Time-Based Associations Between COVID-19 Cases and Community-Level Risk Factors in Massachusetts

There has been a disproportionate burden of COVID-19 cases in the U.S. among racial and ethnic minority populations, workers in essential services, people living in poverty or crowded housing, and among those with reduced access to testing or health care. Previous studies assessing increased risk for COVID-19 exposure or incidence have predominantly considered only one point in time. A recent study supported by NIMHD examined COVID-19 risk at the community-level in Massachusetts across multiple timepoints during the pandemic. The study investigators examined whether sociodemographic factors were predictive of changes in COVID-19 cases over time.

The investigators compiled publicly available datasets for 351 towns and cities in Massachusetts across five periods of time from March 2020 to October 2020. They used mixed-effect, adjusted Poisson regression models to identify sociodemographic factors associated with town-level COVID-19 case incidence.

The datasets included:

  • COVID-19 case data from the Massachusetts Department of Public Health.
  • Sociodemographic, occupational, and economic data from the 2014-2018 American Community Survey (ACS).
  • Estimates of the percentage of essential and service workers (i.e., healthcare practitioners, transportation occupation, food preparation) modified from the ACS using a dataset created by the American Civil Liberties Union.
  • Cell phone mobility data to calculate the percentage of residents commuting to work using SafeGraph Social Distancing Metrics.

The five timepoints examined were:

  • March 2 – April 14, 2020
  • April 15 – June 3, 2020
  • June 4 – July 15, 2020
  • July 16 – September 2, 2020
  • September 3 – October 20, 2020

The investigators reported that one of the strongest predictors of COVID-19 incidence across all timepoints was an increased percentage of town residents classified as essential workers. At the beginning of the pandemic, there was also a positive association between COVID-19 cases and the percentage of residents over 80 years of age, as well as the number of long-term care facility beds. However, the association of incidence with residents over 80 years of age and number of long-term care facility beds decreased over time from the first time point to the last. There was also variable association between COVID-19 cases in cities and towns with higher percentages of residents with no health insurance or with greater population density.

The investigators also reported that in cities or towns with a higher percentage of Black or African American residents, there was an increase in COVID-19 cases at the onset of the pandemic. However, this association decreased over the five time points studied and by September - October 2020, there was no significant association. In cities and towns with a higher percentage of residents identifying as Hispanic/Latino (referred to as Latinx in the study), there was a significant association with increased COVID-19 cases during all five time points.

Together, this is one of the first studies to examine and identify community-level sociodemographic factors associated with increased COVID-19 cases during different stages of the pandemic. The investigators identified that while the percentage of residents identifying as Hispanic/Latino was associated with increased COVID-19 cases over time, the percentage of essential workers was the strongest predictor. It is likely that this elevated risk is associated with long-standing systemic inequalities experienced by racial and ethnic minority populations. These findings support the need to further identify systemic factors associated with COVID-19 exposure and risk and highlight shifting patterns during the pandemic.


Tieskens, K.F., Patil, P., Levy, J.I., Brochu, P., Lane, K.J. Fabian, M.P.,…Leibler, J.H. (2021). Time-varying associations between COVID-19 case incidence and community-level sociodemographic, occupational, environmental, and mobility risk factors in Massachusetts. BMC Infect Dis, 12, 686. doi:10.1186/s12879-021-06389-w

Page updated January 14, 2022