Race and Ethnic Health Inequality - Essay Example

Race and Ethnic Health Inequality: Columbia and Latin America By s of College 2,949 Words 2007 Abstract This paper will explore the race and ethnic health inequalities in Colombia and the surrounding Latin American region. It will first characterize the situation of Afro-Colombians and indigenous populations in Colombia. Secondly, it will document racial/ethnic disparities in health outcomes and access to health care using data from the Living Standards Survey and the evaluation of the "Familias en Accin program". The results will indicate that most racial and ethnic disparities in health and access to health care disappear once we control the socioeconomic characteristics of individuals, employment status and characteristics of the job and geographic location among other things. Based on these findings it is possible to make some specific policy recommendations aimed at improving the status of racial minorities in Colombia and the surrounding areas. Introduction and Background Health inequalities have recently started to receive a good deal of attention in developing countries. However scarce, some preliminary literature has begun to explore the extent of health inequalities in developing countries, in particular, across the socioeconomic dimension. In other words, trying to understand how large the differences in health outcomes are across socioeconomic groups. Conclusions of these studies coincide with what has already been found for industrialized countries: health outcomes are significantly better for individuals that are better-off in terms of income and socioeconomic conditions. In a recent paper, Wagstaff (2002) presents measures of health inequality, much in the spirit of concentration indices commonly used to measure income inequality. In a nutshell, the measure is calculated by plotting the cumulative proportion of individuals experiencing a given health outcome (e.g., deaths in the case of mortality measures) against the cumulative proportion of population at risk, ranked by economic status. The concentration index is then calculated as twice the area between the resulting curve and the diagonal. By convention, if the concentration index is negative, it implies that the constructed curve lies above the diagonal, i.e. that the penetration of that outcome (mortality in our example) is higher among poorer individuals and inequalities in mortality are therefore to the advantage of better-off children. Strikingly, during the nineties Latin America and the Caribbean exhibited the largest inequalities on all measures of health which include: infant mortality rate (IMR), under-five years of age mortality rate (U5MR), percent of children stunted (percent of children whose height measurement is more than two standards deviation below the median reference standard for their age as established by the World Health Organization), percent of children underweight, diarrhea prevalence (percent of surviving children under three, four, or five years old who had diarrhea in the two weeks preceding the survey) and acute respiratory infection prevalence (ARI). While Colombia exhibits worse health inequalities than the world average, these are slightly above the Latin American average. The growing interest in health inequalities in developing countries reflects the extent of the broad interpretation being given to the term "poverty" in the academic literature, and the increasing tendency of defining goals of multilateral institutions and aid organizations in terms of poverty reduction. At the same time, there is a growing consensus that health inequalities (defined between the poor and the rich, and or in other dimensions that characterize different populations, like minorities) are unjust. In other words, reducing the cross-country and intra-country gaps between different population groups does not simply imply reducing poverty, but also improves social justice and equity (Alleyne, Casas, and Castillo-Salgado, 2000). The main objective of this paper is to explore race and ethnic health inequalities in Colombia. Not only literature about race inequality in health in Colombia is non-existing but also the literature on the more general topic of social and economic exclusion of minorities defined by race and ethnicity in Colombia is rather limited. A remarkable exception is a study by Florez, Medina and Urrea (2003) who review the literature about social exclusion by race in Latin America and the Caribbean. In spite of the fact that data about race and ethnicity is rather scarce in Latin America and the Caribbean, the authors show some evidence that minorities (blacks and indigenous populations) in Latin America have lower levels of income and human capital. A few studies (Psacharopoulos and Patrinos, 1994, Patrinos, Velez and Psacharopoulos, 1993) indicate that after controlling for a set of observable characteristics, a significant part of the difference in income and human capital between blacks/indigenous populations and whites is still explained by race itself. Conceptual Considerations about Race and Ethnicity Numerous studies have documented significant racial and ethnic disparities in health (incidence, mortality and severity of diseases), particularly in the United States but also around the world (http://www.od/nih/gov/ormh/history.html). The way in which these differences are interpreted is very important in terms of public health policy design. These disparities are generally explained by two factors: genetic susceptibility to disease or differences in cultural practices. However, these explanations are unsatisfactory in the light of scientific evidence and are mostly uninformative in terms of public policy since in both cases, it is minorities (their inheritance or their culture and behaviors) that are seen as problematic. The difficulty in interpreting racial and ethnic disparities in health is partly related to the fact that there is no agreement about the definition of race and ethnicity as well as the fact that the way in which racial status is assigned varies across societies and has changed over time. The term race has been commonly defined in terms of biological differences between groups that are assumed to be genetic. For example, some authors argue that five major racial groups can be identified in the U.S.: Africans, Caucasians, Pacific Islanders, Asians, and Native Americans (Risch et al. 2002). However, human races are not and never were pure. Additionally, research within the biological sciences has also provided strong evidence that these broad groupings of the population explain little in terms of the overall genetic variation of human beings. In other words, that human genetic diversity cannot be portioned into genetically determined racial categories. For