Racial inequalities in multimorbidity: baseline of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil).

BACKGROUND: Evidence of multimorbidity has come mainly from high-income regions, while disparities among racial groups have been less explored. This study examined racial differences in multimorbidity in the multiracial cohort of the Longitudinal Study of Adult Health (Estudo Longitudinal de Saúde do Adulto), ELSA-Brasil. METHODS: The study examined baseline (2008-2010) data for 14 099 ELSA-Brasil participants who self-reported being white, mixed-race, or black. A list of 16 morbidities was used to evaluate multimorbidity, operationalised by simple count into ≥ 2, ≥ 3, ≥ 4, ≥ 5 and ≥ 6 morbidities, in addition to evaluating the number of coexisting conditions. Prevalence ratios (PR) were estimated from logistic models and a quantile model was used to examine racial differences graphically in the distribution quantiles for the number of morbidities. RESULTS: Overall prevalence of multimorbidity (≥ 2 morbidities) was 70% and, after controlling for age and sex, was greater among mixed-race and black participants - by 6% (PR: 1.06; 95% CI: 1.03-1.08) and 9% (PR: 1.09; 95% CI: 1.06-1.12), respectively - than among white participants. As the cutoff value for defining multimorbidity was raised, so the strength of the association increased, especially among blacks: if set at ≥ 6 morbidities, the prevalence was 27% greater for those of mixed-race (PR: 1.27; 95% CI: 1.07-1.50) and 47% greater for blacks (PR: 1.47; 95% CI: 1.22-1.76) than for whites. The disparities were smaller in the lower morbidity distribution quantiles and larger in the upper quantiles, indicating a heavier burden of disease, particularly on blacks. CONCLUSIONS: Multimorbidity was common among adults and older adults in a Brazilian cohort, but important racial inequalities were found. Raising the cutoff point for defining multimorbidity revealed stronger associations between race/skin colour and multimorbidity, indicating a higher prevalence of multimorbidity among mixed-race and black individuals than among whites and that the former groups coexisted more often with more complex health situations (with more coexisting morbidities). Interventions to prevent and manage the condition of multimorbidity that consider the social determinants of health and historically discriminated populations in low- and middle-income regions are necessary.

Incidence of SARS-CoV-2 over four epidemic waves in a low-resource community in Rio de Janeiro, Brazil: A prospective cohort study.

Background: Incidence rates of SARS-CoV-2 infections in low-resource communities can inform vaccination strategies and non-pharmaceutical interventions (NPIs). Our objective was to estimate incidence over four epidemic waves in a slum in Rio de Janeiro, a proxy for economically deprived areas in the Global South. Methods: Prospective cohort of children and household contacts screened for SARS-CoV-2 by PCR and serology (IgG). The incidence density of PCR positive infections estimated for each wave - the first wave, Zeta, Gamma and Delta - was compared to an index combining NPIs and vaccination coverage. Findings: 718 families and 2501 individuals were enrolled, from May 2020 to November 2021. The incidence density of SARS-CoV-2 infection due to the first wave was 2, 3 times that of the other waves. The incidence among children was lower than that of older participants, except in later waves, when vaccination of the elderly reached 90%. Household agglomeration was significantly associated with incidence only during the first wave. Interpretation: The incidence of infection greatly exceeded rates reported in similar cohorts. The observed reduction in incidence in the elderly during the Delta variant wave, in spite of the rollback of NPIs, can be attributed to increased vaccine coverage. The high incidence in young people reinforces the importance of vaccination in this age group, a policy that has yet to receive the full support of some sectors of society. Funding: UK Medical Research Council, Foundation for the Advancement of Science of the State of Rio de Janeiro, National Council for Scientific and Technological Development.

[Socio-spatial inequalities in COVID-19 mortality in the three waves: an intraurban analysis in Argentina]. / Desigualdades socioespaciales de la mortalidad por COVID-19 en tres olas de propagación: un análisis intraurbano en Argentina.

