Geography of disease and mortality

Health is a state of complete physical, mental and social well-being, and not merely the absence of disease or infirmity (the World Health Organization definition).

It is therefore a positive state of well-being across three dimensions — physical, mental and social — rather than simply 'not being ill'.

Communicable (infectious) diseases are caused by pathogens and can spread from person to person, or via a vector. Examples include malaria (spread by the Anopheles mosquito), tuberculosis (TB) and HIV/AIDS.

Non-communicable diseases (NCDs) are not infectious and cannot be passed between people. They include cardiovascular (heart) disease, cancer and type-2 diabetes, and are often linked to ageing and lifestyle.

The key distinction is transmission: communicable diseases spread between hosts, whereas NCDs develop within an individual and cannot be caught. Communicable disease dominates in many LICs; NCDs dominate in HICs.

Infant mortality rate (IMR) is the number of deaths of infants under one year of age per 1000 live births per year. It is a sensitive indicator because infant survival depends closely on clean water, sanitation, nutrition, maternal health and healthcare access — so a high IMR signals weak health provision.

Life expectancy is the average number of years a person can expect to live from birth given current mortality rates. It summarises overall mortality across the whole population in a single figure.

Both are useful because they capture different aspects of health: IMR focuses on the vulnerable first year of life and reacts quickly to changes in healthcare and sanitation, while life expectancy gives a broad, whole-population summary. Together they reveal large development gaps — e.g. Japan (IMR ~2, life expectancy ~84) versus Chad (IMR ~68, life expectancy ~53).

The resource shows a very large health gap between the high-income and low-income country. Japan's life expectancy (~84 years) is about 29 years higher than Sierra Leone's (~55 years), and Japan's infant mortality rate (~2 per 1000) is roughly 39 times lower than Sierra Leone's (~78 per 1000).

Both indicators point the same way: in Japan most infants survive and people live into old age, whereas in Sierra Leone many children die young and average survival is much shorter. Because life expectancy is a birth-average, Sierra Leone's very high infant mortality drags the figure down sharply.

The contrast reflects different disease profiles and development levels. Sierra Leone's burden is dominated by communicable disease (malaria, diarrhoeal and respiratory infections) and maternal/child mortality, linked to limited healthcare, sanitation and clean water. Japan's burden is mainly non-communicable, degenerative disease affecting the elderly, so few die young.

Overall the resource demonstrates that the two countries sit at very different stages of the epidemiological transition, with health outcomes closely tied to wealth, healthcare and sanitation.

The epidemiological transition model describes how the main causes of illness and death change as a country develops, shifting from communicable to non-communicable disease.

Stage	Dominant disease	Mortality / life expectancy
Age of Pestilence and Famine	Infectious disease (cholera, TB, malaria) and famine	Very high mortality; life expectancy ~20–40
Age of Receding Pandemics	Epidemics decline as sanitation, water and medicine improve	Death rate falls; life expectancy ~30–50
Age of Degenerative & Man-Made Diseases	Non-communicable disease — cardiovascular disease, cancer, diabetes	Low mortality; life expectancy 70+

The model is closely linked to development. In the least developed countries, poverty, weak sanitation and limited healthcare mean infectious disease and child mortality dominate (an early stage). As a country develops, improved clean water, sanitation, nutrition, vaccination and medical care reduce infectious disease, death rates fall and life expectancy rises. In the most developed, ageing societies, infectious disease is largely controlled, so most deaths are from degenerative NCDs linked to old age and lifestyle.

This parallels the Demographic Transition Model — as death rates fall, the disease profile shifts. However, the transition is not perfectly linear: many middle-income countries face a 'double burden', still fighting infectious disease while NCDs rise, so development and disease profile are linked but not a simple one-way switch.

Health indicators vary systematically with development: as a country becomes wealthier and more developed, life expectancy rises while mortality indicators and the infectious-disease burden fall. This can be analysed indicator by indicator.

Life expectancy rises steeply with development. In high-income countries it reaches ~84 in Japan and the low-80s across Western Europe and Australia, whereas in low-income sub-Saharan Africa it is only ~53–55 (Chad, Central African Republic, Sierra Leone) — a gap of roughly 30 years. Greater wealth funds healthcare, clean water and nutrition that let people survive into old age.

Infant and under-5 mortality show the sharpest contrast. IMR is around 2 per 1000 in Japan but 68–78 per 1000 in Chad and Sierra Leone. Because infant survival depends on sanitation, clean water, vaccination and maternal health, IMR is a very sensitive indicator of development, and high infant mortality also pulls down life expectancy at birth.

