Objectives. To address evidence gaps in COVID-19 mortality inequities resulting from inadequate race/ethnicity data and no socioeconomic data.

Methods. We analyzed age-standardized death rates in Massachusetts by weekly time intervals, comparing rates for January 1 to May 19, 2020, with the corresponding historical average for 2015 to 2019 stratified by zip code social metrics.

Results. At the surge peak (week 16, April 15–21), mortality rate ratios (comparing 2020 vs 2015–2019) were 2.2 (95% confidence interval [CI] = 1.4, 3.5) and 2.7 (95% CI = 1.4, 5.5) for the lowest and highest zip code tabulation area (ZCTA) poverty categories, respectively, with the 2020 peak mortality rate 1.1 (95% CI = 1.0, 1.3) times higher in the highest than the lowest poverty ZCTA. Similarly, rate ratios were significantly elevated for the highest versus lowest quintiles with respect to household crowding (1.7; 95% CI = 1.0, 2.9), racialized economic segregation (3.1; 95% CI = 1.8, 5.3), and percentage population of color (1.8; 95% CI = 1.6, 2.0).

Conclusions. The COVID-19 mortality surge exhibited large inequities.

Public Health Implications. Using zip code social metrics can guide equity-oriented COVID-19 prevention and mitigation efforts.

Evidence about the societal distribution of COVID-19 is crucial for prevention and mitigation.1,2 In the United States, such evidence has been limited because of inadequate data on race/ethnicity and no data on socioeconomic position.1–3 Available data on inequities typically pertain solely to higher proportions of populations of color among COVID-19 cases and deaths relative to the total population at the local, state, and national levels.2,3

Counts of reported COVID-19 deaths, however, are likely to be underestimates given differential access to testing and diagnosis, uncertainties in case definitions, and misclassification.4,5 An alternative strategy is to tally the overall number of deaths relative to the same time period in prior years.4,5 This technique enables capturing not only deaths due to COVID-19 that have been misclassified but also other deaths attributable to the pandemic (e.g., deaths due to avoiding hospitals and medical care) while taking into account seasonal mortality fluctuations and pandemic-related mortality declines (e.g., due to reduced air pollution from economic shutdowns).4,5

Here we present novel evidence on inequities in COVID-19–related surges in all-cause mortality. Using the validated methods of our Public Health Disparities Geocoding Project,6,7 we analyzed Massachusetts death rates stratified by zip code social metrics, comparing weekly intervals from January 1 through May 29 for 2020 versus 2015 to 2019. Our transparent, easy-to-replicate methodology (see Table A, available as a supplement to the online version of this article at http://www.ajph.org, for details regarding zip code metrics and our Web site for programming code and zip code data7) relies on reported data (i.e., there are no complex model-based estimates or assumptions), the zip codes are part of the records (i.e., no geocoding is required), and the zip code metrics can be readily used by any local or state health agency to monitor the societal patterning of COVID-19 outcomes (deaths, tests, or hospitalizations).

We obtained provisional records of all deaths from January 1 to May 19 from the Massachusetts Vital Statistics Registry Fact of Death files for 2015 to 2020. These records included data on decedents’ age, sex/gender, and address but not their race/ethnicity, education, or occupation, despite the latter 3 variables being standard components of death certificate data.

Between January 1 and May 19, 2020, there were a total of 30 048 deaths, whereas the annual average for 2015 to 2019 was 23 032. We obtained population estimates of age and zip code tabulation area (ZCTA) data for the 530 Massachusetts ZCTAs from the 2014 to 2018 American Community Survey.7,8

Zip Code Tabulation Area Social Metrics

ZCTA social metrics included percentage of individuals living below the poverty line, percentage of household crowding, percentage population of color (defined as the percentage of the population that is not White non-Hispanic), and the Index of Concentration at the Extremes for racialized economic segregation (Table A).7,9 The Index of Concentration at the Extremes quantifies the extent to which an area’s population belongs to 1 of 2 extremes (in this case, high-income White non-Hispanics vs low-income populations of color) and thus ranges from 1 (everyone in the best-off extreme) to −1 (everyone in the worst-off extreme).7 We used a priori cut points for ZCTA percentage of individuals living below the poverty line (0%–4.9%, 5%–9.9%, 10%–14.9%, 15%–19.9%, 20%–100%) and ZCTA quintile cut points based on the Massachusetts distribution for the remaining ZCTA metrics (weighted by population size).7

Statistical Analyses

We linked death records to ZCTA characteristics7,8 by zip code of the recorded residence at time of death (data were missing for 42 [0.1%] deaths in 2020 and 612 [0.05% annual average] deaths during 2015 to 2019). Not all postal zip codes have a corresponding ZCTA in the US Census files, and as a result 27 deaths (0.1% of the total) from 2020 and 441 deaths (0.5% of the total) from 2015 to 2019 were unmatched. We aggregated deaths by ZCTA and age category and linked them to stratified population estimates from the 2014 to 2018 American Community Survey and ZCTA social metrics.

