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He estimated that life expectancy in relatively egalitarian and relatively inegalitarian countries differed by 10 years. 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. However, few studies have explored the relation ship between political variables and population health in groups of countries.

Navarro et al. 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, 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. 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.

Katherine M. Keyes and Sandro Galea

We develop a theoretical model that integrates previous findings and provides a blueprint for the macro-social causation of child health outcomes. This is why we draw from the field of comparative welfare state politics for our model. And partisan politics, in turn, was strongly related to social structural features, most importantly the strength of organized labor. According to this conceptual framework, politics e. We modified Navarro et al. See Fig.

On Mechanisms vs. Foundations

Ones in grey are the ones that are not considered in this analysis. We measure political environment in two dimensions: the level of political participation and the ideological orienta tion. 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.

The dominance of pro-egalitarian political ideology, which is measured by the votes gamed by leftwing parties is positively correlated with better population health Muntaner et al. 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 Le. 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.

We also included income inequality because it has been associated with population health averages in a number of studies e. In epidemiology, the mechanism backing this prediction is based largely on two explanations: psychosocial e. In the welfare-state literature, income inequality is more a result of government policies, that is, an endogenous variable. Based on the theoretical model described above, we hypothesize that egalitarian political and welfare state variables e. 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 year period from to Other explanatory variables were obtained from Huber et al.

In choosing indicators corresponding to our theoretical model we faced two problems: one was data avaliability. Variables and data sources are presented in Table 1. Statistical analysis: We conducted an unbalanced panel data analysis of the 19 countries, using the robust-cluster variance estimator. The robust-cluster variance estimator is a variant of the Huber-White robust estimator that remains valid le. Thus, the robust-cluster standard errors are unaffected by the presence of unmeasured stable country-specific factors causing correlation among errors of observations for the same country, or for that matter by any other form of within-unit error correlation.

By generating successive adjusted variable plots, we confirmed that all explanatory variables were in linear relationships with the outcome variables of interest except GDPpc. We used a logarithrmc term for GDPpc, because it provided a better model fit than other transformations. We built our final models 4 and 5 to evaluate the effect of the Gini coefficient on other explanatory variables and viceversa. However, in doing so, many of the data points were dropped, mainly because of missing data points in the Gini coefficients and a few in other variables.

This may represent valid data or a mistake in experimentation, data collection, or data entry. However, we chose to include the US in the analysis. First, our sample is the whole universe of advanced capitalist countries, and therefore, the distant values of the US are not a result of any fault in sampling process, but a result of distinct historical process of that country.

Also, we do not have a rationale to expect that our theoretical model regarding the impact of political and welfare state factors of population health does not apply to the US. This is in part due to the theoretical reason Peters, as the US is part of the system of industrialized welfare state regimes.

Katherine M. Keyes and Sandro Galea

It also reflects the public health importance of the US as a large nation. The possible correlation among clusters through time i. To assess the reliability of our analysis, we conducted a couple of sensitivity tests, namely extreme bound analyses and a kind of jackknife method, and the results can be provided at request. A clear declining trend in infant and under-five mortality rates was observed during the year analyzed.

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The low birth weight rate decreases until the mids and starts to increase from the mids. 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 coeffi- cient are quite unstable. Results are presented in Tables 2 , 3 y 4.

Coefficients can be interpreted in the same way as in OLS regressions. When political variables are added, models predict 76 and 71 percent of the variability. Both political variables are signifi- cantly correlated with health outcomes.

Determinants of Health and an Ecological Framework for Population Health

Left vote shows stronger associations with health outcomes than voter turnout. Voter turnout is associated with IMR and U5MR but not in the expected direction: higher voter turnout is associated with higher mortality rates. The two welfare state variables accounted for more of variability in mortality rates than the two political variables. When we include all significant variables together in a single model, the explanatory power increases in both IMR and U5MR models.

The Gini coeffi- cient weakened the association of both voter turnout and left vote with infant mortality rate, while strengthened that of log GDPpc and total public medical care. We could not fit the model with the Gini coefficient for under-five mortality rate because of insuffi- cient data points. Low birth weight rate: Findings for the low birth weight rate clearly differ from results obtained with the infant and the under-five mortality rates. The model is not significant pvalue — 0. Left vote is significantly associated with LBW p-value — 0.

