Development and implementation of a treatment pathway to reduce coronary angiograms - lessons from a failure.

BACKGROUND: The rates of coronary angiograms (CA) and related procedures (percutaneous intervention [PCI]) are significantly higher in Germany than in other Organisation for Economic Co-ordination and Development (OECD) countries. The current guidelines recommend non-invasive diagnosis of coronary heart disease (CHD); CA should only have a limited role in choosing the appropriate revascularisation procedure. The aim of the present study was to explore whether improvements in guideline adherence can be achieved through the implementation of regional treatment pathways. We chose four regions of Germany with high utilisation of CAs for the study. Here we report the results of the concomitant qualitative study. METHODS: General practitioners and specialist physicians (cardiologists, hospital-based cardiologists, emergency physicians, radiologists and nuclear medicine specialists) caring for patients with suspected CHD were invited to develop regional treatment pathways. Four academic departments provided support for moderation, provision of materials, etc. The study team observed session discussions and took notes. After the development of the treatment pathways, 45 semi-structured interviews were conducted with the participating physicians. Interviews and field notes were transcribed verbatim and underwent qualitative content analysis. RESULTS: Pathway development received little support among the participants. Although consensus documents were produced, the results were unlikely to improve practice. The participants expressed very little commitment to change. Although this attempt clearly failed in all study regions, our experience provides relevant insights into the process of evidence appraisal and implementation. A lack of organisational skills, ignorance of current evidence and guidelines, and a lack of feedback regarding one's own clinical behaviour proved to be insurmountable. CA was still seen as the diagnostic gold standard by most interviewees. CONCLUSIONS: Oversupply and overutilisation can be assumed to be present in study regions but are not immediately perceived by clinicians. The problem is unlikely to be solved by regional collaborative initiatives; optimised resource planning within the health care system combined with appropriate economic incentives might best address these issues.

Spatial Bayesian distributed lag non-linear models (SB-DLNM) for small-area exposure-lag-response epidemiological modelling.

BACKGROUND: Distributed lag non-linear models (DLNMs) are the reference framework for modelling lagged non-linear associations. They are usually used in large-scale multi-location studies. Attempts to study these associations in small areas either did not include the lagged non-linear effects, did not allow for geographically-varying risks or downscaled risks from larger spatial units through socioeconomic and physical meta-predictors when the estimation of the risks was not feasible due to low statistical power. METHODS: Here we proposed spatial Bayesian DLNMs (SB-DLNMs) as a new framework for the estimation of reliable small-area lagged non-linear associations, and demonstrated the methodology for the case study of the temperature-mortality relationship in the 73 neighbourhoods of the city of Barcelona. We generalized location-independent DLNMs to the Bayesian framework (B-DLNMs), and extended them to SB-DLNMs by incorporating spatial models in a single-stage approach that accounts for the spatial dependence between risks. RESULTS: The results of the case study highlighted the benefits of incorporating the spatial component for small-area analysis. Estimates obtained from independent B-DLNMs were unstable and unreliable, particularly in neighbourhoods with very low numbers of deaths. SB-DLNMs addressed these instabilities by incorporating spatial dependencies, resulting in more plausible and coherent estimates and revealing hidden spatial patterns. In addition, the Bayesian framework enriches the range of estimates and tests that can be used in both large- and small-area studies. CONCLUSIONS: SB-DLNMs account for spatial structures in the risk associations across small areas. By modelling spatial differences, SB-DLNMs facilitate the direct estimation of non-linear exposure-response lagged associations at the small-area level, even in areas with as few as 19 deaths. The manuscript includes an illustrative code to reproduce the results, and to facilitate the implementation of other case studies by other researchers.

Oxycodone initiation in Australia (2014-2018): Sociodemographic factors and preceding health service use.

AIMS: Oxycodone is the most commonly prescribed strong opioid in Australia. This study describes health service antecedents and sociodemographic factors associated with oxycodone initiation. METHODS: Population-based new user cohort study linking medicine dispensings, hospitalizations, emergency department visits, medical services and cancer notifications from New South Wales (NSW) for 2014-2018. New users had no dispensings of any opioid in the preceding year. We analysed health service use in the 5 days preceding initiation and proportion of people on treatment over 1 year and fitted an area-based, multivariable initiation model with sociodemographic covariates. RESULTS: Oxycodone accounted for 30% of opioid initiations. Annually, 3% of the NSW population initiated oxycodone, and 5-6% were prevalent users; the new user cohort comprised 830 963 people. Discharge from hospital (39.3%), therapeutic procedures (21.4%) and emergency department visits (19.7%) were common; a hospital admission for injury (6.0%) or a past-year history of cancer (7.2%) were less common. At 1 year after initiation, 4.6% of people were using oxycodone. In the multivariable model, new use of oxycodone increased with age and was higher for people outside major cities, for example, an incidence rate ratio of 1.43 (95% confidence interval 1.36-1.51) for inner regional areas relative to major cities; there was no evidence of variation in rates of new use by social disadvantage. CONCLUSION: About half of new oxycodone use in NSW was preceded by a recent episode of hospital care or a therapeutic procedure. Higher rates of oxycodone initiation in rural and regional areas were not explained by sociodemographic factors.

