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2.
PLoS One ; 15(10): e0239569, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33002036

RESUMO

We provide results on the level of COVID-19 excess mortality in the Italian region of Lombardy and in the province of Bergamo using official and original data sources. Since February 2020 Lombardy and in particular the province of Bergamo have been severely hit by the novel COVID-19 infectious disease. Combining official statistics, retrospective data and original data (i.e., obituaries and death notices) we provide a tentative estimate of the number of deaths either directly or indirectly, associated with COVID-19 as well as the total number of persons infected. Our findings suggest that the reported number of deaths attributable to COVID-19 identified by public authorities accounts only for one half of the observed excess mortality between March 2020 and previous years.


Assuntos
Infecções por Coronavirus/mortalidade , Notificação de Doenças/estatística & dados numéricos , Mortalidade/tendências , Pneumonia Viral/mortalidade , Adolescente , Adulto , Idoso , Betacoronavirus , Feminino , Humanos , Itália/epidemiologia , Masculino , Pessoa de Meia-Idade , Pandemias , Adulto Jovem
3.
Medwave ; 20(8): e8031, 2020 Sep 25.
Artigo em Espanhol | MEDLINE | ID: mdl-33017383

RESUMO

Objective: To compare excess mortality by district quintiles according to the Human Development Index (HDI) in Metropolitan Lima, the capital of Peru, and analyze the socioeconomic factors associated with excess mortality within the context of COVID-19. Methods: Retrospective cross-sectional analysis of the mortality records from non-violent causes registered in the National Death Information System in the 50 districts of Metropolitan Lima of the first 24 weeks of the years 2019 and 2020. Descriptive analysis was performed using contingency tables and time series graphs by sex, age group, and quintile of the district of residence according to the HDI. Negative binomial regression analysis was performed to identify possible explanatory factors for excess mortality. Results: An excess of 20 093 non-violent deaths and 2,979 confirmed deaths from COVID-19 were registered in Metropolitan Lima during the study period. The increase was observed primarily in men and adults aged 60 and over. Residents in the districts belonging to the fifth quintile, according to HDI, presented, in most cases, the lowest rates. Multivariate analysis revealed that a higher HDI level (p = 0.009) and a higher proportion of inhabitants living in extreme poverty (p = 0.014) decreased the excess mortality. Conclusion: Excess of non-violent deaths in Metropolitan Lima is higher in the quintiles with the lowest HDI, in men, and the age group from 60 to more years of age. The study of social and economic health determinants in Peru is crucial for the design of measures to be taken by the government against the COVID-19 pandemic.


Assuntos
Causas de Morte , Infecções por Coronavirus/epidemiologia , Mortalidade/tendências , Pneumonia Viral/epidemiologia , Adolescente , Adulto , Distribuição por Idade , Criança , Pré-Escolar , Infecções por Coronavirus/mortalidade , Estudos Transversais , Feminino , Humanos , Lactente , Recém-Nascido , Masculino , Pessoa de Meia-Idade , Pandemias , Peru/epidemiologia , Pneumonia Viral/mortalidade , Pobreza , Estudos Retrospectivos , Distribuição por Sexo , Fatores Socioeconômicos , Adulto Jovem
5.
Medicine (Baltimore) ; 99(38): e22155, 2020 Sep 18.
Artigo em Inglês | MEDLINE | ID: mdl-32957338

RESUMO

To investigate the risk of mortality associated with exposure to codeine, considering various risk groups, using population-based national insurance claims data.National sample cohort data from the National Health Insurance Service of South Korea (2002-2013) was used in this case-control study. Cases were defined as patients with a death record between January 1, 2002 and December 31, 2013. Each case was matched to 10 controls based on age, sex, baseline comorbidities, and year of death. Definition of exposure was codeine prescription in 30 days prior to death and sensitivity analyses were performed for 15 and 60-day exposures. Adjusted odds ratios (aORs) with 95% confidence intervals (CIs) were estimated using conditional logistic regression adjusting for benzodiazepine, other opioids, anesthetics, hypnotics, CYP2D6 inducer, CYP3A4 inducer, and the Charlson comorbidity index.A total of 19,341 cases and 185,700 matched controls were included. The overall risk associated with codeine use and mortality risk was not significant (aOR 1.08, 95% CI 1.00-1.16). Sensitivity analyses with different exposure time window also presented similar insignificant results. However, in the subgroup analyses, codeine use was associated with an increased risk of mortality in the >85-year-old age group (aOR 2.38, 95% CI 1.26-4.48) and patients with respiratory disease (aOR 1.29, 95% CI 1.17-1.42).Although no statistically significant association was found in codeine exposure and mortality risk between cases and controls, we demonstrated that the elderly over 85 years old and patients with respiratory disease are associated with a higher risk with codeine exposure. Therefore, a more cautious practice of codeine prescription in these groups might be needed.