Welfare-state variables together are stronger predictors of LBW R2 — 0. Percentage of population under public medical care is significantly associated with LBW p-value — 0. The Gini coefficient does not explain much of the variation in LBW p — 0. Sensitivity analyses: To test the stability of our analyses, we conducted two different types of sensitivity analyses by each outcome variable. Because of insufficient data points, we excluded the Gini coefficient from this test. We also performed a kind of jackknife test generating 19 bivariate regressions by using subsets of our data set with one country omitted at a time.

In most instances, the results from extreme bound analysis and jackknife method are congruent, and the direction of association between the variables being tested and the outcome is stable. Results from are available from authors on request. Regressions when the US is omitted yielded mini mum or maximum values about half of the times, but the direction of the associations does not change, and the values are not far off from the range.

Therefore, the results from sensitivity tests did not substantially modify the conclusions of our analyses. In conclusion, our results show that the strongest predictor of these three population health indicators was the percentage of population under public medical coverage. And welfare state variables had stronger explanatory power than political variables.

Only Income was measured as the current monthly per capita household income. The study also showed a high morbidity rate involving complications related to unsafe abortion, especially hemorrhage and infections Tables 1 and 2 show the results of the univariate analyses.

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No factors were significantly associated with SA Tables 1 and 2. Tables 3 and 4 show the multiple multinomial logistic regression models. After stepwise removal of the variables that lost significance, 6 variables remained that were statistically significant for the total group of women, of which 5 remained in the group of women with a history of pregnancy Tables 3 and 4. Marital status at the time of the first event showed borderline significance, but was left in the model because of its importance in the specialized literature. None of the variables was significantly associated with SA.

Similar proportions were seen in single black women The lowest proportions of induced abortions were verified in non-black women with at least complete primary schooling 6.

Ubiquity and the Macrosocial Determinants of Population Health - Oxford Medicine

Importantly, unsafe abortion is not an issue of "free choice", but the consequence of social determinants and different health opportunities between individuals and groups For analysis of the main fieldwork results, the reference adopted here was the concept proposed and used by the WHO International Commission on Social Determinants of Health, focused on health interventions In Brazil, persons in situations of vulnerability and social risk who belong to families with a per capita income of up to one-half the minimum wage are classified by the Ministry of Social Development as affected by poverty and social exclusion.

Societies with steeper income inequality gradients, such as Brazil, present more social and health problems as compared to more egalitarian countries Rich and poor differ in more than income, since the latter, among others, is also responsible for health. The vast disparity between rich and poor is a growing threat to health, as reflected in the morbidity and mortality gradients both within and between countries Unsafe induced abortion in poor women involves health inequities due to the higher number of abortions, the inadequate conditions in which they occur, and the higher resulting morbidity, also leading to a higher mortality rate difficult to measure.

Meanwhile, women with better financial resources that submit to induced abortion, even though illegal or partially illegal, are able to do so without this abortion becoming unsafe, even though it is clandestine Numerous studies have shown higher complication and mortality rates resulting from unsafe abortion among women with low socioeconomic status The acceptance of induced abortion as conditioned by economic or financial factors, lack of material conditions to have and raise a child despite the resistance to accepting the abortion itself , denotes the gravity of income disparity in the study population.

In a sense, schooling represents income when the latter cannot be measured, and thus its dual importance. Countries with greater income inequality also perform worse in the level of schooling, and even more so in the lower economic strata The complex relationship between education and other demographic characteristics, fertility preferences, contraception, and abortion is reflected in the very different patterns found in the few studies in which this information educational or schooling level is available As an example of the indirect role of education in pregnancy and abortion, contraceptive use is closely related to the women's level of schooling.

Better educated women have more knowledge of contraceptive methods and their use and can choose the more effective methods as compared to their less educated peers 26,27, Meanwhile, the teenage pregnancy rate is strongly associated with relative poverty and inequality and thus also with low schooling This is confirmed in the current study population, in which the mean age of women at the time of their induced abortions for the first pregnancy was The vulnerability to unintended pregnancy is strongly influenced by access and use, or non-use, of effective contraception, in addition to exposure to undesired sex, early marriage, and sexual violence, factors correlated with low schooling Women with more schooling not only acquire more knowledge, but also have greater autonomy and capacity to choose This also explains the higher odds of unsafe abortion in women with less schooling.