Exploring the use of experimental small area estimates to examine the relationship between individual-level and area-level community belonging and self-rated health.

Background: Small area estimation refers to statistical modelling procedures that leverage information or "borrow strength" from other sources or variables. This is done to enhance the reliability of estimates of characteristics or outcomes for areas that do not contain sufficient sample sizes to provide disaggregated estimates of adequate precision and reliability. There is growing interest in secondary research applications for small area estimates (SAEs). However, it is crucial to assess the analytic value of these estimates when used as proxies for individual-level characteristics or as distinct measures that offer insights at the area level. This study assessed novel area-level community belonging measures derived using small area estimation and examined associations with individual-level measures of community belonging and self-rated health. Data and methods: SAEs of community belonging within census tracts produced from the 2016-2019 cycles of the Canadian Community Health Survey (CCHS) were merged with respondent data from the 2020 CCHS. Multinomial logistic regression models were run between area-level SAEs, individual-level sense of community belonging, and self-rated health on the study sample of people aged 18 years and older. Results: Area-level community belonging was associated with individual-level community belonging, even after adjusting for individual-level sociodemographic characteristics, despite limited agreement between individual- and area-level measures. Living in a neighbourhood with low community belonging was associated with higher odds of reporting being in fair or poor health, versus being in very good or excellent health (odds ratio: 1.53; 95% confidence interval: 1.22, 1.91), even after adjusting for other factors such as individual-level sense of community belonging, which was also associated with self-rated health. Interpretation: Area-level and individual-level sense of community belonging were independently associated with self-rated health. The novel SAEs of community belonging can be used as distinct measures of neighbourhood-level community belonging and should be understood as complementary to, rather than proxies for, individual-level measures of community belonging.

Understanding Factors Influencing Geographic Variation in Healthcare Expenditures: A Small Areas Analysis Study.

Dramatic geographic variations in healthcare expenditures were documented by developed countries, but little is known about such variations under China's context, and what causes such variations. This study aims to examine variations of healthcare expenditures among small areas and to determine the associations between demand-, supply-factors, and per capita inpatient expenditures. This cross-sectional study utilized hospital discharge data aggregated within delineated hospital service areas (HSAs) using the small-area analysis approach. Linear multivariate regression modeling with robust standard errors was used to estimate the sources of variation of per capita inpatient expenditures across HSAs covering the years 2017 to 2019; the Shapley value decomposition method was used to measure the respective contributions of demand-, supply-side to such variations. Among 149 HSAs, demand factors explained most of the (87.4%) overall geographic variation among HSAs. With each 1% increase in GDP per capita and urbanization rate was associated with 0.099% and 0.9% increase in inpatient expenditure per capita, respectively, while each 1% increase in the share of females and the unemployment rate was associated with a 0.7% and 0.4% reduction in the per capita inpatient expenditures, respectively. In supply-side, for every 1 increase in hospital beds per 1000 population, the per capita inpatient expenditures rose by 2.9%, while with every 1% increase in the share of private hospitals, the per capita inpatient expenditures would decrease by 0.4%. With Herfindahl-Hirschman Index decrease 10%, the per capita inpatient expenditures would increase 1.06%. This study suggests demand-side factors are associated with large geographic variation in per capita inpatient expenditures among HSAs, while supply-side factors played an important role. The evaluation of geographic variations in per capita inpatient expenditures as well as its associated factors have great potential to provide an indirect approach to identify possibly existing underutilized or overutilized healthcare procedures.

Mapping the prevalence of cancer risk factors at the small area level in Australia.

