Impact of the use of small-area models on estimation of attributable mortality at a regional level.

The objective of this study is to assess the impact of applying prevalences derived from a small-area model at a regional level on smoking-attributable mortality (SAM). A prevalence-dependent method was used to estimate SAM. Prevalences of tobacco use were derived from a small-area model. SAM and population attributable fraction (PAF) estimates were compared against those calculated by pooling data from three national health surveys conducted in Spain (2011-2014-2017). We calculated the relative changes between the two estimates and assessed the width of the 95% CI of the PAF. Applying surveys-based prevalences, tobacco use was estimated to cause 53 825 (95% CI: 53 182-54 342) deaths in Spain in 2017, a figure 3.8% lower obtained with the small-area model prevalences. The lowest relative change was observed in the Castile-La Mancha region (1.1%) and the highest in Navarre (14.1%). The median relative change between regions was higher for women (26.1%), population aged ≥65 years (6.6%), and cardiometabolic diseases (9.0%). The differences between PAF by cause of death were never greater than 2%. Overall, the differences between estimates of SAM, PAF, and confidence interval width are small when using prevalences from both sources. Having these data available by region will allow decision-makers to implement smoking control measures based on more accurate data.

Time Dependence Between Tobacco Consumption and Lung Cancer Mortality in Spain.

OBJECTIVES: Lung cancer is the leading cause of cancer death and the second most common cancer in both sexes worldwide, with tobacco being its main risk factor. The aim of this study is to establish the temporal relationship between smoking prevalence and lung cancer mortality in Spain. METHODS: To model the time dependence between smoking prevalence and lung cancer mortality, a distributed lag non-linear model was applied adjusting for sex, age, year of mortality and population at risk. Smoking prevalence data from 1991-2020 were used. Considering a maximum lag of 25 years, mortality data from 2016-2020 were included. The effect of prevalence on mortality for each lag is presented in terms of relative risk (RR). To identify the lag at which smoking prevalence has the greatest effect on mortality, the RR of the different lags were compared. RESULTS: The optimal lag observed between smoking prevalence and lung cancer mortality in Spain was 15 years. The maximum RR was 2.9 (95%CI: 2.0-4.3) for a prevalence of 71% and a 15-year lag. The RR was 1.8 for a prevalence of 33%, an approximate median value between 1991-2020, and a 15-year lag. CONCLUSIONS: In Spain, lung cancer mortality is affected by smoking prevalence 15 years prior. Knowing the evolution of the smoking prevalence series in a country and establishing a lag time is essential to predict how lung cancer incidence and mortality will evolve.

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.

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 estimation of expenditure means and ratios under a unit-level bivariate linear mixed model.

Under a unit-level bivariate linear mixed model, this paper introduces small area predictors of expenditure means and ratios, and derives approximations and estimators of the corresponding mean squared errors. For the considered model, the REML estimation method is implemented. Several simulation experiments, designed to analyze the behavior of the introduced fitting algorithm, predictors and mean squared error estimators, are carried out. An application to real data from the Spanish household budget survey illustrates the behavior of the proposed statistical methodology. The target is the estimation of means of food and non-food household annual expenditures and of ratios of food household expenditures by Spanish provinces.

[A Proposed Epidemiologic Risk Threshold for SARS-CoV-2 for Assisting Healthcare Decision-Making]. / Sistema de ayuda a la toma de decisiones sanitarias. Propuesta de umbrales de riesgo epidemiológico ante SARS-CoV-2.

INTRODUCTION: The SARS-CoV-2 pandemic is the most important health challenge observed in 100 years, and since its emergence has generated the highest excess of non-war-related deaths in the western world. Since this disease is highly contagious and 33% of cases are asymptomatic, it is crucial to develop methods to predict its course. We developed a predictive model for Covid-19 infection in Spanish provinces. METHODS: We applied main components analysis to epidemiological data for Spanish provinces obtained from the National Centre of Epidemiology, based on the epidemiological curve between 24 February and 8 June 2020. Using this method, we classified provinces according to their epidemiological progress (worst, intermediate, and good). RESULTS: We identified 2 components that explained 99% of variability in the 52 epidemiological curves. The first component can be interpreted as the crude incidence rate trend and the second component as the speed of increase or decrease in the incidence rate during the period analysed. We identified 10 provinces in the group with the worst progress and 17 in the intermediate group. The threshold values for the 7-day incidence rate for an alert 1 (intermediate) were 134 cases/100,000 inhabitants, and 167 for alert 2 (high), respectively, showing a high discriminative power between provinces. CONCLUSIONS: These alert levels might be useful for deciding which measures may affect population mobility and other public health decisions when considering community transmission of SARS-CoV-2 in a given geographical area. This information would also facilitate intercomparison between healthcare areas and Autonomous Communities.

