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1.
medRxiv ; 2024 Jan 16.
Artigo em Inglês | MEDLINE | ID: mdl-38293208

RESUMO

To assess the excess mortality burden of Covid-19 in the United States, we estimated sex, age and race stratified all-cause excess deaths in each county of the US during 2020 and 2021. Using spatial Bayesian models trained on all recorded deaths between 2003-2019, we estimated 463,187 (95% uncertainty interval (UI): 426,139 - 497,526) excess deaths during 2020, and 544,105 (95% UI: 492,202 - 592,959) excess deaths during 2021 nationally, with considerable geographical heterogeneity. Excess mortality rate (EMR) nearly doubled for each 10-year increase in age and was consistently higher among men than women. EMR in the Black population was 1.5 times that of the White population nationally and as high as 3.8 times in some states. Among the 25-54 year population excess mortality was highest in the American Indian/Alaskan Native (AI/AN) population among the four racial groups studied, and in a few states was as high as 6 times that of the White population. Strong association of EMR with county-level social vulnerability was estimated, including positive associations with prevalence of disability (standardized effect: 40.6 excess deaths per 100,000), older population (37.6), poverty (23.6), and unemployment (18.5), whereas population density (-50), higher education (-38.6), and income (-35.4) were protective. Together, these estimates provide a more reliable and comprehensive understanding of the mortality burden of the pandemic in the US thus far. They suggest that Covid-19 amplified social and racial disparities. Short-term measures to protect more vulnerable groups in future Covid-19 waves and systemic corrective steps to address long-term societal inequities are necessary.

2.
Am J Epidemiol ; 193(2): 256-266, 2024 Feb 05.
Artigo em Inglês | MEDLINE | ID: mdl-37846128

RESUMO

Suicide rates in the United States have increased over the past 15 years, with substantial geographic variation in these increases; yet there have been few attempts to cluster counties by the magnitude of suicide rate changes according to intercept and slope or to identify the economic precursors of increases. We used vital statistics data and growth mixture models to identify clusters of counties by their magnitude of suicide growth from 2008 to 2020 and examined associations with county economic and labor indices. Our models identified 5 clusters, each differentiated by intercept and slope magnitude, with the highest-rate cluster (4% of counties) being observed mainly in sparsely populated areas in the West and Alaska, starting the time series at 25.4 suicides per 100,000 population, and exhibiting the steepest increase in slope (0.69/100,000/year). There was no cluster for which the suicide rate was stable or declining. Counties in the highest-rate cluster were more likely to have agricultural and service economies and less likely to have urban professional economies. Given the increased burden of suicide, with no clusters of counties improving over time, additional policy and prevention efforts are needed, particularly targeted at rural areas in the West.


Assuntos
Suicídio , Humanos , Estados Unidos/epidemiologia , População Rural
4.
Emerg Infect Dis ; 29(8): 1711-1713, 2023 08.
Artigo em Inglês | MEDLINE | ID: mdl-37486228

RESUMO

Surveillance of COVID-19 is challenging but critical for mitigating disease, particularly if predictive of future disease burden. We report a robust multiyear lead-lag association between internet search activity for loss of smell or taste and COVID-19-associated hospitalization and deaths. These search data could help predict COVID-19 surges.


Assuntos
COVID-19 , Transtornos do Olfato , Humanos , Paladar , SARS-CoV-2 , Anosmia , Transtornos do Olfato/epidemiologia , Transtornos do Olfato/etiologia
5.
Proc Natl Acad Sci U S A ; 120(28): e2300590120, 2023 07 11.
Artigo em Inglês | MEDLINE | ID: mdl-37399393

