Sociodemographic disparities and contextual factors in obesity: updated evidence from a National Survey of Risk Factors for Chronic Diseases

Objective: To assess the association of sociodemographic and environmental factors with the obesity occurrence in Argentina from a sex- and age-comparative perspective and a multilevel approach. Design: Cross-sectional study based on secondary data from the National Survey of Chronic Diseases Risk Factors (CDRF) 2018, Argentina. Two-level logistic regression models stratified by sex and age were used. Setting: The nationwide probabilistic sample of the CDRF survey and 24 geographical units. Participants: 16 410 adult people, living in Argentine towns of at least 5 000 people, nested into 24 geographical units. Sex-age groups were defined as young (aged 18-44y), middle-aged (45-64y), and older (65y and older) men and women. Results: Single men (all age groups) and divorced/widowed men (aged 45y or older) had a lower obesity risk compared to married ones. In the middle-aged group, men with higher education showed a lower risk than men with incomplete primary education. In young women, a marked social gradient by educational level was observed. A low-income level coupled with highly urbanized contexts represents an unfavourable scenario for young and middle-aged women. Having a multiperson household was a risk factor for obesity (OR=1.26, p=0.038) in middle-aged women. Contextual factors linked to the availability of socially constructed recreational resources and green spaces were associated with obesity among young adults. Conclusions: Socio-environmental determinants of obesity seem to operate differently according to sex and age in Argentina. This entails the need to address the obesity epidemic considering gender inequalities and the socio-environmental context at each stage of life.

Differences in depressive symptoms by rurality in Japan: a cross-sectional multilevel study using different aggregation units of municipalities and neighborhoods (JAGES)

Background Rurality can reflect many aspects of the community, including community characteristics that may be associated with mental health. In this study, we focused on geographical units to address multiple layers of a rural environment. By evaluating rurality at both the municipality and neighborhood (i.e., a smaller unit within a municipality) levels in Japan, we aimed to elucidate the relationship between depression and rurality. To explore the mechanisms linking rurality and depression, we examined how the association between rurality and depression can be explained by community social capital according to geographical units. Methods We used cross-sectional data from the 2016 wave of the Japan Gerontological Evaluation Study involving 144,822 respondents aged 65 years or older residing in 937 neighborhoods across 39 municipalities. The population density quintile for municipality-level rurality and the quintile for the time required to reach densely inhabited districts for neighborhood-level rurality were used. We calculated the prevalence ratios of depressive symptoms by gender using a three-level (individual, neighborhood, and municipality) Poisson regression. Community social capital was assessed using three components: civic participation, social cohesion, and reciprocity. Results The prevalence of depressive symptoms was higher in municipalities with lower population density than those with the highest population density; the ratios were 1.22 (95% confidence intervals: 1.15, 1.30) for men and 1.22 (1.13, 1.31) for women. In contrast, when evaluating rurality at the neighborhood level, the prevalence of depressive symptoms was 0.9 times lower for men in rural areas; no such association was observed for women. In rural municipalities, community civic participation was associated with an increased risk of depressive symptoms. In rural neighborhoods, community social cohesion and reciprocity were linked to a lower risk of depressive symptoms. Conclusions The association between rurality and depression varied according to geographical unit. In rural municipalities, the risk of depression may be higher for both men and women, and the presence of an environment conducive to civic participation may contribute to a higher risk of depression, as observed in this study. The risk of depression in men may be lower in rural neighborhoods in Japan, which may be related to high social cohesion and reciprocity.