example, Lewontin (1972) showed that over 85% of the observed genetic variation (using internal proteins as markers of genetic variability) occurred within racial groups, only 6.3 percent of variability occurred between racial groups, and approximately 8.3 percent of variability between ethnic groups within a race. More recently, work by Barbujani et al (1997) shows that 84 percent of genetic diversity occurred within populations using 109 polymorphic DNA sequences. This evidence does not suggest that there are no genetic differences between races, but rather that very few differences have been found which directly relate to health (Cooper, R.S. 2003). Even if some diseases have been found to be purely hereditary, the constant interaction between genes and the environment means that it is difficult to disentangle genetic from environmental factors. In fact, genetic predisposition is not a useful explanation for racial and ethnic disparities in health. If racial and ethnic groups do not represent distinct gene pools, then genetic explanations for health inequities are weakly (if at all) informative. The way in which racial/ethnicity status is assigned in the U.S. and most countries ultimately relates to social and political concepts more than genetics or scientific rigor. In short, the definition of ethnicity builds on a complex construct that includes biology, history, cultural practices, language, religion and lifestyle, all of which affect health outcomes. The lack of major systematic genetic differences between ethnic groups, together with significant differences in lifestyle (nutrition, alcohol, smoking, etc), means that ethnic differences in morbidity and mortality to some extent provide evidence against the importance of genetic factors and for the importance of environmental factors. In sum, it is important to understand that race is not necessarily a biological concept but rather a complex definition which involves social and cultural factors. Interpreting the race variable as purely genetic leaves little room for interventions at the disposal of governments or institutions that can be effective in reducing race-associated health differentials. A purely behavioral interpretation suggests that all interventions should focus on modifying the individuals' behavior. Finally, a more comprehensive conceptualization that includes social and individual behavioral factors suggests that changes can be made at the social (for example, health services, sanitation, nature of employment, etc.) and at the individual level. Description of Racial and Ethnic Minorities in Colombia The main source of data for this section (and this paper) is the Living Standard Survey (LSS) carried out by the National Department of Statistics (DANE) during 2003 at the national level. The objective of the LSS is to provide measures of socioeconomic status of the Colombian population and understand the incidence of poverty and the relevance of various determinants of poverty. This survey was applied to a basic sample of 22,949 households which are expanded to the 11,194,108 households that constitute the total Colombian population. Basic Descriptive Statistics According to the LSS, around 6.6% of the population is afro-colombian (afrodescendent, "mulato" or palenquero), 2% is indigenous and less than 1% reports being either "raizal del archipilago" or gipsy in 2003. That means that approximately 9.2% of the Colombian population belongs to a racial or ethnic minority. Minorities are mainly concentrated in the Pacific (54.71% of the population in that region is either afro-colombian, indigenous or raizal del archipelago), San Andrs y Providencia (46.79%), Valle del Cauca (20.3%) and the Atlantic region (13.1%). Around 75% of minorities in the Pacific region are afro-colombian while 25% are indigenous. In Valle del Cauca, approximately 97% of minorities are afro-colombian and only 3% are indigenous groups. In the Atlantic region, around 69% of minorities correspond to afro-colombians while 31% are indigenous and 90% of minorities in San Andrs y Providencia are "raizal del archipilago" and 8.7% are afro-colombians. Finally, 72% of ethnic minorities in the Amazon are indigenous while the remaining 27% are afro-colombians. Child's birth weight is significantly lower for minorities (3.4 kgs vs. 3.6 kgs) while child's length at birth is not significantly different between black and indigenous populations and the rest of the population. Child's and mother's height and weight at the time of the survey are significantly lower for minorities than for their non-minority counterparts. Finally, the number of standard deviations from the international height-for-age and weight-for-age standards are significantly higher for black and indigenous children than for the rest of the population. For example, while black and indigenous children are 1.07 standard deviations below the international height-for-age standard, non-minorities are 0.89 standard deviations below. The Statistical Model The starting point for the empirical analysis is a theoretical model of health production (Becker 1993) which constitutes the main building block in the health literature. According to this framework, households produce certain goods like human capital and health using a number of inputs. The main variable of interest is race which belongs to the vector Xi. If the associated regression coefficient is significant, it would imply that even after conditioning on a wide range of observable characteristics that include education, age and income, race itself explains part of the variation in health outcomes. Vector Xi includes as many observable characteristics as possible in order to avoid omitted variable bias. In other words, being black/indigenous might be highly correlated with low income, certain types of employment, residing in certain regions of the country, etc., which could be in turn, correlated with poor health outcomes. Omitting some of these relevant variables might induce a bias in the coefficient associated with the race dummy variable. For instance, it seems plausible to argue that minorities and individuals with lower income will be more likely to be unemployed which will significantly affect the probabilities of accessing and affording health care and thus, have an effect on the individual's health outcomes. Hence, excluding the individual's employment status from vector Xi could cause a significant bias on the coefficient associated with the race dummy variable since part of the effect of employment status will be attributed to race. Additionally, in terms of policy, this seems extremely relevant in the sense that if health inequalities are present, and the results indicate that the employment situation minorities plays an important role in explaining them, then there is potential for