COVID-19 Variability Within European Countries Sourced From ECDC Data. Is Variability Explained by Specific Country Policies?

Background: Europe has had a large variability in COVID-19 incidence between and within countries, particularly after June 2020. We aim to assess the variability between European countries and regions located in a given country. Methods: We used ECDC information including countries having 7 regions or more. The metric used to assess the regional variability within a country was the intercuartilic range in a weekly basis for 32 weeks between June 29th 2020 and February 1st 2021. We also calculated each country's overall variability across the 32 weeks using the distances from the regional curves of the 14-day incidence rates to the corresponding national curve, using the L2 metric for functional data. We afterwards standardised this metric to a scale from 0 to 100 points. We repeated the calculations excluding island regions. Results: The variability between and within countries was large. Slovenia, Spain and Portugal have the greatest variability. Spain and Slovenia held also the top three places for the greatest number of weeks (Spain for 19 weeks and Slovenia for 10) with the highest variability. For variability among the incidence curves across the 32-week period, Slovenia, Portugal and Spain ranked first in functional variability, when all the regions were analysed but also when the island regions were excluded. Conclusions: These differences might be due to how countries tackled the epidemiological situation. The persistent variability in COVID-19 incidence between regions of a given country suggests that governmental action may have an important role in applying epidemiological control measures.

Sistema de ayuda a la toma de decisiones sanitarias. propuesta de umbrales de riesgo epidemiológico ante SARS-CoV-2 / A proposed epidemiologic risk threshold for SARS-CoV-2 for assisting healthcare decision-making

INTRODUCCIÓN: La pandemia por SARS-CoV-2 es el mayor desafío sanitario en los últimos 100 años, ocasionando el mayor exceso de mortalidad no bélico en este período en el mundo occidental. Ante una enfermedad de elevada contagiosidad y asintomática en un tercio de los casos, es fundamental disponer de modelos que predigan su evolución. Pretendemos desarrollar un modelo de predicción de infección por COVID19 en provincias españolas. MÉTODO: Análisis de componentes principales funcional a datos epidemiológicos de las provincias españolas en función de su curva epidémica entre el 24 de febrero y el 8 de junio. Con este método se han clasificado las provincias en función de su evolución (peor, intermedia y mejor). Se han empleado los datos del Centro Nacional de Epidemiología. RESULTADOS: Se identificaron dos componentes que explican el 99% de la variabilidad de las 52 curvas. La primera componente es la tendencia global de la tasa de incidencia, y la segunda componente es la velocidad de crecimiento o decrecimiento de la incidencia durante el período. Se identificaron 10 provincias en el grupo de peor evolución y 17 en el de evolución intermedia. Los valores umbrales de la tasa de incidencia a 7 días fueron 134 casos/100.000 habitantes para un nivel de alerta 1 (medio) y 167 para el nivel 2 (alto), consiguiendo un elevado poder de discriminación entre provincias. CONCLUSIONES: Estos niveles de alerta podrían ser de utilidad para decidir medidas que puedan afectar a la movilidad de la población, siempre y cuando haya una situación de transmisión comunitaria de SARS-CoV-2. Esta información sería intercomparable entre áreas sanitarias o CCAA

INTRODUCTION: The SARS-CoV-2 pandemic is the most important health challenge encountered in 100 years, and since its emergence has generated the highest excess of non-war-related deaths in the western world. Since this disease is highly contagious and 33% of cases are asymptomatic, it is crucial to develop methods to predict its course. We developed a predictive model for Covid-19 infection in Spanish provinces. METHODS: We applied main components analysis to epidemiological data for Spanish provinces obtained from the National Centre of Epidemiology, based on the epidemiological curve between 24 February and 8 June 2020. Using this method, we classified provinces according to their epidemiological progress (worst, intermediate, and good). RESULTS: We identified two components that explained 99% of variability in the 52 epidemiological curves. The first component can be interpreted as the crude incidence rate trend and the second component as the speed of increase or decrease in the incidence rate during the period analysed. We identified 10 provinces in the group with the worst progress and 17 in the intermediate group. The threshold values for the 7-day incidence rate for an alert 1 (intermediate) were 134 cases/100,000 inhabitants, and 167 for alert 2 (high), respectively, showing a high discriminative power between provinces. CONCLUSIONS: These alert levels might be useful for deciding which measures may affect population mobility and other public health decisions when considering community transmission of SARS-CoV-2 in a given geographical area. This information would also facilitate intercomparison between healthcare areas and Autonomous Communities