We then computed, for 2020 and 2015 to 2019, all-cause age-standardized mortality rates per 100 000 person-years overall and by ZCTA metric categories for weekly periods spanning January 1 to May 19 using the year 2000 standard million6,7 (for detailed data, see Table B, available as a supplement to the online version of this article at http://www.ajph.org). We computed 2020 versus 2015 to 2019 age-standardized rate differences and rate ratios with 95% confidence intervals (CIs) using standard formulas,10 and we used Poisson models to calculate weekly 2020 mortality rate ratios by ZCTA strata starting on March 25 (week 13).

In Massachusetts, the surge in all-cause age-standardized mortality rates in 2020 commenced in late March and peaked in mid-April (Figure 1 and Table B; see also Figure A, available as a supplement to the online version of this article at http://www.ajph.org), yielding 7016 more deaths between January 1 and May 19 relative to the average for this time period during 2015 to 2019. This surge affected all sectors of the population but was greatest among individuals in zip codes in the top 2 poverty and household crowding categories and the top quintile in terms of percentage population of color and racialized economic segregation (Figure 1, Figure A, and Table B).

At the surge peak (week 16, April 15–21), mortality rate ratios (comparing 2020 vs 2015–2019) were 2.2 (95% CI = 1.4, 3.5) and 2.7 (95% = 1.4, 5.5) for the lowest and highest ZCTA poverty categories, respectively, with the 2020 peak mortality rate 1.1 (95% CI = 1.0, 1.3) times higher in the highest than the lowest poverty ZCTA. The additional best versus worst quintile comparisons showed similar patterns of elevated risk: the corresponding rate ratios were 2.0 (95% CI = 1.2, 3.4), 2.9 (95% CI = 1.7, 5.0), and 1.4 (95% CI = 1.3, 1.6) for household crowding; 2.2 (95% CI = 1.3, 4.0), 2.9 (95% CI = 1.7, 5.1), and 1.3 (95% CI = 1.1, 1.4) for the Index of Concentration at the Extremes (for racialized economic segregation); and 1.7 (95% = 1.0, 2.9), 3.1 (95% CI = 1.8, 5.3), and 1.8 (95% CI = 1.6, 2.0) for percentage population of color.

Our investigation demonstrates that it is feasible and critical to document social inequities in the toll of COVID-19. The stark social gradients in the mortality surge we detected in Massachusetts—in relation to zip code poverty, percentage of household crowding, percentage population of color, and racialized economic segregation—are unlikely to be unique to that state.1–3,5 The greater mortality surge observed in the worst-off than in the best-off ZCTA in relation to all of these metrics points to the role of both economic inequality and racial inequality in elevating risk. Such data can aid identification of communities in need of resources for testing, personal protective equipment, and facilities for self-isolation.1–3 Future research could explore the use of additional ZCTA metrics as well as their combined impact.

Our study is limited by its focus on only 1 state and use of only ZCTA data. However, our preliminary analysis of US county COVID-19 death rates and ZCTA analysis of COVID cases revealed similar inequities,11 as did our complementary analyses at the city and town levels in Massachusetts.12

As underscored by the New York City Department of Health and Mental Hygiene, the approach of “monitoring . . . all-cause deaths and estimating excess mortality during the pandemic provides a more sensitive measure of the total number of deaths [than] counting laboratory-confirmed or probable COVID-19–associated deaths” and also a “faster and more inclusive measure of the pandemic’s impact on mortality than [reliance] on national COVID-19 reporting mechanisms.”5(pp603–604) Thus, monitoring death rate trends in relation to zip code social metrics can provide real-time data that are crucial in documenting mortality surges, identifying communities at risk, and aiding efforts to prevent and mitigate COVID-19 inequities.

ACKNOWLEDGMENTS

We thank the Boston Globe for its assistance (uncompensated) in obtaining Massachusetts mortality data.

CONFLICTS OF INTEREST

The authors have no conflicts of interest to report.

HUMAN PARTICIPANT PROTECTION

No protocol approval was needed for this study because no human participants were involved.

References

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Nancy Krieger, PhD, Pamela D. Waterman, MPH, and Jarvis T. Chen, ScDThe authors are with the Department of Social and Behavioral Sciences, Harvard T. H. Chan School of Public Health, Boston, MA. “COVID-19 and Overall Mortality Inequities in the Surge in Death Rates by Zip Code Characteristics: Massachusetts, January 1 to May 19, 2020”, American Journal of Public Health 110, no. 12 (December 1, 2020): pp. 1850-1852.

https://doi.org/10.2105/AJPH.2020.305913

PMID: 33058698