BACKGROUND: Cancer is a significant health issue globally and it is well known that cancer risk varies geographically. However in many countries there are no small area-level data on cancer risk factors with high resolution and complete reach, which hinders the development of targeted prevention strategies. METHODS: Using Australia as a case study, the 2017-2018 National Health Survey was used to generate prevalence estimates for 2221 small areas across Australia for eight cancer risk factor measures covering smoking, alcohol, physical activity, diet and weight. Utilising a recently developed Bayesian two-stage small area estimation methodology, the model incorporated survey-only covariates, spatial smoothing and hierarchical modelling techniques, along with a vast array of small area-level auxiliary data, including census, remoteness, and socioeconomic data. The models borrowed strength from previously published cancer risk estimates provided by the Social Health Atlases of Australia. Estimates were internally and externally validated. RESULTS: We illustrated that in 2017-2018 health behaviours across Australia exhibited more spatial disparities than previously realised by improving the reach and resolution of formerly published cancer risk factors. The derived estimates revealed higher prevalence of unhealthy behaviours in more remote areas, and areas of lower socioeconomic status; a trend that aligned well with previous work. CONCLUSIONS: Our study addresses the gaps in small area level cancer risk factor estimates in Australia. The new estimates provide improved spatial resolution and reach and will enable more targeted cancer prevention strategies at the small area level. Furthermore, by including the results in the next release of the Australian Cancer Atlas, which currently provides small area level estimates of cancer incidence and relative survival, this work will help to provide a more comprehensive picture of cancer in Australia by supporting policy makers, researchers, and the general public in understanding the spatial distribution of cancer risk factors. The methodology applied in this work is generalisable to other small area estimation applications and has been shown to perform well when the survey data are sparse.

Regional disparities in major cancer incidence in Korea, 1999-2018.

OBJECTIVES: This study investigated regional disparities in the incidence of 8 major cancers at the municipal level in Korea during 1999-2018 and evaluated the presence or absence of hot spots of cancer clusters during 2014-2018. METHODS: The Korea National Cancer Incidence Database was used. Age-standardized incidence rates were calculated by gender and region at the municipal level for 4 periods of 5 years and 8 cancer types. Regional disparities were calculated as both absolute and relative measures. The possibility of clusters was examined using global Moran's I with a spatial weight matrix based on adjacency or distance. RESULTS: Regional disparities varied depending on cancer type and gender during the 20-year study period. For men, the regional disparities of stomach, colon and rectum, lung, and liver cancer declined, and those of thyroid and prostate cancer recently decreased, despite an overall increasing incidence. For women, regional disparities in stomach, colon and rectum, lung, liver, and cervical cancer declined, that of thyroid cancer recently decreased, despite an overall increasing incidence, and that of breast cancer steadily increased. In 2014-2018, breast cancer (I, 0.61; 95% confidence interval [CI], 0.53 to 0.70) showed a high probability of cancer clusters in women, and liver cancer (I, 0.48; 95% CI, 0.40 to 0.56) showed a high probability of cancer clusters in men. CONCLUSIONS: Disparities in cancer incidence that were not seen at the national level were discovered at the municipal level. These results could provide important directions for planning and implementing local cancer policies.

Emergency Department Syndromic Surveillance to Monitor Tick-Borne Diseases: A 6-Year Small-Area Analysis in Northeastern Italy.

Tick-borne diseases (TBD) are endemic in Europe. However, surveillance is currently incomplete. Alternative strategies need to be considered. The aim of this study was to test an Emergency Department Syndromic Surveillance (EDSyS) system as a complementary data source to describe the impact of tick bites and TBD using a small-area analysis approach and to monitor the risk of TBD to target prevention. ED databases in the Local Health Authority 8 District (Veneto, Italy) were queried for tick-bite and TBD-related visits between January 2017 and December 2022. Hospitalisations were also collected. Events involving the resident population were used to calculate incidence rates. A total of 4187 ED visits for tick-bite and 143 for TBD were recorded; in addition, 62 TBD-related hospitalisations (of which 72.6% in over 50 s and 22.6% in over 65 s). ED visits peaked in spring and in autumn, followed by a 4-week lag in the increase in hospital admissions. The small-area analysis identified two areas at higher risk of bites and TBD. The use of a EDSyS system allowed two natural foci to be identified. This approach proved useful in predicting temporal and geographic risk of TBD and in identifying local endemic areas, thus enabling an effective multidisciplinary prevention strategy.

Validation of a small-area model for estimation of smoking prevalence at a subnational level.

INTRODUCTION: Small-area estimation methods are an alternative to direct survey-based estimates in cases where a survey's sample size does not suffice to ensure representativeness. Nevertheless, the information yielded by small-area estimation methods must be validated. The objective of this study was thus to validate a small-area model. METHODS: The prevalence of smokers, ex-smokers, and never smokers by sex and age group (15-34, 35-54, 55-64, 65-74, ≥75 years) was calculated in two Spanish Autonomous Regions (ARs) by applying a weighted ratio estimator (direct estimator) to data from representative surveys. These estimates were compared against those obtained with a small-area model applied to another survey, specifically the Spanish National Health Survey, which did not guarantee representativeness for these two ARs by sex and age. To evaluate the concordance of the estimates, we calculated the intraclass correlation coefficient (ICC) and the 95% confidence intervals of the differences between estimates. To assess the precision of the estimates, the coefficients of variation were obtained. RESULTS: In all cases, the ICC was ≥0.87, indicating good concordance between the direct and small-area model estimates. Slightly more than eight in ten 95% confidence intervals for the differences between estimates included zero. In all cases, the coefficient of variation of the small-area model was <30%, indicating a good degree of precision in the estimates. CONCLUSIONS: The small-area model applied to national survey data yields valid estimates of smoking prevalence by sex and age group at the AR level. These models could thus be applied to a single year's data from a national survey, which does not guarantee regional representativeness, to characterize various risk factors in a population at a subnational level.