The study aimed to analyze the socio-spatial differences in COVID-19 mortality in the pandemic's three waves in the city of Buenos Aires, Argentina. COVID-19 mortality data were obtained from the COVID-19 Database and reported by the Buenos Aires Autonomous Government from March 7, 2020, to September 30, 2021. Three waves were identified: the first from March to December 2020, the second from December 2020 to March 2021, and the third from March to September 2021. Multivariate regressions were calculated for each wave to analyze the association between risk of COVID-19 mortality in two age groups (0-59 years and 60 years or older) and the percentage of households with unmet basic needs as indicator of neighborhood poverty level, and population density. During the first wave and in both age groups, the neighborhood in the tertile with the highest percentages of households with unmet basic needs showed higher risk of COVID-19 mortality when compared to neighborhoods in the tertile with the lowest percentages of households with unmet basic needs. These inequalities disappeared in the second wave in both age groups, while the third wave saw a similar geographic pattern to the first wave. Higher levels of immunity in neighborhoods with high poverty levels might partially explain the absence of socio-spatial inequalities in the second wave, while the emergence of the gamma and lambda variants could partially explain the return to inequalities observed in the first wave.

Nuestro propósito fue investigar las diferencias de las desigualdades socioespaciales de la mortalidad por COVID-19 entre tres olas de propagación del virus en la Ciudad Autónoma de Buenos Aires (CABA), Argentina. Los datos de mortalidad por COVID-19 se obtuvieron de la base de datos de casos de COVID-19, informados por el gobierno de la CABA, desde el 7 de marzo de 2020 hasta el 30 de septiembre de 2021. Se determinaron tres olas: la primera ola, entre los meses de marzo y diciembre de 2020, la segunda ola, entre diciembre y marzo de 2021, y la tercera ola, entre marzo y septiembre de 2021. En cada ola se calcularon regresiones multivariadas para analizar la asociación entre el riesgo de mortalidad por COVID-19, en dos grupos etarios (0-59 años y 60 o más años), y el porcentaje de hogares con necesidades básicas insatisfechas, como indicador del nivel de pobreza de los barrios, y la densidad poblacional. Durante la primera ola y en ambos grupos etarios, los barrios del tercil con mayores porcentajes de hogares con necesidades básicas insatisfechas tuvieron un riesgo mayor de mortalidad por COVID-19, en comparación a los barrios del tercil con menores porcentajes de hogares con necesidades básicas insatisfechas. Estas desigualdades desaparecieron durante la segunda ola en ambos grupos etarios, mientras que en la tercera ola pareció emerger un patrón geográfico similar al de la primera ola. Es posible que mayores niveles de inmunidad en barrios con niveles altos de pobreza pudieran explicar parcialmente la ausencia de desigualdades socioespaciales durante la segunda ola, mientras que la irrupción de las variantes gamma y lambda podría explicar parcialmente el retorno a las desigualdades observadas en la primera ola.

A proposta era investigar as diferenças nas desigualdades socioespaciais da mortalidade por COVID-19 entre três ondas de propagação do vírus na Cidade Autônoma de Buenos Aires, Argentina. Os dados de mortalidade por COVID-19 foram obtidos a partir da base de dados dos casos de COVID-19 informados pelo governo da Cidade Autônoma de Buenos Aires, do dia 7 de março de 2020, até 30 de setembro de 2021. Foram identificadas três ondas: a primeira, entre os meses de março e dezembro de 2020, a segunda, entre dezembro e março de 2021, e a terceira, entre março e setembro de 2021. Para cada uma delas, foram calculadas regressões multivariadas, visando analisar a associação entre o risco de mortalidade por COVID-19 em dois grupos etários (0-59 anos e 60 anos ou mais), e o percentual de domicílios com necessidades básicas não atendidas, como indicador do nível de pobreza dos bairros, e a densidade populacional. Durante a primeira onda e em ambos grupos etários, os bairros do tercil com maiores percentuais de domicílios com necessidades básicas não atendidas apresentaram um risco maior de mortalidade por COVID-19 na comparação com os bairros do tercil com menores taxas de domicílios com necessidades básicas não atendidas. Estas desigualdades desapareceram durante a segunda onda nos dois grupos etários, ao passo que, na terceira onda parece ter emergido um padrão geográfico similar ao da primeira onda. Maiores níveis de imunidade em bairros com altas taxas de pobreza poderiam explicar parcialmente a ausência de desigualdades socioespaciais durante a segunda onda, sendo que a irrupção das variantes gama e lambda poderia explicar parcialmente a volta para as desigualdades observadas na primeira onda.

Severe acute respiratory infection surveillance in Brazil: the role of public, private and philanthropic healthcare units.

