scholarly journals Monitoring Health Systems by Estimating Excess Mortality

2020 ◽  
Author(s):  
Rolando J. Acosta ◽  
Rafael A. Irizarry
Keyword(s):  
Puerto Rico ◽  
New Jersey ◽  
Natural Disasters ◽  
Health Systems ◽  
Excess Mortality ◽  
Data Driven ◽  
Mortality Data ◽  
List Type ◽  
Individual Level ◽  
The Us

AbstractImportance: Monitoring health systems during and after natural disasters, epidemics, or outbreaks is critical for guiding policy decisions and interventions. When the effects of an event are long-lasting and difficult to detect in the short term, the accumulated effects can be devastating.Objective: We aim to leverage the improved access to mortality data to develop data-driven approaches that can help monitor health systems and quantify the effects on mortality of natural disasters, epidemics, or outbreaks.Design, Setting, and Participants: To demonstrate the utility of our approach we conducted several retrospective time-series analyses of mortality assessment after natural disasters. We obtained individual-level mortality records from the Department of Health of Puerto Rico from January 1985 to May 2020 to study the effects of hurricanes Hugo, Georges, and María in September 1989, September 1998, and September 2017, respectively. Further, we obtained daily mortality counts from Florida, New Jersey, and Louisiana’s Vital Statistic systems from January 2015 to December 2018, January 2007 to December 2015, and January 2003 to December 2006, respectively, to study the effects of hurricanes Irma in 2017, Sandy in 2013, and Katrina in 2005. Finally, we obtained individual-level mortality data from the Cook county, IL, medical examiners office, and state-specific weekly mortality counts from the Center for Disease Control and Prevention to assess the effect of the COVID-19 pandemic on the US health system.Exposures: Hurricanes María, Georges, and Hugo in Puerto Rico, Irma in Florida, Sandy in New Jersey, and Katrina in Louisiana, the Chikungunya outbreak in Puerto Rico, and the COVID-19 pandemic in the United States.Main Outcomes: We estimate and provide uncertainty assessments for percent increase from expected mortality, estimated excess deaths, and difference across groups.Results: We found that the death rate increase in Puerto Rico after María and Georges was substantially higher than the other hurricanes we examined. Further, we find that excess mortality in the US was already above 100,000 on May 9, 2020, with over 58% of these occurred in New York, New Jersey, Massachusetts, and Pennsylvania, and that effects of this pandemic were worse for elderly black individuals compared to whites of the same age.Conclusions and Relevance: Our approach can be used to monitor or assess health systems by estimating increased mortality rates and excess deaths from mortality records.Key PointsQuestion: Can we estimate excess mortality and provide accurate uncertainty assessments from vital statistics data?Findings: We developed statistical methodology that accounts for key sources of variability and provides accurate estimates and their standard errors for excess mortality. We applied the approach to datasets from several US states including periods affected by hurricanes and epidemics. We found an elevated and persistent increase in mortality after hurricanes in Puerto Rico that was substantially higher than in other US states. We also found that excess mortality in the US during the COVID-19 pandemic reached 100,000 by May 9, 2020. Finally, we found significant differences in the effects of this pandemic across racial groups in the US.Meaning: Data-driven approaches can help monitor health systems and quantify the effects on mortality of natural disasters, epidemics, or outbreaks.

scholarly journals Excess Mortality in the United States During the First Peak of the COVID-19 Pandemic

2020 ◽  
Author(s):  
R. Rivera ◽  
J. E. Rosenbaum ◽  
W. Quispe
Keyword(s):  
United States ◽  
New York ◽  
New Jersey ◽  
Excess Mortality ◽  
Case Fatality ◽  
The United States ◽  
Mortality Data ◽  
Surveillance Systems ◽  
Pandemic Response ◽  
Mortality Surveillance