Assuntos
Codeína/efeitos adversos , Mortalidade/tendências , Doenças Respiratórias/complicações , Idoso de 80 Anos ou mais , Estudos de Casos e Controles , Feminino , Humanos , Masculino , República da Coreia/epidemiologia , Doenças Respiratórias/epidemiologia , Estudos Retrospectivos , Risco
6.
Lancet ; 396(10255): 918-934, 2020 09 26.
Artigo em Inglês | MEDLINE | ID: mdl-32891217

RESUMO

The Sustainable Development Goal (SDG) target 3.4 is to reduce premature mortality from non-communicable diseases (NCDs) by a third by 2030 relative to 2015 levels, and to promote mental health and wellbeing. We used data on cause-specific mortality to characterise the risk and trends in NCD mortality in each country and evaluate combinations of reductions in NCD causes of death that can achieve SDG target 3.4. Among NCDs, ischaemic heart disease is responsible for the highest risk of premature death in more than half of all countries for women, and more than three-quarters for men. However, stroke, other cardiovascular diseases, and some cancers are associated with a similar risk, and in many countries, a higher risk of premature death than ischaemic heart disease. Although premature mortality from NCDs is declining in most countries, for most the pace of change is too slow to achieve SDG target 3.4. To investigate the options available to each country for achieving SDG target 3.4, we considered different scenarios, each representing a combination of fast (annual rate achieved by the tenth best performing percentile of all countries) and average (median of all countries) declines in risk of premature death from NCDs. Pathways analysis shows that every country has options for achieving SDG target 3.4. No country could achieve the target by addressing a single disease. In at least half the countries, achieving the target requires improvements in the rate of decline in at least five causes for women and in at least seven causes for men to the same rate achieved by the tenth best performing percentile of all countries. Tobacco and alcohol control and effective health-system interventions-including hypertension and diabetes treatment; primary and secondary cardiovascular disease prevention in high-risk individuals; low-dose inhaled corticosteroids and bronchodilators for asthma and chronic obstructive pulmonary disease; treatment of acute cardiovascular diseases, diabetes complications, and exacerbations of asthma and chronic obstructive pulmonary disease; and effective cancer screening and treatment-will reduce NCD causes of death necessary to achieve SDG target 3.4 in most countries.


Assuntos
Mortalidade Prematura/tendências , Doenças não Transmissíveis/mortalidade , Desenvolvimento Sustentável , Adulto , Idoso , Doenças Cardiovasculares/mortalidade , Causas de Morte , Doença Crônica , Diabetes Mellitus/mortalidade , Feminino , Humanos , Masculino , Saúde Mental , Pessoa de Meia-Idade , Mortalidade/tendências , Isquemia Miocárdica/mortalidade , Neoplasias/mortalidade , Prevenção Primária , Doenças Respiratórias/mortalidade , Prevenção Secundária , Acidente Vascular Cerebral/mortalidade
8.
PLoS One ; 15(9): e0239175, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32941485

RESUMO

The COVID-19 outbreak has forced most of the global population to lock-down and has put in check the health services all over the world. Current predictive models are complex, region-dependent, and might not be generalized to other countries. However, a 150-year old epidemics law promulgated by William Farr might be useful as a simple arithmetical model (percent increase [R1] and acceleration [R2] of new cases and deaths) to provide a first sight of the epidemic behavior and to detect regions with high predicted dynamics. Thus, this study tested Farr's Law assumptions by modeling COVID-19 data of new cases and deaths. COVID-19 data until April 10, 2020, was extracted from available countries, including income, urban index, and population characteristics. Farr's law first (R1) and second ratio (R2) were calculated. We constructed epidemic curves and predictive models for the available countries and performed ecological correlation analysis between R1 and R2 with demographic data. We extracted data from 210 countries, and it was possible to estimate the ratios of 170 of them. Around 42·94% of the countries were in an initial acceleration phase, while 23·5% already crossed the peak. We predicted a reduction close to zero with wide confidence intervals for 56 countries until June 10 (high-income countries from Asia and Oceania, with strict political actions). There was a significant association between high R1 of deaths and high urban index. Farr's law seems to be a useful model to give an overview of COVID-19 pandemic dynamics. The countries with high dynamics are from Africa and Latin America. Thus, this is a call to urgently prioritize actions in those countries to intensify surveillance, to re-allocate resources, and to build healthcare capacities based on multi-nation collaboration to limit onward transmission and to reduce the future impact on these regions in an eventual second wave.


Assuntos
Betacoronavirus , Infecções por Coronavirus/prevenção & controle , Modelos Biológicos , Pandemias/legislação & jurisprudência , Pneumonia Viral/prevenção & controle , África/epidemiologia , Ásia/epidemiologia , Infecções por Coronavirus/epidemiologia , Previsões , Geografia Médica , Humanos , Incidência , América Latina/epidemiologia , Morbidade/tendências , Mortalidade/tendências , Pandemias/prevenção & controle , Pneumonia Viral/epidemiologia , Dinâmica Populacional , Saúde da População Urbana
9.
J Psychiatr Pract ; 26(5): 394-399, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32865940

RESUMO

This article explains how the mortality rate of an illness such as Coronavirus Disease 2019 (COVID-19) is calculated as well as how the definition of what is a "case" has changed from the earliest days of the pandemic to now. Many factors were not known about The Sudden Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) which causes COVID-19 at the beginning of the pandemic because it is a novel human pathogen. One key factor that was not known in the earliest days of the pandemic was that many patients are either asymptomatic or have symptoms so mild that they may not seek medical attention and hence these patients would not be identified as a "case" if that term is defined as being sufficiently symptomatic to be seeking medical attention. Cases in the earliest days of the pandemic were defined as based on having symptoms (eg, fever, cough, respiratory distress) after ruling out other possible causes. Cases now are defined by tests confirming that the person is shedding the SARS-CoV-2 (ie, a laboratory vs. a symptomatic diagnosis). The mortality rate of this virus dropped as a function of this change. On the basis of the results of an unintended, naturalistic experiment on an expeditionary cruise in March of 2020, there was more than a 5-fold drop in the calculated mortality rate due to this definitional change in what constituted a case. This column explains this issue and discusses its implications for effectively dealing with the SARS-CoV-2 (or COVID-19) pandemic.


Assuntos
Controle de Doenças Transmissíveis , Infecções por Coronavirus , Transmissão de Doença Infecciosa/prevenção & controle , Máscaras , Mortalidade/tendências , Pandemias , Pneumonia Viral , Distância Social , Betacoronavirus , Técnicas de Laboratório Clínico/estatística & dados numéricos , Controle de Doenças Transmissíveis/instrumentação , Controle de Doenças Transmissíveis/métodos , Controle de Doenças Transmissíveis/organização & administração , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/tratamento farmacológico , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/terapia , Infecções por Coronavirus/transmissão , Humanos , Pandemias/prevenção & controle , Pneumonia Viral/epidemiologia , Pneumonia Viral/prevenção & controle , Pneumonia Viral/terapia , Pneumonia Viral/transmissão , Medição de Risco , Fatores de Risco , Índice de Gravidade de Doença
10.
N Engl J Med ; 383(7): 640-649, 2020 08 13.
Artigo em Inglês | MEDLINE | ID: mdl-32786189

RESUMO

BACKGROUND: Lung cancer is made up of distinct subtypes, including non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC). Although overall mortality from lung cancer has been declining in the United States, little is known about mortality trends according to cancer subtype at the population level because death certificates do not record subtype information. METHODS: Using data from Surveillance, Epidemiology, and End Results (SEER) areas, we assessed lung-cancer mortality and linked deaths from lung cancer to incident cases in SEER cancer registries. This allowed us to evaluate population-level mortality trends attributed to specific subtypes (incidence-based mortality). We also evaluated lung-cancer incidence and survival according to cancer subtype, sex, and calendar year. Joinpoint software was used to assess changes in incidence and trends in incidence-based mortality. RESULTS: Mortality from NSCLC decreased even faster than the incidence of this subtype, and this decrease was associated with a substantial improvement in survival over time that corresponded to the timing of approval of targeted therapy. Among men, incidence-based mortality from NSCLC decreased 6.3% annually from 2013 through 2016, whereas the incidence decreased 3.1% annually from 2008 through 2016. Corresponding lung cancer-specific survival improved from 26% among men with NSCLC that was diagnosed in 2001 to 35% among those in whom it was diagnosed in 2014. This improvement in survival was found across all races and ethnic groups. Similar patterns were found among women with NSCLC. In contrast, mortality from SCLC declined almost entirely as a result of declining incidence, with no improvement in survival. This result correlates with limited treatment advances for SCLC in the time frame we examined. CONCLUSIONS: Population-level mortality from NSCLC in the United States fell sharply from 2013 to 2016, and survival after diagnosis improved substantially. Our analysis suggests that a reduction in incidence along with treatment advances - particularly approvals for and use of targeted therapies - is likely to explain the reduction in mortality observed during this period.


Assuntos
Carcinoma Pulmonar de Células não Pequenas/mortalidade , Neoplasias Pulmonares/mortalidade , Carcinoma Pulmonar de Células não Pequenas/epidemiologia , Feminino , Humanos , Incidência , Neoplasias Pulmonares/epidemiologia , Masculino , Mortalidade/tendências , Programa de SEER , Fatores Sexuais , Estados Unidos/epidemiologia
11.
BMC Public Health ; 20(1): 1238, 2020 Aug 14.
Artigo em Inglês | MEDLINE | ID: mdl-32795276

RESUMO

BACKGROUND: Standardized mortality surveillance data, capable of detecting variations in total mortality at population level and not only among the infected, provide an unbiased insight into the impact of epidemics, like COVID-19 (Coronavirus disease). We analysed the temporal trend in total excess mortality and deaths among positive cases of SARS-CoV-2 by geographical area (north and centre-south), age and sex, taking into account the deficit in mortality in previous months. METHODS: Data from the Italian rapid mortality surveillance system was used to quantify excess deaths during the epidemic, to estimate the mortality deficit during the previous months and to compare total excess mortality with deaths among positive cases of SARS-CoV-2. Data were stratified by geographical area (north vs centre and south), age and sex. RESULTS: COVID-19 had a greater impact in northern Italian cities among subjects aged 75-84 and 85+ years. COVID-19 deaths accounted for half of total excess mortality in both areas, with differences by age: almost all excess deaths were from COVID-19 among adults, while among the elderly only one third of the excess was coded as COVID-19. When taking into account the mortality deficit in the pre-pandemic period, different trends were observed by area: all excess mortality during COVID-19 was explained by deficit mortality in the centre and south, while only a 16% overlap was estimated in northern cities, with quotas decreasing by age, from 67% in the 15-64 years old to 1% only among subjects 85+ years old. CONCLUSIONS: An underestimation of COVID-19 deaths is particularly evident among the elderly. When quantifying the burden in mortality related to COVID-19, it is important to consider seasonal dynamics in mortality. Surveillance data provides an impartial indicator for monitoring the following phases of the epidemic, and may help in the evaluation of mitigation measures adopted.


Assuntos
Infecções por Coronavirus/mortalidade , Mortalidade/tendências , Pneumonia Viral/mortalidade , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Cidades/epidemiologia , Feminino , Humanos , Itália/epidemiologia , Masculino , Pessoa de Meia-Idade , Pandemias , Análise Espaço-Temporal , Adulto Jovem
12.
BMJ ; 370: m2533, 2020 08 19.
Artigo em Inglês | MEDLINE | ID: mdl-32816755

RESUMO

OBJECTIVES: To examine the long term mortality associated with preterm delivery in a large population based cohort of women, and to assess for potential confounding by shared familial factors. DESIGN: National cohort study. SETTING: Sweden. PARTICIPANTS: All 2 189 477 women with a singleton delivery in 1973-2015. MAIN OUTCOME MEASURES: All cause and cause specific mortality up to 2016, identified from nationwide death records. Cox regression was used to calculate hazard ratios while adjusting for confounders, and co-sibling analyses assessed the potential influence of unmeasured shared familial (genetic and environmental) factors. RESULTS: In 50.7 million person years of follow-up, 76 535 (3.5%) women died (median age at death was 57.6). In the 10 years after delivery, the adjusted hazard ratio for all cause mortality associated with preterm delivery (<37 weeks) was 1.73 (95% confidence interval 1.61 to 1.87), and when further stratified was 2.20 (1.63 to 2.96) for extremely preterm delivery (22-27 weeks), 2.28 (2.01 to 2.58) for very preterm delivery (28-33 weeks), 1.52 (1.39 to 1.67) for late preterm delivery (34-36 weeks), and 1.19 (1.12 to 1.27) for early term delivery (37-38 weeks) compared with full term delivery (39-41 weeks). These risks declined but remained significantly raised after longer follow-up times: for preterm versusfull term births, 10-19 years after delivery, the adjusted hazard ratio was 1.45 (95% confidence interval 1.37 to 1.53); 20-44 years after delivery, the adjusted hazard ratio was 1.37 (1.33 to 1.41). These findings did not seem to be attributable to shared genetic or environmental factors within families. Several causes were identified, including cardiovascular and respiratory disorders, diabetes, and cancer. CONCLUSIONS: In this large national cohort of women, the findings suggested that preterm and early term delivery were independent risk factors for premature mortality from several major causes. These associations declined over time but remained raised up to 40 years later.


Assuntos
Causas de Morte , Mortalidade/tendências , Mães/estatística & dados numéricos , Nascimento Prematuro/epidemiologia , Irmãos , Feminino , Seguimentos , Humanos , Pessoa de Meia-Idade , Gravidez , Prevalência , Sistema de Registros , Fatores de Risco , Suécia/epidemiologia
13.
MMWR Morb Mortal Wkly Rep ; 69(34): 1173-1176, 2020 Aug 28.
Artigo em Inglês | MEDLINE | ID: mdl-32853188

RESUMO

State and local health departments in the United States are using various indicators to identify differences in rates of reported coronavirus disease 2019 (COVID-19) and severe COVID-19 outcomes, including hospitalizations and deaths. To inform mitigation efforts, on May 19, 2020, the Kentucky Department for Public Health (KDPH) implemented a reporting system to monitor five indicators of state-level COVID-19 status to assess the ability to safely reopen: 1) composite syndromic surveillance data, 2) the number of new COVID-19 cases,* 3) the number of COVID-19-associated deaths,† 4) health care capacity data, and 5) public health capacity for contact tracing (contact tracing capacity). Using standardized methods, KDPH compiles an indicator monitoring report (IMR) to provide daily analysis of these five indicators, which are combined with publicly available data into a user-friendly composite status that KDPH and local policy makers use to assess state-level COVID-19 hazard status. During May 19-July 15, 2020, Kentucky reported 12,742 COVID-19 cases, and 299 COVID-19-related deaths (1). The mean composite state-level hazard status during May 19-July 15 was 2.5 (fair to moderate). IMR review led to county-level hotspot identification (identification of counties meeting criteria for temporal increases in number of cases and incidence) and facilitated collaboration among KDPH and local authorities on decisions regarding mitigation efforts. Kentucky's IMR might easily be adopted by state and local health departments in other jurisdictions to guide decision-making for COVID-19 mitigation, response, and reopening.


Assuntos
Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/prevenção & controle , Monitoramento Epidemiológico , Pandemias/prevenção & controle , Pneumonia Viral/epidemiologia , Pneumonia Viral/prevenção & controle , Infecções por Coronavirus/mortalidade , Infecções por Coronavirus/terapia , Hospitalização/estatística & dados numéricos , Humanos , Kentucky/epidemiologia , Mortalidade/tendências , Pneumonia Viral/mortalidade , Pneumonia Viral/terapia , Prática de Saúde Pública
14.
BMJ ; 370: m2688, 2020 08 13.
Artigo em Inglês | MEDLINE | ID: mdl-32816805

RESUMO

OBJECTIVE: To describe trends in the burden of mortality due to subtypes of heart disease from 1999 to 2018 to inform targeted prevention strategies and reduce disparities. DESIGN: Serial cross sectional analysis of cause specific heart disease mortality rates using national death certificate data in the overall population as well as stratified by race-sex, age, and geography. SETTING: United States, 1999-2018. PARTICIPANTS: 12.9 million decedents from total heart disease (49% women, 12% black, and 19% <65 years old). MAIN OUTCOME MEASURES: Age adjusted mortality rates (AAMR) and years of potential life lost (YPLL) for each heart disease subtype, and respective mean annual percentage change. RESULTS: Deaths from total heart disease fell from 752 192 to 596 577 between 1999 and 2011, and then increased to 655 381 in 2018. From 1999 to 2018, the proportion of total deaths from heart disease attributed to ischemic heart disease decreased from 73% to 56%, while the proportion attributed to heart failure increased from 8% to 13% and the proportion attributed to hypertensive heart disease increased from 4% to 9%. Among heart disease subtypes, AAMR was consistently highest for ischemic heart disease in all subgroups (race-sex, age, and region). After 2011, AAMR for heart failure and hypertensive heart disease increased at a faster rate than for other subtypes. The fastest increases in heart failure mortality were in black men (mean annual percentage change 4.9%, 95% confidence interval 4.0% to 5.8%), whereas the fastest increases in hypertensive heart disease occurred in white men (6.3%, 4.9% to 9.4%). The burden of years of potential life lost was greatest from ischemic heart disease, but black-white disparities were driven by heart failure and hypertensive heart disease. Deaths from heart disease in 2018 resulted in approximately 3.8 million potential years of life lost. CONCLUSIONS: Trends in AAMR and years of potential life lost for ischemic heart disease have decelerated since 2011. For almost all other subtypes of heart disease, AAMR and years of potential life lost became stagnant or increased. Heart failure and hypertensive heart disease account for the greatest increases in premature deaths and the largest black-white disparities and have offset declines in ischemic heart disease. Early and targeted primary and secondary prevention and control of risk factors for heart disease, with a focus on groups at high risk, are needed to avoid these suboptimal trends beginning earlier in life.


Assuntos
Cardiopatias/mortalidade , Mortalidade/tendências , Adulto , Idoso , Idoso de 80 Anos ou mais , Cardiopatias/classificação , Cardiopatias/etnologia , Humanos , Masculino , Pessoa de Meia-Idade , Estados Unidos/epidemiologia , Estatísticas Vitais
16.
Eur J Epidemiol ; 35(8): 733-742, 2020 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-32780189

RESUMO

Forecasting models have been influential in shaping decision-making in the COVID-19 pandemic. However, there is concern that their predictions may have been misleading. Here, we dissect the predictions made by four models for the daily COVID-19 death counts between March 25 and June 5 in New York state, as well as the predictions of ICU bed utilisation made by the influential IHME model. We evaluated the accuracy of the point estimates and the accuracy of the uncertainty estimates of the model predictions. First, we compared the "ground truth" data sources on daily deaths against which these models were trained. Three different data sources were used by these models, and these had substantial differences in recorded daily death counts. Two additional data sources that we examined also provided different death counts per day. For accuracy of prediction, all models fared very poorly. Only 10.2% of the predictions fell within 10% of their training ground truth, irrespective of distance into the future. For accurate assessment of uncertainty, only one model matched relatively well the nominal 95% coverage, but that model did not start predictions until April 16, thus had no impact on early, major decisions. For ICU bed utilisation, the IHME model was highly inaccurate; the point estimates only started to match ground truth after the pandemic wave had started to wane. We conclude that trustworthy models require trustworthy input data to be trained upon. Moreover, models need to be subjected to prespecified real time performance tests, before their results are provided to policy makers and public health officials.


