate changes and environmental degradation have resulted in explicit inequalities among the vulnerable and marginalized population compared to the affluent in exacerbating all health consequences. Limited economic and technological resources imply increased impacts on this population to cope with the risks of climate change (Khine & Langkulsen, 2023). Environmental degradation has a significant effect in difficulty in addressing the poverty issues in the impoverished society and results in difficulty in achieving the desired goals.
Socio-economic impact
Environmental degradation can have significant socioeconomic impacts. When the environment is degraded, not only agriculture, and farming but also other sectors including tourism are affected resulting in economic loss. Depletion of natural resources can disrupt the supply chain and increase prices of goods and services. In the long run, this results in a significant burden on healthcare expenditure which further continues the vicious cycle.
Mitigating the Effect of environmental degradation on human Health:
Climate change and environmental degradation have profound impacts on human health and need to be properly addressed in a multifaceted approach. Adopting a One Health approach, enhancing cross-sector, transboundary communication, as well as collaboration among multiple stakeholders is prudent in minimizing the risks related to environmental degradation. The severity of the health risks depends on the ability of the existing public health system to adequately intervene based on the local resources.
To mitigate the existing and future consequences of environmental degradation, it is imperative to have early preparedness, develop climate resilient infrastructure and sustainable practices, use renewable sources of energy, reduce emissions of short-lived climate pollutants, address pollution issues as well as public education, reinforcing controlling strategies, reforestation, crop rotation as well as reducing exposure to harmful pollutants. Besides, it is essential to address environmental health disparities and risks.
Conclusion:
Government Laws and policies should be developed and strictly implemented prioritizing both the environment and human health. International cooperation among different countries and partners is essential to achieve desired goals and its sustainability. A strong environmental education (EE) system with the responsiveness of humans towards nature for sustainability and environmental security is the need of time.
References:
Cohen, L., & Jefferies, A. (2019). Environmental exposures and cancer: Using the precautionary principle. Ecancermedicalscience, 13, ed91. https://doi.org/10.3332/ecancer.2019.ed91
Keesing, F., & Ostfeld, R. S. (2021). Impacts of biodiversity and biodiversity loss on zoonotic diseases. Proceedings of the National Academy of Sciences, 118(17), e2023540118. https://doi.org/10.1073/pnas.2023540118
Khine, M. M., & Langkulsen, U. (2023). The Implications of Climate Change on Health among Vulnerable Populations in South Africa: A Systematic Review. International Journal of Environmental Research and Public Health, 20(4), 3425. https://doi.org/10.3390/ijerph20043425
Leal Filho, W., Balasubramanian, M., Purcell, W., & Paz, S. (2022). Handling the health impacts of extreme climate events. Environmental Sciences Europe, 34(1), 45. https://doi.org/10.1186/s12302-022-00621-3
Prüss-Üstün, A., Corvalán, C., & World Health Organization. (2006). Preventing disease through healthy environments: Towards an estimate of the environmental burden of disease. World Health Organization.
Sarah, R. H., Shariful Islam, M., Zamiur Rahaman, M., Afrin, S., Rahman, M., & Saif-Ur-Rahman, K. M. (2022). Pivotal role of environmental toxicants on developing infectious diseases in LMICs: A protocol for a systematic review and meta-analysis. BMJ Open, 12(7), e058927. https://doi.org/10.1136/bmjopen-2021-058927
Schwerdtle, P., Bowen, K., & McMichael, C. (2018). The health impacts of climate-related migration. BMC Medicine, 16(1), 1. https://doi.org/10.1186/s12916-017-0981-7
Shayo, G. M., Elimbinzi, E., Shao, G. N., & Fabian, C. (2023). Severity of waterborne diseases in developing countries and the effectiveness of ceramic filters for improving water quality. Bulletin of the National Research Centre, 47(1), 113. https://doi.org/10.1186/s42269-023-01088-9
Subramaniam, Y., & Masron, T. A. (2021). Food security and environmental degradation: Evidence from developing countries. GeoJournal, 86(3), 1141–1153. https://doi.org/10.1007/s10708-019-10119-w
Wigand, M. E., Timmermann, C., Scherp, A., Becker, T., & Steger, F. (2022). Climate Change, Pollution, Deforestation, and Mental Health: Research Trends, Gaps, and Ethical Considerations. GeoHealth, 6(11), e2022GH000632. https://doi.org/10.1029/2022GH000632
Zhang, Z., Xue, T., & Jin, X. (2020). Effects of meteorological conditions and air pollution on COVID-19 transmission: Evidence from 219 Chinese cities. The Science of the Total Environment, 741, 140244. https://doi.org/10.1016/j.scitotenv.2020.140244
Noise: Types
Impulsive noise:
Continuous noise:
Intermittent noise:
Major Sources
Natural sources – lightening, wind, rainfall, etc
Man-made sources
SAFETY LIMITS- How loud is too loud?
The maximum exposure limit is 85dB for eight hours a day
WHO guidelines
The WHO guidelines for community noise recommend
The WHO guidelines for night noise recommend less than 40 dB(A) of annual average outside of bedrooms to prevent adverse health effects from night noise.