1AbstractDeaths are frequently under-estimated during emergencies, times when accurate mortality estimates are crucial for pandemic response and public adherence to non-pharmaceutical interventions. This study estimates excess all-cause, pneumonia, and influenza mortality during the COVID-19 health emergency using the June 12, 2020 release of weekly mortality data from the United States (U.S.) Mortality Surveillance Survey (MSS) from September 27, 2015 to May 9, 2020, using semiparametric and conventional time-series models in 9 states with high reported COVID-19 deaths and apparently complete mortality data: California, Colorado, Florida, Illinois, Massachusetts, Michigan, New Jersey, New York, and Washington. The May 9 endpoint was chosen due to apparently increased reporting lags in provisional mortality counts. We estimated greater excess mortality than official COVID-19 mortality in the U.S. (excess mortality 95% confidence interval (CI) (80862, 107284) vs. 78834 COVID-19 deaths) and 6 states: California (excess mortality 95% CI (2891, 5873) vs. 2849 COVID-19 deaths); Illinois (95% CI (4412, 5871) vs. 3525 COVID-19 deaths); Massachusetts (95% CI (5061, 6317) vs. 5050 COVID-19 deaths); New Jersey (95% CI (12497, 15307) vs. 10465 COVID-19 deaths); and New York (95% CI (30469, 37722) vs. 26584 COVID-19 deaths). Conventional model results were consistent with semiparametric results but less precise.Official COVID-19 mortality substantially understates actual mortality, suggesting greater case-fatality rates. Mortality reporting lags appeared to worsen during the pandemic, when timeliness in surveillance systems was most crucial for improving pandemic response.

scholarly journals Excess mortality in the United States during the first three months of the COVID-19 pandemic

2020 ◽  
Vol 148 ◽  
Author(s):  
R. Rivera ◽  
J. E. Rosenbaum ◽  
W. Quispe
Keyword(s):  
United States ◽  
New York ◽  
New Jersey ◽  
Excess Mortality ◽  
The United States ◽  
Mortality Data ◽  
Surveillance Systems ◽  
Pandemic Response ◽  
Mortality Surveillance ◽  
Mortality Estimates

Abstract Deaths are frequently under-estimated during emergencies, times when accurate mortality estimates are crucial for emergency response. This study estimates excess all-cause, pneumonia and influenza mortality during the coronavirus disease 2019 (COVID-19) pandemic using the 11 September 2020 release of weekly mortality data from the United States (U.S.) Mortality Surveillance System (MSS) from 27 September 2015 to 9 May 2020, using semiparametric and conventional time-series models in 13 states with high reported COVID-19 deaths and apparently complete mortality data: California, Colorado, Connecticut, Florida, Illinois, Indiana, Louisiana, Massachusetts, Michigan, New Jersey, New York, Pennsylvania and Washington. We estimated greater excess mortality than official COVID-19 mortality in the U.S. (excess mortality 95% confidence interval (CI) 100 013–127 501 vs. 78 834 COVID-19 deaths) and 9 states: California (excess mortality 95% CI 3338–6344) vs. 2849 COVID-19 deaths); Connecticut (excess mortality 95% CI 3095–3952) vs. 2932 COVID-19 deaths); Illinois (95% CI 4646–6111) vs. 3525 COVID-19 deaths); Louisiana (excess mortality 95% CI 2341–3183 vs. 2267 COVID-19 deaths); Massachusetts (95% CI 5562–7201 vs. 5050 COVID-19 deaths); New Jersey (95% CI 13 170–16 058 vs. 10 465 COVID-19 deaths); New York (95% CI 32 538–39 960 vs. 26 584 COVID-19 deaths); and Pennsylvania (95% CI 5125–6560 vs. 3793 COVID-19 deaths). Conventional model results were consistent with semiparametric results but less precise. Significant excess pneumonia deaths were also found for all locations and we estimated hundreds of excess influenza deaths in New York. We find that official COVID-19 mortality substantially understates actual mortality, excess deaths cannot be explained entirely by official COVID-19 death counts. Mortality reporting lags appeared to worsen during the pandemic, when timeliness in surveillance systems was most crucial for improving pandemic response.

