Does community deprivation determine longevity after the age of 75? A cross-national analysis

Objectives: Analyze the association between socioeconomic deprivation and old-age survival in Europe, and investigate whether it varies by country and gender. Methods: Our study incorporated five countries (Portugal, Spain, France, Italy, and England). A 10-year survival rate expressing the proportion of population aged 75–84 years who reached 85–94 years old was calculated at area-level for 2001–11. To estimate associations, we used Bayesian spatial models and a transnational measure of deprivation. Attributable/prevention fractions were calculated. Results: Overall, there was a significant association between deprivation and survival in both genders. In England that association was stronger, following a dose–response relation. Although lesser in magnitude, significant associations were observed in Spain and Italy, whereas in France and Portugal these were even weaker. The elimination of socioeconomic differences between areas would increase survival by 7.1%, and even a small reduction in socioeconomic differences would lead to a 1.6% increase. Conclusions: Socioeconomic deprivation was associated with survival among older adults at ecological-level, although with varying magnitude across countries. Reasons for such cross-country differences should be sought. Our results emphasize the importance of reducing socioeconomic differences between areas.This work was supported by Portuguese funds through FCT—Fundação para a Ciência e a Tecnologia in the framework of project UID/BIM/04293/2013. AIR and MFP would also like to thank to FC—Fundação para a Ciência e a Tecnologia for the Grants PTDC/SAU-EPI/113424/2009 and SFRH/BD/82529/2011. MSC was supported by CNpQ (309692/2013-0) and FAPERJ (E-26/203.557/2014).We are very grateful to the National Statistic Offices for sending us the required data and to all the members of the European Deprivation Index (EDI) team. The authors would like to thank Rogério Ribeiro for the help in preparing visual supports, Alexandra Guttentag for her work as language editor, and the anonymous reviewers for their highly valuable corrections and suggestions

Geographic patterns and hotspots of pediatric tuberculosis: the role of socioeconomic determinants

Objective: Children are an important demographic group for understanding overall tuberculosis epidemiology, and monitoring of childhood tuberculosis is essential for appropriate prevention. The present study sought to characterize the spatial distribution of childhood tuberculosis notification rates in continental Portugal; identify high-risk areas; and evaluate the association between childhood tuberculosis notification rates and socioeconomic deprivation. Methods: Using hierarchical Bayesian spatial models, we analyzed the geographic distribution of pediatric tuberculosis notification rates across 278 municipalities between 2016 and 2020 and determined high- risk and low-risk areas. We used the Portuguese version of the European Deprivation Index to estimate the association between childhood tuberculosis and area-level socioeconomic deprivation. Results: Notification rates ranged from 1.8 to 13.15 per 100,000 children under 5 years of age. We identified seven high- risk areas, the relative risk of which was significantly above the study area average. All seven high-risk areas were located in the metropolitan area of Porto or Lisbon. There was a significant relationship between socioeconomic deprivation and pediatric tuberculosis notification rates (relative risk = 1.16; Bayesian credible interval, 1.05-1.29). Conclusions: Identified high-risk and socioeconomically deprived areas should constitute target areas for tuberculosis control, and these data should be integrated with other risk factors to define more precise criteria for BCG vaccination.Objetivo: As crianças são um grupo demográfico importante para a compreensão da epidemiologia da tuberculose em geral, e o monitoramento da tuberculose infantil é essencial para a prevenção adequada. O presente estudo procurou caracterizar a distribuição espacial das taxas de notificação de tuberculose infantil em Portugal continental; identificar áreas de alto risco e avaliar a associação entre taxas de notificação de tuberculose infantil e privação socioeconômica. Métodos: Por meio de modelos espaciais hierárquicos bayesianos, analisamos a distribuição geográfica das taxas de notificação de tuberculose pediátrica em 278 municípios entre 2016 e 2020 e determinamos as áreas de alto e baixo risco. Usamos a versão portuguesa do European Deprivation Index para calcular a associação entre a tuberculose infantil e a privação socioeconômica em cada área. Resultados: As taxas de notificação variaram de 1,8 a 13,15 por 100.000 crianças com idade < 5 anos. Identificamos sete áreas de alto risco, cujo risco relativo era significativamente maior que a média da área de estudo. Todas as sete áreas de alto risco situavam-se na área metropolitana do Porto e de Lisboa. Houve uma relação significativa entre a privação socioeconômica e as taxas de notificação de tuberculose pediátrica (risco relativo = 1,16; intervalo de credibilidade de 95%: 1,05-1,29). Conclusões: Áreas identificadas como sendo de alto risco e desfavorecidas socioeconomicamente devem constituir áreas-alvo para o controle da tuberculose, e esses dados devem ser integrados a outros fatores de risco para definir critérios mais precisos para a vacinação com BCG.This study received financial support from the Fundo Europeu de Desenvolvimento Regional (FEDER, European Regional Development Fund), through the Programa Operacional Competitividade e Internacionalizacao; from the Ministerio da Ciencia, Tecnologia e Ensino Superior de Portugal, Fundacao para a Ciencia e a Tecnologia - FCT - Unidade de Investigacao em Epidemiologia, Instituto de Saude Publica, Universidade do Porto - EPIUnit (Grant no. UIDB/04750/2020); and from the Laboratorio para a Investigacao Integrativa e Translacional em Saude Populacional - ITR (Grant no. LA/P/0064/2020). Ana Isabel Ribeiro is the recipient of a grant from the FCT Estimulo ao Emprego Cientifico, Apoio Individual (Grant no. CEECIND/02386/2018)