policy aimed at improving the status of minorities in Colombia. Conclusions and Recommendations Studies about social and economic exclusion of minorities (defined by race and ethnicity) in Colombia are rather scarce. Furthermore, the literature about racial and ethnic health disparities is basically inexistent. This discussion is an attempt to document the socioeconomic situation of black and indigenous populations in Colombia with a particular focus on health outcomes and access to health care. Additionally, it setup a statistical model to test whether health racial disparities remain after controlling for a broad set of socioeconomic characteristics of individuals. We use data from the Living Standards Survey (2003), data collected by the National Planning Department for 68 municipalities with a majority of black population and data from the evaluation of the Familias en Accin program to document the situation of minorities in the country and understand the source of racial and ethnic health disparities. Some basic stylized facts indicate that minorities (who account for approximately 9.2% of the Colombian population) are worse off in terms of socioeconomic status (Sisben level), income, unemployment rates, access to formal employment, unsatisfied basic needs, education and access to basic utilities (water, electricity, sewer). In regards to health, minorities are significantly less likely to have health insurance. In particular, while 31.41% of non-minorities do not have health insurance, 48% of black and indigenous populations do not. Also minorities have a worse perception of their own health status (according to data from the LSS) than the rest of the population and a higher likelihood of having been ill during the 30 days prior to the date of the interview but are less likely to suffer from a chronic disease or having been hospitalized within the 12 months prior to the date of the interview than non-minorities. In sum, evidence from self-reported health measures suggests that there are no significant differences (at least against minorities) in health outcomes between racial/ethnic groups. In the case of health outcomes, the results presented suggest that after controlling for socioeconomic characteristics, employment status and geographic location, the minority dummy variable turns out to be insignificant in explaining the variation in health outcomes. In other words, the racial and ethnic disparities in access to health care insurance can be fully accounted by the fact that minorities are worse off in almost every single socioeconomic dimension (employment, education, income, etc.). Finally, using data from the evaluation of the Familias en Accion program the date has shown that differences in height and weight to age (with respect to international standards) between blacks and indigenous populations and the rest of the population are fully accounted for by the family's socioeconomic status, parents' education, inheritance and geographic location. In other words, after controlling for this set of variables, the race dummy variable does not have any additional explanatory power in explaining the variation in weight and height measures. A very interesting result indicates that while the average schooling attainment of the child's parents is positive and very significant in explaining weight variation it is insignificantly related to height. This is in agreement with the basic intuition that weight is likely to be associated with healthy behaviors like eating habits, the likelihood of exercising, etc., and these in turn, tend to be highly correlated with education while height is typically thought of as being associated with heritable features. The implications of these results in terms of policy are straightforward. Racial and ethnic disparities in health outcomes and access to health care exist mainly because minorities are worse off in terms of socioeconomic status (Sisben level), income, unemployment rates, access to formal employment, unsatisfied basic needs, education and access to basic utilities (water, electricity, sewer). Given this, it is clear that policy should be designed with the objective of improving the socioeconomic status of minorities in the country instead of aimed at changing the structure of institutions, for example, health care providers. In particular, it seems like education plays a very important role as does access to formal employment. Policies aimed at increasing education coverage and improving literacy rates in regions of the country with high concentration of black and indigenous populations can prove useful in improving minorities' health outcomes and access to health care. A possibility could be to consider implementing affirmative action policies for schools and universities. This alone could also increase the access of minorities to formal employment which, in turn, is associated with better health outcomes and higher probability of having health insurance according to the results presented in this paper. High unemployment rates in some regions of the country can be significantly contributing to the disadvantageous health situation of minorities. Hence, policies aimed at improving labor market outcomes in general could improve the overall status of minorities and hence reduce racial/ethnic socioeconomic and health disparities. However, it seems that blacks are worse off in terms of access to health care even after conditioning on a wide range of individual characteristics while the opposite is true in the case of indigenous populations. This suggests that a public policy design to provide access to health care to afro-colombians through a publicly funded system, similar in nature the one that is available for indigenous reservations, could prove extremely useful in reducing ethnic disparities in access to health care. In other words, it finds the significant differences between blacks and indigenous groups are related to policy choices, specifically in the context of insurance provided by the government. Further research aimed at understanding the reasons why minorities have less access to education and formal employment would be useful in understanding the possible consequences of implementing a policy like affirmative action. References Alleyne, G., J. Casas and C. Castillo-Delgado, 2000, "Equality, equity: why bother", Bulletin of the World Health Organization 78(1): 76-66. Balsa, A. T. Mcguire and L. Meredith, 2004, "Testing for Statistical Discrimination in Health Care", manuscript Harvard Medical School, February. Barbujani, G., et al., 1997, "An apportionment of human DNA diversity", Proc. Natl. Acad. Sciences, 94: 4516-19. Braun, L.,2002, "Race, Ethnicity and Health", Perspectives in Biology and Medicine, Vol.45, No. 2: 159-74. 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