Factors influencing food insecurity rates: A county by county observation from 2017 in New Jersey

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
K Ignace ◽  
M Souza ◽  
C Borges
Keyword(s):  
New Jersey ◽  
Food Insecurity ◽  
Median Income ◽  
Individual Level ◽  
The Past ◽  
Total Food ◽  
High Deprivation ◽  
The Us ◽  
Poverty Status ◽  
Us Census

Abstract This observational study aimed to investigate food insecurity prevalence for children under 18 in New Jersey using secondary data from 2017. Total food insecurity rates per county for children under 18 and all people were gathered from the NJ Department of Health. Age and Sex 5-year estimates for total population and children were collected from the US Census. The median income in the past 12 months from 2013-2017 was collected from the US Census in addition to family poverty status in the past 12 months from 2013-2017. Additional data was collected from the Social Capital Index. A bivariate analysis and a chi-square test revealed that nine independent variables were correlated to food insecurity prevalence by county in New Jersey among children under the age of 18. The significant variables were total food insecurity prevalence for all people in a county (p=.000), median income in the past 12 months in 2017 age-adjusted dollars (p=.000), percent of families below the poverty level (p=.000), county-level index (p=.000), family unity (p=.000), institutional health (p=.002), collective efficacy (p=.003), percent of adults over the age of 18 not covered under any type of health coverage (p=.006), and percent of families headed by a single parent (p=.000). Regarding the multivariate analysis, only two variables were still significant. Median income (p=.030) and poverty status (p=.007). These two variables are strongly associated with food insecurity prevalence among children under the age of 18. Health insurance status and household income are correlated with food insecurity. Food insecure children were more likely to live in areas with high deprivation and experience both individual-level poverty and neighborhood deprivation. Key messages Median income and poverty status are strongly associated with food insecurity prevalence among children under the age of 18. Food insecure children were more likely to live in areas with high deprivation and experience both individual level poverty and neighborhood deprivation.

scholarly journals Excess mortality in England and Wales during the first wave of the COVID-19 pandemic

2020 ◽  
Author(s):  
Evangelos Kontopantelis ◽  
Mamas A Mamas ◽  
John Deanfield ◽  
Miqdad Asaria ◽  
Tim Doran
Keyword(s):  
Excess Mortality ◽  
Age Groups ◽  
Mortality Rates ◽  
Mortality Data ◽  
List Type ◽  
Age Group ◽  
The West ◽  
England And Wales ◽  
West Midlands ◽  
Excess Deaths

AbstractBackgroundDeaths during the COVID-19 pandemic result directly from infection and exacerbation of other diseases and indirectly from deferment of care for other conditions, and are socially and geographically patterned. We quantified excess mortality in regions of England and Wales during the pandemic, for all causes and for non-COVID-19 associated deaths.MethodsWeekly mortality data for 1 Jan 2010 to 1 May 2020 for England and Wales were obtained from the Office of National Statistics. Mean-dispersion negative binomial regressions were used to model death counts based on pre-pandemic trends and exponentiated linear predictions were subtracted from: i) all-cause deaths; and ii) all-cause deaths minus COVID-19 related deaths for the pandemic period (07-13 March to 25 April to 8 May).FindingsBetween 7 March and 8 May 2020, there were 47,243 (95%CI: 46,671 to 47,815) excess deaths in England and Wales, of which 9,948 (95%CI: 9,376 to 10,520) were not associated with COVID-19. Overall excess mortality rates varied from 49 per 100,000 (95%CI: 49 to 50) in the South West to 102 per 100,000 (95%CI: 102 to 103) in London. Non-COVID-19 associated excess mortality rates ranged from −1 per 100,000 (95%CI: −1 to 0) in Wales (i.e. mortality rates were no higher than expected) to 26 per 100,000 (95%CI: 25 to 26) in the West Midlands.InterpretationThe COVID-19 pandemic has had markedly different impacts on the regions of England and Wales, both for deaths directly attributable to COVID-19 infection and for deaths resulting from the national public health response.FundingNoneSummary boxWhat is already known on the subjectThe number of deaths due to COVID-19 have been quantified by the Office of National StatisticsThese have also been reported across age groups and regionsWhat this study addsWe report the number of excess deaths, using weekly mortality data from 1/1/2010We also quantify the number of excess deaths, excluding COVID-19 associated deaths, which can be attributed to COVID-19 directly (but not coded as such) or indirectly (due to other urgent but unmet health need)Highest excess mortality, excluding COVID-19 deaths, was observed in the West Midlands, followed by London and the North WestAlthough males had larger excess mortality rates than females across all age groups, female excess mortality rates excluding COVID-19 were higher in the 85+ age group, indicating a large undocumented impact of the virus on older females (direct and/or indirect)The three provided appendices will be updated weekly on the BMJ-JECH website, to provide up-to-date information of excess mortality by region, sex and age group

scholarly journals Local mortality estimates during the COVID-19 pandemic in Italy

2021 ◽  
Author(s):  
Augusto Cerqua ◽  
Roberta Di Stefano ◽  
Marco Letta ◽  
Sara Miccoli
Keyword(s):  
Machine Learning ◽  
Excess Mortality ◽  
Control Method ◽  
Local Level ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Mortality Data ◽  
Official Method ◽  
Learning Techniques ◽  
Mortality Estimates

AbstractEstimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The “official” approach adopted by public institutions to estimate the “excess mortality” during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in “ordinary” years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.

scholarly journals Leading cancers contributing to educational disparities in cancer mortality in the US, 2017

2021 ◽  
Author(s):  
Diana R. Withrow ◽  
Neal D. Freedman ◽  
James T. Gibson ◽  
Mandi Yu ◽  
Anna M. Nápoles ◽  
...  
Keyword(s):  
Lung Cancer ◽  
High School ◽  
Mortality Rate ◽  
Cancer Mortality ◽  
Death Certificate ◽  
Bachelor's Degree ◽  
Educational Disparities ◽  
Individual Level ◽  
Death Certificate Data ◽  
The Us

Abstract Purpose To inform prevention efforts, we sought to determine which cancer types contribute the most to cancer mortality disparities by individual-level education using national death certificate data for 2017. Methods Information on all US deaths occurring in 2017 among 25–84-year-olds was ascertained from national death certificate data, which include cause of death and educational attainment. Education was classified as high school or less (≤ 12 years), some college or diploma (13–15 years), and Bachelor's degree or higher (≥ 16 years). Cancer mortality rate differences (RD) were calculated by subtracting age-adjusted mortality rates (AMR) among those with ≥ 16 years of education from AMR among those with ≤ 12 years. Results The cancer mortality rate difference between those with a Bachelor's degree or more vs. high school or less education was 72 deaths per 100,000 person-years. Lung cancer deaths account for over half (53%) of the RD for cancer mortality by education in the US. Conclusion Efforts to reduce smoking, particularly among persons with less education, would contribute substantially to reducing educational disparities in lung cancer and overall cancer mortality.

scholarly journals Effects of transition on HIV and non-HIV services and health systems in Kenya: a mixed methods evaluation of donor transition

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Daniela C. Rodríguez ◽  
Diwakar Mohan ◽  
Caroline Mackenzie ◽  
Jess Wilhelm ◽  
Ezinne Eze-Ajoku ◽  
...  
Keyword(s):  
Mixed Methods ◽  
Service Delivery ◽  
Health Systems ◽  
Local Level ◽  
Health Facilities ◽  
Government Support ◽  
Hiv Care ◽  
Local Context ◽  
Mixed Methods Evaluation ◽  
The Us

Abstract Background In 2015 the US President’s Emergency Plan for AIDS Relief (PEPFAR) initiated its Geographic Prioritization (GP) process whereby it prioritized high burden areas within countries, with the goal of more rapidly achieving the UNAIDS 90–90-90 targets. In Kenya, PEPFAR designated over 400 health facilities in Northeastern Kenya to be transitioned to government support (known as central support (CS)). Methods We conducted a mixed methods evaluation exploring the effect of GP on health systems, and HIV and non-HIV service delivery in CS facilities. Quantitative data from a facility survey and health service delivery data were gathered and combined with data from two rounds of interviews and focus group discussions (FGDs) conducted at national and sub-national level to document the design and implementation of GP. The survey included 230 health facilities across 10 counties, and 59 interviews and 22 FGDs were conducted with government officials, health facility providers, patients, and civil society. Results We found that PEPFAR moved quickly from announcing the GP to implementation. Despite extensive conversations between the US government and the Government of Kenya, there was little consultation with sub-national actors even though the country had recently undergone a major devolution process. Survey and qualitative data identified a number of effects from GP, including discontinuation of certain services, declines in quality and access to HIV care, loss of training and financial incentives for health workers, and disruption of laboratory testing. Despite these reports, service coverage had not been greatly affected; however, clinician strikes in the post-transition period were potential confounders. Conclusions This study found similar effects to earlier research on transition and provides additional insights about internal country transitions, particularly in decentralized contexts. Aside from a need for longer planning periods and better communication and coordination, we raise concerns about transitions driven by epidemiological criteria without adaptation to the local context and their implication for priority-setting and HIV investments at the local level.

