Ecologic Study of Influenza Vaccination Uptake and COVID-19 Death Rate in New York City

The Centers for Disease Control and Prevention (CDC) and local health jurisdictions have been using HIV surveillance data to monitor mortality among people with HIV in the United States with age-standardized death rates, but the principles of age standardization have not been consistently followed, making age standardization lose its purpose—comparison over time, across jurisdictions, or by other characteristics. We review the current practices of age standardization in calculating death rates among people with HIV in the United States, discuss the principles of age standardization including those specific to the HIV population whose age distribution differs markedly from that of the US 2000 standard population, make recommendations, and report age-standardized death rates among people with HIV in New York City. When we restricted the analysis population to adults aged between 18 and 84 years in New York City, the age-standardized death rate among people with HIV decreased from 20.8 per 1000 (95% confidence interval [CI] = 19.2, 22.3) in 2013 to 17.1 per 1000 (95% CI = 15.8, 18.3) in 2017, and the age-standardized death rate among people without HIV decreased from 5.8 per 1000 in 2013 to 5.5 per 1000 in 2017.

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Increasing influenza vaccination in New York City taxi drivers: A community driven approach

Vaccine ◽

10.1016/j.vaccine.2015.03.027 ◽

2015 ◽

Vol 33 (22) ◽

pp. 2521-2523 ◽

Cited By ~ 2

Author(s):

Francesca Gany ◽

Rohini Rau-Murthy ◽

Imran Mujawar ◽

Lakshmi Prasad ◽

Nicole Roberts

Keyword(s):

New York ◽

New York City ◽

Influenza Vaccination ◽

York City ◽

Taxi Drivers

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Deaths From Pneumonia—New York City, 1999–2015

Open Forum Infectious Diseases ◽

10.1093/ofid/ofy020 ◽

2018 ◽

Vol 5 (2) ◽

Cited By ~ 8

Author(s):

Evette Cordoba ◽

Gil Maduro ◽

Mary Huynh ◽

Jay K Varma ◽

Neil M Vora

Keyword(s):

New York ◽

New York City ◽

Cause Of Death ◽

York City ◽

Death Rate ◽

Multivariable Analysis ◽

Etiologic Agent ◽

Staten Island ◽

Underlying Cause Of Death ◽

Poverty Levels

Abstract Background “Pneumonia and influenza” are the third leading cause of death in New York City. Since 2012, pneumonia and influenza have been the only infectious diseases listed among the 10 leading causes of death in NYC. Most pneumonia and influenza deaths in NYC list pneumonia as the underlying cause of death, not influenza. We therefore analyzed death certificate data for pneumonia in NYC during 1999–2015. Methods We calculated annualized pneumonia death rates (overall and by sociodemographic subgroup) and examined the etiologic agent listed. Results There were 41 400 pneumonia deaths during the study period, corresponding to an annualized age-adjusted death rate of 29.7 per 100 000 population. Approximately 17.5% of pneumonia deaths specified an etiologic agent. Age-adjusted pneumonia death rate declined over the study period and across each borough. Males had an annualized age-adjusted pneumonia death rate 1.5 (95% confidence interval [CI], 1.5–1.5) times that of females. Non-Hispanic blacks had an annualized age-adjusted pneumonia death rate 1.2 (95% CI, 1.2–1.2) times that of non-Hispanic whites. The annualized pneumonia death rate increased with age group above 5–24 years and neighborhood-level poverty. Staten Island had an annualized age-adjusted pneumonia death rate 1.3 (95% CI, 1.2–1.3) times that of Manhattan. In the multivariable analysis, pneumonia deaths were more likely to occur among males, non-Hispanic blacks, persons aged ≥65 years, residents of neighborhoods with higher poverty levels, and in Staten Island. Conclusions While the accuracy of death certificates is unknown, investigation is needed to understand why certain populations are disproportionately recorded as dying from pneumonia in NYC.

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Hospital-level factors associated with death during pneumonia-associated hospitalization among adults—New York City, 2010–2014

PLoS ONE ◽

10.1371/journal.pone.0256678 ◽

2021 ◽

Vol 16 (10) ◽

pp. e0256678

Author(s):

Kate Whittemore ◽

Kristian M. Garcia ◽

Chaorui C. Huang ◽

Sungwoo Lim ◽

Demetre C. Daskalakis ◽

...