The association between income inequality and adult mental health at the subnational level—a systematic review

Purpose A systematic review was undertaken to determine whether research supports: (i) an association between income inequality and adult mental health when measured at the subnational level, and if so, (ii) in a way that supports the Income Inequality Hypothesis (i.e. between higher inequality and poorer mental health) or the Mixed Neighbourhood Hypothesis (higher inequality and better mental health). Methods Systematic searches of PsycINFO, Medline and Web of Science databases were undertaken from database inception to September 2020. Included studies appeared in English-language, peer-reviewed journals and incorporated measure/s of objective income inequality and adult mental illness. Papers were excluded if they focused on highly specialised population samples. Study quality was assessed using a custom-developed tool and data synthesised using the vote-count method. Results Forty-two studies met criteria for inclusion representing nearly eight million participants and more than 110,000 geographical units. Of these, 54.76% supported the Income Inequality Hypothesis and 11.9% supported the Mixed Neighbourhood Hypothesis. This held for highest quality studies and after controlling for absolute deprivation. The results were consistent across mental health conditions, size of geographical units, and held for low/middle and high income countries. Conclusions A number of limitations in the literature were identified, including a lack of appropriate (multi-level) analyses and modelling of relevant confounders (deprivation) in many studies. Nonetheless, the findings suggest that area-level income inequality is associated with poorer mental health, and provides support for the introduction of social, economic and public health policies that ameliorate the deleterious effects of income inequality. Clinical registration number PROSPERO 2020 CRD42020181507.

Sociodemographic determinants of intraurban variations in COVID-19 incidence: the case of Barcelona

BackgroundIntraurban sociodemographic risk factors for COVID-19 have yet to be fully understood. We investigated the relationship between COVID-19 incidence and sociodemographic factors in Barcelona at a fine-grained geography.MethodsThis cross-sectional ecological study is based on 10 550 confirmed cases of COVID-19 registered during the first wave in the municipality of Barcelona (population 1.64 million). We considered 16 variables on the demographic structure, urban density, household conditions, socioeconomic status, mobility and health characteristics for 76 geographical units of analysis (neighbourhoods), using a lasso analysis to identify the most relevant variables. We then fitted a multivariate Quasi-Poisson model that explained the COVID-19 incidence by neighbourhood in relation to these variables.ResultsNeighbourhoods with: (1) greater population density, (2) an aged population structure, (3) a high presence of nursing homes, (4) high proportions of individuals who left their residential area during lockdown and/or (5) working in health-related occupations were more likely to register a higher number of cases of COVID-19. Conversely, COVID-19 incidence was negatively associated with (6) percentage of residents with post-secondary education and (7) population born in countries with a high Human Development Index.ConclusionLike other historical pandemics, the incidence of COVID-19 is associated with neighbourhood sociodemographic factors with a greater burden faced by already deprived areas. Because urban social and health injustices already existed in those geographical units with higher COVID-19 incidence in Barcelona, the current pandemic is likely to reinforce both health and social inequalities, and urban environmental injustice all together.

Infection Rates from Covid-19 in Great Britain by Geographical Units: A Model-based Estimation from Mortality Data

This study estimates cumulative infection rates from Covid-19 in Great Britain by geographical units and investigates spatial patterns in infection rates. We propose a model-based approach to calculate cumulative infection rates from data on observed and expected deaths from Covid-19. Our analysis of mortality data shows that between 5 and 6% of people in Great Britain were infected by Covid-19 by the last third of April 2020. It is unlikely that the infection rate was lower than 3% or higher than 12%. Secondly, England had higher infection rates than Scotland and Wales, although the differences between countries were not large. Thirdly, we observed a substantial variation in virus infection rates in Great Britain by geographical units. Estimated infection rates were highest in the capital city of London where more than 10% of the population might have been infected and also in other major urban regions, while the lowest were in small towns and rural areas. Finally, spatial regression analysis showed that the virus infection rates increased with the increasing population density of the area and the level of deprivation. The results suggest that people from lower socioeconomic groups in urban areas (including those with minority backgrounds) were most affected by the spread of coronavirus in March and April.