Small-area models to assess the geographical distribution of tobacco consumption by sex and age in Spain.

INTRODUCTION: Complete and accurate data on smoking prevalence at a local level would enable health authorities to plan context-dependent smoking interventions. However, national health surveys do not generally provide direct estimates of smoking prevalence by sex and age groups at the subnational level. This study uses a small-area model-based methodology to obtain precise estimations of smoking prevalence by sex, age group and region, from a population-based survey. METHODS: The areas targeted for analysis consisted of 180 groups based on a combination of sex, age group (15-34, 35-54, 55-64, 65-74, and ≥75 years), and Autonomous Region. Data on tobacco use came from the 2017 Spanish National Health Survey (2017 SNHS). In each of the 180 groups, we estimated the prevalence of smokers (S), ex-smokers (ExS) and never smokers (NS), as well as their coefficients of variation (CV), using a weighted ratio estimator (direct estimator) and a multinomial logistic model with random area effects. RESULTS: When smoking prevalence was estimated using the small-area model, the precision of direct estimates improved; the CV of S and ExS decreased on average by 26%, and those of NS by 25%. The range of S prevalence was 11-46% in men and 4-37% in women, excluding the group aged ≥75 years. CONCLUSIONS: This study proposes a methodology for obtaining reliable estimates of smoking prevalence in groups or areas not covered in the survey design. The model applied is a good alternative for enhancing the precision of estimates at a detailed level, at a much lower cost than that involved in conducting large-scale surveys. This method could be easily integrated into routine data processing of population health surveys. Having such estimates directly after completing a health survey would help characterize the tobacco epidemic and/or any other risk factor more precisely.

Understanding the differential effect of local socio-economic conditions on the relation between prescription opioid supply and drug overdose deaths in US counties.

BACKGROUND AND AIMS: Both local socio-economic conditions and prescription opioid supply are associated with drug overdose deaths, which exhibit substantial geographical heterogeneity across the United States. We measured whether the associations of prescription opioid supply with drug overdose deaths vary by local socio-economic conditions. DESIGN: Ecological county-level study, including 3109 US counties between 2006 and 2019 (n = 43 526 county-years) using annual mortality data. SETTING: United States. CASES: A total of 711 447 drug overdose deaths. MEASUREMENTS: We modeled overdose counts using Bayesian hierarchical Poisson models, estimating associations between four types of drug overdose deaths (deaths involving any drugs, any opioid, prescription opioids only and heroin), prescription opioid supply and five socio-economic indicators: unemployment, poverty rate, income inequality, Rey index (components include mean household income, % high school graduates, % blue-collar workers and unemployment rate), and American human development index (HDI; an indicator of community wellbeing). FINDINGS: Drug overdose deaths and all substance-specific overdose deaths were higher in counties with higher income inequality [adjusted odds ratios (aORs) = 1.09-1.13], Rey index (aORs = 1.15-1.21) and prescription opioid supply (aORs = 1.14-1.21), and lower in counties with higher HDI scores (aORs = 0.75-0.92). Poverty rate, income inequality and HDI scores were found to modify the effect of prescription opioid supply on heroin overdose deaths. The plot of the interactions showed that when disadvantage is high, increasing prescription opioid supply does not increase heroin overdose deaths. The less disadvantage there is, indicated by lower poverty rates, higher HDI scores and lower income inequality, the greater the effect of increasing prescription opioid supply relative to population size on heroin overdose deaths in US counties. CONCLUSIONS: In the United States, prescription opioid supply is associated with higher drug overdose deaths; associations are stronger in counties with less disadvantage and less income inequality, but only for heroin overdose deaths.

Geographic variation and sociodemographic correlates of prescription psychotropic drug use among children and youth in Ontario, Canada: a population-based study.

