[PENDING] Socioeconomic determinants of health: A worldwide study

I am also grateful to my tutors in each of the four courses of this MBA program, as each of them contributed significantly to enhancing my overall program experience. Across the entire sample of countries, the results showed that income, poverty and education were the strongest socioeconomic determinants of health.

Introduction

The concepts presented in the chapter are "population health" with emphasis on the different categories of population health measures, "social stratification" with a deeper look at education, occupation and income as the main socio-economic indicators. The analyzes were repeated for subgroups classified as low-, lower-middle-, upper-middle-, and high-income countries, and the results are presented in the same format to allow comparisons.

Theoretical framework

Population health

• Health outcomes and determinants
• Measures of population health

The "population health perspective" recognizes shared responsibility for population health outcomes due to the multiple factors that influence population health. Population health measures are broadly categorized as measures of mortality and life expectancy, subjective well-being, and summary measures of population health.

Social stratification

• Socioeconomic indicators
• The healthcare system

Therefore, investing in education and health can show the way out of poverty (Behrman & Rosenzweig, 2004). Understanding the sources and direction of causality between income and health is a key factor in improving population health as well as reducing societal health inequalities.

Health inequalities

Especially among the developed countries, the Organization for Economic Co-operation and Development (OECD) in the health system or in other social structures has identified remarkable variations in various health indicators such as life expectancy and mortality. Also at that time the average life expectancy for men in India was 62 and only 54 for men living in the poorest area of ​​Glasgow - Calton (Marmot, 2015).

Literature review

The relationship between income inequality and health status is one of the most studied and the results are often contradictory. Most cross-sectional studies support the existence of an inverse relationship between income inequality and health (Wilkinson, 1995; Kawachi. & Kennedy, 1999; Epstein et al., 2009), but they are generally based on simplistic health models that do not take into account factors such as health improvement policies and health services. Other cross-sectional studies that have investigated the relationship between life expectancy and infant mortality, and the Gini coefficient (the higher its value, the greater the inequality) for a sample of countries, conclude that the Gini coefficient is indeed related negatively with life expectancy and positively with infant mortality (Mellor & Milyo, 2001).

The relationship between poverty and health applies both to developing countries (due to absolute poverty) and to more developed countries due to the effects of relative poverty (Subramanian et al., 2002). Another study that analyzed the relationship between per capita gross domestic product (GDP), extreme poverty, the Gini coefficient, and common measures of public health found that an increase in GDP had a large positive impact on population health, although the strength of the relationship was affected. with poverty and inequality (Biggs et al., 2010). There has also been a long debate about the importance of the contribution of health services to the health of the population (McKee, 1999).

Material and methods

• Research questions and hypotheses
• Study design and variable selection
• Sample and data collection
• Statistical analyses
• Endogeneity testing

Life expectancy at birth is "the average number of years a newborn is expected to live if mortality patterns at the time of birth remain constant in the future". The infant mortality rate per 1,000 live births is "the number of infants who die before the age of one, per 1,000 live births in a given year." The under-five mortality rate is "the probability, per 1,000, that a newborn infant will die before reaching the age of five, subject to age-specific mortality rates for the specified year".

Unemployment refers to "the proportion of the labor force that is out of work but available for and looking for work. Public health expenditure refers to "the sum of recurrent and capital expenditure from central and local government budgets, external loans, grants, donations from international agencies and non-governmental organizations and social health insurance funds", expressed as a percentage of the country's GDP. Doctor density refers to "the number of general practitioners and specialists in the country per 1,000 people".

Results

Sample formation

Tests of linear regression assumptions

For both the more general Kolmogorov-Smirnov test and the normality-specific Shapiro-Wilk test, the null hypotheses of a normal distribution of the residuals were not rejected, as p>0.05 for all tests. The existence of heterogeneous error variances across the range of measured values ​​was tested with the Breusch-Pagan and Koenker tests, and the results are shown in Table 5.4. For all models and tests, the p-values ​​exceeded 0.05, which means that the null hypothesis of homoscedasticity is not rejected.

The possibility of high correlations between the independent variables was tested with the VIF by taking each predictor as the dependent variable and regressing it on all the other predictors. For all the tests, the VIF was less than the suggested threshold of 10, implying the absence of any serious collinearity. Even better, the VIF was between one and five across all tests, suggesting only moderate correlation, but not severe enough to require corrective measures (Belsley, 1991).