Maternal mortality is likewise development-linked: only around 5–10 per 100 000 live births in HICs, but over 500, and above 1000 in the worst-affected LICs (e.g. South Sudan, Chad), reflecting whether women have skilled birth attendants and emergency obstetric care.

Disease burden (DALYs) and disease profile also shift. In LICs, DALYs are dominated by communicable disease, maternal and child conditions and malnutrition; in HICs, DALYs come mainly from non-communicable, degenerative disease — the pattern predicted by the epidemiological transition model.

In conclusion, all four indicators move in the same direction with development: higher development means higher life expectancy and lower infant, child and maternal mortality, with the disease burden shifting from communicable to non-communicable. The relationship is strong but not perfect — middle-income countries face a double burden, and factors such as conflict, HIV/AIDS and inequality can distort the pattern (e.g. HIV lowering life expectancy in parts of southern Africa).

The idea that health mirrors development is central to the geography of disease and mortality: as countries develop, the epidemiological transition predicts a shift from communicable disease and high mortality towards non-communicable disease and long life. This essay examines how far development really determines disease and mortality, using Japan (a high-income country) and Chad/Sierra Leone (low-income countries) as contrasting cases, before weighing the factors that complicate the relationship.

The case that development is the key determinant. The health data show an overwhelming link with development. Life expectancy ranges from ~84 in Japan to ~53–55 in Chad, the Central African Republic and Sierra Leone — a gap of roughly 30 years. Infant mortality is around 2 per 1000 in Japan but 68–78 per 1000 in Chad and Sierra Leone, and maternal mortality is under 10 per 100 000 in HICs but exceeds 1000 in the worst-affected LICs. Development explains this because wealth funds universal healthcare, clean water, sanitation, vaccination and good nutrition, which control the communicable diseases (malaria, diarrhoeal disease, TB) and maternal/child deaths that dominate LIC mortality. In Japan, by contrast, these are largely eliminated, so deaths are mainly from degenerative NCDs of old age — exactly the pattern of the epidemiological transition. On this evidence, development is the single most powerful control.

The case that other factors also matter. However, development does not explain everything. Conflict and governance can devastate health independently of average wealth — war and state collapse can raise mortality and cut life expectancy even where resources exist. Specific epidemics distort the pattern: HIV/AIDS sharply reduced life expectancy in parts of southern Africa (e.g. Lesotho, Botswana) during its peak, a health outcome not fully captured by income alone. Environment and disease ecology matter too — malaria is concentrated in warm, humid tropical regions regardless of development, so physical geography shapes the communicable-disease burden. Culture, diet and lifestyle also drive NCDs: rising obesity, smoking and processed diets raise heart disease and diabetes even in middle-income countries.

The 'double burden' complication. Rapidly developing, middle-income countries such as India show that the development–disease link is not linear. They face a double burden: infectious disease persists in poorer, rural and slum populations while NCDs rise rapidly among the urban middle class. This shows that internal inequality, not just national development level, shapes the geography of disease — a country can sit across two stages of the epidemiological transition at once.

Conclusion. On balance, development is the dominant determinant of disease and mortality: comparing Japan (life expectancy ~84, NCDs) with Chad or Sierra Leone (~53–55, communicable disease and child mortality) shows how strongly wealth, healthcare and sanitation shape health. However, it is not the sole factor. Conflict, HIV/AIDS epidemics, disease ecology, lifestyle and internal inequality all modify the pattern, and the double burden shows the transition is uneven. The most accurate view is that development sets the broad framework of a country's disease and mortality profile, but specific physical, social and political factors determine the detail — precisely the kind of nuanced conclusion a Unit 4 research enquiry should reach.

The epidemiological transition model proposes that as countries develop, their dominant disease burden shifts from communicable/infectious disease and famine through an age of receding pandemics to an age of degenerative, non-communicable disease. This essay assesses how useful the model is as a framework for understanding global patterns of disease and mortality, testing it against real examples before reaching a judgement.

Why the model is useful. Its greatest strength is that it captures the real, systematic link between development and disease profile. The extremes fit it well. Japan, a highly developed, ageing society, has completed the transition: life expectancy is ~84, infant mortality is ~2 per 1000, and the leading causes of death are cancer, cardiovascular disease and stroke — degenerative NCDs, exactly as the model predicts. At the other end, Chad and Sierra Leone remain in an early stage: life expectancy ~53–55, infant mortality 68–78 per 1000, and mortality dominated by communicable disease (malaria, diarrhoeal, respiratory infections) and maternal/child deaths. The model also usefully links to the Demographic Transition Model — as death rates fall, the disease profile changes — giving a coherent way to compare countries at different development levels and to predict how a country's health burden will evolve as it develops, which is valuable for planning healthcare.