Impacts of Noise Pollution
The degree of harmful effects depends on its
Control over Noise Pollution
Control techniques
Noise Pollution - Management Principles
Legal provisions
Nepal’s second Nationally Determined Contribution (NDC) broadens the ambition of the 2016 NDC from policy to adaptation practice at all levels of government in terms of overall sectoral coverage and net emission reduction contributions. Nepal’s NDC emphasizes health-specific measures as health in adaptation, health co-benefits, health in mitigation, climate integration, gender equity, and social inclusion. The major key policy provisions to address health risks of climate change in Nepal's NDC include:
Increase the reliable supply of clean energy: Nepal aims to increase the quantity, quality, reliability, and affordability of electricity access from renewable sources to reduce reliance on fossil fuels and improve air quality. By 2030, it aims to expand clean energy generation from approximately 1,400 MW to 15,000 MW.
Strengthen transmission and distribution links: The NDC emphasizes the need to strengthen transmission and distribution links to support the upscaling of e-cooking, e-heating, e-transport, and charging stations, promoting the use of clean energy sources and anticipated co-benefits in terms of reducing air pollutant emissions and enhancing energy security and energy equity. It aims for 15% of total energy demand to come from clean sources, with electric vehicles accounting for 90% of all private vehicle sales.
Promote public electric mobility: The NDC includes policies to promote public electric mobility through policy incentives, including subsidy policies and other financial mechanisms, to reduce air pollution from transportation. By 2030, it aims to develop 200 km of the electric rail network to support public transportation and the mass movement of goods.
Improve waste management: Nepal aims to create an enabling environment for treating industrial and municipal waste, including fecal sludge, and promotes waste segregation, recycling, and waste-to-energy programs to reduce the spread of diseases. By 2030, it aims to adopt and implement waste segregation, recycling, and waste-to-energy programs in at least 100 municipalities.
Enhance climate-smart agriculture: Nepal’s NDC also prioritizes the agriculture sector and includes targets like operationalization of institutional structure for climate change management at the local level, establishment of 200 climate-smart villages and 500 climate-smart farms, promotion of inter-cropping, crop, and livestock waste management to reduce carbon emission, and climate-resilient and gender-responsive adaptation plan preparation in all 753 local units. It further aims to reduce the vulnerability of farmers to climate change impacts. Besides, it aims to maintain 45% of the total land area under forest cover by 2030.
Nepal is preparing a detailed NDC implementation framework/ road map to have the targets reached in the stipulated time covering energy, agriculture, forestry, and other land use, waste, industrial processes and product use, urban settlements and tourism, gender equality, and social inclusion, and governance sectors, which in turn aims to address the climate change impacts.
References:
Shrestha, S., Chapagain, P. S., Dhakal, M., Rai, S. C., Islam, M. N., Zhang, Y., Liu, L., & Paudel, B. (2024). Climate change policy instruments in the agriculture sector: From policy to adaptation practice at the local level in Nepal. Geographical Journal of Nepal, 17(01), 1–22. https://doi.org/10.3126/gjn.v17i01.63932
Nepal's Second Nationally Determined Contributions
UNFCCC. (2020). Nepal's Long-term Strategy for Net-zero Emissions.https://unfccc.int/sites/default/files/resource/NepalLTLEDS.pdf
Nepal is a country with fragile geology. It has eight physiographic regions with a variability in the soil types due to distinct topographic, climatic, and vegetation characteristics (1). Soils of Nepal are highly variable and are dependent on various environmental parameters such as climate, parent material, organisms, and time factor, reflecting different kinds and degrees of soil formation factors and their combinations (Kang and Tripathi, 1992). Rapid population growth, increasing economies, and anthropogenic pollution have further impacted.
Poor soil fertility, low levels of mineral nutrients in soil, improper nutrient management, and lack of plant genotypes are major constraints contributing to food insecurity, malnutrition, and ecosystem degradation in developing countries like Nepal (2).
Multitudes of factors are posing challenges to soil pollution in Nepal, which can be
Nepal is a member country advocating for the Sustainable Development Goal (SDG). SDG target 3.9 entails “By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water, and soil pollution and contamination”, which currently lacks any measurable indicator for soil pollution.
Soil pollution is a serious issue in Nepal that requires a multisectoral approach and the involvement of various stakeholders for sustainable soil management.
Addressing the soil pollution in Nepal warrants
References:
1. Anup KC, Kalu S, editors. Soil Pollution Status and Its Remediation in Nepal2015.
2. K C A. Status of Soil Fertility in a Community Forest of Nepal. International Journal of Environment. 2013;1:56-67.
3. Lamichhane G, Acharya A, Marahatha R, Modi B, Paudel R, Adhikari A, et al. Microplastithe cs in the environment: global concern, challenges, and controlling measures. International Journal of Environmental Science and Technology. 2023;20(4):4673-94.
4. Aichner B, Glaser B, Zech W. Polycyclic aromatic hydrocarbons and polychlorinated biphenyls in urban soils from Kathmandu, Nepal. Organic Geochemistry. 2007;38:700-15.
5. Pandey R. Solid Waste Management Practice and Health Implications: A Case of Kathmandu Metropolitan City, Nepal. Himalayan Review. 2009;35(0):33-47.
6. Chalise D, Kumar L, Kristiansen P. Land Degradation by Soil Erosion in Nepal: A Review. Soil Systems. 2019;3(1):12.7. Atreya K, Subedi BP, Ghimire PL, Khanal SC, Charmakar S, Adhikari R. Agroforestry for mountain development: Prospects, challenges and ways forward in Nepal. Archives of Agriculture and Environmental Science. 2021;6(1):87-99.