Keyword(s):

New York ◽

New York City ◽

York City ◽

Death Rate ◽

Teaching Hospitals ◽

Hospital Death ◽

Negative Binomial Regression Model ◽

Hospital Level ◽

American Hospital ◽

Factors Associated

Background In New York City (NYC), pneumonia is a leading cause of death and most pneumonia deaths occur in hospitals. Whether the pneumonia death rate in NYC reflects reporting artifact or is associated with factors during pneumonia-associated hospitalization (PAH) is unknown. We aimed to identify hospital-level factors associated with higher than expected in-hospital pneumonia death rates among adults in NYC. Methods Data from January 1, 2010–December 31, 2014 were obtained from the New York Statewide Planning and Research Cooperative System and the American Hospital Association Database. In-hospital pneumonia standardized mortality ratio (SMR) was calculated for each hospital as observed PAH death rate divided by expected PAH death rate. To determine hospital-level factors associated with higher in-hospital pneumonia SMR, we fit a hospital-level multivariable negative binomial regression model. Results Of 148,172 PAH among adult NYC residents in 39 hospitals during 2010–2014, 20,820 (14.06%) resulted in in-hospital death. In-hospital pneumonia SMRs varied across NYC hospitals (0.77–1.23) after controlling for patient-level factors. An increase in average daily occupancy and membership in the Council of Teaching Hospitals were associated with increased in-hospital pneumonia SMR. Conclusions Differences in in-hospital pneumonia SMRs between hospitals might reflect differences in disease severity, quality of care, or coding practices. More research is needed to understand the association between average daily occupancy and in-hospital pneumonia SMR. Additional pneumonia-specific training at teaching hospitals can be considered to address higher in-hospital pneumonia SMR in teaching hospitals.

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Seasonal Influenza Vaccination among Homebound Elderly Receiving Home-Based Primary Care in New York City

Journal of Community Health ◽

10.1007/s10900-011-9409-z ◽

2011 ◽

Vol 37 (1) ◽

pp. 10-14 ◽

Cited By ~ 4

Author(s):

David B. Banach ◽

Katherine Ornstein ◽

Stephanie H. Factor ◽

Theresa A. Soriano

Keyword(s):

Primary Care ◽

New York ◽

New York City ◽

Influenza Vaccination ◽

York City ◽

Seasonal Influenza ◽

Seasonal Influenza Vaccination ◽

Home Based Primary Care ◽

Home Based

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Predict new cases of the Coronavirus 19; in Michigan, U.S.A. or other countries using Crow-AMSAA Method (Preprint)

10.2196/preprints.19555 ◽

2020 ◽

Author(s):

Yanshuo Wang

Keyword(s):

New York ◽

New York City ◽

Social Distance ◽

York City ◽

Death Rate ◽

Transmission Rate ◽

Complex Problem ◽

Mitigation Strategies ◽

Summary Table ◽

The Government

BACKGROUND Statistical predictions are useful to predict events based on statistical models. The data is useful to determine outcomes based on inputs and calculations. The Crow-AMSAA method will be explored to predict new cases of Coronavirus 19 (COVID19). This method is currently used within engineering reliability design to predict failures and evaluate the reliability growth. The author intents to use this model to predict the COVID19 cases by using daily reported data from Michigan, New York City, U.S.A and other countries. The piece wise Crow-AMSAA (CA) model fits the data very well for the infected cases and deaths at different phases during the start of the COVID19 outbreak. The slope β of the Crow-AMSAA line indicates the speed of the transmission or death rate. The traditional epidemiological model is based on the exponential distribution, but the Crow-AMSAA is the Non Homogeneous Poisson Process (NHPP) which can be used to modeling the complex problem like COVID19, especially when the various mitigation strategies such as social distance, isolation and locking down were implemented by the government at different places. OBJECTIVE This paper is to use piece wise Crow-AMSAA method to fit the COVID19 confirmed cases in Michigan, New York City, U.S.A and other countries. METHODS piece wise Crow-AMSAA method to fit the COVID19 confirmed cases RESULTS From the Crow-AMSAA analysis above, at the beginning of the COVID 19, the infectious cases did not follow the Crow-AMSAA prediction line, but during the outbreak start, the confirmed cases does follow the CA line, the slope β value indicates the pace of the transmission rate or death rate in each case. The piece wise Crow-AMSAA describes the different phases of spreading. This indicates the speed of the transmission rate could change according to the government interference, social distance order or other factors. Comparing the piece wise CA β slopes (β: 1.683-- 0.834--0.092) in China and in U.S.A (β:5.138--10.48--5.259), the speed of infectious rate in U.S.A is much higher than the infectious rate in China. From the piece wise CA plots and summary table 1 of the CA slope βs, the COVID19 spreading has the different behavior at different places and countries where the government implemented the different policy to slow down the spreading. CONCLUSIONS From the analysis of data and conclusions from confirmed cases and deaths of COVID 19 in Michigan, New York city, U.S.A, China and other countries, the piece wise Crow-AMSAA method can be used to modeling the spreading of COVID19.

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