4. DISCUSSION
By using individual geocoded addresses of COVID-19 cases and deaths in
MA, we were able to both evaluate community-level risk for these
outcomes at high spatial resolution and distinguish institutional
outcomes from those in non-institutional settings in community models of
disease over time. Removing institutional cases from our models,
especially in the context of mortality endpoints where institutional
facilities contributed an appreciable percentage, allowed for a more
nuanced understanding of local risk and disease drivers. Additionally,
assessing trends over time across both case and mortality outcomes shed
light on differential case fatality by subpopulation over time. Overall,
our efforts highlight the value of collaboration between state public
health departments and academic researchers to access, analyze, and
interpret COVID-19 data to maximize its effective use in public health
practice.
We observed key disparities in models of both cases and death outcomes
associated with the proportion of Black and Latinx populations by tract,
findings that parallel those at town-level resolution across a shorter
time period as well as findings from other studies. The variability
observed in the size of these estimates over time reinforces how these
race and ethnicity variables are proxies for social constructs and not
inherent or constant risk factors. Broader assessments of the structural
factors that result in these disparities, including systemic racism,
socioeconomic status, housing inequities, and multigenerational family
stressors, are vital to comprehensively understand why these communities
have experienced elevated COVID-19 risk. The variable IRRs over time
could be coupled with other community-level information to better assess
the particular factors driving disparities in each phase of the
pandemic.
The finding that the association with % Black residents was smaller
during Phase 2 than Phase 1 may indicate that public health policies and
other measures enacted by Fall 2020 among communities with higher
proportions of Black residents successfully reduced risk relative to
other communities. This could include greater availability of testing
than existed in the early months of the pandemic. Notably, we did not
see as substantial of a reduction for communities with higher
proportions of Latinx residents, which points toward the need for a
closer look at testing as well as structural barriers to implementing
risk-reduction methods (such as ability to self-isolate, work from home,
or physically distance) across these communities.
Our study period captured only the early months of vaccination
availability in the state, reflecting availability for healthcare
workers (beginning on December 15, 2020), residents of long-term care
facilities (December 28, 2020), and all individuals aged 75 and over
(February 1, 2021). It is possible that our findings related to
racial/ethnic patterns for Phase 2 reflect, in part, differential
vaccination patterns from early 2021; however, this is unlikely to have
a large influence on our overall findings, given the limited number of
individuals fully vaccinated by the end of the time period assessed
here. Given that LTCF residents were eligible for vaccination for
several weeks of the study period, it is plausible that our estimates
for the LTCF beds per capita variable is an underestimate of the true
association.
Case risk associated with proportion essential workers by tract remained
elevated across both time periods in our study. This finding may reflect
the limitations of workplace interventions to reduce exposure risk among
this workforce, including challenges to physical distancing in essential
service jobs, and lack of paid sick leave. Our study period did not
overlap with vaccine availability targeting essential workers outside of
healthcare settings (March 22, 2021); analyses utilizing more recent
data could consider the effect of targeted vaccination on these
estimates by tract. The elevated mortality risk observed in association
with proportion essential workers by tract, especially in Phase 2, may
suggest that communities with more essential workers faced a higher case
fatality rate, although it is difficult to conclude with certainty from
our data.
One important aspect of modeling cases and deaths separately across
pandemic phases is the ability to effectively control for differential
testing availability and utilization. This was particularly salient
during Phase 2, which saw increased availability of testing to the
public and widespread asymptomatic surveillance testing at workplaces
and schools. Since testing is not uniformly distributed across all
census tracts, IRRs are likely higher in places with more testing,
irrespective of case severity. By contrast, COVID-19 deaths in Phase 2
are expected to have much more consistent identification, making
comparisons less susceptible to testing biases. The greater mortality
risk for Black/Latinx populations, essential workers, and older
residents that we observed in Phase 2 may thus provide a more accurate
depiction of racial and ethnic inequities in COVID-19 impacts than case
rates alone, which could potentially be more useful for informing
targeted public health interventions.
By retaining LTCF facilities in non-institutional models, we attempted
to estimate the impact of LTCF cases on disease incidence outside of
these facilities. Our data indicate that density of long-term care
services was not a major correlate of infections or deaths in the
community at large (among individuals who were not residing in these
institutions; section 3.2). This observation may indicate successful
biocontainment within these facilities or limited interaction between
residents/employees and community members. However, we lack key
information to determine this definitively, including controls within
facilities and residential location of LTCF employees. It also may
indicate that risk factors for institutional cases and deaths differed
in meaningful ways from what was observed at the community level. While
beyond the scope of this study, considerations of risk factors for
mortality specifically within institutions, and comparisons between
these within-institution and community-level risk factors, would be a
valuable addition to this literature.
Our analysis is limited by a few key factors. First, census tracts are
heterogenous and not distinctly classified by the variables in our
models, which complicates the interpretation of our findings. We
identified tracts using continuous demographic and occupational
characteristics, and the same town may have elevated proportions of
some, but not all, of the covariates in our model. As such, our analyses
can serve as a guide for understanding differential risk by population
subgroups but not to identify specific tracts to target with public
health interventions, as would be possible with spatial methods.
Additionally, as mentioned previously, limited availability of testing
during Phase 1 resulted in testing and diagnosis of only symptomatic
cases early in the pandemic, while testing was widely available in Phase
2, both for symptomatic cases and asymptomatic surveillance. It is
difficult to concretely assess the directionality of these biases, but
these trends may indicate that our data from Phase 1 reflects an
underestimate of the true association with the covariate analyzed. In
addition, the variables we included might not be all or the strongest
predictors of cases or deaths. Another limitation is that patient
address information likely contains errors, which cascade into the
geocoding process, resulting in misclassification of LTCF residents and
tract of residence; however, this is likely non-differential with
respect to outcome and, moreover, there is no feasible way to ameliorate
this type of error. Our age-related covariates may imprecisely classify
risk associated with age. Finally, ACS data were derived pre-pandemic
and may not fully reflect conditions during the pandemic, especially
with respect to employment and housing.
Our project is strengthened by our geocoded individual-level data, which
was facilitated by a cross-sectoral partnership. Using address geocodes
of individual patients who were diagnosed or died of COVID-19, we
observed significant disparities in both case and mortality burden in
association with population proportions of Black and Latinx residents,
as well as essential workers at the census tract level in the first year
of the pandemic in Massachusetts. Modeling cases and deaths separately,
as well as with and without institutional outcomes, allowed us to more
comprehensively understand health disparities experienced by vulnerable
subgroups during the first year of the pandemic.
DISCLOSURES : The authors have no conflicts of interest to
disclose.
AUTHOR CONTRIBUTIONS: JIL, KJP, MPF and JHL conceptualized the
project. KRS and XP conducted statistical analyses. PP, KRS, KFT, JIL,
EAE, KJL and JHL guided data analysis and data interpretation, with
support from all authors. FC, TST and KJL conducted and supervised
geocoding. RMK and TST contributed individual-level data from DPH and
guided its use and interpretation. JHL and KRS wrote the paper with
contributions from all authors.
FUNDING SOURCES: This project is supported by NIH/NIMHD grant
P50MD010428 (PIs: JIL and F. Laden) and by an unrestricted gift from
Google LLC to support research on COVID-19 and health equity (PI:
JIL).
Table 1. Descriptive statistics by phase of COVID-19
cases, deaths, and average census tract characteristics in
Massachusetts, USA. 1Reflects the number of cases and
deaths that were successfully geocoded to a census tract (total number
of individuals excluded due to lack of geocoding is 1,360 [0.27%]).