Evidence map›Paper›PMID 33729070›Full record

ArticlePublic health reports (Washington, D.C. : 1974)2021

Neighborhood Disadvantage Measures and COVID-19 Cases in Boston, 2020.

Margaret E Samuels-Kalow, Stephen Dorner, Rebecca E Cash, Sayon Dutta, Benjamin White, Gia E Ciccolo, David F M Brown, Carlos A Camargo

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Public health reports (Washington, D.C. : 1974), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
  2. Article
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  8. Article
  9. Nurse-sensitive indicators during COVID-19.International journal of nursing knowledge · 2022
    Article
  10. Review
  11. Factors associated with the death of healthcare workers due to COVID-19 in the state of Amapá, Brazil.Revista brasileira de medicina do trabalho : publicacao oficial da Associacao Nacional de Medicina do Trabalho-ANAMT
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Margaret E Samuels-Kalow2348 Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Stephen Dorner2348 Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Rebecca E Cash2348 Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Sayon Dutta2348 Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Benjamin White2348 Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Gia E Ciccolo2348 Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
David F M Brown2348 Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Carlos A Camargo2348 Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

Funding

The Harvard Clinical and Translational Science CenterUL1TR002541 · NCATS · HARVARD MEDICAL SCHOOL · PI NADLER, LEE MARSHALL · 2018 to 2022
$93.0M
NCATS NIH HHS UL1 TR002541
6 · The paper itself

Abstract

objectiveUnderstanding the pattern of population risk for coronavirus disease 2019 (COVID-19) is critically important for health systems and policy makers. The objective of this study was to describe the association between neighborhood factors and number of COVID-19 cases. We hypothesized an association between disadvantaged neighborhoods and clusters of COVID-19 cases.

methodsWe analyzed data on patients presenting to a large health care system in Boston during February 5-May 4, 2020. We used a bivariate local join-count procedure to determine colocation between census tracts with high rates of neighborhood demographic characteristics (eg, Hispanic race/ethnicity) and measures of disadvantage (eg, health insurance status) and COVID-19 cases. We used negative binomial models to assess independent associations between neighborhood factors and the incidence of COVID-19.

resultsA total of 9898 COVID-19 patients were in the cohort. The overall crude incidence in the study area was 32 cases per 10 000 population, and the adjusted incidence per census tract ranged from 2 to 405 per 10 000 population. We found significant colocation of several neighborhood factors and the top quintile of cases: percentage of population that was Hispanic, non-Hispanic Black, without health insurance, receiving Supplemental Nutrition Assistance Program benefits, and living in poverty. Factors associated with increased incidence of COVID-19 included percentage of population that is Hispanic (incidence rate ratio [IRR] = 1.25; 95% CI, 1.23-1.28) and percentage of households living in poverty (IRR = 1.25; 95% CI, 1.19-1.32).

conclusionsWe found a significant association between neighborhoods with high rates of disadvantage and COVID-19. Policy makers need to consider these health inequities when responding to the pandemic and planning for subsequent health needs.

Indexed as

Residence CharacteristicsAdultAgedCOVID-19EthnicityFemaleFood AssistanceGeographic MappingHumansIncidenceMaleMassachusettsMedically UninsuredMiddle AgedPovertySocioeconomic FactorsCOVID-19geospatial analysissocial determinants of health

Identifiers

PMID33729070
PMCPMC8580391

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.