RESUMO

When an influenza pandemic emerges, temporary school closures and antiviral treatment may slow virus spread, reduce the overall disease burden, and provide time for vaccine development, distribution, and administration while keeping a larger portion of the general population infection free. The impact of such measures will depend on the transmissibility and severity of the virus and the timing and extent of their implementation. To provide robust assessments of layered pandemic intervention strategies, the Centers for Disease Control and Prevention (CDC) funded a network of academic groups to build a framework for the development and comparison of multiple pandemic influenza models. Research teams from Columbia University, Imperial College London/Princeton University, Northeastern University, the University of Texas at Austin/Yale University, and the University of Virginia independently modeled three prescribed sets of pandemic influenza scenarios developed collaboratively by the CDC and network members. Results provided by the groups were aggregated into a mean-based ensemble. The ensemble and most component models agreed on the ranking of the most and least effective intervention strategies by impact but not on the magnitude of those impacts. In the scenarios evaluated, vaccination alone, due to the time needed for development, approval, and deployment, would not be expected to substantially reduce the numbers of illnesses, hospitalizations, and deaths that would occur. Only strategies that included early implementation of school closure were found to substantially mitigate early spread and allow time for vaccines to be developed and administered, especially under a highly transmissible pandemic scenario.


Assuntos
Vacinas contra Influenza , Influenza Humana , Humanos , Influenza Humana/tratamento farmacológico , Influenza Humana/epidemiologia , Influenza Humana/prevenção & controle , Preparações Farmacêuticas , Pandemias/prevenção & controle , Vacinas contra Influenza/uso terapêutico , Antivirais/farmacologia , Antivirais/uso terapêutico
6.
Psychiatr Serv ; 74(9): 978-981, 2023 09 01.
Artigo em Inglês | MEDLINE | ID: mdl-36872897

RESUMO

OBJECTIVE: Utilization of the 988 Suicide and Crisis Lifeline (Lifeline; formerly called the National Suicide Prevention Lifeline) was analyzed in relation to suicide deaths in U.S. states between 2007 and 2020 to identify states with potential unmet need for mental health crisis hotline services. METHODS: Annual state call rates were calculated from calls routed to the Lifeline during the 2007-2020 period (N=13.6 million). Annual state suicide mortality rates (standardized) were calculated from suicide deaths reported to the National Vital Statistics System (2007-2020 cumulative deaths=588,122). Call rate ratio (CRR) and mortality rate ratio (MRR) were estimated by state and year. RESULTS: Sixteen U.S. states demonstrated a consistently high MRR and a low CRR, suggesting high suicide burden and relatively low Lifeline use. Heterogeneity in state CRRs decreased over time. CONCLUSIONS: Prioritizing states with a high MRR and a low CRR for messaging and outreach regarding the availability of the Lifeline can ensure more equitable, need-based access to this critical resource.


Assuntos
Linhas Diretas , Prevenção do Suicídio , Suicídio Consumado , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Criança , Pré-Escolar , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Adulto Jovem , Linhas Diretas/estatística & dados numéricos , Linhas Diretas/provisão & distribuição , Linhas Diretas/tendências , Prevenção do Suicídio/métodos , Prevenção do Suicídio/estatística & dados numéricos , Prevenção do Suicídio/provisão & distribuição , Prevenção do Suicídio/tendências , Suicídio Consumado/estatística & dados numéricos , Suicídio Consumado/tendências , Estados Unidos/epidemiologia , Classificação Internacional de Doenças , Grupos Raciais/estatística & dados numéricos , Serviços de Saúde Mental/provisão & distribuição , Serviços de Saúde Mental/tendências , Populações Vulneráveis/estatística & dados numéricos
7.
PLoS Comput Biol ; 19(3): e1010945, 2023 03.
Artigo em Inglês | MEDLINE | ID: mdl-36913441

RESUMO

Deaths by suicide, as well as suicidal ideations, plans and attempts, have been increasing in the US for the past two decades. Deployment of effective interventions would require timely, geographically well-resolved estimates of suicide activity. In this study, we evaluated the feasibility of a two-step process for predicting suicide mortality: a) generation of hindcasts, mortality estimates for past months for which observational data would not have been available if forecasts were generated in real-time; and b) generation of forecasts with observational data augmented with hindcasts. Calls to crisis hotline services and online queries to the Google search engine for suicide-related terms were used as proxy data sources to generate hindcasts. The primary hindcast model (auto) is an Autoregressive Integrated Moving average model (ARIMA), trained on suicide mortality rates alone. Three regression models augment hindcast estimates from auto with call rates (calls), GHT search rates (ght) and both datasets together (calls_ght). The 4 forecast models used are ARIMA models trained with corresponding hindcast estimates. All models were evaluated against a baseline random walk with drift model. Rolling monthly 6-month ahead forecasts for all 50 states between 2012 and 2020 were generated. Quantile score (QS) was used to assess the quality of the forecast distributions. Median QS for auto was better than baseline (0.114 vs. 0.21. Median QS of augmented models were lower than auto, but not significantly different from each other (Wilcoxon signed-rank test, p > .05). Forecasts from augmented models were also better calibrated. Together, these results provide evidence that proxy data can address delays in release of suicide mortality data and improve forecast quality. An operational forecast system of state-level suicide risk may be feasible with sustained engagement between modelers and public health departments to appraise data sources and methods as well as to continuously evaluate forecast accuracy.