Analyses with the entire sample

• Descriptive statistics
• Correlations between variables
• Multiple linear regressions
• Instrumental variables and two-stage residual inclusion

At the other end of the health spectrum are low-income sub-Saharan countries. The results of pairwise correlations support the inclusion of these specific variables in multiple regression models as potential predictors of health. The main elements of the three models with data from 172 countries are presented in Table 5.11.

Thus, the cross-country analysis showed that GNI/capita and poverty predicted +80% of the variability of IMR and U5MR. The same two variables and school years also explained most of the variability of LE. Regarding the strength of the effect of each socio-economic variable on the dependent (health) variable, which is expressed by the absolute values ​​of the corresponding beta coefficients, poverty (and not years of schooling despite the highest R2) had the strongest effect on LE.

Analyses by country income group

• Analysis for low-income countries
• Analysis for lower middle-income countries
• Analysis for upper middle-income countries
• Analysis for high-income countries

Poverty and expected years of schooling cumulatively explained 48.1% of the variance in LE in low-middle-income countries. The relative contribution of each socio-economic variable to measures of population health, in the group of low-middle-income countries, is shown graphically in Figure 5.3, which also shows the unexplained variance in each model. The results of the regressions for the 49 upper-middle-income countries are presented in Table 5.17.

The relative contribution of each socio-economic variable to population health indicators, in the group of upper middle-income countries, is shown graphically in Figure 5.4. The striking feature of the three models for the richer countries is the non-significant contribution of the two health care variables. That is, to investigate causality, the 2SRI approach was implemented, using the same IVs as in the entire sample (section 5.3.4). and the results are shown in Table 5.19.

Discussion

Interpretation of the results

In an analysis including all countries, GNI/capita and poverty predicted more than 80% of infant variability. In the regression models, income inequality was significant when the health outcome was infant or child mortality, but not life expectancy. In this study, all bivariate correlations between public health spending and health indicators and between physician density and health indicators were strong (r>0.7).

Poverty was strongly associated with all health outcomes regardless of income level, while unemployment was a significant predictor of population health in the low- and high-income groups. The signs of the coefficients for unemployment were surprising in the group of high-income countries (a positive association with LE and a negative one with IMR and U5M). Our results corroborate studies that have found an effect of public spending on health, especially in the poorer countries (Bidani & Ravallion, 1997; Gupta et al., 2003; Houweling et al., 2005).

Limitations

The countries were not segmented by SES or geographic location, which could have provided more information about the particular groups most affected. This may be important as people who are disadvantaged in socio-economic status typically have poorer health services and economic hardship would likely have a greater negative effect on the poorest section of a society. Furthermore, this study focuses on the amount of money the government spends on health care and does not consider how effectively or efficiently that money is spent.

Indeed, it is possible for a country to spend less money on health care but achieve better results in terms of health outcomes due to better efficiency, or by avoiding ineffective interventions.

Implications and future research

We examined the impact of public health spending on the health of the entire population of each nation. Future studies should perhaps investigate whether existing health system models work differently in this aspect. For example, at the local level emphasis can be placed on transport and housing policies, at the national level on environmental, educational and social policies and at the global level on financial, trade and agricultural policies (WHO, 2011).

Future studies in this area of ​​research should explicitly examine the complex, multi-level relationship between population health and key socioeconomic determinants. Collecting more publicly available data at the individual and community levels is needed to confirm and strengthen confidence in country-level findings such as those from this study. The advantage of individual-level data is the potential to investigate the determinants of the health of individuals rather than that of the entire population, and therefore these data may be better suited to inform health policy making .

Conclusions

Impact of national income level, inequality and poverty on public health in Latin America. Closing the gap in one generation: health equity through action on the social determinants of health. Determinants of under-5 mortality among the poor and the rich: an international analysis of 43 developing countries.

Estimating the effect of income on health and mortality using lottery prizes as an exogenous source of variation in income. The socio-economic determinants of health: Economic growth and health in the OECD countries during the last three decades. Short-term effects of job loss on health conditions, health insurance, and health care utilization.