Why does a child born in Oman today expect to live nearly 30 years longer than a child born in Somalia? Why have some OIC (Organization of Islamic Cooperation) countries made stunning progress in life expectancy while others have stagnated?
A systematic review published in a leading public health journal has analyzed 54 studies covering dozens of Muslim-majority countries to answer exactly these questions. The findings are clear, evidence-based, and deeply hopeful: The secrets to longer life are not mysteries. They are measurable, modifiable, and within reach of every nation.
The review, which synthesized research from 2006 to 2025, examined three broad categories of determinants: economic and macroeconomic factors, social and demographic conditions, environmental quality, and healthcare resources. The result is a roadmap for policymakers, public health officials, and citizens who want to understand what actually works.
The Big Picture: Life Expectancy Varies Dramatically Across OIC Countries
Average life expectancy in OIC countries has increased from 62.2 years in 2000 to 68.9 years in 2023. But this average hides enormous variation:
- High-income Gulf Cooperation Council (GCC) states (Oman, Qatar, Saudi Arabia, UAE, Bahrain, Kuwait) have the highest life expectancy, often exceeding 75-80 years.
- Upper-middle-income countries (Indonesia, Iran, Turkey, Malaysia, Kazakhstan) generally have moderate to high life expectancy, with steady improvements.
- Lower-middle-income countries (Nigeria, Pakistan, Bangladesh, Palestine) show modestly higher life expectancy but remain highly sensitive to poverty, education, and environmental pressures.
- Low-income countries (Sudan, Somalia, Afghanistan) consistently report the lowest life expectancy and the slowest gains.
The gap is not inevitable. The review identifies specific, actionable factors that explain these differences.
What Works – Factors Consistently Associated with Longer Life Expectancy
| Determinant Category | Specific Factor | Direction of Association | Strength of Evidence |
|---|---|---|---|
| Economic | GDP per capita | Positive (+) | Very strong (44 studies) |
| Economic | Health expenditure (% of GDP) | Positive (+) | Strong (35 studies) |
| Economic | Financial deepening / domestic credit | Positive (+) | Moderate |
| Economic | Inflation | Negative (-) | Moderate |
| Economic | Income inequality (GINI) | Negative (-) | Strong |
| Economic | Poverty headcount | Negative (-) | Strong |
| Social | Education (schooling years, literacy, enrollment) | Positive (+) | Very strong (44 studies) |
| Social | Employment rate | Positive (+) | Strong |
| Social | Unemployment | Negative (-) | Strong |
| Social | Smoking prevalence | Negative (-) | Strong |
| Environmental | CO₂ emissions | Negative (-) | Strong (33 studies) |
| Environmental | Air pollution (PM2.5, PM10, SO₂, CO) | Negative (-) | Very strong |
| Environmental | Ecological footprint deficit | Negative (-) | Strong |
| Healthcare | Health expenditure per capita | Positive (+) | Strong (35 studies) |
| Healthcare | Physician density / hospital beds | Positive (+) | Strong |
| Healthcare | Vaccination coverage | Positive (+) | Strong |
| Healthcare | Infant mortality rate | Negative (-) | Very strong |
| Healthcare | Maternal mortality ratio | Negative (-) | Very strong |
The Economic Engine: Money Matters, But How You Spend It Matters More
Higher national income is consistently associated with longer life expectancy. In a 46-country OIC panel study, log GDP per capita showed a large positive effect on life expectancy alongside health expenditure and schooling.
But here is the crucial nuance: Not all economic growth is equal. Inflation, income inequality, and poverty all undermine the health benefits of growth.
- In Nigeria, poverty headcount (the percentage of the population living below the poverty line) was strongly negatively associated with life expectancy (β=−0.1672,p<0.001β=−0.1672,p<0.001). The poverty gap and squared poverty gap showed similar negative effects.
- In Pakistan, income inequality (GINI index) had a significant negative effect on life expectancy (β=−0.25060,p=0.0044β=−0.25060,p=0.0044), even after controlling for GDP per capita and health spending.
- Inflation also showed negative associations in several models.
The takeaway: Economic growth alone is not enough. To translate wealth into health, countries must reduce inequality, control inflation, and ensure that the benefits of growth reach the poorest citizens.
The Social Foundation: Education, Employment, and the Burden of Poverty
Education emerged as one of the most powerful social determinants of life expectancy. In the 46-country OIC panel, mean years of schooling had a large positive effect (β=0.575393,p<0.001β=0.575393,p<0.001). Smoking prevalence, by contrast, was strongly negative (β=−0.220921,p<0.001β=−0.220921,p<0.001).
Country-specific studies reinforced this pattern:
- In 19 Arab OIC countries, female and male literacy and enrollment rates correlated strongly with life expectancy (female enrollment: r = 0.87 with female life expectancy).
- In Malaysia, literacy correlated positively with life expectancy (r = 0.69, p < 0.05).
- In Turkey, secondary school enrollment was strongly associated with life expectancy in both crude (r = 0.9897) and adjusted models (β=0.176652,p<0.001β=0.176652,p<0.001).
Employment also matters. In Bangladesh, a higher employment rate was positively associated with life expectancy (β=0.558,p<0.01β=0.558,p<0.01), while unemployment (β=−0.411,p<0.05β=−0.411,p<0.05), population growth (β=−0.443,p<0.01β=−0.443,p<0.01), and higher age dependency (β=−0.393,p<0.05β=−0.393,p<0.05) were negative predictors.
