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. 2014 Jul;25(4):494-504.
doi: 10.1097/EDE.0000000000000104.

Jim Crow and premature mortality among the US Black and White population, 1960-2009: an age-period-cohort analysis

Nancy Krieger  1 , Jarvis T Chen, Brent A Coull, Jason Beckfield, Mathew V Kiang, Pamela D Waterman
Affiliations

Jim Crow and premature mortality among the US Black and White population, 1960-2009: an age-period-cohort analysis

Nancy Krieger et al. Epidemiology. 2014 Jul.

Abstract

Background: Scant research has analyzed the health impact of abolition of Jim Crow (ie, legal racial discrimination overturned by the US 1964 Civil Rights Act).

Methods: We used hierarchical age-period-cohort models to analyze US national black and white premature mortality rates (death before 65 years of age) in 1960-2009.

Results: Within a context of declining US black and white premature mortality rates and a persistent 2-fold excess black risk of premature mortality in both the Jim Crow and non-Jim Crow states, analyses including random period, cohort, state, and county effects and fixed county income effects found that, within the black population, the largest Jim Crow-by-period interaction occurred in 1960-1964 (mortality rate ratio [MRR] = 1.15 [95% confidence interval = 1.09-1.22), yielding the largest overall period-specific Jim Crow effect MRR of 1.27, with no such interactions subsequently observed. Furthermore, the most elevated Jim Crow-by-cohort effects occurred for birth cohorts from 1901 through 1945 (MRR range = 1.05-1.11), translating to the largest overall cohort-specific Jim Crow effect MRRs for the 1921-1945 birth cohorts (MRR ~ 1.2), with no such interactions subsequently observed. No such interactions between Jim Crow and either period or cohort occurred among the white population.

Conclusion: Together, the study results offer compelling evidence of the enduring impact of both Jim Crow and its abolition on premature mortality among the US black population, although insufficient to eliminate the persistent 2-fold black excess risk evident in both the Jim Crow and non-Jim Crow states from 1960 to 2009.

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Conflict of interest statement

The authors report no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
In black, the US states with legal racial discrimination outlawed by the 1964 US Civil Rights Act. (“Jim Crow states”).
FIGURE 2
FIGURE 2
US age-standardized premature mortality rates (death before 65 years of age, standardized to the 2000 standard million), 1960–2009: black and white population by Jim Crow polity. Difference between Jim Crow and non-Jim crow areas: for blacks, black bars; for whites, white bars. Difference between blacks and whites: for Jim Crow areas, light gray bars; for non-Jim Crow areas, medium gray bars.
See this image and copyright information in PMC

References

    1. Holford TR. Temporal factors in public health surveillance: sorting out age, period, and cohort effects. In: Brookmeyer R, Stroup DF, editors. Monitoring the Health of Populations: Statistical Principles and Methods for Public Health Surveillance. New York, NY: Oxford University Press; 2003. pp. 99–126.
    1. Bell A, Jones K. Don’t birth cohorts matter? A commentary and simulation exercise on Reither, Hauser, and Yang’s (2009) age-period-cohort study of obesity. Soc Sci Med. 2014;101:176–180. - PubMed
    1. O’Brien RM, Hudson K, Stockard J. A mixed model estimation of age, period, and cohort effects. Sociol Methods Rev. 2008;36:402–428.
    1. Richardson S, Abellan JJ, Best N. Bayesian spatiotemporal analysis of joint patterns of male and female lung cancer risks in Yorkshire (UK) Stat Methods Med Res. 2006;15:385–407. - PubMed
    1. Raudenbush SW, Bryk AS. Hierarchical Linear Models: Applications and Data Analysis Methods. Thousand Oaks, Calif: Sage; 2002.

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