BACKGROUND: Population-based research examining geographic variability in psychotropic medication dispensing to children and youth and the sociodemographic correlates of such variation is lacking. Variation in psychotropic use could reflect disparities in access to non-pharmacologic interventions and identify potentially concerning use patterns. METHODS: We conducted a population-based study of all Ontario residents aged 0 to 24 years who were dispensed a benzodiazepine, stimulant, antipsychotic or antidepressant between January 1, 2018, and December 31, 2018. We conducted small-area variation analyses and identified determinants of dispensing using negative binomial generalized estimating equation models. RESULTS: The age- and sex-standardized rate of psychotropic dispensing to children and youth was 76.8 (range 41.7 to 144.4) prescriptions per 1000 population, with large variation in psychotropic dispensing across Ontario's census divisions. Males had higher antipsychotic [rate ratio (RR) 1.40; 95% confidence interval (CI) 1.36 to 1.44) and stimulant (RR 1.75; 95% CI 1.70 to 1.80) dispensing rates relative to females, with less use of benzodiazepines (RR 0.85; 95% CI 0.83 to 0.88) and antidepressants (RR 0.81; 95% CI 0.80 to 0.82). Lower antipsychotic dispensing was observed in the highest income neighbourhoods (RR 0.72; 95% CI 0.70 to 0.75) relative to the lowest. Benzodiazepine (RR 1.12; 95% CI 1.01 to 1.24) and stimulant (RR 1.11; 95% CI 1.01 to 1.23) dispensing increased with the density of mental health services in census divisions, whereas antipsychotic use decreased (RR 0.82; 95% CI 0.73 to 0.91). The regional density of child and adolescent psychiatrists and developmental pediatricians (RR 1.00; 95% CI 0.99 to 1.01) was not associated with psychotropic dispensing. CONCLUSION: We found significant variation in psychotropic dispensing among young Ontarians. Targeted investment in regions with long wait times for publicly-funded non-pharmacological interventions and novel collaborative service models may minimize variability and promote best practices in using psychotropics among children and youth.

Objective and subjective neighbourhood characteristics and suicidality: a multilevel analysis.

BACKGROUND: Characteristics of the neighbourhood environment, including population density, social fragmentation, and trust, have been linked to mental health outcomes. Using a longitudinal population-based cohort, we explored the relationship between objective and subjective neighbourhood characteristics and the odds of suicidal thoughts and attempts. METHODS: We conducted a longitudinal study of 20764 participants living in Stockholm County who participated in the Stockholm Public Health Survey. We used multilevel modelling to examine if suicidal thoughts and attempts were associated with neighbourhood characteristics, independent of individual associations. We included objective and subjective measures to explore if there was a different relationship between these measures of the neighbourhood environment and suicidality. RESULTS: Associations between neighbourhood factors and suicidality were predominantly explained by individual characteristics, with the exception of neighbourhood-level deprivation and average residential trust. Each unit increase of deprivation was linked to increased odds of suicidal thoughts [Odds ratio (OR) 1.04, 95% confidence interval (CI) 1.00-1.07] and attempts (OR 1.11, 95% CI 1.06-1.17). Decreasing residential trust was associated with increased odds of suicide attempts (OR 1.09, 95% CI 1.02-1.17). There was no evidence that neighbourhood-level fragmentation or average trust in public and political institutions had an independent effect on suicidality once individual and sociodemographic factors were accounted for. CONCLUSIONS: This study showed that much of the neighbourhood-level variation in suicidal thoughts and attempts could be explained by compositional factors, including sociodemographic clustering within neighbourhoods. The independent effect of neighbourhood-level deprivation and average residential trust provide evidence that the neighbourhood context may exert an independent effect on suicidality beyond the impact of individual characteristics.

Mental health service areas in Switzerland.

OBJECTIVES: Small area analysis is a health services research technique that facilitates geographical comparison of services supply and utilization rates between health service areas (HSAs). HSAs are functionally relevant regions around medical facilities within which most residents undergo treatment. We aimed to identify HSAs for psychiatric outpatient care (HSA-PSY) in Switzerland. METHODS: We used HSAr, a new and automated methodological approach, and comprehensive psychiatric service use data from insurances to identify HSA-PSY based on travel patterns between patients' residences and service sites. Resulting HSA-PSY were compared geographically, demographically and regarding the use of inpatient and outpatient psychiatric services. RESULTS: We identified 68 HSA-PSY, which were reviewed and validated by local mental health services experts. The population-based rate of inpatient and outpatient service utilization varied considerably between HSA-PSY. Utilization of inpatient and outpatient services tended to be positively associated across HSA-PSY. CONCLUSIONS: Wide variation of service use between HSA-PSY can hardly be fully explained by underlying differences in the prevalence or incidence of disorders. Whether other factors such as the amount of services supply did add to the high variation should be addressed in further studies, for which our functional mapping on a small-scale regional level provides a good analytical framework.

