Cambridge Syllabus Notes

Disease and Geography – Health‑Care Access & Inequality

Objective

Analyse how geographical factors shape the availability, affordability, acceptability and quality of health‑care; interpret patterns of health inequality; evaluate case‑studies that illustrate spatial causes, effects and policy responses – all in line with Cambridge International AS & A Level Geography (9696) Paper 4 requirements.

1. Key‑Concepts Checklist (AO1)

For each concept, the table shows where it is addressed in the notes and an example you can use in an exam answer.

Concept	Where Covered	Exam‑style Example
Scale	Section 5 (Spatial patterns) & case‑studies	“Local clinic in Kibera versus national referral hospital in Nairobi”
Change over time	Case‑studies (HIV, Malaria, TB, COVID‑19)	“HIV prevalence fell from >15 % pre‑2000 to ~7 % by 2020 after donor programmes”
Place	All case‑studies (specific locations named)	“Rural highlands of Tanzania”
Spatial variation	Section 5 & Core‑Periphery model	“Physician density of 3.2 per 1 000 in Nairobi vs 0.4 per 1 000 in remote districts”
Cause‑and‑effect	Determinants (Four‑Dimension Model) & case‑studies	“Poor roads (physical barrier) → longer travel time → delayed malaria treatment”
Systems	Section 2 (Models) & case‑studies	“Interaction between disease‑transmission system and health‑service delivery system”
Environmental interactions	Malaria case‑study; COVID‑19 slums	“Monsoon‑driven flooding creates mosquito breeding sites”
Challenges & opportunities	Section 7 (Policy) & case‑studies	“Tele‑medicine can overcome distance in remote highlands”
Diversity / Equality	Each case‑study (gender, age, migrant groups)	“Higher TB rates among prison populations in Eastern Europe”

2. Exam‑style Definitions (AO1)

Core‑Periphery Model: A spatial theory that explains concentration of economic activity, services and skilled labour in a central “core” with a gradual decline in provision toward the “periphery”. In health‑care geography it accounts for the gradient in facility density, staff numbers and disease burden from city centre to remote rural areas.
Epidemiological Transition: The shift in a population’s disease profile from predominately infectious diseases to chronic, non‑communicable diseases as income, education and lifestyle change.
Disease‑Diffusion Models: Frameworks (hierarchical, contagion, relocation) that describe how diseases spread through networks of places, influenced by transport links, population density and migration.
Four‑Dimension Model of Health‑Care Access: Four inter‑related dimensions that determine utilisation: Physical (distance, travel time), Financial (costs, insurance), Acceptability (cultural, gender, trust) and Quality (staffing, equipment, standards).
Gini Coefficient (Health): A statistical measure ranging from 0 (perfect equality) to 1 (maximum inequality) that quantifies the distribution of a health variable (e.g., life expectancy, health‑care utilisation) across a population.

3. Geographic Models & Theories (AO2)

Model / Theory	Core Idea (2‑3 sentences)	Geographic Relevance to Health‑Care
Core‑Periphery Model	Economic and service provision are concentrated in metropolitan cores; beyond the core, the density of jobs, infrastructure and specialised services falls sharply, creating a gradient of opportunity and wellbeing.	Explains urban‑rural differences in physician density, drug availability and disease burden; useful for analysing why remote peripheries experience higher malaria incidence or lower ART coverage.
Epidemiological Transition	As societies industrialise, mortality from infectious diseases declines while chronic diseases (cardiovascular, cancer) rise, reflecting changes in diet, activity, and health‑service demand.	Links economic development to shifting health‑care needs; e.g., rising non‑communicable disease (NCD) rates in rapidly urbanising China require different service provision than rural malaria control.
Disease‑Diffusion Models (hierarchical, contagion, relocation)	Hierarchical diffusion spreads from larger to smaller settlements; contagion spreads through direct contact; relocation follows population movement.	Helps map the spread of HIV from port cities to inland areas, or COVID‑19 from global travel hubs to suburban neighbourhoods.
Four‑Dimension Health‑Care Access Framework	Physical, financial, acceptability and quality dimensions interact to determine whether people can obtain needed services.	Provides a diagnostic checklist for any case study and a basis for evaluating policy interventions (e.g., mobile clinics address physical and quality barriers).

4. Determinants of Health‑Care Access (Four‑Dimension Model)

Physical accessibility – distance, travel time, terrain, transport infrastructure.
Financial accessibility – service fees, insurance coverage, out‑of‑pocket payments.
Acceptability – cultural beliefs, language, gender norms, trust in providers.
Quality – staffing levels, equipment, drug availability, adherence to standards.

Concept‑check (Scale & Place): Compare how each dimension differs between an urban hospital in Nairobi and a remote health post in the Tanzanian highlands.

5. Measuring Health Inequality (AO2)

Indicator	Definition	Typical Data Source
Gini coefficient (health)	Statistical measure of inequality in a health outcome or health‑care use (0 = perfect equality, 1 = maximum inequality).	World Bank, National Statistics Offices
Health‑care density	Number of physicians (or nurses) per 1 000 population.	WHO Global Health Observatory
Out‑of‑pocket expenditure (% of total health spend)	Share of health costs paid directly by households.	UNDP Human Development Reports
Travel time to nearest facility (minutes)	Average time required to reach a primary‑care centre, calculated with GIS.	National health surveys, GIS analyses

6. Spatial Patterns of Inequality (AO2)

The core‑periphery model typically produces the following gradient:

Urban cores: high facility density, well‑maintained transport, but may contain deprived pockets (e.g., slums).
Sub‑urban fringe: mixed service provision; transitional in both population density and health‑care access.
Rural peripheries: low facility density, poor roads, long travel times, higher out‑of‑pocket costs.

Concept‑check (Spatial variation & Cause‑and‑effect): Explain why malaria incidence often peaks in the rural periphery of the Ganges‑Brahmaputra floodplain.

7. Case Studies

7.1 HIV/AIDS – Sub‑Saharan Africa

Physical accessibility: Sparse clinics in remote districts; travel times often >2 h.
Financial accessibility: Limited public funding; high out‑of‑pocket costs for antiretroviral therapy (ART) before donor subsidies.
Acceptability: Stigma, gender norms and traditional beliefs deter testing and treatment adherence.
Quality: Shortages of trained staff, diagnostic kits and reliable drug supplies.
Change over time: Pre‑2000 prevalence >15 %; after PEPFAR and Global Fund programmes prevalence fell to ~7 % in many countries by 2020.
Diversity/equality: Higher prevalence among women, adolescents and key populations (sex workers, MSM).

Concept‑check (Disease‑diffusion & Change over time): Use the hierarchical diffusion model to describe how HIV moved from urban ports to rural interiors.

Data‑Interpretation Mini‑Exercise (AO2): The table below shows ART coverage (% of PLHIV) for three districts. Calculate the average coverage and comment on the spatial pattern.

District	ART Coverage (%)
Kigoma (urban)	78
Mwanza (semi‑urban)	62
Kigoma Rural	41

Evaluation Rubric (AO3): Assess the success of donor‑funded ART programmes using criteria:

Coverage (percentage of PLHIV receiving ART)
Cost‑effectiveness (cost per life‑year saved)
Sustainability (reliance on external funding vs national budget)
Equity (reduction in gender/age differentials)

What would an examiner look for? Clear link to the Four‑Dimension Model, use of quantitative data, explanation of spatial trends, and balanced evaluation of strengths and weaknesses of the response.

7.2 Malaria – South Asia (India, Bangladesh, Nepal)

Environmental interaction: Low‑lying, humid floodplains and irrigated rice fields provide ideal breeding sites for Anopheles mosquitoes.
Physical accessibility: Remote hill villages lack paved roads; travel to the nearest health centre can exceed 4 h.
Financial accessibility: Inadequate public provision of insecticide‑treated nets (ITNs); households must purchase them.
Acceptability: Traditional healers sometimes replace biomedical treatment, especially during monsoon peaks.
Change over time: National malaria control programmes (2005‑2020) reduced incidence by ~40 % but a resurgence occurred after funding cuts in 2021.

Concept‑check (Environmental interactions & Systems): Discuss how monsoon season amplifies the physical‑accessibility barrier.

Data‑Interpretation Mini‑Exercise (AO2): Using the GIS raster below (travel‑time to nearest health post), identify the three villages with the highest travel times and suggest a spatial solution.

Evaluation Rubric (AO3): Judge the effectiveness of ITN distribution programmes based on:

Coverage (% of households owning at least one ITN)
Correct usage rates
Cost per case averted
Adaptability to seasonal migration

7.3 Tuberculosis (TB) – Eastern Europe (Russia, Ukraine, Belarus)

Place: Post‑industrial cities with dense, poorly ventilated housing and legacy coal‑mining districts.
Financial accessibility: Economic transition reduced health‑care budgets; patients often pay for diagnostics and drugs.
Quality: Outdated drug regimens, limited laboratory capacity and occasional drug‑resistance.
Acceptability: Stigma attached to TB, especially among migrant workers and prison inmates.
Change over time: Incidence peaked in the 1990s after the Soviet collapse; modest decline after DOTS implementation (2000‑2015).
Diversity/equality: Higher rates among prison populations, internal migrants and people living in informal settlements.

Concept‑check (Challenge & Opportunity): Identify one geographic opportunity that could reduce TB inequality in these cities.

Data‑Interpretation Mini‑Exercise (AO2): The chart shows TB incidence (per 100 000) for three city districts. Which district should be prioritised for a mobile‑clinic programme and why?