Where the model works less well. However, the model has real limitations. First, it can be too linear and deterministic: it implies a neat one-way progression, but many middle-income countries such as India and Brazil face a 'double burden' — persisting infectious disease in poorer areas AND rapidly rising NCDs among wealthier urban populations — so they occupy two stages at once. Second, the model underplays re-emergence and new pandemics: infectious disease has not vanished from developed countries. HIV/AIDS caused major mortality even in HICs, drug-resistant TB is rising, and COVID-19 showed a communicable disease striking rich and poor countries alike — events the classic model did not anticipate. Third, it downplays sudden reversals: HIV/AIDS reduced life expectancy in parts of southern Africa (e.g. Lesotho, Botswana), a backward step the smooth transition does not capture. Fourth, it treats countries as internally uniform, ignoring the large inequalities within countries (rich vs poor, urban vs rural) that mean a single national 'stage' hides very different local realities.

Balancing the argument. Despite these weaknesses, the model remains a valuable organising framework. Its critics generally accept the core insight — that development shifts the balance from infectious to non-communicable disease — and later versions of the model add a fourth stage (delayed degenerative diseases) and acknowledge re-emerging disease, showing it can be refined rather than rejected. As a starting framework for a research enquiry it is powerful; it simply needs to be supplemented with ideas about inequality, disease ecology, epidemics and the double burden.

Conclusion. Overall, the epidemiological transition model is highly useful but not complete. It successfully explains the broad global pattern — the strong link between development level, falling mortality and the shift from communicable to non-communicable disease, seen clearly in the Japan–Chad contrast. However, it is too linear, underestimates re-emerging and new infectious diseases (HIV, COVID-19), ignores within-country inequality, and cannot easily accommodate the double burden of emerging economies. The most accurate assessment is that the model provides an excellent broad framework for understanding global disease and mortality, but must be used alongside concepts of inequality, disease ecology and epidemic shocks to explain the full, messy reality — the balanced judgement expected in a Unit 4 research essay.

Wealth clearly shapes health: richer countries can afford the healthcare, clean water and nutrition that reduce disease and death. But is wealth the single most important factor in the geography of disease and mortality, or do other controls matter as much? This essay evaluates the statement using contrasting named examples.

The case that wealth is the main factor. The strongest evidence is the close correlation between income and health indicators. High-income Japan has a life expectancy of ~84 and infant mortality of ~2 per 1000; low-income Chad and Sierra Leone have life expectancies of ~53–55 and infant mortality of 68–78 per 1000. Wealth explains much of this because national income funds universal healthcare, sanitation, clean water, vaccination and good nutrition, which control the communicable diseases and maternal/child deaths that dominate poorer countries. Where wealth is lacking, preventable infectious disease persists. On this reading, wealth is the master variable that determines the resources a country can devote to health.

The case that other factors matter as much. However, wealth alone is an incomplete explanation. Governance and public policy can produce good health at low income: countries that prioritise primary healthcare, female education and clean water achieve life expectancies well above what their income alone predicts, showing that how money is used matters, not just how much there is. Conflict and instability can wreck health regardless of underlying wealth, raising mortality where states collapse. Disease ecology and physical geography are independent controls: malaria thrives in warm, humid tropical environments, so climate shapes the communicable-disease burden irrespective of income. Culture and lifestyle drive the NCD burden — diet, smoking and activity levels influence heart disease and diabetes even in wealthy societies. And specific epidemics distort the pattern: HIV/AIDS sharply cut life expectancy in relatively better-off southern African states (e.g. Botswana, Lesotho), a shock that income did not prevent.

Inequality within countries. Wealth also fails to explain the health differences within a single country. Middle-income countries such as India show a double burden in which poorer, rural populations still suffer infectious disease while wealthier urban groups develop NCDs — so internal inequality, not just average national wealth, shapes who gets which disease. This suggests the distribution of wealth matters as much as its total.

Conclusion. On balance, I partly agree: wealth is the single most powerful factor in the broad global pattern, because it funds the healthcare, sanitation and nutrition that most strongly separate healthy from unhealthy countries — the Japan–Chad contrast makes this clear. However, the statement is too absolute. Governance, conflict, disease ecology, lifestyle, epidemics and internal inequality all shape disease and mortality in ways wealth alone cannot explain, and good policy can deliver health above a country's income level. The most accurate view is that wealth sets the broad framework, but it is how that wealth is distributed and used — alongside environment, governance and disease ecology — that determines the detailed geography of disease and mortality.