Prevalence of adult smokers in Brazilian capitals according to socioeconomic deprivation / Prevalência de fumantes adultos nas capitais brasileiras, segundo privação socioeconômica

ABSTRACT Objective: To estimate the prevalence of adult smokers in the 26 capitals and the Federal District according to the Brazilian Deprivation Index (Índice Brasileiro de Privação - IBP). Methods: Dataset on smoking were obtained from the Surveillance of Risk and Protective Factors for Noncommunicable Diseases by Survey (Vigitel) system for the 26 capitals and the Federal District, in the period from 2010 to 2013. The IBP classifies the census sectors according to indicators such as: income less than ½ minimum wage, illiterate population and without sanitary sewage. In the North and Northeast regions, the census sectors were grouped into four categories (low, medium, high and very high deprivation) and in the South, Southeast and Midwest regions into three (low, medium and high deprivation). Prevalence estimates of adult smokers were obtained using the indirect estimation method in small areas. To calculate the prevalence ratios, Poisson models are used. Results: The positive association between prevalence and deprivation of census sector categories was found in 16 (59.3%) of the 27 cities. In nine (33.3%) cities, the sectors with the greatest deprivation had a higher prevalence of smokers when compared to those with the least deprivation, and in two (7.4%) there were no differences. In Aracaju, Belém, Fortaleza, João Pessoa, Macapá and Salvador, the prevalence of adult smokers was three times higher in the group of sectors with greater deprivation compared to those with less deprivation. Conclusion: Sectors with greater social deprivation had a higher prevalence of smoking, compared with less deprivation, pointing to social inequalities.

RESUMO Objetivo: Estimar as prevalências de adultos fumante nas 26 capitais e no Distrito Federal segundo o Índice Brasileiro de Privação. Métodos: Os dados sobre tabagismo foram obtidos junto ao sistema de Vigilância de Fatores de Risco e Proteção para Doenças Crônicas por Inquérito (Vigitel) para as 26 capitais e o Distrito Federal, no período de 2010 a 2013. O Índice Brasileiro de Privação classifica os setores censitários segundo indicadores como: renda menor que meio salário mínimo, população não alfabetizada e sem esgotamento sanitário. Nas regiões Norte e Nordeste, os setores censitários foram agrupados em quatro categorias (baixa, média, alta e muito alta privação) e, nas regiões Sul, Sudeste e Centro-Oeste, em três (baixa, média e alta privação). As estimativas de prevalências de adultos fumantes foram obtidas pelo método indireto de estimação em pequenas áreas. Para o cálculo das razões de prevalências, empregram-se modelos de Poisson. Resultados: A associação positiva entre a prevalência e a privação das categorias de setores censitários foi encontrada em 16 (59,3%) das 27 cidades. Em nove (33,3%) cidades, os setores de maior privação apresentaram maior prevalência de fumantes quando comparados aos de menor privação e, em duas (7,4%), não apresentaram diferenças. Em Aracaju, Belém, Fortaleza, João Pessoa, Macapá e Salvador, as prevalências de adultos fumantes foram três vezes maiores no grupo de setores com maior privação em relação aos de menor privação. Conclusão: Setores de maior privação social apresentaram maiores prevalências de tabagismo, comparados com menor privação, apontando desigualdades sociais.

Premature mortality due to noncommunicable diseases in Brazilian capitals: redistribution of garbage causes and evolution by social deprivation strata / Mortalidade prematura por doenças crônicas não transmissíveis em capitais brasileiras: redistribuição de causas garbage e evolução por estratos de privação social

ABSTRACT Objective: To analyze premature mortality due to noncommunicable chronic diseases (NCDs) in Brazilian capitals and the Federal District (DF) after redistribution of garbage causes and the temporal evolution according to social deprivation strata in the 2010 to 2012 and 2017 to 2019 triennia. Methods: Corrections were applied to the Mortality Information System (Sistema de Informação sobre Mortalidade - SIM) data such as the redistribution of garbage codes (GC). Premature mortality rates due to NCDs were calculated and standardized by age. The differences among NCDs mortality rates were analyzed according to the Brazilian Deprivation Index (Índice Brasileiro de Privação - IBP) categories and between the three-year periods. Results: In the capitals as a whole, rates increased between 8 and 12% after GC redistribution and the greatest increases occurred in areas of high deprivation: 11.9 and 11.4%, triennia 1 and 2, respectively. There was variability between the capitals. There was a reduction in rates in all strata of deprivation between the three-year periods, with the greatest decrease in the stratum of low deprivation (-18.2%) and the lowest in the stratum of high deprivation (-7.5%). Conclusion: The redistribution of GC represented an increase in mortality rates, being higher in the strata of greater social deprivation. As a rule, a positive gradient of mortality was observed with increasing social deprivation. The analysis of the temporal evolution showed a decrease in mortality from NCDs between the triennia, especially in areas of lower social deprivation.