Assuntos
Infecções por Coronavirus/mortalidade , Previsões/métodos , Unidades de Terapia Intensiva/estatística & dados numéricos , Pandemias/prevenção & controle , Pneumonia Viral/mortalidade , Ocupação de Leitos , Betacoronavirus , Humanos , Unidades de Terapia Intensiva/provisão & distribução , Modelos Estatísticos , Mortalidade/tendências , New York/epidemiologia , Saúde Pública
17.
Med Care ; 58(9): 785-792, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32732787

RESUMO

BACKGROUND: Telephone call programs are a common intervention used to improve patients' transition to outpatient care after hospital discharge. OBJECTIVE: To examine the impact of a follow-up telephone call program as a readmission reduction initiative. RESEARCH DESIGN: Pragmatic randomized controlled real-world effectiveness trial. SUBJECTS: We enrolled and randomized all patients discharged home from a hospital general medicine service to a follow-up telephone call program or usual care discharge. Patients discharged against medical advice were excluded. The intervention was a hospital program, delivering a semistructured follow-up telephone call from a nurse within 3-7 days of discharge, designed to assess understanding and provide education, and assistance to support discharge plan implementation. MEASURES: Our primary endpoint was hospital inpatient readmission within 30 days identified by the electronic health record. Secondary endpoints included observation readmission, emergency department revisit, and mortality within 30 days, and patient experience ratings. RESULTS: All 3054 patients discharged home were enrolled and randomized to the telephone call program (n=1534) or usual care discharge (n=1520). Using a prespecified intention-to-treat analysis, we found no evidence supporting differences in 30-day inpatient readmissions [14.9% vs. 15.3%; difference -0.4 (95% confidence interval, 95% CI), -2.9 to 2.1; P=0.76], observation readmissions [3.8% vs. 3.6%; difference 0.2 (95% CI, -1.1 to 1.6); P=0.74], emergency department revisits [6.1% vs. 5.4%; difference 0.7 (95% CI, -1.0 to 2.3); P=0.43], or mortality [4.4% vs. 4.9%; difference -0.5 (95% CI, -2.0 to 1.0); P=0.51] between telephone call and usual care groups. CONCLUSIONS: We found no evidence of an impact on 30-day readmissions or mortality due to the postdischarge telephone call program.


Assuntos
Continuidade da Assistência ao Paciente/organização & administração , Readmissão do Paciente/estatística & dados numéricos , Telefone/estatística & dados numéricos , Idoso , Idoso de 80 Anos ou mais , Serviço Hospitalar de Emergência/estatística & dados numéricos , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Mortalidade/tendências , Recursos Humanos de Enfermagem no Hospital/organização & administração , Satisfação do Paciente , Avaliação de Programas e Projetos de Saúde , Inquéritos e Questionários , Fatores de Tempo
18.
SEMERGEN, Soc. Esp. Med. Rural Gen. (Ed. Impr.) ; 46(supl.1): 12-19, ago. 2020. tab, graf
Artigo em Espanhol | IBECS | ID: ibc-192612

RESUMO

INTRODUCCIÓN: La pandemia por el virus SARS-CoV-2 ha supuesto un auténtico reto para los sistemas sanitarios. En España, la distribución heterogénea del virus y las diferentes estrategias sanitarias han condicionado la morbilidad y la letalidad. El objetivo de este estudio fue analizar la letalidad de la infección por sexo y rangos de edad en las comunidades autónomas (CC.AA.) de España. MATERIAL Y MÉTODOS: Para realizar el análisis, los datos se extrajeron del Ministerio de Sanidad, Consejerías y Departamentos de Salud Pública de las diferentes CC.AA. Se estimó la población infectada a partir de los resultados del ENE-COVID19 y de la población censada a 1 de enero de 2020 (INE) para una validez del test de anticuerpos IgG con 80% de sensibilidad y 100% de especificidad. La tasa de letalidad (TL) (fallecidos/1.000 infectados estimados) por sexo y edad (< 20 años, 20-64 y ≥ 65 años) se calculó para cada CC.AA. Se calculó la razón estandarizada de letalidad (REL) por el método exacto (EPIDAT). RESULTADOS: La prevalencia estimada de infección por SARS-CoV-2 en España fue del 6% (rango, 1,4% [Ceuta] - 14,1% [Comunidad de Madrid]). La TL para el conjunto de España fue del 9,6/1.000, oscilando entre el 1/1.000 en Melilla y el 26,6/1.000 en La Rioja, sin que se encontrara correlación entre letalidad y prevalencia de la infección. La TL fue mayor en hombres (10,2/1.000, razón 1,17 respecto de mujeres), excepto en Cataluña (razón 0,92), y especialmente elevada en los mayores de 64 años en La Rioja (143,5/1.000), Asturias (69,2/1.000) y País Vasco (46,6/1.000). Se encontró un exceso de letalidad (REL) global superior al 30% en La Rioja (2,91; IC 95%: 2,36-3,57), Asturias (1,51; IC 95%: 1,27-1,80), País Vasco (1,42; IC 95%: 1,31-1,54) y Extremadura (1,37; IC 95%: 1,20-1,57) y en los mayores de 64 años en Madrid y Canarias. CONCLUSIONES: La infección por el virus SARS-CoV-2 se ha distribuido de forma muy irregular en las diferentes CCAA, existiendo una gran diferencia en la tasa de letalidad entre comunidades, siendo especialmente elevada en La Rioja, Asturias y País Vasco. Es relevante el exceso de letalidad respecto a la media nacional en la población mayor de 64 años en las CC.AA. de Madrid y Canarias


