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Kishore, Nishant

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Kishore

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Nishant

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Kishore, Nishant

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2018 - 20202

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Author's Publications: search.filters.isAuthorOfPublication.aba46ba2-eb44-464d-adf7-dfd76df6e9bf

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    Reductions in commuting mobility predict geographic differences in SARS-CoV-2 prevalence in New York City
    (2020) Kissler, Stephen; Kishore, Nishant; Prabhu, Malavika; Goffman, Dena; Beilin, Yaakov; Landau, Ruth; Gyamfi-Bannerman, Cynthia; Bateman, Brian; Katz, Daniel; Gal, Jonathan; Bianco, Angela; Stone, Joanne; Larremore, Daniel; Buckee, Caroline; Grad, Yonatan
    Importance: New York City is the epicenter of the SARS-CoV-2 pandemic in the United States. Mortality and hospitalizations have differed substantially between different neighborhoods. Mitigation efforts in the coming months will require knowing the extent of geographic variation in SARS-CoV-2 prevalence and understanding the drivers of these differences. Objective: To estimate the prevalence of SARS-CoV-2 infection by New York City borough between March 22nd and May 3rd, 2020, and to associate variation in prevalence with antecedent reductions in mobility, defined as aggregated daily physical movements into and out of each borough. Design: Observational study of universal SARS-CoV-2 test results obtained from women hospitalized for delivery. Setting: Four New York-Presbyterian hospital campuses and two Mount Sinai hospital campuses in New York City. Participants: 1,746 women with New York City ZIP codes hospitalized for delivery. Exposures: Infection with SARS-CoV-2. Main outcomes: Population prevalence of SARS-CoV-2 by borough and correlation with the reduction in daily commuting-style movements into and out of each borough. Results: The estimated population prevalence of SARS-CoV-2 ranged from 11.3% (95% credible interval 8.9%, 13.9%) in Manhattan to 26.0% (95% credible interval 15.3%, 38.9%) in South Queens, with an estimated city-wide prevalence of 15.6% (95% credible interval 13.9%, 17.4%). The peak city-wide prevalence was during the week of March 30th, though temporal trends in prevalence varied substantially between boroughs. Population revalence was lowest in boroughs with the greatest reductions in morning commutes out of and evening commutes into the borough (Pearson R = –0.88, 95% credible interval –0.52, –0.99). Conclusions and relevance: Reductions in between-borough mobility predict geographic differences in the prevalence of SARS-CoV-2 infection in New York City. Large parts of the city may remain at risk for substantial SARS-CoV-2 outbreaks. Widespread testing should be conducted to identify geographic disparities in prevalence and assess the risk of future outbreaks.
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    Mortality in Puerto Rico after Hurricane Maria
    (New England Journal of Medicine (NEJM/MMS), 2018) Kishore, Nishant; Marqués, Domingo; Mahmud, Ayesha; Kiang, Mathew; Rodriguez, Irmary; Fuller, Arlan; Ebner, Peggy; Sorensen, Cecilia; Racy, Fabio De Castro Jorge; Lemery, Jay; Maas, Leslie; Leaning, Jennifer; Irizarry, Rafael; Balsari, Satchit; Buckee, Caroline
    BACKGROUND Quantifying the effect of natural disasters on society is critical for recovery of public health services and infrastructure. The death toll can be difficult to assess in the aftermath of a major disaster. In September 2017, Hurricane Maria caused massive infrastructural damage to Puerto Rico, but its effect on mortality remains contentious. The official death count is 64. METHODS Using a representative, stratified sample, we surveyed 3299 randomly chosen households across Puerto Rico to produce an independent estimate of all-cause mortality after the hurricane. Respondents were asked about displacement, infrastructure loss, and causes of death. We calculated excess deaths by comparing our estimated post-hurricane mortality rate with official rates for the same period in 2016. RESULTS From the survey data, we estimated a mortality rate of 14.3 deaths (95% confidence interval [CI], 9.8 to 18.9) per 1000 persons from September 20 through December 31, 2017. This rate yielded a total of 4645 excess deaths during this period (95% CI, 793 to 8498), equivalent to a 62% increase in the mortality rate as compared with the same period in 2016. However, this number is likely to be an underestimate because of survivor bias. The mortality rate remained high through the end of December 2017, and one third of the deaths were attributed to delayed or interrupted health care. Hurricane-related migration was substantial. CONCLUSIONS This household-based survey suggests that the number of excess deaths related to Hurricane Maria in Puerto Rico is more than 70 times the official estimate. (Funded by the Harvard T.H. Chan School of Public Health and others.)