RESUMO Objetivo: Analisar a mortalidade prematura por doenças crônicas não transmissíveis (DCNT) nas capitais brasileiras e Distrito Federal (DF) após redistribuição das causas garbage, e a evolução temporal segundo estratos de privação social nos triênios 2010 a 2012 e 2017 a 2019. Métodos: Foram aplicadas correções ao Sistema de Informação sobre Mortalidade (SIM), sendo empregada metodologia para redistribuição das causas garbage (CG). As taxas de mortalidade prematura por DCNT padronizadas por idade foram estimadas. Foram analisadas as diferenças entre as taxas de mortalidade por DCNT segundo categorias do Índice Brasileiro de Privação (IBP) e entre os triênios. Resultados: No conjunto das capitais, as taxas aumentaram entre 8 e 12% após a redistribuição de CG, e os maiores acréscimos ocorreram em áreas de alta privação: 11,9 e 11,4%, triênios 1 e 2, respectivamente. Houve variabilidade entre as capitais. Observou-se redução das taxas em todos os estratos de privação entre os triênios, sendo maior decréscimo no estrato de baixa privação (-18,2%), e menor no estrato de alta privação (-7,5%). Conclusão: A redistribuição de CG representou aumento das taxas de mortalidade, sendo maior nos estratos de maior privação social. Via de regra, observou-se gradiente positivo de mortalidade com o aumento da privação social. A análise da evolução temporal evidenciou decréscimo da mortalidade por DCNT entre os triênios, sobretudo em áreas de menor privação social.

Life Expectancy and Causes of Premature Death by Subgroup for Community-Based Action in Marin County, California, 2017-2021.

INTRODUCTION: Marin is a medium-sized county in California's San Francisco Bay Area. Despite its historically higher-than-average life expectancy and socioeconomic level, known economic and health disparities by race, ethnicity, and geography became more visible during the COVID-19 pandemic.  Methods: We calculated life expectancy, measured years of potential life lost (YPLLs), and described premature mortality for the five years of 2017-2021 by race, ethnicity, census tract, and resource level (as measured by Healthy Places Index [HPI]) to provide data on inequities to guide community-centered action to reduce premature mortality.  Results: Life expectancy for the county was 85.2 years. The non-Hispanic African American/Black population experienced the lowest life expectancy of 77.1 years, 11.6 years lower than the non-Hispanic Asian population which had the highest life expectancy (88.7 years). There was a 14.9-year difference in life expectancy between the census tracts with the lowest (77.1 years) and highest (92.0 years) estimates. We found a moderate, positive association between census tract resource level (HPI) and life expectancy (r=0.58, p<0.01). The leading causes of premature death were cancer, diseases of the circulatory system, and accidental overdoses, with variation by subgroup.  Conclusion: These data highlight health disparities that persist in Marin County and can inform data-driven public health strategies to narrow gaps in longevity between communities.

Geographical variation in compulsory hospitalisation - ethical challenges.

BACKGROUND: Compulsory hospitalisation in mental health care restricts patients' liberty and is experienced as harmful by many. Such hospitalisations continue to be used due to their assumed benefit, despite limited scientific evidence. Observed geographical variation in compulsory hospitalisation raises concern that rates are higher and lower than necessary in some areas. METHODS/DISCUSSION: We present a specific normative ethical analysis of how geographical variation in compulsory hospitalisation challenges four core principles of health care ethics. We then consider the theoretical possibility of a "right", or appropriate, level of compulsory hospitalisation, as a general norm for assessing the moral divergence, i.e., too little, or too much. Finally, we discuss implications of our analysis and how they can inform the future direction of mental health services.

Premature mortality due to noncommunicable diseases in Brazilian capitals: redistribution of garbage causes and evolution by social deprivation strata / Mortalidade prematura por doenças crônicas não transmissíveis em capitais brasileiras: redistribuição de causas garbage e evolução por estratos de privação social

Objectives: To analyze premature mortality due to noncommunicable diseases (NCDs) in Brazilian capitals and the Federal District (DF) according to the redistribution of garbage causes and the temporal evolution according to social deprivation strata in the trienniums 2010 to 2012 and 2017 to 2019. Methods: Corrections were applied to the Mortality Information System (SIM) such as: proportional redistribution of ignored and blank data and garbage causes (GC). Municipal premature mortality rates from NCDs were calculated using the local empirical Bayesian estimator and standardized by age. Differences between NCD mortality rates according to the Brazilian Deprivation Index (IBP) categories and between the three-year periods were analyzed. Results: In the capitals as a whole, rates increased between 8 and 12% after the GC redistribution and the greatest increases occurred in areas of high deprivation: 11.9% and 11.4%, triennia 1 and 2. There was variability between the capitals. There was a reduction in rates in all strata of deprivation between the three-year periods, with the greatest decrease in the stratum of low deprivation (-18.2%) and the lowest in the stratum of high deprivation (-7.5%). Conclusion: The redistribution of GC represented an increase in mortality rates, being higher in the strata of greater social deprivation. As a rule, a positive gradient of mortality was observed with increasing social deprivation. The analysis of the temporal evolution showed a decrease in mortality from NCDs between the three years, especially in areas of less social deprivation.

