BackgroundIt is widely claimed that racial and ethnic minorities, especially in the US, are less willing than non-minority individuals to participate in health research. Yet, there is a paucity of empirical data to substantiate this claim.Methods and FindingsWe performed a comprehensive literature search to identify all published health research studies that report consent rates by race or ethnicity. We found 20 health research studies that reported consent rates by race or ethnicity. These 20 studies reported the enrollment decisions of over 70,000 individuals for a broad range of research, from interviews to drug treatment to surgical trials. Eighteen of the twenty studies were single-site studies conducted exclusively in the US or multi-site studies where the majority of sites (i.e., at least 2/3) were in the US. Of the remaining two studies, the Concorde study was conducted at 74 sites in the United Kingdom, Ireland, and France, while the Delta study was conducted at 152 sites in Europe and 23 sites in Australia and New Zealand. For the three interview or non-intervention studies, African-Americans had a nonsignificantly lower overall consent rate than non-Hispanic whites (82.2% versus 83.5%; odds ratio [OR] = 0.92; 95% confidence interval [CI] 0.84–1.02). For these same three studies, Hispanics had a nonsignificantly higher overall consent rate than non-Hispanic whites (86.1% versus 83.5%; OR = 1.37; 95% CI 0.94–1.98). For the ten clinical intervention studies, African-Americans' overall consent rate was nonsignificantly higher than that of non-Hispanic whites (45.3% versus 41.8%; OR = 1.06; 95% CI 0.78–1.45). For these same ten studies, Hispanics had a statistically significant higher overall consent rate than non-Hispanic whites (55.9% versus 41.8%; OR = 1.33; 95% CI 1.08–1.65). For the seven surgery trials, which report all minority groups together, minorities as a group had a nonsignificantly higher overall consent rate than non-Hispanic whites (65.8% versus 47.8%; OR = 1.26; 95% CI 0.89–1.77). Given the preponderance of US sites, the vast majority of these individuals from minority groups were African-Americans or Hispanics from the US.ConclusionsWe found very small differences in the willingness of minorities, most of whom were African-Americans and Hispanics in the US, to participate in health research compared to non-Hispanic whites. These findings, based on the research enrollment decisions of over 70,000 individuals, the vast majority from the US, suggest that racial and ethnic minorities in the US are as willing as non-Hispanic whites to participate in health research. Hence, efforts to increase minority participation in health research should focus on ensuring access to health research for all groups, rather than changing minority attitudes.

Objectives. We evaluated use of the Index of Concentration at the Extremes (ICE) for public health monitoring.Methods. We used New York City data centered around 2010 to assess cross-sectional associations at the census tract and community district levels, for (1) diverse ICE measures plus the US poverty rate, with (2) infant mortality, premature mortality (before age 65 years), and diabetes mortality.Results. Point estimates for rate ratios were consistently greatest for the novel ICE P ublic health monitoring data need to be informative about not only health outcomes, but also their societal distribution and determinants, so that the data can be useful for policies, programs, and advocacy focused on improving population health and advancing health equity.1-3 Both the global and US literature increasingly recognize the importance of assessing progress and setbacks in reducing health inequities (i.e., unfair, unnecessary, and preventable health differences between the groups at issue). [1][2][3][4][5][6][7][8][9][10][11] Adding to the urgency of using measures that illuminate inequitable health gaps is growing concern about 21st-century rising concentrations of income and wealth [12][13][14][15][16][17][18][19] and their implications for public health and health inequities. 12,20,21 Most public health monitoring systems, however, do not employ metrics that convey societal distributions of concentrations of privilege and deprivation.1,2 Instead, the typical practice is to present health data in relation to characteristics measured at the individual or household level, such as income, educational level, and also, chiefly in the United States, race/ethnicity. Health outcomes are then compared across groups defined in relation to the chosen characteristics, which may be modeled either continuously or categorically. 1-3,22-24Some analyses additionally employ variants of these measures aggregated to the neighborhood level (e.g., percentage of persons or households below poverty, percentage of persons with less than a highschool education, percentage of persons who are Black). [22][23][24] In either case, although gaps in health outcomes can be quantified by comparing groups with less versus more resources, distributional information on the extent to which the population is divided into the groups at issue is not part of the metric. The excess risk of societal groups that get the proverbial short end of the stick becomes the focus, and these groups effectively become characterized as the "problem"; by contrast, the societal groups holding the stick's other, longer end simply stand as a referent group, and the problematic economic, political, and social relationships that produce health inequities are hidden from view. 11,12,25,26 A troubling feature of our era, however, is not a property of individuals or households but instead pertains to increasing spatial social polarization, part and parcel of growing concentrations of extreme income and wealth. [12][13][14][15][16][17][18][19][20][21]26,27