INTRODUCTION: The SARS-CoV-2 pandemic has posed a real challenge to health systems. In Spain, the heterogeneous distribution of the virus infection and the different health strategies have conditioned the morbidity and fatality rate. The aim of this study was to analyse the lethality of the infection by sex and age range in the Autonomous Communities (AC) of Spain. MATERIAL AND METHODS: To perform the analysis, data were extracted from the Ministry of Health, Regional and Public Health Departments of the different AC. The infected population was estimated from the results of the ENE-COVID19 and the population registered on 1 January 2020 (INE) for the validity of the IgG antibody test with 80% sensitivity and 100% specificity. The case fatality rate (TL) (deaths/1000 estimated infected) by sex and age (< 20 years, 20-64 and ≥ 65 years) was calculated for each AC. The standardized case fatality ratio (REL) was calculated by the exact method (EPIDAT). RESULTS: The estimated prevalence of SARS-CoV-2 infection in Spain was 6% (range, 1.4% [Ceuta] - 14.1% [Community of Madrid]). The TL in Spain was 9,6/1000, ranged per AC from 1/1000 in Melilla to 26.6/1000 in La Rioja, with no correlation between case fatality and prevalence of infection. The TL was higher in men (10.2/1000, ratio 1.17 with respect to women), except in Cataluña (ratio 0.92), and especially high in those over 64 years of age in La Rioja (143.5/1000), Asturias (69.2/1000) and Basque Country (46.6/1000). Overall excess REL was found to be over 30% in La Rioja (2.91; 95% CI: 2.36-3.57), Asturias (1.51; 95% CI: 1.27-1.80), Basque Country (1.42; 95% CI: 1.31-1.54) and Extremadura (1.37; 95% CI: 1.20-1.57) and in those over 64 years in Madrid and the Canary Islands. CONCLUSIONS: SARs-CoV-2 virus infection has been very unevenly distributed in the different ACs, with notably differences in TL between ACs, particularly high in La Rioja, Asturias and the Basque Country. Is important to study the excess in TL the population over 64 years of age in the ACs of Madrid and the Canary Islands


Assuntos
Humanos , Infecções por Coronavirus/mortalidade , Mortalidade/tendências , Síndrome Respiratória Aguda Grave/mortalidade , Vírus da SARS/patogenicidade , Distribuição por Idade e Sexo , Causas de Morte/tendências , Sensibilidade e Especificidade , Imunoglobulina G/análise , Espanha/epidemiologia , Epidemiologia Descritiva
19.
Lancet Diabetes Endocrinol ; 8(10): 823-833, 2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-32798471

RESUMO

BACKGROUND: Diabetes has been associated with increased COVID-19-related mortality, but the association between modifiable risk factors, including hyperglycaemia and obesity, and COVID-19-related mortality among people with diabetes is unclear. We assessed associations between risk factors and COVID-19-related mortality in people with type 1 and type 2 diabetes. METHODS: We did a population-based cohort study of people with diagnosed diabetes who were registered with a general practice in England. National population data on people with type 1 and type 2 diabetes collated by the National Diabetes Audit were linked to mortality records collated by the Office for National Statistics from Jan 2, 2017, to May 11, 2020. We identified the weekly number of deaths in people with type 1 and type 2 diabetes during the first 19 weeks of 2020 and calculated the percentage change from the mean number of deaths for the corresponding weeks in 2017, 2018, and 2019. The associations between risk factors (including sex, age, ethnicity, socioeconomic deprivation, HbA1c, renal impairment [from estimated glomerular filtration rate (eGFR)], BMI, tobacco smoking status, and cardiovascular comorbidities) and COVID-19-related mortality (defined as International Classification of Diseases, version 10, code U07.1 or U07.2 as a primary or secondary cause of death) between Feb 16 and May 11, 2020, were investigated by use of Cox proportional hazards models. FINDINGS: Weekly death registrations in the first 19 weeks of 2020 exceeded the corresponding 3-year weekly averages for 2017-19 by 672 (50·9%) in people with type 1 diabetes and 16 071 (64·3%) in people with type 2 diabetes. Between Feb 16 and May 11, 2020, among 264 390 people with type 1 diabetes and 2 874 020 people with type 2 diabetes, 1604 people with type 1 diabetes and 36 291 people with type 2 diabetes died from all causes. Of these total deaths, 464 in people with type 1 diabetes and 10 525 in people with type 2 diabetes were defined as COVID-19 related, of which 289 (62·3%) and 5833 (55·4%), respectively, occurred in people with a history of cardiovascular disease or with renal impairment (eGFR <60 mL/min per 1·73 m2). Male sex, older age, renal impairment, non-white ethnicity, socioeconomic deprivation, and previous stroke and heart failure were associated with increased COVID-19-related mortality in both type 1 and type 2 diabetes. Compared with people with an HbA1c of 48-53 mmol/mol (6·5-7·0%), people with an HbA1c of 86 mmol/mol (10·0%) or higher had increased COVID-19-related mortality (hazard ratio [HR] 2·23 [95% CI 1·50-3·30, p<0·0001] in type 1 diabetes and 1·61 [1·47-1·77, p<0·0001] in type 2 diabetes). In addition, in people with type 2 diabetes, COVID-19-related mortality was significantly higher in those with an HbA1c of 59 mmol/mol (7·6%) or higher than in those with an HbA1c of 48-53 mmol/mol (HR 1·22 [95% CI 1·15-1·30, p<0·0001] for 59-74 mmol/mol [7·6-8·9%] and 1·36 [1·24-1·50, p<0·0001] for 75-85 mmol/mol [9·0-9·9%]). The association between BMI and COVID-19-related mortality was U-shaped: in type 1 diabetes, compared with a BMI of 25·0-29·9 kg/m2, a BMI of less than 20·0 kg/m2 had an HR of 2·45 (95% CI 1·60-3·75, p<0·0001) and a BMI of 40·0 kg/m2 or higher had an HR of 2·33 (1·53-3·56, p<0·0001); the corresponding HRs for type 2 diabetes were 2·33 (2·11-2·56, p<0·0001) and 1·60 (1·47-1·75, p<0·0001). INTERPRETATION: Deaths in people with type 1 and type 2 diabetes rose sharply during the initial COVID-19 pandemic in England. Increased COVID-19-related mortality was associated not only with cardiovascular and renal complications of diabetes but, independently, also with glycaemic control and BMI. FUNDING: None.


