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Ethnicity/race and economics in COVID-19: meta-regression of data from counties in the New York metropolitan area
  1. Hisato Takagi1,
  2. Toshiki Kuno2,
  3. Yujiro Yokoyama3,
  4. Hiroki Ueyama2,
  5. Takuya Matsushiro1,
  6. Yosuke Hari1,
  7. Tomo Ando4
  1. 1 Shizuoka Medical Center, Shizuoka, Japan
  2. 2 Mount Sinai Beth Israel Hospital, New York City, New York, USA
  3. 3 Easton Hospital, Easton, Pennsylvania, USA
  4. 4 New York Presbyterian Hospital/Columbia University Medical Center, New York City, New York, USA
  1. Correspondence to Hisato Takagi, Shizuoka Medical Center, 762-1 Nagasawa, Shimizu-cho, Sunto-gun 411-8611, Japan; kfgth973{at}

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‘NYC residents from low-income communities have tested positive for COVID-19 antibodies at a higher-than-average rate, underscoring the disproportionate impact of the disease on people of colour, Governor Andrew Cuomo said on Wednesday’(20 May 2020) ( Ethnicity/race and economics may affect prevalence/case fatality of COVID-19. To determine whether COVID-19 prevalence/fatality is modulated by ethnicity/race and economics, meta-regression of data from the counties in the New York (NY) metropolitan area was conducted.

We selected 31 counties in the NY metropolitan area (New York–Newark, NY-NJ-CT-PA Combined Statistical Area). (1) Prevalence/case-fatality rates of confirmed COVID-19 cases (20 May 2020) and (2) ethnicity/race and income/poverty estimates were obtained in each county. We performed random-effects meta-regression using OpenMetaAnalyst ( The covariates were listed in online supplemental table S1. Statistically significant (p=0.05) covariates in the univariable model were together entered into the multivariable model.

Supplemental material

Results of the meta-regression are summarised in table 1. A slope (coefficient) of the univariable meta-regression line for COVID-19 prevalence was not significant for household income, whereas the coefficient was significantly positive for black (p=0.015), Hispanic/Latino (p<0.001) and poverty (p=0.026) (figure 1), which indicated that COVID-19 prevalence increased significantly as black, Hispanic/Latino and poverty increased. The multivariable model revealed that the slope was significantly positive for only Hispanic/Latino (p<0.001). The coefficient in the univariable model for COVID-19 fatality, however, was not significant for all the covariates (table 1).

Table 1

Summary of meta-regression

Figure 1

Meta-regression graph depicting the COVID-19 prevalence (plotted as the logarithm transformed prevalence on the y-axis) as a function of a given factor (plotted on the x-axis).

The present meta-regression suggests that black, Hispanic/Latino and poverty may be positively associated with COVID-19 prevalence. Especially, Hispanic/Latino may be ‘independently’ associated with COVID-19 prevalence. According to the most recent (19 May 2020) data of the NYC Health, the age-adjusted case/death rate (/100 000-people) was higher (1.5-/2.0-fold) in Hispanic/Latino and black/African-American than in White.1 The case/death rate (/100 000-people) was also higher (1.5-/2.3-fold) in very-high poverty than in low poverty.1 These findings may partially strengthen the present results. It is unclear (1) why Hispanic/Latino (neither black nor poverty) is independently associated with COVID-19 prevalence or (2) why none of black, Hispanic/Latino and poverty is associated with COVID-19 fatality, which demonstrated in the present study. Disparity of ethnic and racial health should be identified and addressed in the pandemic of COVID-19.2 Furthermore, long-term legislation to improve social welfare would be introduced to address vulnerability in subjects with the most economical disadvantage.3

In conclusion, black, Hispanic/Latino and poverty, especially Hispanic/Latino independently, may be associated with COVID-19 prevalence. There may be no association of black, Hispanic/Latino, poverty and household income with COVID-19 fatality.



  • Contributors HT had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: HT; acquisition, analysis or interpretation of data: HT, TK, YH, TA; drafting of the manuscript: HT; critical revision of the manuscript for important intellectual content: TK, YH, TA; statistical analysis: HT, TK, TA; administrative, technical or material support: YY, HU, TM; supervision: TA.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; internally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.