Political and welfare state determinants of infant and child health indicators: An analysis of wealthy countries

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Abstract

Economic indicators such as income inequality are gaining attention as putative determinants of population health. On the other hand, we are just beginning to explore the health impact on population health of political and welfare state variables such as political orientation of government or type of medical care coverage. To determine the socially structured impact of political and welfare state variables on low birth weight rate, infant mortality rate, and under-five mortality rate, we conducted an ecological study with unbalanced time-series data from 19 wealthy OECD countries for the years from 1960 to 1994. Among the political/welfare state variables, total public medical coverage was the most significant predictor of the mortality outcomes. The low birth weight rate was more sensitive to political predictors such as percentage of vote obtained by social democratic or labor parties. Overall, political and welfare state variables (including indicators of health policies) are associated with infant and child health indicators. While a strong medical care system seems crucial to some population health outcomes (e.g., the infant mortality rate), other population health outcomes might be impacted by social policies enacted by parties supporting strong welfare states (the low birth weight rate). Our investigation suggests that strong political will that advocates for more egalitarian welfare policies, including public medical services, is important in maintaining and improving the nation's health.

Introduction

The goal of this investigation is to examine the relationship between political and welfare state variables and average levels of population health among wealthy countries. Researchers in comparative social epidemiology and adjacent disciplines characteristically study countries belonging to the Organization for Economic Cooperation and Development (OECD) because of a greater availability and quality of data on economic factors (e.g., income inequality and national income: Preston, 1975; Rodgers, 1979; Wilkinson, 1996). In fact, studying the relationship between income inequality and population health is one of the most heuristic research programs in contemporary social epidemiology (Wilkinson, 1996; Wilkinson, 2005). However, critics have argued that this model suffers from the omission of political factors that are necessary to explain health inequalities (Coburn, 2000; Muntaner & Lynch, 1999). Thus, new approaches to international health comparisons pay attention to political and health policy variables (Coburn, 2000; Conley & Springer, 2001, for American states; Lynch et al., 2004; Macinko, Starfield, & Shi, 2003; Macinko, Shi, & Starfield, 2004; Muntaner et al., 2002; Navarro & Shi, 2001).

For example, the relationship between income inequality and population health has been examined in several cross-national studies during the last three decades (Lynch et al., 1994; Wagstaff & van Doorslaer, 2000). In spite of recent challenges to the notion that, in wealthy countries, the link between income inequality and health has the generality of a natural law (Wilkinson (1996), Wilkinson (2005)), there is still some evidence of a positive association between income inequality and mortality rates in a wide variety of contexts (e.g., American states: Lynch et al., 2004). In one of the first studies, Rodgers examined the cross-sectional relationship between income distribution, mean income per capita, and all-cause mortality in 56 countries (Rodgers, 1979). He estimated that life expectancy in relatively egalitarian and relatively inegalitarian countries differed by 5–10 years. Rodgers suggested that the relationship was significant even in countries with per capita incomes below US$1000. Analysis restricted to countries with low per capita income found a similar relationship in the areas of life expectancy at birth and life expectancy at fifth birthday. The relationship was weaker in the area of infant mortality. Thus, Rodgers’ and later studies on income inequality have contributed to establish that ecological designs in comparative international health are justified because they provide unique macro-level insights into the global distribution of health inequalities and its determinants.

However, few studies have explored the relationship between political variables and population health in groups of countries. Navarro et al.'s (2003) study might be the only study that has included a comprehensive number of political variables while adjusting for economic determinants. A key assumption of our theoretical approach is that understanding the association between social factors and health requires analyzing political as well as economic determinants (Coburn, 2000). Thus, although countries’ income distribution and GDP have been associated with several population health outcomes such as infant mortality and low birth weight (Lynch et al., 2001), recent studies suggest that political and welfare state variables (e.g., access to health care) could also be important determinants of population health outcomes (David & Collins, 1997; Macinko, Starfield et al., 2003; Macinko et al., 2004; Muntaner et al., 2002; Navarro & Shi, 2001; Raphael & Bryant, 2003). For example Conley and Springer used a country-level fixed-effects model to determine whether public health spending had a significant impact in lowering infant mortality rates, and whether that effect was cumulative over a 5-year period (Conley & Springer, 2001). They found that state spending, which varied according to the institutional structure of the welfare state, affected infant mortality through both health and social policies. Raphael and Bryant reviewed literatures on welfare state and women's health in Canada, to find out that “characteristics associated with the advanced welfare state in industrialized nations are primary contributors of women's quality of life.” (Raphael & Bryant, 2003) Muntaner and colleagues used political and welfare state variables, as well as social capital and economic indicators to examine GDP adjusted partial correlations with cause- and age-specific mortality rates. Among the outcome measures, the five variables related to birth and infant survival and non-intentional injuries were most consistently associated with economic inequality and political/welfare state variables (Muntaner et al., 2002). They found Gini coefficient, household income inequality, 90/10 percentile, 50/10 percentile, household poverty rate, voter turnout, social pact (a measure of pact between labor and employers), percentage of “left” (i.e., social democratic or labor) vote and “left” seats, women in government, and total public medical care to be significantly correlated with infant mortality rates (p<0.05) in both males and females. In addition, the low birth weight rate was significantly associated with the Gini coefficient, household income inequality, 90/10 percentile, 50/10 percentile, household poverty rate, voter turnout, social pact, “left” votes, women in government, and total public medical care.