Assuntos
Suicídio , Humanos , Saúde Pública , Previsões , Ferramenta de Busca
8.
Lancet Public Health ; 8(3): e184-e193, 2023 03.
Artigo em Inglês | MEDLINE | ID: mdl-36702142

RESUMO

BACKGROUND: Suicide is one of the leading causes of death in the USA and population risk prediction models can inform decisions on the type, location, and timing of public health interventions. We aimed to develop a prediction model to estimate county-level suicide risk in the USA using population characteristics. METHODS: We obtained data on all deaths by suicide reported to the National Vital Statistics System between Jan 1, 2005, and Dec 31, 2019, and age, sex, race, and county of residence of the decedents were extracted to calculate baseline risk. We also obtained county-level annual measures of socioeconomic predictors of suicide risk (unemployment, weekly wage, poverty prevalence, median household income, and population density) and state-level prevalence of major depressive disorder and firearm ownership from US public sources. We applied conditional autoregressive models, which account for spatiotemporal autocorrelation in response and predictors, to estimate county-level suicide risk. FINDINGS: Estimates derived from conditional autoregressive models were more accurate than from models not adjusted for spatiotemporal autocorrelation. Inclusion of suicide risk and protective covariates further reduced errors. Suicide risk was estimated to increase with each SD increase in firearm ownership (2·8% [95% credible interval (CrI) 1·8 to 3·9]), prevalence of major depressive episode (1·0% [0·4 to 1·5]), and unemployment rate (2·8% [1·9 to 3·8]). Conversely, risk was estimated to decrease by 4·3% (-5·1 to -3·2) for each SD increase in median household income and by 4·3% (-5·8 to -2·5) for each SD increase in population density. An increase in the heterogeneity in county-specific suicide risk was also observed during the study period. INTERPRETATION: Area-level characteristics and the conditional autoregressive models can estimate population-level suicide risk. Availability of near real-time situational data are necessary for the translation of these models into a surveillance setting. Monitoring changes in population-level risk of suicide could help public health agencies select and deploy targeted interventions quickly. FUNDING: US National Institute of Mental Health.


Assuntos
Transtorno Depressivo Maior , Armas de Fogo , Suicídio , Humanos , Estados Unidos , Pobreza , Fatores de Risco
9.
medRxiv ; 2023 Dec 11.
Artigo em Inglês | MEDLINE | ID: mdl-38168429

RESUMO

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. Forecasting teams were asked to provide national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one through four weeks ahead for the 2021-22 and 2022-23 influenza seasons. Across both seasons, 26 teams submitted forecasts, with the submitting teams varying between seasons. Forecast skill was evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperformed the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble was the 2nd most accurate model measured by WIS in 2021-22 and the 5th most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degraded over longer forecast horizons and during periods of rapid change. Current influenza forecasting efforts help inform situational awareness, but research is needed to address limitations, including decreased performance during periods of changing epidemic dynamics.

10.
Nat Commun ; 13(1): 6307, 2022 10 23.
Artigo em Inglês | MEDLINE | ID: mdl-36274183

RESUMO

Understanding SARS-CoV-2 transmission within and among communities is critical for tailoring public health policies to local context. However, analysis of community transmission is challenging due to a lack of high-resolution surveillance and testing data. Here, using contact tracing records for 644,029 cases and their contacts in New York City during the second pandemic wave, we provide a detailed characterization of the operational performance of contact tracing and reconstruct exposure and transmission networks at individual and ZIP code scales. We find considerable heterogeneity in reported close contacts and secondary infections and evidence of extensive transmission across ZIP code areas. Our analysis reveals the spatial pattern of SARS-CoV-2 spread and communities that are tightly interconnected by exposure and transmission. We find that locations with higher vaccination coverage and lower numbers of visitors to points-of-interest had reduced within- and cross-ZIP code transmission events, highlighting potential measures for curtailing SARS-CoV-2 spread in urban settings.


Assuntos
COVID-19 , Busca de Comunicante , Humanos , COVID-19/epidemiologia , SARS-CoV-2 , Cidade de Nova Iorque/epidemiologia , Pandemias/prevenção & controle
11.
Res Sq ; 2022 Jul 27.
Artigo em Inglês | MEDLINE | ID: mdl-35923312

RESUMO

Understanding SARS-CoV-2 transmission within and among communities is critical for tailoring public health policies to local context. However, analysis of community transmission is challenging due to a lack of high-resolution surveillance and testing data. Here, using contact tracing records for 644,029 cases and their contacts in New York City during the second pandemic wave, we provide a detailed characterization of the operational performance of contact tracing and reconstruct exposure and transmission networks at individual and ZIP code scales. We find considerable heterogeneity in reported close contacts and secondary infections and evidence of extensive transmission across ZIP code areas. Our analysis reveals the spatial pattern of SARS-CoV-2 spread and communities that are tightly interconnected by exposure and transmission. We find that higher vaccination coverage and reduced numbers of visitors to points-of-interest are associated with fewer within- and cross-ZIP code transmission events, highlighting potential measures for curtailing SARS-CoV-2 spread in urban settings.

12.
Am J Epidemiol ; 191(11): 1897-1905, 2022 10 20.
Artigo em Inglês | MEDLINE | ID: mdl-35916364

RESUMO

We aimed to determine whether long-term ambient concentrations of fine particulate matter (particulate matter with an aerodynamic diameter less than or equal to 2.5 µm (PM2.5)) were associated with increased risk of testing positive for coronavirus disease 2019 (COVID-19) among pregnant individuals who were universally screened at delivery and whether socioeconomic status (SES) modified this relationship. We used obstetrical data collected from New-York Presbyterian Hospital/Columbia University Irving Medical Center in New York, New York, between March and December 2020, including data on Medicaid use (a proxy for low SES) and COVID-19 test results. We linked estimated 2018-2019 PM2.5 concentrations (300-m resolution) with census-tract-level population density, household size, income, and mobility (as measured by mobile-device use) on the basis of residential address. Analyses included 3,318 individuals; 5% tested positive for COVID-19 at delivery, 8% tested positive during pregnancy, and 48% used Medicaid. Average long-term PM2.5 concentrations were 7.4 (standard deviation, 0.8) µg/m3. In adjusted multilevel logistic regression models, we saw no association between PM2.5 and ever testing positive for COVID-19; however, odds were elevated among those using Medicaid (per 1-µg/m3 increase, odds ratio = 1.6, 95% confidence interval: 1.0, 2.5). Further, while only 22% of those testing positive showed symptoms, 69% of symptomatic individuals used Medicaid. SES, including unmeasured occupational exposures or increased susceptibility to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) due to concurrent social and environmental exposures, may explain the increased odds of testing positive for COVID-19 being confined to vulnerable pregnant individuals using Medicaid.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , COVID-19 , Gravidez , Feminino , Humanos , Material Particulado/análise , SARS-CoV-2 , Poluição do Ar/efeitos adversos , Poluentes Atmosféricos/análise , Cidade de Nova Iorque/epidemiologia , Prevalência , Exposição Ambiental/efeitos adversos , Classe Social
13.
J Am Acad Child Adolesc Psychiatry ; 61(9): 1131-1140.e5, 2022 09.
Artigo em Inglês | MEDLINE | ID: mdl-35031449

RESUMO

OBJECTIVE: Deaths by suicide correlate both spatially and temporally, leading to suicide clusters. This study aimed to estimate racial patterns in suicide clusters since 2000. METHOD: Data from the US National Vital Statistics System included all International Classification of Diseases, Tenth Revision (ICD-10)-coded suicide cases from 2000-2019 among American Indian/Alaska Native (AI/AN), Asian/Pacific Islander (A/PI), Black, or White youth and young adults, aged 5-34 years. We estimated age, period, and cohort (APC) trends and identified spatiotemporal clusters using the SaTScan space-time statistic, which identified lower- and higher-than-expected suicide rates (cold and hot clusters) in a prespecified area (150 km) and time interval (15 months). We also calculated the average proportion of deaths by suicide contained in clusters, to quantify the relative importance of spatiotemporal patterning as a driver of overall suicide rates. RESULTS: From 2010-2019, suicide rates increased from between 37% among AI/AN (95% CI = 1.22, 1.55) to 81% among A/PI (95% CI = 1.65, 2.01) groups. Suicide clusters accounted for 0.8%-10.3% of all suicide deaths, across racial groups. Since 2000, the likelihood of detecting cluster increased over time, with considerable differences in the number of clusters in each racial group (4 among AI/AN to 72 among White youth). Among Black youth and young adults, 27 total clusters were identified. Hot clusters were concentrated in southeastern and mid-Atlantic counties. CONCLUSION: Suicide rates and clusters in youth and young adults have increased in the past 2 decades, requiring attention from policy makers, clinicians, and caretakers. Racially distinct patterns highlight opportunities to tailor individual- and population-level prevention efforts to prevent suicide deaths in emerging high-risk groups.


Assuntos
Suicídio , Adolescente , Criança , Humanos , Grupos Raciais , Estados Unidos/epidemiologia , Adulto Jovem
14.
Influenza Other Respir Viruses ; 16(1): 56-62, 2022 01.
Artigo em Inglês | MEDLINE | ID: mdl-34647421

RESUMO

BACKGROUND: The COVID-19 pandemic has overrun hospital systems while exacerbating economic hardship and food insecurity on a global scale. In an effort to understand how early action to find and control the virus is associated with cumulative outcomes, we explored how country-level testing capacity affects later COVID-19 mortality. METHODS: We used the Our World in Data database to explore testing and mortality records in 27 countries from December 31, 2019, to September 30, 2020; we applied Cox proportional hazards regression to determine the relationship between early COVID-19 testing capacity (cumulative tests per case) and later COVID-19 mortality (time to specified mortality thresholds), adjusting for country-level confounders, including median age, GDP, hospital bed capacity, population density, and nonpharmaceutical interventions. RESULTS: Higher early testing implementation, as indicated by more cumulative tests per case when mortality was still low, was associated with a lower risk for higher per capita deaths. A sample finding indicated that a higher cumulative number of tests administered per case at the time of six deaths per million persons was associated with a lower risk of reaching 15 deaths per million persons, after adjustment for all confounders (HR = 0.909; P = 0.0001). CONCLUSIONS: Countries that developed stronger COVID-19 testing capacity at early timepoints, as measured by tests administered per case identified, experienced a slower increase of deaths per capita. Thus, this study operationalizes the value of testing and provides empirical evidence that stronger testing capacity at early timepoints is associated with reduced mortality and improved pandemic control.


Assuntos
COVID-19 , Teste para COVID-19 , Humanos , Pandemias , Pobreza , SARS-CoV-2
16.
PLoS One ; 16(12): e0260931, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34936666

RESUMO

During the COVID-19 pandemic, US populations have experienced elevated rates of financial and psychological distress that could lead to increases in suicide rates. Rapid ongoing mental health monitoring is critical for early intervention, especially in regions most affected by the pandemic, yet traditional surveillance data are available only after long lags. Novel information on real-time population isolation and concerns stemming from the pandemic's social and economic impacts, via cellular mobility tracking and online search data, are potentially important interim surveillance resources. Using these measures, we employed transfer function model time-series analyses to estimate associations between daily mobility indicators (proportion of cellular devices completely at home and time spent at home) and Google Health Trends search volumes for terms pertaining to economic stress, mental health, and suicide during 2020 and 2021 both nationally and in New York City. During the first pandemic wave in early-spring 2020, over 50% of devices remained completely at home and searches for economic stressors exceeded 60,000 per 10 million. We found large concurrent associations across analyses between declining mobility and increasing searches for economic stressor terms (national proportion of devices at home: cross-correlation coefficient (CC) = 0.6 (p-value <0.001)). Nationally, we also found strong associations between declining mobility and increasing mental health and suicide-related searches (time at home: mood/anxiety CC = 0.53 (<0.001), social stressor CC = 0.51 (<0.001), suicide seeking CC = 0.37 (0.006)). Our findings suggest that pandemic-related isolation coincided with acute economic distress and may be a risk factor for poor mental health and suicidal behavior. These emergent relationships warrant ongoing attention and causal assessment given the potential for long-term psychological impact and suicide death. As US populations continue to face stress, Google search data can be used to identify possible warning signs from real-time changes in distributions of population thought patterns.