Age at first intercourse is a good predictor of initial exposure to a pregnancy 31 which in turn is a necessary condition for the occurrence of abortion. Differential vulnerability to unintended pregnancy is much greater in this age bracket, especially among women with low income and poor schooling, either due to lack of the necessary knowledge or experience, or to erratic sexual activity, lack of communication with sex partners, and lack of control over life circumstances The number of sex partners during the year prior to the study probably reflects more systematic pregnancy-prone behavior, besides denoting lack of adherence to contraceptive use, which leads consequently to induced abortion In the WHO model, such behaviors are on the threshold between individual factors and social determinants of health, since behaviors, which are often interpreted as merely involving individual responsibility, depending on persons' free will choices, in reality can also be considered part of the social determinants of health, since such choices are heavily conditioned by social determinants In this study population, there was a high proportion of women with at least as many live born children as compared to what they reported as the ideal number 30 , with an increased probability of continuing to submit to unsafe abortions, which is also not due to free choice, but rather demonstrates the use of abortion to control fertility, within the specific context and given the social determinants acting on it.

Marital status is also entangled in this threshold between individual factors and social determinants of health. This socio-demographic characteristic is one of the possible factors in the occurrence, or lack thereof, of an induced abortion in the presence of an unplanned pregnancy. The distribution of abortions by marital status varies according to the scientific literature, and particularly according to the specific region In the United States and Cuba, countries with distinct socioeconomic, political, and cultural realities including religion , induced abortion is more frequent among single women, as in Brazil in poor communities.

Single women in the United States have a fourfold probability of submitting to induced abortion, as compared to married women In this study sample, the women's marital status varied greatly over the course of their childbearing history, between single, "cohabiting", and married, but unsafe abortion was associated with "living alone" at the time of this event. According to an international review on induced abortion, including countries in which abortion is legal and others whether it is restricted by law, developed and developing countries, published in , the authors concluded: " In more than half of the countries studied, married women obtain a larger proportion of abortions than unmarried women.

However, once pregnant, unmarried women are more likely than married women to choose abortion " 25 p. According to Buss 33 , color is a proxy for social situation in Brazil, with the presence of health inequities belonging to this context. As is known, " With regard to health status, differences between blacks and whites were striking " 34 p. Black African-descendant women in Brazil have less access to education, worse living and housing conditions, less access to contraceptive methods, and higher odds of becoming pregnant although not always intentionally Thus, the North, Northeast, and Central-West of Brazil concentrate the majority of deaths of black women due to problems in the pregnancy cycle pregnancy, delivery, and postpartum , including abortion, as well as the majority of maternal deaths The structural and intermediate determinants influence each other in a feedback loop, leading to health inequities, which in the case of the black population result from all the possible interactions between the diverse determinants The women that submitted to unsafe abortion included a higher proportion of black, low-income, and single women with less than 4 years of schooling as compared to white women, which alone demonstrates the presence of health inequities.

The majority of the migrant population consists of young, African-descendant, unmarried women with low schooling and high poverty and high unemployment rates In this study, migrant women showed higher odds of suffering spontaneous abortion. This finding merits more in-depth investigation. Accumulated health problems?


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A response bias? Finally, in this study population the morbidity rate was much higher than that reported in higher income women's populations or those with access to safe abortion 5, It seems to me that these macrosocial phenomena merit careful attention, and this volume could have helped advance our understanding of their impacts on population health.

Additionally, I found the politically biased views in some chapters somewhat disappointing. For example, the chapter on corporate practices missed an opportunity to convey insights from the science of corporate action and organizational theory. Instead of addressing governance structures and the efficiency imperative 4 , the chapter tends toward a politically leftist rant against the alcohol, automotive, firearms, food, pharmaceutical, and tobacco industries.

The second-to-last chapter conceptual framework for action also appears to have missed important opportunities by focusing exclusively on the amelioration of health disparities and associated social inequalities. Consideration of the scholarship addressing Rawlsian justice maximize the welfare of the disadvantaged or perhaps even utilitarian theory maximize the greatest good for the greatest number would have balanced and thus strengthened the arguments made 5.

Just as when advances in biochemistry and single nucleotide polymorphism research extend our understanding of the microscopic processes at work, advances in our understanding of and appreciation for macrosocial determinants of health are healthy for the discipline. Galea's new text certainly represents such an advance and portends a substantial step in a useful direction. That said, I close with two cautions about the broader effort to incorporate macrosocial determinants into epidemiologic analysis.

First, it seems clear that the scientific utility of macrosocial determinants will depend on the key, if not impossible, task of explaining exactly how and why these phenomena e. Second, practically speaking, a fundamental question is the extent to which the salubrious influences of macrosocial factors can be increased without yielding unacceptable, if unintended, macrosocial consequences such as economic depression or totalitarian regimes. There is much work to be done. Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide.

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