Epidemiological surveillance and notification of respiratory infections are important for management and control of epidemics and pandemics. Fact-based decisions, like social distancing policies and preparation of hospital beds, are taken based on several factors, including case numbers; hence, health authorities need quick access to reliable and well-analysed data. We aimed to analyse the role of the Brazilian public health system in the notification and hospitalization of patients with severe acute respiratory infection (SARI). Data of SARI cases in Brazil (2013-20) were obtained from SIVEP-Gripe platform, and legal status of each healthcare unit (HCU) responsible for case notification and hospitalization was obtained from the National Registry of Health Facilities (CNES) database. HCUs that are part of the hospital network were classified as 'Public Administration', 'Business Entities', 'Philanthropic Entities' or 'Individuals'. SARI notification data from Brazilian macro-regions (North, Northeast, Midwest, Southeast and South) were analysed and compared between administrative spheres. This study reveals that hospitalizations due to SARI increased significantly in Brazil during the coronavirus disease 2019 (COVID-19) pandemic, especially in HCUs of Public Administration. In the Southeast and South, where incidence of SARI is high, philanthropic HCUs also contribute to hospitalization of SARI cases and attend up to 7.4% of the cases notified by the Public Administration. The number of cases is usually lower in other regions, but in 2020 the Northeast showed more hospitalizations than the South. In the South, SARI season occurs later; however, in 2020, an early peak was observed because of COVID-19. Notably, the contribution of each administrative sphere that manages hospital networks in Brazil in the control and management of SARI varies between regions. Our approach will allow managers to assess the use of public resources, given that there are different profiles of healthcare in each region of Brazil and that the public health system has a major role in notifying and attending SARI cases.

Desigualdades socioespaciales de la mortalidad por COVID-19 en tres olas de propagación: un análisis intraurbano en Argentina / Socio-spatial inequalities in COVID-19 mortality in the three waves: an intraurban analysis in Argentina / Desigualdades socioespaciais da mortalidade por COVID-19 em três ondas de propagação: una análise intraurbana na Argentina

Nuestro propósito fue investigar las diferencias de las desigualdades socioespaciales de la mortalidad por COVID-19 entre tres olas de propagación del virus en la Ciudad Autónoma de Buenos Aires (CABA), Argentina. Los datos de mortalidad por COVID-19 se obtuvieron de la base de datos de casos de COVID-19, informados por el gobierno de la CABA, desde el 7 de marzo de 2020 hasta el 30 de septiembre de 2021. Se determinaron tres olas: la primera ola, entre los meses de marzo y diciembre de 2020, la segunda ola, entre diciembre y marzo de 2021, y la tercera ola, entre marzo y septiembre de 2021. En cada ola se calcularon regresiones multivariadas para analizar la asociación entre el riesgo de mortalidad por COVID-19, en dos grupos etarios (0-59 años y 60 o más años), y el porcentaje de hogares con necesidades básicas insatisfechas, como indicador del nivel de pobreza de los barrios, y la densidad poblacional. Durante la primera ola y en ambos grupos etarios, los barrios del tercil con mayores porcentajes de hogares con necesidades básicas insatisfechas tuvieron un riesgo mayor de mortalidad por COVID-19, en comparación a los barrios del tercil con menores porcentajes de hogares con necesidades básicas insatisfechas. Estas desigualdades desaparecieron durante la segunda ola en ambos grupos etarios, mientras que en la tercera ola pareció emerger un patrón geográfico similar al de la primera ola. Es posible que mayores niveles de inmunidad en barrios con niveles altos de pobreza pudieran explicar parcialmente la ausencia de desigualdades socioespaciales durante la segunda ola, mientras que la irrupción de las variantes gamma y lambda podría explicar parcialmente el retorno a las desigualdades observadas en la primera ola.