New York City (NYC) was an epicenter of the coronavirus disease 2019 (COVID-19) outbreak in the United States during spring 2020 (1). During March-May 2020, approximately 203,000 laboratory-confirmed COVID-19 cases were reported to the NYC Department of Health and Mental Hygiene (DOHMH). To obtain more complete data, DOHMH used supplementary information sources and relied on direct data importation and matching of patient identifiers for data on hospitalization status, the occurrence of death, race/ethnicity, and presence of underlying medical conditions. The highest rates of cases, hospitalizations, and deaths were concentrated in communities of color, high-poverty areas, and among persons aged ≥75 years or with underlying conditions. The crude fatality rate was 9.2% overall and 32.1% among hospitalized patients. Using these data to prevent additional infections among NYC residents during subsequent waves of the pandemic, particularly among those at highest risk for hospitalization and death, is critical. Mitigating COVID-19 transmission among vulnerable groups at high risk for hospitalization and death is an urgent priority. Similar to NYC, other jurisdictions might find the use of supplementary information sources valuable in their efforts to prevent COVID-19 infections. This report describes cases of laboratory-confirmed COVID-19 among NYC residents diagnosed during February 29-June 1, 2020, that were reported to DOHMH. DOHMH began COVID-19 surveillance in January 2020 when testing capacity for SARS-CoV-2 (the virus that causes COVID-19) using real-time reverse transcription-polymerase chain reaction (RT-PCR) was limited by strict testing criteria because of limited test availability only through CDC. The NYC and New York State public health laboratories began testing hospitalized patients at the end of February and early March. DOHMH encouraged patients with mild symptoms to remain at home rather than seek health care because of shortages of personal protective equipment and laboratory tests at hospitals and clinics. Commercial laboratories began testing for SARS-CoV-2 in mid-to late March. During February 29-March 15, patients with laboratory-confirmed COVID-19 were interviewed by DOHMH, and close contacts were identified for monitoring. The rapid rise in laboratory-confirmed cases (cases) quickly made interviewing all patients, as well as contact tracing, unsustainable. Subsequent case investigations

Background As the COVID-19 pandemic continues to unfold, the infection-fatality risk (ie, risk of death among all infected individuals including those with asymptomatic and mild infections) is crucial for gauging the burden of death due to COVID-19 in the coming months or years. Here, we estimate the infection-fatality risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in New York City, NY, USA, the first epidemic centre in the USA, where the infection-fatality risk remains unclear.Methods In this model-based analysis, we developed a meta-population network model-inference system to estimate the underlying SARS-CoV-2 infection rate in New York City during the 2020 spring pandemic wave using available case, mortality, and mobility data. Based on these estimates, we further estimated the infection-fatality risk for all ages overall and for five age groups (<25, 25-44, 45-64, 65-74, and ≥75 years) separately, during the period March 1 to June 6, 2020 (ie, before the city began a phased reopening). FindingsDuring the period March 1 to June 6, 2020, 205 639 people had a laboratory-confirmed infection with SARS-CoV-2 and 21 447 confirmed and probable COVID-19-related deaths occurred among residents of New York City. We estimated an overall infection-fatality risk of 1•39% (95% credible interval 1•04-1•77) in New York City. Our estimated infection-fatality risk for the two oldest age groups (65-74 and ≥75 years) was much higher than the younger age groups, with a cumulative estimated infection-fatality risk of 0•116% (0•0729-0•148) for those aged 25-44 years and 0•939% (0•729-1•19) for those aged 45-64 years versus 4•87% (3•37-6•89) for those aged 65-74 years and 14•2% (10•2-18•1) for those aged 75 years and older. In particular, weekly infection-fatality risk was estimated to be as high as 6•72% (5•52-8•01) for those aged 65-74 years and 19•1% (14•7-21•9) for those aged 75 years and older.Interpretation Our results are based on more complete ascertainment of COVID-19-related deaths in New York City than other places and thus probably reflect the true higher burden of death due to COVID-19 than that previously reported elsewhere. Given the high infection-fatality risk of SARS-CoV-2, governments must account for and closely monitor the infection rate and population health outcomes and enact prompt public health responses accordingly as the COVID-19 pandemic unfolds.

Objectives. To assess if historical redlining, the US government’s 1930s racially discriminatory grading of neighborhoods’ mortgage credit-worthiness, implemented via the federally sponsored Home Owners’ Loan Corporation (HOLC) color-coded maps, is associated with contemporary risk of preterm birth (< 37 weeks gestation). Methods. We analyzed 2013–2017 birth certificate data for all singleton births in New York City (n = 528 096) linked by maternal residence at time of birth to (1) HOLC grade and (2) current census tract social characteristics. Results. The proportion of preterm births ranged from 5.0% in grade A (“best”—green) to 7.3% in grade D (“hazardous”—red). The odds ratio for HOLC grade D versus A equaled 1.6 and remained significant (1.2; P < .05) in multilevel models adjusted for maternal sociodemographic characteristics and current census tract poverty, but was 1.07 (95% confidence interval = 0.92, 1.20) after adjustment for current census tract racialized economic segregation. Conclusions. Historical redlining may be a structural determinant of present-day risk of preterm birth. Public Health Implications. Policies for fair housing, economic development, and health equity should consider historical redlining’s impacts on present-day residential segregation and health outcomes.