Abstract
The magnitude of Black–White differences in infant mortality rates varies considerably across U.S. counties. Many prior studies of racial disparities in infant mortality rely on rate ratios (RRs) and rate differences (RDs) to measure relative and absolute inequalities in the risk of infant mortality between Black and White infants. In this paper, I draw on linked birth and death records from 2004 to 2013 to systematically evaluate RRs and RDs as tools for assessing variation in Black–White disparities in infant mortality across U.S. counties. I present evidence that both metrics have limitations in identifying counties that can serve as a model for, or target of, institutional interventions. For example, rather than reflecting an advantaged position for Black infants, counties with the lowest RRs tend to be places with high White infant mortality rates. I then introduce a new approach to measuring relative and absolute inequalities in infant mortality and evaluate the utility of these new metrics compared to conventional approaches.
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Notes
Author’s calculations based on linked birth and death records from 2004 to 2013.
Odds ratios are the odds of an outcome in one group divided by the odds of the outcome in the other group. Although they represent another example of a relative comparisons based on ratios, odds ratios are subject to several limitations. In addition to being less intuitive to interpret, odds ratios are not directly comparable to relative risks. Both measures are similar when the outcome of interest is rare in both groups, but as it becomes more common in either group, the two ratios diverge and are no longer interchangeable (compared to relative risks, odds ratios will be larger in these scenarios) (Davies et al., 1998; Ranganathan et al., 2015). Moreover, odds ratios present difficulties when the goal is to make comparisons across groups and models (Mood 2010).
I focus on rate ratios and rate differences in this paper, but the limitations of these metrics when comparing disparities across counties also pertain to other measures of absolute and relative disparities that rely on comparisons of the risk of Black and White infant mortality (e.g., a ratio or difference of two predicted probabilities).
Although the Black infant mortality rate exceeds the White infant mortality rate in all U.S. counties (Rossen et al., 2016), White infants do not represent the racial group with the best health outcomes; even lower rates of mortality are observed among Asian American infants (Ely and Driscoll 2020).
As Online Resource 1 indicates, the precision of the estimates varies considerably, with especially wide confidence intervals in counties with fewer than 100 deaths to infants of White and Black mothers. In order to assess the sensitivity of the results to the inclusion of these counties, I replicate all analyses presented in the paper using only the sample of 111 counties with at least 100 infant deaths to both White and Black mothers. These supplemental analyses (available from the author upon request) provide reassurance that the results are not driven solely by counties with small numbers of infant deaths. As in the full sample, I find that Rate ratios are more informative about White infant mortality rates than Black infant mortality rates, but the opposite is true for rate differences. Further, when used to measure the magnitude of the Black–White disparity, both metrics prove to be inconsistent in identifying counties that can serve as a model for or target of institutional interventions. Finally, using the adjusted measures of rate ratios improves the ability to identify counties with below and above average Black infant mortality rates. In contrast to the results from the full sample, the advantages of the adjusted rate difference measure over the conventional measure are less pronounced when restricting the analysis to counties with at least 100 infant deaths to both White and Black mothers.
An alternative approach that achieves a similar objective to LPA is to regress the White infant mortality rate on the socio-demographic variables in order to generate predictions for each county’s White infant mortality rate based on its socio-demographic characteristics. Overall, this regression-adjusted approach produces results that are similar to the LPA analysis. The correlation between the regression-adjusted rate ratios and the LPA-adjusted rate ratios presented in the paper is .918 and the correlation between the regression-adjusted rate differences and the LPA-adjusted rate differences is .987. However, a detailed comparison of these two approaches indicates that the LPA-adjusted metrics perform slightly better in identifying the type of disparities that are most relevant to health policy as described in this paper (see Online Resource 2 Figs. SI1, SI2; Tables SI1, SI2, SI3, SI4).
Information on county population, population density, % Black, median income, poverty rate, and % of adults with a BA comes from the American Community Study’s 2006–2010 5-year estimates data (U.S. Census Bureau 2010). Unemployment rates are from the Bureau of Labor Statistics Local Area Unemployment Statistics (LAUS) program (U.S. Bureau of Labor Statistics 2013). The distributions of population, population density, and median income are heavily right-skewed so I use the natural log of these variables in the analyses.
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Acknowledgements
The author would like to thank Chong-Min Fu-Sosnaud, Rourke O’Brien, Sarah Beth Kaufman, Jennifer Mathews, Tahir Naqvi, Richard Reed, Hana Kruger, the anonymous reviewers, and the participants in the UTSA Demography Lecture Series seminar for helpful input and suggestions.
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Appendix
Appendix
See Tables 3 , 4, and 5 and Figs. 9 , 10, and 11.
Comparison and infant mortality rates, rate ratios (RR), and rate differences (RD) in four U.S. counties
Example of using the average White infant mortality rate in counties with similar socio-demographic characteristics to calculate adjusted rate ratios (aRR) and adjusted rate differences (aRD) in Chesterfield County, SC and Ocean County, NJ
Comparison of conventional and adjusted measures of rate ratios as measures of high (Q4) Black–White inequalities in county infant mortality rates (n = 108 counties)
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Sosnaud, B. Reconceptualizing Measures of Black–White Disparity in Infant Mortality in U.S. Counties. Popul Res Policy Rev 41, 1779–1808 (2022). https://doi.org/10.1007/s11113-022-09711-9
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DOI: https://doi.org/10.1007/s11113-022-09711-9