Assuntos
COVID-19/psicologia , Telefone Celular/estatística & dados numéricos , Ferramenta de Busca/estatística & dados numéricos , Fatores Socioeconômicos , Suicídio/psicologia , Sistemas de Informação Geográfica , Humanos , Saúde Mental/estatística & dados numéricos , Cidade de Nova Iorque , Quarentena/estatística & dados numéricos , Ferramenta de Busca/tendências , Estresse Psicológico , Fatores de Tempo , Estados Unidos
17.
PLoS Med ; 18(10): e1003793, 2021 10.
Artigo em Inglês | MEDLINE | ID: mdl-34665805

RESUMO

BACKGROUND: The importance of infectious disease epidemic forecasting and prediction research is underscored by decades of communicable disease outbreaks, including COVID-19. Unlike other fields of medical research, such as clinical trials and systematic reviews, no reporting guidelines exist for reporting epidemic forecasting and prediction research despite their utility. We therefore developed the EPIFORGE checklist, a guideline for standardized reporting of epidemic forecasting research. METHODS AND FINDINGS: We developed this checklist using a best-practice process for development of reporting guidelines, involving a Delphi process and broad consultation with an international panel of infectious disease modelers and model end users. The objectives of these guidelines are to improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting. The guidelines are not designed to advise scientists on how to perform epidemic forecasting and prediction research, but rather to serve as a standard for reporting critical methodological details of such studies. CONCLUSIONS: These guidelines have been submitted to the EQUATOR network, in addition to hosting by other dedicated webpages to facilitate feedback and journal endorsement.


Assuntos
Pesquisa Biomédica/normas , COVID-19/epidemiologia , Lista de Checagem/normas , Epidemias , Guias como Assunto/normas , Projetos de Pesquisa , Pesquisa Biomédica/métodos , Lista de Checagem/métodos , Doenças Transmissíveis/epidemiologia , Epidemias/estatística & dados numéricos , Previsões/métodos , Humanos , Reprodutibilidade dos Testes
18.
Am J Prev Med ; 61(6): 795-803, 2021 12.
Artigo em Inglês | MEDLINE | ID: mdl-34420829

RESUMO

INTRODUCTION: In the U.S., state-level household firearm ownership is strongly associated with firearm suicide mortality rates. Whether the recent increases in firearm suicide are explained by state-level household firearm ownership rates and trends remains unknown. METHODS: Mortality data from the U.S. National Vital Statistics System and an estimate of state-level household firearm ownership rate were used to conduct hierarchical age-period-cohort (random-effects) modeling of firearm suicide mortality between 2001 and 2016. Models were adjusted for individual-level race and sex and for state-level poverty rate, unemployment rate, median household income in U.S. dollars, population density, and elevation. RESULTS: Between 2001 and 2016, the crude national firearm suicide mortality rate increased from 6.8 to 8.0 per 100,000, and household firearm ownership rate remained relatively stable, at around 40%. Both variables were markedly heterogeneous and correlated at the state level. Age-period-cohort models revealed period effects (affecting people across ages) and cohort effects (affecting specific birth cohorts) underlying the recent increases in firearm suicide. Individuals born after 2000 had higher firearm suicide rates than most cohorts born before. A 2001-2006 decreasing period effect was followed, after 2009, by an increasing period effect that peaked in 2015. State-level household firearm ownership rates and trends did not explain cohort effects and only minimally explained period effects. CONCLUSIONS: State-level firearm ownership rates largely explain the state-level differences in firearm suicide but only marginally explain recent increases in firearm suicide. Although firearms in the home increase firearm suicide risk, the recent national rise in firearm suicide might be the result of broader, more distal causes of suicide risk.