Flexible modelling of spatial variation in agricultural field trials with the R package INLA

The objective of this paper was to fit different established spatial models for analysing agricultural field trials using the open-source R package INLA. Spatial variation is common in field trials, and accounting for it increases the accuracy of estimated genetic effects. However, this is still hindered by the lack of available software implementations. We compare some established spatial models and show possibilities for flexible modelling with respect to field trial design and joint modelling over multiple years and locations. We use a Bayesian framework and for statistical inference the integrated nested Laplace approximations (INLA) implemented in the R package INLA. The spatial models we use are the well-known independent row and column effects, separable first-order autoregressive ( AR1⊗AR1 ) models and a Gaussian random field (Matérn) model that is approximated via the stochastic partial differential equation approach. The Matérn model can accommodate flexible field trial designs and yields interpretable parameters. We test the models in a simulation study imitating a wheat breeding programme with different levels of spatial variation, with and without genome-wide markers and with combining data over two locations, modelling spatial and genetic effects jointly. The results show comparable predictive performance for both the AR1⊗AR1 and the Matérn models. We also present an example of fitting the models to a real wheat breeding data and simulated tree breeding data with the Nelder wheel design to show the flexibility of the Matérn model and the R package INLA

Using Bayesian spatial models to map and to identify geographical hotspots of multidrug-resistant tuberculosis in Portugal between 2000 and 2016

Multidrug-resistant tuberculosis (MDR-TB) is a major threat to the eradication of tuberculosis. TB control strategies need to be adapted to the necessities of different countries and adjusted in high-risk areas. In this study, we analysed the spatial distribution of the MDR- and non-MDR-TB cases across municipalities in Continental Portugal between 2000 and 2016. We used Bayesian spatial models to estimate age-standardized notification rates and standardized notification ratios in each area, and to delimitate high- and low-risk areas, those whose standardized notification ratio is significantly above or below the country’s average, respectively. The spatial distribution of MDR- and non-MDR-TB was not homogeneous across the country. Age-standardized notification rates of MDR-TB ranged from 0.08 to 1.20 and of non-MDR-TB ranged from 7.73 to 83.03 notifications per 100,000 population across the municipalities. We identified 36 high-risk areas for non-MDR-TB and 8 high-risk areas for MDR-TB, which were simultaneously high-risk areas for non-MDR-TB. We found a moderate correlation (ρ = 0.653; 95% CI 0.457–0.728) between MDR- and non-MDR-TB standardized notification ratios. We found heterogeneity in the spatial distribution of MDR-TB across municipalities and we identified priority areas for intervention against TB. We recommend including geographical criteria in the application of molecular drug resistance to provide early MDR-TB diagnosis, in high-risk areas.This work was developed under the scope of the project NORTE-01-0145-FEDER-000013, supported by the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER) and project PTDC/SAU-PUB/29521/2017. Olena Oliveira is supported by the project NORTE-08-5369-FSE-000041, financed by the Operational Program NORTE 2020 and co-financed by the European Social Fund through a doctoral grant (UMINHO/BD/47/2016). This study was also supported by FEDER through the Operational Programme Competitiveness and Internationalization and national funding from the Foundation for Science and Technology—FCT (Portuguese Ministry of Science, Technology and Higher Education) under the Unidade de Investigação em Epidemiologia—Instituto de Saúde Pública da Universidade do Porto (EPIUnit) (POCI-01-0145-FEDER-006862; Ref. UID/DTP/04750/2019). Ana Isabel Ribeiro was supported by National Funds through FCT, under the programme of ‘Stimulus of Scientific Employment—Individual Support’ within the contract CEECIND/02386/2018

Assessing comorbidity and correlates of wasting and stunting among children in Somalia using cross-sectional household surveys: 2007 to 2010

Wasting and stunting may occur together at the individual child level; however, their shared geographic distribution and correlates remain unexplored. Understanding shared and separate correlates may inform interventions. We aimed to assess the spatial codistribution of wasting, stunting and underweight and investigate their shared correlates among children aged 6-59 months in Somalia.Cross-sectional nutritional assessments surveys were conducted using structured interviews among communities in Somalia biannually from 2007 to 2010. A two-stage cluster sampling methodology was used to select children aged 6-59 months from households across three livelihood zones (pastoral, agropastoral and riverine). Using these data and environmental covariates, we implemented a multivariate spatial technique to estimate the codistribution and divergence of the risks and correlates of wasting and stunting at the 1×1 km spatial resolution.73 778 children aged 6-59 months from 1066 survey clusters in Somalia.Observed pairwise child level empirical correlations were 0.30, 0.70 and 0.73 between weight-for-height and height-for-age; height-for-age and weight-for-age, and weight-for-height and weight-for-age, respectively. Access to foods with high protein content and vegetation cover, a proxy of rainfall or drought, were associated with lower risk of wasting and stunting. Age, gender, illness, access to carbohydrates and temperature were correlates of all three indicators. The spatial codistribution was highest between stunting and underweight with relative risk values ranging between 0.15 and 6.20, followed by wasting and underweight (range: 0.18-5.18) and lowest between wasting and stunting (range: 0.26-4.32).The determinants of wasting and stunting are largely shared, but their correlation is relatively variable in space. Significant hotspots of different forms of malnutrition occurred in the South Central regions of the country. Although nutrition response in Somalia has traditionally focused on wasting rather than stunting, integrated programming and interventions can effectively target both conditions to alleviate common risk factors