scholarly journals The Feasibility of Renewable Natural Gas in New Jersey

2021 ◽  
Vol 13 (4) ◽  
pp. 1618
Author(s):  
Anneliese Dyer ◽  
Amelia Christine Miller ◽  
Brianna Chandra ◽  
Juan Galindo Maza ◽  
Carley Tran ◽  
...  
Keyword(s):  
New Jersey ◽  
Natural Gas ◽  
Conceptual Model ◽  
Greenhouse Gas Emission ◽  
Wastewater Treatment Plants ◽  
The United States ◽  
Outreach Program ◽  
Financial Barriers ◽  
The Us ◽  
Renewable Natural Gas

With traditional natural gas being one of the top options for heating in the United States and the present threat of climate change, there is a demand for an alternative clean fuel source. A Renewable Natural Gas Implementation Decision-Making Conceptual Model was created to provide a framework for considering the feasibility of renewable natural gas (RNG) projects and applied to New Jersey, specifically investigating landfills and wastewater treatment plants (WWTPs). Data from the US EPA’s Landfill Methane Outreach Program and New Jersey’s Department of Environmental Protection Sewage Sludge databases were used to identify seven landfills and 22 WWTPs as possible locations for RNG projects. Landfills were found to have a higher potential for producing RNG, on average potentially producing enough RNG to heat 12,792 homes per year versus 1227 for the average WWTP. Additionally, landfills, while having higher capital expenses, have lower projected payback periods, averaging 5.19 years compared to WWTP’s 11.78 years. WWTPs, however, generally are located closer to existing natural gas pipelines than landfills and when they produce more than 362 million standard cubic feet per year (MMSCFY) of biogas are financially feasible. RNG projects at Monmouth County Reclamation Center, Ocean County Landfill, and Passaic Valley Sewerage Commission WWTP show the greatest potential. Greenhouse gas emission reductions from RNG projects at these facilities utilizing all available biogas would be 1.628 million metric tons CO2 equivalents per year, synonymous to removing over 351,000 passenger vehicles from the road each year. In addition, expanding federal and state incentives to encompass RNG as a heating fuel is necessary to reduce financial barriers to RNG projects throughout the US. Overall, this paper supports the hypothesized conceptual model in examining the feasibility of RNG projects through examples from New Jersey and confirms the potential for RNG production utilizing existing waste streams.

Avenues to News and Diverse News Exposure Online: Comparing Direct Navigation, Social Media, News Aggregators, Search Queries, and Article Hyperlinks

2021 ◽  
pp. 194016122110091
Author(s):  
Magdalena Wojcieszak ◽  
Ericka Menchen-Trevino ◽  
Joao F. F. Goncalves ◽  
Brian Weeks
Keyword(s):  
Social Media ◽  
Search Engines ◽  
Direct Access ◽  
Data Set ◽  
Individual Level ◽  
News Sources ◽  
Internet Users ◽  
News Websites ◽  
The Us ◽  
Social Media Platforms

The online environment dramatically expands the number of ways people can encounter news but there remain questions of whether these abundant opportunities facilitate news exposure diversity. This project examines key questions regarding how internet users arrive at news and what kinds of news they encounter. We account for a multiplicity of avenues to news online, some of which have never been analyzed: (1) direct access to news websites, (2) social networks, (3) news aggregators, (4) search engines, (5) webmail, and (6) hyperlinks in news. We examine the extent to which each avenue promotes news exposure and also exposes users to news sources that are left leaning, right leaning, and centrist. When combined with information on individual political leanings, we show the extent of dissimilar, centrist, or congenial exposure resulting from each avenue. We rely on web browsing history records from 636 social media users in the US paired with survey self-reports, a unique data set that allows us to examine both aggregate and individual-level exposure. Visits to news websites account for about 2 percent of the total number of visits to URLs and are unevenly distributed among users. The most widespread ways of accessing news are search engines and social media platforms (and hyperlinks within news sites once people arrive at news). The two former avenues also increase dissimilar news exposure, compared to accessing news directly, yet direct news access drives the highest proportion of centrist exposure.

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