The study aimed to analyze the socio-spatial differences in COVID-19 mortality in the pandemic's three waves in the city of Buenos Aires, Argentina. COVID-19 mortality data were obtained from the COVID-19 Database and reported by the Buenos Aires Autonomous Government from March 7, 2020, to September 30, 2021. Three waves were identified: the first from March to December 2020, the second from December 2020 to March 2021, and the third from March to September 2021. Multivariate regressions were calculated for each wave to analyze the association between risk of COVID-19 mortality in two age groups (0-59 years and 60 years or older) and the percentage of households with unmet basic needs as indicator of neighborhood poverty level, and population density. During the first wave and in both age groups, the neighborhood in the tertile with the highest percentages of households with unmet basic needs showed higher risk of COVID-19 mortality when compared to neighborhoods in the tertile with the lowest percentages of households with unmet basic needs. These inequalities disappeared in the second wave in both age groups, while the third wave saw a similar geographic pattern to the first wave. Higher levels of immunity in neighborhoods with high poverty levels might partially explain the absence of socio-spatial inequalities in the second wave, while the emergence of the gamma and lambda variants could partially explain the return to inequalities observed in the first wave.

A proposta era investigar as diferenças nas desigualdades socioespaciais da mortalidade por COVID-19 entre três ondas de propagação do vírus na Cidade Autônoma de Buenos Aires, Argentina. Os dados de mortalidade por COVID-19 foram obtidos a partir da base de dados dos casos de COVID-19 informados pelo governo da Cidade Autônoma de Buenos Aires, do dia 7 de março de 2020, até 30 de setembro de 2021. Foram identificadas três ondas: a primeira, entre os meses de março e dezembro de 2020, a segunda, entre dezembro e março de 2021, e a terceira, entre março e setembro de 2021. Para cada uma delas, foram calculadas regressões multivariadas, visando analisar a associação entre o risco de mortalidade por COVID-19 em dois grupos etários (0-59 anos e 60 anos ou mais), e o percentual de domicílios com necessidades básicas não atendidas, como indicador do nível de pobreza dos bairros, e a densidade populacional. Durante a primeira onda e em ambos grupos etários, os bairros do tercil com maiores percentuais de domicílios com necessidades básicas não atendidas apresentaram um risco maior de mortalidade por COVID-19 na comparação com os bairros do tercil com menores taxas de domicílios com necessidades básicas não atendidas. Estas desigualdades desapareceram durante a segunda onda nos dois grupos etários, ao passo que, na terceira onda parece ter emergido um padrão geográfico similar ao da primeira onda. Maiores níveis de imunidade em bairros com altas taxas de pobreza poderiam explicar parcialmente a ausência de desigualdades socioespaciais durante a segunda onda, sendo que a irrupção das variantes gama e lambda poderia explicar parcialmente a volta para as desigualdades observadas na primeira onda.

Risk assessment for HIV infection in men who have sex with men and the contribution of sexual partner networks. / Avaliação de risco para infecção HIV em homens que fazem sexo com homens e a contribuição das redes de parceiros sexuais.

This study aimed to evaluate the risk of HIV infection in men who have sex with men (MSM) by developing an index that considers sex partner networks. The index variables were age, ethnicity/skin color, schooling, relationship type, condom use in receptive and insertive relationships, self-perception of the possibility of HIV infection, sexually transmitted infections, and rapid HIV testing results. We used data from a cross-sectional MSM egocentric network survey conducted in Rio de Janeiro between 2014 and 2015. The initial research volunteer is called ego, each partner is called alter, and each pair of people in a relationship is called the dyad. Multiple logistic regression was used to define the coefficients of the equations for the elaboration of the indices. The index ranged from 0 to 1; the closer to 1, the higher the risk of HIV infection. HIV prevalence was 13.9% among egos. The mean egos index with an HIV-reactive test was 57% higher than non-reactive, and the same profile was observed in the index values of dyads. The index allowed the incorporation of network data through the dyads and contributed to the identification of individuals with a higher likelihood of acquiring HIV.

O objetivo do estudo foi avaliar o risco de infecção por HIV em homens que fazem sexo com homens (HSH) a partir do desenvolvimento de um índice que considere as redes de parceiros sexuais. As variáveis do índice foram faixa etária, raça/cor, escolaridade, tipo de relacionamento, uso de preservativo em relações receptivas e insertivas, autopercepção da chance de se infectar pelo HIV, história de infecções sexualmente transmissíveis, além dos resultados dos testes rápidos para HIV. Foram utilizados dados de uma pesquisa de rede egocêntricas HSH, com desenho transversal, realizada no Rio de Janeiro entre 2014 e 2015. O voluntário inicial da pesquisa é denominado ego, cada parceiro é alter, e cada par de pessoas em um relacionamento é a díade. Utilizou-se regressão logística múltipla para definição dos coeficientes das equações para elaboração dos índices. O índice variou de 0 a 1, quanto mais próximo de 1, maior o risco de infecção por HIV. A prevalência de HIV dos egos foi de 13,9%. A média do índice dos egos com teste HIV reagente foi 57% maior do que aqueles não reagentes, o mesmo perfil foi observado nos valores dos índices das díades. O índice permitiu incorporar os dados das redes por meio das díades e contribuiu para a identificação de indivíduos com maior chance de aquisição do HIV.