Assuntos
Armas de Fogo , Suicídio , Humanos , Propriedade , Pobreza , Desemprego , Estados Unidos/epidemiologia
19.
Nature ; 598(7880): 338-341, 2021 10.
Artigo em Inglês | MEDLINE | ID: mdl-34438440

RESUMO

The COVID-19 pandemic disrupted health systems and economies throughout the world during 2020 and was particularly devastating for the United States, which experienced the highest numbers of reported cases and deaths during 20201-3. Many of the epidemiological features responsible for observed rates of morbidity and mortality have been reported4-8; however, the overall burden and characteristics of COVID-19 in the United States have not been comprehensively quantified. Here we use a data-driven model-inference approach to simulate the pandemic at county-scale in the United States during 2020 and estimate critical, time-varying epidemiological properties underpinning the dynamics of the virus. The pandemic in the United States during 2020 was characterized by national ascertainment rates that increased from 11.3% (95% credible interval (CI): 8.3-15.9%) in March to 24.5% (18.6-32.3%) during December. Population susceptibility at the end of the year was 69.0% (63.6-75.4%), indicating that about one third of the US population had been infected. Community infectious rates, the percentage of people harbouring a contagious infection, increased above 0.8% (0.6-1.0%) before the end of the year, and were as high as 2.4% in some major metropolitan areas. By contrast, the infection fatality rate fell to 0.3% by year's end.


Assuntos
COVID-19/epidemiologia , COVID-19/transmissão , SARS-CoV-2 , Número Básico de Reprodução , COVID-19/economia , COVID-19/mortalidade , Calibragem , Efeitos Psicossociais da Doença , Humanos , Incidência , Pandemias , Prevalência , Estados Unidos/epidemiologia
20.
PLoS Med ; 18(7): e1003693, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-34255766

RESUMO

BACKGROUND: With the availability of multiple Coronavirus Disease 2019 (COVID-19) vaccines and the predicted shortages in supply for the near future, it is necessary to allocate vaccines in a manner that minimizes severe outcomes, particularly deaths. To date, vaccination strategies in the United States have focused on individual characteristics such as age and occupation. Here, we assess the utility of population-level health and socioeconomic indicators as additional criteria for geographical allocation of vaccines. METHODS AND FINDINGS: County-level estimates of 14 indicators associated with COVID-19 mortality were extracted from public data sources. Effect estimates of the individual indicators were calculated with univariate models. Presence of spatial autocorrelation was established using Moran's I statistic. Spatial simultaneous autoregressive (SAR) models that account for spatial autocorrelation in response and predictors were used to assess (i) the proportion of variance in county-level COVID-19 mortality that can explained by identified health/socioeconomic indicators (R2); and (ii) effect estimates of each predictor. Adjusting for case rates, the selected indicators individually explain 24%-29% of the variability in mortality. Prevalence of chronic kidney disease and proportion of population residing in nursing homes have the highest R2. Mortality is estimated to increase by 43 per thousand residents (95% CI: 37-49; p < 0.001) with a 1% increase in the prevalence of chronic kidney disease and by 39 deaths per thousand (95% CI: 34-44; p < 0.001) with 1% increase in population living in nursing homes. SAR models using multiple health/socioeconomic indicators explain 43% of the variability in COVID-19 mortality in US counties, adjusting for case rates. R2 was found to be not sensitive to the choice of SAR model form. Study limitations include the use of mortality rates that are not age standardized, a spatial adjacency matrix that does not capture human flows among counties, and insufficient accounting for interaction among predictors. CONCLUSIONS: Significant spatial autocorrelation exists in COVID-19 mortality in the US, and population health/socioeconomic indicators account for a considerable variability in county-level mortality. In the context of vaccine rollout in the US and globally, national and subnational estimates of burden of disease could inform optimal geographical allocation of vaccines.


Assuntos
COVID-19/epidemiologia , COVID-19/mortalidade , Feminino , Humanos , Masculino , Fatores Socioeconômicos , Estados Unidos/epidemiologia
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