Biogeography of Medically Important Insects using Quantitative Analysis

We summarized distributional information of medically important insects from 76 families and 4531 genera occurring worldwide. The continents were divided into 67 basic geographical units. Using a new similarity formula and a new clustering method for quantitative analysis, 67 basic geographical units were clustered into 7 large unit groups and 20 small unit groups. The results were superior to the traditional single linkage method, average group linkage method, or sum of squares method. The cluster results were similar with the result of mainly phytophagous insects 104,344 genera in the world, but were different from the Wallace’s mammal geographical division scheme. Based on these seemingly contradictory results, we infer that animals, insect and plants may have the same distribution pattern and that it is necessary to conduct precise quantitative analysis for animals and plants worldwide.

The Contribution of Age Structure to the Number of Deaths from Covid-19 in the UK by Geographical Units

This study investigates the contribution of population age structure to mortality from Covid-19 in the UK by geographical units. We project death rates at various spatial scales by applying data on age-specific fatality rates to the area’s population by age and sex. Our analysis shows a significant variation in the projected death rates between the constituent countries of the UK, between its regions and within regions. First, Scotland and Wales have higher projected fatality levels from Covid-19 than England, whereas Northern Ireland has lower rate. Second, the infection fatality rates are projected to be substantially higher in small towns and rural areas than those in large urban areas. Third, our analysis shows that within urban regions there are also ‘pockets’ of high projected death rates. Overall, the areas with high and low fatality rates tend to cluster because of the high residential separation of different population age-groups in the UK. Our analysis also reveals that the Welsh-, Gaelic- and Cornish-speaking communities with relatively old populations are likely to experience heavy population losses if the virus spreads widely across the UK.

Using reflectance spectroscopy for detecting land-use effects on soil quality in drylands

<p>The rapid growth in the global population over the past few decades has resulted in the transformation of many natural ecosystems into human-dominated ones. Land-use (LU) dynamics are accompanied by an increase in resource exploitation, often causing deteriorated environmental conditions that are reflected in the soil quality. Soil quality differences between LUs can be observed and measured using near-infrared reflectance spectroscopy (NIRS) methods. The research goal was to apply, measure, and evaluate soil properties based solely on the spectral differences between both natural and human-dominated LU practices, in the dryland environment of the central Negev Desert, Israel. This goal was achieved through the development and implementation of chemometrics techniques that were generated from soil point spectroscopy. Soil quality index (SQI) values, based on 14 physical, biological, and chemical soil properties, were quantified and compared between LUs and geographical units across the study area. Laboratory spectral measurements of soil samples were applied. Significant differences in SQI values were found between the geographical units. The statistical and mathematical methods for evaluating the soil properties’ spectral differences included principal component analysis (PCA), partial least squares-regression (PLS-R), and partial least squares-discriminant analysis (PLS-DA). Correlations between predicted spectral values and measured soil properties and SQI were calculated using PLS-R and evaluated by the coefficient of determination (R<sup>2</sup>), the Root Mean Square Error of Calibration, and Cross-Validation (RMSEC and RMSECV), and the ratio of performance to deviation (RPD). The PLS-R managed to produce “excellent” and “good” prediction values for some of the soil properties, including EC, Cl, Na, Ca + Mg, SAR, NO<sub>3</sub>, P, and SOM. Results of the PLS-R model for SQI are R<sup>2</sup> = 0.90, RPD = 2.46, RMSEC = 0.034, and RMSECV = 0.057. The PLS-DA classification of the laboratory spectroscopy was applied and resulted in high accuracy and kappa coefficient values when comparing LUs. In contrast, comparing the sampling sites resulted in lower overall accuracy (Acc = 0.82) and kappa values (K<sub>c</sub> = 0.80). It is concluded that differentiation between physical, biological, and chemical soil properties, based on their spectral differences, is the key feature in the successful results for recognizing and characterizing various soil processes in an integrative approach.  The results prove that soil quality and most soil properties can be successfully monitored and evaluated using NIRS in a comprehensive, non-destructive, time- and cost-efficient method.</p>