Assuntos
Betacoronavirus , Infecções por Coronavirus/mortalidade , Diabetes Mellitus Tipo 1/mortalidade , Diabetes Mellitus Tipo 2/mortalidade , Pneumonia Viral/mortalidade , Vigilância da População , Adulto , Idoso , Idoso de 80 Anos ou mais , Estudos de Coortes , Infecções por Coronavirus/diagnóstico , Bases de Dados Factuais/tendências , Diabetes Mellitus Tipo 1/diagnóstico , Diabetes Mellitus Tipo 2/diagnóstico , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Mortalidade/tendências , Programas Nacionais de Saúde/tendências , Pandemias , Pneumonia Viral/diagnóstico , Vigilância da População/métodos , Fatores de Risco , Adulto Jovem
20.
Lancet Diabetes Endocrinol ; 8(10): 813-822, 2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-32798472

RESUMO

BACKGROUND: Although diabetes has been associated with COVID-19-related mortality, the absolute and relative risks for type 1 and type 2 diabetes are unknown. We assessed the independent effects of diabetes status, by type, on in-hospital death in England in patients with COVID-19 during the period from March 1 to May 11, 2020. METHODS: We did a whole-population study assessing risks of in-hospital death with COVID-19 between March 1 and May 11, 2020. We included all individuals registered with a general practice in England who were alive on Feb 16, 2020. We used multivariable logistic regression to examine the effect of diabetes status, by type, on in-hospital death with COVID-19, adjusting for demographic factors and cardiovascular comorbidities. Because of the absence of data on total numbers of people infected with COVID-19 during the observation period, we calculated mortality rates for the population as a whole, rather than the population who were infected. FINDINGS: Of the 61 414 470 individuals who were alive and registered with a general practice on Feb 16, 2020, 263 830 (0·4%) had a recorded diagnosis of type 1 diabetes, 2 864 670 (4·7%) had a diagnosis of type 2 diabetes, 41 750 (0·1%) had other types of diabetes, and 58 244 220 (94·8%) had no diabetes. 23 698 in-hospital COVID-19-related deaths occurred during the study period. A third occurred in people with diabetes: 7434 (31·4%) in people with type 2 diabetes, 364 (1·5%) in those with type 1 diabetes, and 69 (0·3%) in people with other types of diabetes. Unadjusted mortality rates per 100 000 people over the 72-day period were 27 (95% CI 27-28) for those without diabetes, 138 (124-153) for those with type 1 diabetes, and 260 (254-265) for those with type 2 diabetes. Adjusted for age, sex, deprivation, ethnicity, and geographical region, compared with people without diabetes, the odds ratios (ORs) for in-hospital COVID-19-related death were 3·51 (95% CI 3·16-3·90) in people with type 1 diabetes and 2·03 (1·97-2·09) in people with type 2 diabetes. These effects were attenuated to ORs of 2·86 (2·58-3·18) for type 1 diabetes and 1·80 (1·75-1·86) for type 2 diabetes when also adjusted for previous hospital admissions with coronary heart disease, cerebrovascular disease, or heart failure. INTERPRETATION: The results of this nationwide analysis in England show that type 1 and type 2 diabetes were both independently associated with a significant increased odds of in-hospital death with COVID-19. FUNDING: None.


Assuntos
Betacoronavirus , Infecções por Coronavirus/mortalidade , Diabetes Mellitus Tipo 1/mortalidade , Diabetes Mellitus Tipo 2/mortalidade , Mortalidade Hospitalar/tendências , Pneumonia Viral/mortalidade , Vigilância da População , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Criança , Pré-Escolar , Comorbidade , Infecções por Coronavirus/diagnóstico , Diabetes Mellitus Tipo 1/diagnóstico , Diabetes Mellitus Tipo 2/diagnóstico , Inglaterra/epidemiologia , Feminino , Humanos , Lactente , Recém-Nascido , Masculino , Pessoa de Meia-Idade , Mortalidade/tendências , Pandemias , Pneumonia Viral/diagnóstico , Vigilância da População/métodos , Adulto Jovem
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