Evaluation Rubric (AO3): Evaluate mobile‑clinic interventions using:

Coverage of high‑risk groups
Impact on treatment success rates
Cost per patient treated
Long‑term sustainability (integration with permanent facilities)

7.4 COVID‑19 – Urban Slums (Kibera, Nairobi; Dharavi, Mumbai)

Physical accessibility: Overcrowded housing (average >5 persons / 10 m²) limits physical distancing; health‑centre catchment areas are large and understaffed.
Financial accessibility: Informal employment means loss of income when staying home; limited health‑insurance coverage.
Acceptability: Mistrust of authorities and misinformation hinder testing and vaccine uptake.
Quality: Scarcity of testing centres, oxygen supplies and ICU beds.
Environmental interaction: Poor sanitation and lack of clean water reduce effectiveness of hand‑washing campaigns.
Change over time: First wave (2020) saw rapid spread; community‑based testing and contact‑tracing in 2021 reduced case‑fatality rates by ~30 %.
Diversity/equality: Disproportionate impact on women in informal market work and on children living in crowded households.

Concept‑check (Systems & Challenges): Explain how integrating community health workers into the formal health system addresses both physical‑ and acceptability‑access barriers.

Data‑Interpretation Mini‑Exercise (AO2): A bar graph shows weekly testing rates before and after community‑health‑worker involvement. Calculate the percentage increase and comment on its significance.

Evaluation Rubric (AO3): Assess the community‑health‑worker model using:

Increase in testing and vaccination uptake
Cost per additional case detected
Community trust and compliance
Potential for scaling to other informal settlements

8. Linking Geography to Policy – Addressing Spatial Inequality (AO3)

Transport upgrades & Tele‑medicine: Road improvements reduce travel time (physical barrier); satellite‑based tele‑health provides remote consultations (quality & acceptability).
Financial protection schemes: National health insurance or cash‑transfer programmes lower out‑of‑pocket costs (financial barrier) and encourage utilisation.
Culturally appropriate health promotion: Materials in local languages, involvement of community leaders and gender‑sensitive messaging improve acceptability.
Cross‑border collaboration: Joint surveillance, data‑sharing and harmonised treatment protocols reduce disease spread across political boundaries (e.g., malaria elimination in the Greater Mekong Subregion).
Targeted service density: Mobile clinics, pop‑up vaccination sites and outreach teams fill gaps identified by health‑care density GIS maps (physical & quality barriers).

Concept‑check (Systems, Challenges & Opportunities): Choose one policy above and discuss how it simultaneously tackles a physical, financial and acceptability barrier.

9. Global Health‑Care Governance (New – AO2)

International frameworks: WHO’s “Universal Health Coverage” agenda, Sustainable Development Goal 3 (Good Health and Well‑being) and the International Health Regulations (IHR) shape national policies.
Funding mechanisms: Global Fund, Gavi, World Bank health‑sector loans – provide resources for disease‑specific programmes and health‑system strengthening.
Data‑sharing & surveillance: Global Health Data Exchange, WHO Global Health Observatory, and pandemic‑early‑warning networks enable coordinated responses.
Equity focus: The “Leave No One Behind” principle drives targeted interventions for marginalised groups (e.g., refugees, slum dwellers).

10. Suggested Diagram

Map showing a gradient of health‑care facility density (dots) from an urban core outward, overlaid with disease prevalence rates (colour‑shaded choropleth) for the same region.

11. Revision Checklist (AO1‑AO3)

Define the four dimensions of health‑care access and give a real‑world example for each.
Explain the Gini coefficient and illustrate how it can be applied to health outcomes.
Describe the core‑periphery, epidemiological transition and disease‑diffusion models; link each to at least one case study.
Identify three geographical factors that create inequality in each of the four case studies.
Discuss one policy response that directly addresses a spatial barrier, referencing the relevant model or concept.
Use the concept‑check boxes to confirm you have considered: scale, change over time, place, spatial variation, cause‑and‑effect, systems, environmental interactions, challenges & opportunities, and diversity/equality.
Complete at least one data‑interpretation mini‑exercise per case study.
Provide an AO3‑style evaluation for each policy or intervention you study.

12. Sample Resource Tasks (AO2)

Map interpretation: A choropleth shows physician density per 1 000 population across Kenya. Identify the core, fringe and peripheral zones and explain the likely health outcomes in each.
Graph analysis: A line graph tracks out‑of‑pocket health expenditure (% of total health spend) from 2000‑2020 for three countries. Calculate the average annual change for each country and discuss what this reveals about financial accessibility.
Table reading: A table lists travel time to the nearest health post for 10 villages. Determine the mean travel time and assess whether the “physical accessibility” dimension meets the WHO recommendation of ≤30 minutes.
GIS raster exercise: Using a provided travel‑time raster, locate the 5 villages with the longest travel times and propose a spatial solution (e.g., mobile clinic, new road).

Health care: access, inequality, case studies