Obtaining adjusted prevalence ratios from logistic regression models in cross-sectional studies / Obtendo razões de chance prevalentes de modelos de regressão logística em estudos transversais / La obtención de las prevalencias ajustadas a partir de los modelos de regresión logística en los estudios transversales

In the last decades, the use of the epidemiological prevalence ratio (PR) instead of the odds ratio has been debated as a measure of association in cross-sectional studies. This article addresses the main difficulties in the use of statistical models for the calculation of PR: convergence problems, availability of tools and inappropriate assumptions. We implement the direct approach to estimate the PR from binary regression models based on two methods proposed by Wilcosky & Chambless and compare with different methods. We used three examples and compared the crude and adjusted estimate of PR, with the estimates obtained by use of log-binomial, Poisson regression and the prevalence odds ratio (POR). PRs obtained from the direct approach resulted in values close enough to those obtained by log-binomial and Poisson, while the POR overestimated the PR. The model implemented here showed the following advantages: no numerical instability; assumes adequate probability distribution and, is available through the R statistical package.

Nas últimas décadas, tem sido discutido o uso da razão de prevalência (RP) ao invés da razão de chance como a medida de associação a ser estimada em estudos transversais. Discute-se as principais dificuldades no uso de modelos estatísticos para o cálculo da RP: problemas de convergência, disponibilidade de ferramentas e pressupostos não apropriados. O objetivo deste estudo é implementar uma abordagem direta para estimar a RP com base em modelos logísticos binários baseados em dois métodos propostos por Wilcosky & Chamblers, e comparar com outros métodos. Utilizou-se três exemplos e comparou-se as estimativas bruta e ajustada da RP obtidas pela função com as estimativas obtidas pelos modelos log-binomial, Poisson e razão de chance prevalente (RCP). As RP da abordagem proposta resultaram em valores próximos aos obtidos pelos modelos log-binomial e Poisson, e a RCP superestimou a RP. O modelo aqui implementado apresentou as seguintes vantagens: não apresenta instabilidade numérica; assume a distribuição de probabilidades adequada; e está disponível no programa estatístico R.

En las últimas décadas, se ha discutido el uso de la razón de prevalencia (RP), en lugar del odds ratio como medida de asociación que se estima en estudios transversales. Se analizan las principales dificultades en el uso de modelos estadísticos para el cálculo de la RP: problemas de convergencia, disponibilidad de herramientas y supuestos no apropiados. El objetivo es realizar un enfoque directo para estimar la RP desde modelos logísticos binarios, basados en dos métodos propuestos por Wilcosky y Chamblers y compararlos con otros métodos. Se han utilizado 3 ejemplos y comparamos las estimaciones crudas y ajustadas de RP con las estimaciones obtenidas por log-binomial, Poisson y odds ratio de prevalencia (ORP). Los RP obtenidos del enfoque directo dieron como resultado valores cercanos a los obtenidos mediante el log- binomial y de Poisson, mientras que la RCP sobreestimó la RP. El modelo que aquí se presenta implementó las siguientes ventajas: no presenta inestabilidad numérica, toma una distribución de probabilidad apropiada y está disponible en software estadístico libre R.

Dynamic survival models with spatial frailty.

In many survival studies, covariates effects are time-varying and there is presence of spatial effects. Dynamic models can be used to cope with the variations of the effects and spatial components are introduced to handle spatial variation. This paper proposes a methodology to simultaneously introduce these components into the model. A number of specifications for the spatial components are considered. Estimation is performed via a Bayesian approach through Markov chain Monte Carlo methods. Models are compared to assess relevance of their components. Analysis of a real data set is performed, showing the relevance of both time-varying covariate effects and spatial components. Extensions to the methodology are proposed along with concluding remarks.