The aim of our study is to build upon the preliminary studies reviewed above on the role of political and welfare state variables in population health. We develop a theoretical model that integrates previous findings and provides a blueprint for the macro-social causation of child health outcomes. We use a time series multivariate regression model that incorporates both GDP and income inequality, as well as political and welfare state variables to enhance the inferential power of the analyses.

The field of (macro) social epidemiology suffers from lack of comprehensive models (Macinko, Shi, Starfield, & Wulu, 2003). This is why we draw from the field of comparative welfare state politics for our model. In the study conducted by Huber & Stephens (2001), the authors emphasized partisan politics as the single most important factor that shaped the development of welfare states through time and that accounted for the variation in welfare state outcomes across countries. And partisan politics, in turn, was strongly related to social structural features, most importantly the strength of organized labor. Navarro, Borrell, and Muntaner's conceptual framework builds upon Huber and Stephen's empirical findings, but adds the dimension of ‘income inequality’, to examine political and economic determinants of population health (Navarro, 2003). According to this conceptual framework, politics (e.g., political orientation of the party in government) determines welfare state policies that affect population health, net of the influence of economic inequality, which is partially determined by welfare state policies (Huber & Stephens, 2001). We modified Navarro et al.'s model based on our review of the empirical literature summarized in the introduction section. (See Fig. 1) Variables in squares are those used in the present analyses, while those in circles are not used or could not be measured. Ones in grey are the ones that are not considered in this analysis.

Our conceptual model thus involves a country's political environment, welfare state policies, health care system, and income inequality. We measure political environment in two dimensions: the level of political participation and the ideological orientation. We hypothesize that the level of political participation is positively correlated with good population health status, based on a couple of partial and multivariate correlation analyses (Muntaner et al., 2002; Navarro et al., 2003). Literatures investigating the relationship between health and social network/cohesion, which is related to civic participation such as voting, support the hypothesis. (e.g., Blakely, Kennedy, & Kawachi, 2001)

The dominance of pro-egalitarian political ideology, which is measured by the votes gained by left-wing parties is positively correlated with better population health (Muntaner et al., 2002; Navarro et al., 2003) possibly through welfare state policies, such as commitment to full-employment, providing universal health coverage, and increase in redistribution of income. We used two indicators of welfare-state policy: social security transfer and percentage of population under public medical coverage. These two indicators are expected to be negatively associated with population ill health (i.e., high infant mortality rate, under-5 mortality rate, and low birthweight rate). While the former directly affects the level of income inequality, the latter primarily is associated with the level of access to medical care. Rather than including these two variables in a single welfare state construct, we separated them conceptually so that we will be able to understand their unique contribution to population health. Because social transfers and health services fall short from measuring the whole effect of different welfare-state arrangements, we included an additional pathway through “other policies” (e.g., labor market and environmental health policies), which might affect population health independently from the welfare-state indicators used in this study.

We also included income inequality because it has been associated with population health averages in a number of studies (e.g., Wilkinson, 1996). In epidemiology, the mechanism backing this prediction is based largely on two explanations: psychosocial (e.g., Wilkinson, 1996) and neo-material (e.g., Kaplan, Pamuk, Lynch, Cohen, & Balfour, 1996). In the welfare-state literature, income inequality is more a result of government policies, that is, an endogenous variable. For example, Bradley, Huber, Moller, Nielsen, and Stephens (2003). concluded that high pre-tax/pre-transfer inequality is determined by a high unemployment rate, a high proportion of female-headed households and by low union density, while reduction in inequality through taxes and transfers is strongly determined by political variables such as leftist cabinet, Christian democratic cabinet, constitutional veto points, and welfare generosity.

Based on the theoretical model described above, we hypothesize that egalitarian political and welfare state variables (e.g., proportion of votes to social democratic parties, universal access to health care) will predict child mortality outcomes at the national level.

Section snippets

Methods

Data sources and variables: The study focuses on 19 wealthy countries from Europe (14), North America (2), and Asia and the Pacific region (3) during the 35-year period from 1960 to 1994. Outcome variables are the infant mortality rate (IMR), the low birth weight rate (LBW) and the under-five mortality rate (U5MR). Data sources are the OECD Health Data (Organization for Economic Co-operation and Development (OECD), 2000) and the annual report “The State of Children.” (United Nations Children's

Results

A clear declining trend in infant and under-five mortality rates was observed during the year analyzed. The low birth weight rate decreases until the mid-1970s and starts to increase from the mid-1980s. The GDPpc continues to increase, but the Gini coefficient shows a rather random picture. But we must keep in mind that there are many missing values in the earlier period so that mean values for the Gini coefficient are quite unstable. Results are presented in Table 2, Table 3, Table 4.

Discussion

Our study contributes to the emerging body of research on the impact of political factors on population health. We used a data set from 19 different countries over a 35-year period. This pooled regression approach helps us to draw more general conclusions than we have been able to, based on previous cross-sectional analyses.

While our study dealt tangentially with the relative income hypothesis, we tried to go a step further by assessing three maternal and child health outcomes in relation to

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