Objetivos: Analisar a mortalidade prematura por doenças crônicas não transmissíveis (DCNT) nas capitais brasileiras e Distrito Federal (DF) segundo redistribuição das causas garbage e a evolução temporal segundo estratos de privação social nos triênios 2010 a 2012 e 2017 a 2019. Métodos: Foram aplicadas correções ao Sistema de Informação sobre Mortalidade (SIM) como: redistribuição proporcional de dados ignorados e em branco, e das causas garbage (CG). As taxas municipais de mortalidade prematura por DCNT foram calculadas pelo estimador bayesiano empírico local e padronizadas por idade. Foram analisadas diferenças entre as taxas de mortalidade por DCNT segundo categorias do Índice Brasileiro de Privação (IBP) e entre os triênios. Resultados: No conjunto das capitais, as taxas aumentaram entre 8 a 12% após a redistribuição de CG e os maiores acréscimos ocorreram em áreas de alta privação: 11,9% e 11,4%, triênios 1 e 2. Houve variabilidade entre as capitais. Observou-se redução das taxas em todos os estratos de privação entre os triênios, sendo maior decréscimo no estrato de baixa privação (-18,2%) e menor no estrato de alta privação (-7,5%). Conclusão: A redistribuição de CG representou aumento das taxas de mortalidade, sendo maior nos estratos de maior privação social. Via de regra, observou-se gradiente positivo de mortalidade com o aumento da privação social. A análise da evolução temporal evidenciou decréscimo da mortalidade por DCNT entre os triênios, sobretudo em áreas de menor privação social.

Neighbourhood income inequalities in mental health in Barcelona 2001-2016: a Bayesian smoothed estimate / Desigualdades de renta por barrios en salud mental en Barcelona 2001-2016: una estimación de alisado bayesiano

Objective: Obtaining reliable health estimates at the small area level (such as neighbourhoods) using survey data usually poses the problem of small sample sizes. To overcome this limitation, we explored smoothing techniques in order to estimate poor mental health prevalence at the neighbourhood level and analyse its profile by income in Barcelona city (Spain). Method: A Bayesian smoothing model with a logit-normal transformation was applied to four repeated cross-sectional waves of the Barcelona health survey for 2001, 2006, 2011 and 2016. Mental health status was identified from the 12-item General Health Questionnaire. Income inequalities were analysed with neighbourhood income in quantiles for each year and trends in the pooled analysis. Results: The prevalence of poor mental health ranged from 14.6% in 2001 to 18.9% in 2016. The yearly difference between neighbourhoods was 12.4% in 2001, 16.7% in 2006, 14.2% in 2011, and 20.0% in 2016. The odds ratio and 95% credible interval (95%CI) of experiencing poor mental health was 1.40 times higher (95%CI: 1.02-1.91) in less advantaged neighbourhoods than in more advantaged neighbourhoods in 2001, 1.61 times higher (95%CI: 1.01-2.59) in 2006 and 2.31 times higher (95%CI: 1.57-3.40) in 2016. Conclusions: This study shows that the Bayesian smoothed techniques allows detection of inequalities in health in neighbourhoods and monitoring of interventions against them. In Barcelona, mental health problems are more prevalent in low-income neighbourhoods and raised in 2016. (AU)

Objetivo: Obtener estimadores de salud en áreas pequeñas (como los barrios) utilizando datos de encuestas supone hacer frente al problema de insuficiente tamaño muestral. Para superar esta limitación exploramos técnicas de alisado con el fin de estimar la prevalencia de mala salud mental a nivel de barrio y analizar su patrón por renta en la ciudad de Barcelona (España). Método: Se aplicó un modelo de alisado bayesiano con transformación logística-normal a cuatro muestras transversales repetidas de la Encuesta de Salud de Barcelona para los años 2001, 2006, 2011 y 2016. La salud mental fue identificada con el Cuestionario General de Salud de 12-items. Las desigualdades de ingreso se analizaron por cuantiles de la renta por barrio para cada año y las tendencias en el análisis conjunto. Resultados: La prevalencia de mala salud mental oscila entre el 14,6% en 2001 y el 18,9% en 2016. La diferencia entre barrios fue del 12,4% en 2001, del 16,7% en 2006, del 14,2% en 2011 y del 20,0% en 2016. La odds ratio y el intervalo creíble al 95% (IC95%) de experimentar mala salud mental fue 1,4 veces superior (IC95%: 1,02-1,91) en los barrios menos aventajados respecto de los más aventajados en 2001, de 1,61 (IC95%: 1,01-2,59) en 2006 y de 2,31 (IC95%: 1,57-3,40) en 2016. Conclusiones: Este estudio muestra que las técnicas de alisado bayesiano permiten la detección de desigualdades en salud a nivel de barrios para su monitorización e intervención con el fin de reducirlas. En Barcelona, los problemas de salud mental son más prevalentes en los barrios de menor renta y se incrementaron en 2016. (AU)