Poverty is deadly. The Nigerian studies consistently showed that poverty intensity remains a significant negative determinant of life expectancy even after controlling for GDP per capita. This finding aligns with the World Health Organization’s emphasis on social determinants of health: the conditions in which people are born, grow, work, live, and age.
The Environmental Threat: Air Pollution Shortens Lives
One of the most striking findings of the review is the consistent, strong negative effect of environmental degradation – especially air pollution and CO₂ emissions – on life expectancy across OIC countries.
- In Nigeria, one study found that CO₂ emissions had a large negative coefficient (β=−45.0359,p=0.0066β=−45.0359,p=0.0066).
- In Pakistan, ARDL models showed that higher CO₂ emissions were associated with lower life expectancy (β=−0.046395,p=0.0007β=−0.046395,p=0.0007), with similar negative effects in FMOLS and DOLS robustness checks.
- In Kazakhstan, energy use (β=−0.0942,p=0.007β=−0.0942,p=0.007) and air pollution proxies (β=−0.1294,p=0.0134β=−0.1294,p=0.0134) were negatively associated with life expectancy.
- In Iran, a detailed time-series study found strong negative associations between particulate matter (PM2.5, PM10), sulfur dioxide (SO₂), carbon monoxide (CO), and life expectancy. For example, CO had a crude correlation of r = -0.944 with life expectancy.
Global context: These findings are consistent with worldwide estimates that ambient air pollution reduces average life expectancy by about 2.9 years – comparable to or exceeding the impact of tobacco smoking.
The hopeful message: Air pollution is modifiable. Policies that reduce emissions – transitioning to renewable energy, improving fuel standards, investing in public transportation – directly improve population health and life expectancy.
The Healthcare Factor: Resources Save Lives
Health-system resources and disease burden were central determinants of life expectancy across the 35 studies that examined this domain.
- In the 46-country OIC panel, health expenditure as a share of GDP was positively associated with life expectancy (β=0.132586,p=0.0400β=0.132586,p=0.0400).
- In GCC countries, structural equation models showed that health resources had strong direct effects on life expectancy (β=0.468,p<0.001β=0.468,p<0.001).
- In Oman and Qatar, health status and resources directly predicted life expectancy with large coefficients (β=0.839β=0.839 and β=0.904β=0.904, respectively).
- In Malaysia, doctors per 10,000 population (r = 0.75) and government health expenditure (r = 0.81) had strong positive correlations with life expectancy.
- In Saudi Arabia, government health expenditure was positively associated with life expectancy (β=0.144,p=0.011β=0.144,p=0.011).
Disease burden matters just as much. In 19 Arab OIC countries, infant mortality rate (IMR) and maternal mortality ratio (MMR) were strongly negatively correlated with life expectancy (IMR: r = -0.95 with female life expectancy; MMR: r = -0.94). Skilled birth attendance, prenatal care, nutrition, and physician density were all positively correlated.
What Hurts – Factors Consistently Associated with Shorter Life Expectancy
| Determinant Category | Specific Factor | Impact | Example Evidence |
|---|---|---|---|
| Economic | Poverty headcount | Strong negative | Nigeria: β = -0.1672 (p < 0.001) |
| Economic | Income inequality (GINI) | Strong negative | Pakistan: β = -0.25060 (p = 0.0044) |
| Environmental | CO₂ emissions | Strong negative | Nigeria: β = -45.04 (p = 0.0066) |
| Environmental | Air pollution (PM2.5, SO₂, CO) | Very strong negative | Iran: CO r = -0.944 with LE |
| Healthcare burden | Infant mortality rate | Very strong negative | Arab countries: r = -0.95 |
| Healthcare burden | Maternal mortality ratio | Very strong negative | Arab countries: r = -0.94 |
| Social | Smoking prevalence | Strong negative | OIC panel: β = -0.221 (p < 0.001) |
| Social | Unemployment | Moderate negative | Bangladesh: β = -0.411 (p < 0.05) |
The Indirect Pathways: How Social Factors Work Through Healthcare
One of the most sophisticated findings comes from structural equation models (SEM) conducted in Oman, Qatar, Indonesia, and GCC countries. These models show that social and economic factors often influence life expectancy indirectly, through their effect on healthcare resources.
- In Oman and Indonesia, sociodemographic factors (education, demographic structure) had large indirect effects on life expectancy via health resources, with total effects of β = 0.675-0.755.
- In GCC countries, health resources mediated the relationship between macroeconomic conditions and life expectancy.
Policy implication: Investing in education and poverty reduction is not just a social good – it is a health investment. Better-educated populations demand and use healthcare more effectively. Wealthier populations fund stronger health systems.
The Bottom Line: A Roadmap for Action
This systematic review, the first of its kind focused exclusively on OIC countries, provides clear, evidence-based answers to the question of why life expectancy varies so dramatically across the Muslim world.
What works:
- Higher GDP per capita, but only when accompanied by poverty reduction and inequality control
- Better education, especially female literacy and enrollment
- Strong employment and lower unemployment
- Higher health expenditure and stronger health systems (physicians, hospitals, vaccines)
- Cleaner air and lower CO₂ emissions
What hurts:
- Poverty and income inequality
- Air pollution and environmental degradation
- High infant and maternal mortality
- Smoking and unemployment
The encouraging news: Every single one of these factors is modifiable. Countries that have made progress – from Oman to Malaysia to Turkey – have done so through deliberate policy choices. They invested in education. They built health systems. They reduced poverty. And their citizens live longer, healthier lives as a result.
For the 57 OIC countries, the path forward is clear. The evidence is in. Now comes the work.
Reference: here
Other Articles:
