Socioeconomic Patterns Research Articles

Social and economic patterning in UPF intake in toddlerhood and middle childhood: longitudinal data from the UK Gemini cohort.

Children's consumption of ultra-processed food (UPF) may contribute to inequalities in obesity and wider health. Socioeconomic patterning in younger UK children's UPF intake is unknown. To investigate socioeconomic patterning of UK toddlers' (21-months) and children's (7-years) UPF intake across several household and neighbourhood indicators. Secondary analysis of data from a prospective longitudinal cohort study using parent-report sociodemographic data and 3-day diet diaries. /setting: Participants were children from the UK Gemini study of n=4,804 twins born in 2007. At 21-months and 7-years, n=2,591 and n=592 children had at least 2-days of dietary data, respectively. Percentage energy from UPF at 21-months and 7-years-of-age, classified using the NOVA system. Unadjusted linear regression models were run for household socioeconomic position (SEP) composite score, Index of multiple deprivation decile, income, occupation level, mother's age, education of mother and partner, child's ethnicity, sex, and age. Adjusted multivariable linear regression models were adjusted for ethnicity and all SEP indicators except SEP composite score (Adjusted 1), in addition to child sex and age (Adjusted 2). Missing data were addressed with multiple imputation and inverse probability weighting. Confidence intervals and P-values were adjusted to account for clustering within families. Children of lower SEP had higher UPF intake across several indicators. Mother's education was the strongest predictor, with postgraduate education associated with 8.64% (95% CI -12.08 to -5.20; P<0.001) and 10.12% (95% CI -15.68 to -4.56; P<0.001) less energy from UPF at 21-months and 7-years, respectively, compared to no educational qualifications in Adjusted model 2. UK children from more disadvantaged backgrounds consumed a greater proportion of their energy from UPF. Mother's education seemed the most influential factor. Socioeconomic inequalities, particularly in maternal education, may drive disparities in diet quality and associated health outcomes. Addressing these gaps is essential to reduce childhood obesity and improve long-term health in socioeconomically disadvantaged populations.

A Nationwide Exploration of Social Inequalities in Cancer Mortality Amidst the COVID-19 Pandemic in Belgium.

The COVID-19 pandemic disrupted global health systems, impacting cancer care and potentially increasing cancer mortality, especially among socioeconomically disadvantaged individuals. We aimed to assess changes in cancer mortality from March 1 to December 31, 2020 relative to the same period in 2019, and to examine potential shifts in cancer mortality's social disparities during the same time frame. We used nationwide individually linked cancer mortality data from the Belgian National Register, the Census 2011, and the tax register. Analyses were stratified by age group (45-59 years, 60-74 years, 75+ years) and sex across all cancer types, including breast, colorectal, lung, pancreatic, and prostate. Direct age-standardized mortality rates were calculated in 2019 and 2020 to calculate absolute and relative changes in cancer mortality by social indicators. Relative inequalities in cancer mortality by social groups were calculated for both time frames using Poisson regression. Sensitivity analysis considered any mention of specified cancer groups on the Belgian death certificate. For both overall and site-specific cancers, our study found decreases in cancer mortality during the pandemic's early stages, particularly among individuals aged 75 and older. These changes did not significantly alter established socioeconomic patterns in cancer mortality. Reductions in reported cancer deaths in 2020 may reflect COVID-19 prioritization in cause-of-death coding and its role as a competing risk, rather than true declines. Persistent educational disparities emphasize the need for continued policy and healthcare collaboration, with future research focused on the pandemic's long-term effects on cancer mortality and social inequalities.

Prevalence and social determinants of smoking among men in Mauritius: a cross-sectional study

ABSTRACT Background Mauritius has implemented a range of stringent policies to control smoking and promote public health. Regular monitoring focuses on the prevalence of tobacco use, yet there is a gap in understanding its socio-economic patterns. Objective The aim of this study was to estimate the prevalence of tobacco smoking and to identify the social determinants associated with smoking among men in Mauritius in 2021. Methods This is a cross-sectional population-based study conducted by the Ministry of Health and Wellness during 2021. In total, 3622 individuals participated (response rate of 84.1%), of which 1663 were men (45.9%). The study mainly focused on men given the low prevalence of smoking among women. Daily smoking was the outcome and a series of sociodemographic and socioeconomic factors were included as independent variables. Prevalence ratios (PR) and their 95% confidence intervals (95% CI) were estimated to fulfill the study objective. Results The prevalence of smoking among men was 30.4%. People in the 25–34 age group (PR = 1.65; 95% CI: 1.12–2.41), those separated, divorced or widowed (PR = 1.57; 95% CI: 1.16–2.11), the ethnic groups Muslim-Mauritians (PR = 1.70; 95% CI: 1.00–2.89) and Creoles (PR = 1.97; 95% CI: 1.16–3.35), and those with secondary (PR = 1.29; 95% CI: 1.00–1.67) and primary education (PR = 1.47; 95% CI: 1.10–1.98) were statistically significantly associated with daily smoking. Conclusions Although a gradual decline in smoking prevalence was observed compared with the previous 2015 survey, the Ministry of Health and Wellness should persist in fortifying its anti-smoking measures and concentrate on crafting tailored interventions aimed at the vulnerable groups identified in this study.

Municipality assessment of temperature-related mortality risks in Norway.

Understanding local vulnerability to heat and cold is crucial for public health planning, yet few studies have provided a nationwide analysis of temperature-related mortality across diverse communities. This study analyses the association between ambient air temperature and non-accidental mortality across mainland Norway, using a constrained hierarchical clustering algorithm to group municipalities with similar geographic, environmental, socioeconomic, and demographic patterns. This study analysed the association between ambient air temperature and non-accidental mortality across 356 Norwegian municipalities, using daily data from 1996 to 2018. We applied a case time series design with distributed lag non-linear models. A downscaling procedure assessed the effect of 21 vulnerability factors on temperature-related mortality risks, using Principal Components Analysis to explore heterogeneity across clusters. Cold temperatures contributed to an estimated 3,879 deaths per year (95% CI 3,718-4,130), while heat was associated with 44 deaths annually (95%CI: 29-58). The highest heat-related mortality risk occurred in the South-East, and the highest cold-related risk in the Central-East. Greater heat-related mortality correlated with medium- to sparsely-populated areas, while higher education levels were linked to reduced vulnerability to both heat and cold. By providing the first comprehensive assessment of temperature-related excess mortality and associated risk factors in Norway, our findings underscore the need for targeted, equitable health policies that integrate environmental and socioeconomic factors. These insights are essential to guide climate adaptation strategies, prioritising vulnerable rural communities and socioeconomically disadvantaged groups to mitigate future climate-related health impacts.

Unravelling demographic and socioeconomic patterns of COVID-19 death and other causes of death: results of an individual-level analysis of exhaustive cause of death data in Belgium, 2020

BackgroundThe COVID-19 pandemic led to significant excess mortality in 2020 in Belgium. By using microlevel cause-specific mortality data for the total adult population in Belgium in 2020, three outcomes were considered in this study aiming at predicting sociodemographic (SD) and socioeconomic (SE) patterns of (1) COVID-19 specific death compared to survival; (2) all other causes of death (OCOD) compared to survival; and (3) COVID-19 specific death compared to all OCOD.MethodsTwo complementary statistical methods were used. First, multivariable logistic regression models providing odds ratios and 95% confidence intervals were fitted for the three study outcomes. In addition, we computed conditional inference tree (CIT) algorithms, a non-parametric class of classification trees, to identify and rank by significance level the strongest predictors of the three study outcomes.ResultsOlder individuals, males, individuals living in collectivities, first-generation migrants, and deprived SE groups experienced higher odds of dying from COVID-19 compared to survival; living in collectivities was identified by the CIT as the strongest predictor followed by age and sex. Education emerged as one of the strongest predictors for individuals not living in collectivities. Overall, similar patterns were observed for all OCOD except for first- and second-generation migrants having lower odds of all OCOD compared to survival; age group was identified by the CIT as the strongest predictor. Older individuals, males, individuals living in collectivities, first- and second-generation migrants, and individuals with lower levels of education had higher odds of COVID-19 death compared to all OCOD; living in collectivities was identified by the CIT as the strongest predictor followed by age, sex, and migration background. Education and income emerged as among the strongest predictors among individuals not living in collectivities.ConclusionsThis study identified important SD and SE disparities in COVID-19 mortality, with living in collectivities highlighted as the strongest predictor. This underlines the importance of implementing preventive measures, particularly within the most vulnerable populations, in infectious disease pandemic preparedness to reduce virus circulation and the resulting lethality.

Inter-Generational Occupational and Socio-Economic Pattern of Bhutias in Sikkim: Case Study of Lachung, Tashiding and Tathangchen

The paper discusses the inter-generational occupational and socio-economic pattern of Bhutias in Sikkim. Case studies of certain Bhutia families were conducted as an oral source for understanding their present status, changing lifestyle and their self perception about the present status. Bhutia families from three Bhutia dominated villages Lachung, Tashiding and Tathangchen in Sikkim were interviewed. The findings reveal that transformations and diversification in the occupational pattern among the Bhutia families in Sikkim has taken place. Agriculture as their traditional occupation has declined as they are pursuing other alternative occupations. The case study shows that the number of Bhutia agricultural households, both as agriculturist and agricultural labour has declined, but they continue as owners of large land plots. The study sheds light on the occupational shift from traditional occupations to business and trade, tourism, and salaried jobs over the years and generations.

Unravelling COVID-19 social mortality patterns using conditional inference tree algorithm

Abstract Background The COVID-19 pandemic led to significant excess mortality in 2020 in Belgium. By using microlevel cause-specific mortality data for the total Belgian adult population in 2020, three outcomes were considered in this study aiming at predicting sociodemographic (SD) and socioeconomic (SE) patterns of 1) COVID-19 specific death compared to survival; 2) all other causes of death (OCOD) compared to survival; 3) COVID-19 specific deaths compared to all OCOD. Methods Multivariable logistic regression models were fitted for the three outcomes. In addition, we computed conditional inference tree (CIT) algorithms, complementing regression models, to identify and rank the strongest SD and SE predictors of the three outcomes. Results Older people, males, people living in collectivities, first-generation migrants, and deprived SE groups experienced higher odds of dying from COVID-19 compared to survival; living in collectivities was identified by the CIT as the strongest predictor followed by age and sex. Education emerged as one of the strongest predictors for people not living in collectivities. Overall, similar patterns were observed for all OCOD except for first- and second-generation migrants having lower odds of all OCOD compared to survival; age group was identified by the CIT as the strongest predictor. Older people, males, people living in collectivities, first- and second-generation migrants, and people with lower levels of education had higher odds of COVID-19 death compared to all OCOD; living in collectivities was identified by the CIT as the strongest predictor followed by age, sex, and migration status. Education and income emerged as the strongest predictors among people not living in collectivities. Conclusions This study identified important SD and SE disparities in COVID-19 mortality underlying the importance of implementing preventive measures, particularly within the most vulnerable populations, in infectious disease pandemic preparedness. Key messages • Living in collectivities appeared to be the strongest predictor of COVID-19 death across all age groups, highlighting the importance of preventive measures in reducing the transmission of the virus. • Poor education plays a crucial role in predicting COVID-19 death among people not living in collectivities, highlighting the importance of targeting preventive measures towards low educated groups.

Patients with refractory musculoskeletal pain syndromes undergoing a multimodal assessment and therapy programme: a cross-sectional study.

Chronic musculoskeletal pain syndromes, including fibromyalgia, are heterogeneous entities with a major socioeconomic burden. Multimodal treatment programmes have shown greater efficacy than conventional approaches for these patients, at least in the short term. A profound understanding of chronic musculoskeletal pain syndrome patients treated in multimodal treatment programmes is important for their development and to provide insight into these conditions. To provide a comprehensive and objective description of medical, psychosocial and sleep characteristics of the treatment-refractory chronic musculoskeletal pain syndrome patients treated at the multimodal treatment programmes provided by our tertiary service in Switzerland. This was a cross-sectional analysis of 202 refractory chronic musculoskeletal pain syndrome patients with or without a concomitant autoimmune disorder hospitalised between 2018 and 2022 in a 12-day Swiss multimodal treatment programme. They underwent a comprehensive self-assessment with eight different questionnaires and assessments by a psychiatrist, rheumatologist, pain specialist, occupational therapist and physiotherapist. Sleep assessment was performed via actigraphy. Clinical and demographic variables were selected by consensus of three experienced rheumatologists and chronic pain specialists. The Fibromyalgia Rapid Screening Test (FiRST), American College of Rheumatology (ACR)-2010 criteria (ACR2010) and Toronto Alexithymia Scale-20 (TAS-20) were also applied. The mean age of the patients was 47 years (SD = 10), 73% were female, and 30% were obese. Half (50%) were not from Switzerland, and 12% came from conflict zones. Almost half (40%) lived alone. Back pain was the principal site (90%). Of the patients, 78% fulfilled the ACR2010 criteria for fibromyalgia, and 17% were diagnosed with an underlying immune-mediated disorder, mostly spondylarthritis. Pain since childhood occurred in 45% of the patients, and 68% had pain since adolescence. Disability financial aid had been pursued by 69%, and 46% were still awaiting a response. Psychiatric comorbidities were highly prevalent (73%), of which 56% consisted of depression. Of all patients, 15% were diagnosed with enduring personality changes after a catastrophic experience (EPCACE), and 10% had post-traumatic stress disorder. Alexithymia affected 34% of patients. Objective sleep disorder was observed in 78% of patients, and 41% were under opioid therapy. This analysis reveals the complex psychosomatic and socioeconomic patterns of the patients treated in Switzerland with refractory chronic musculoskeletal pain syndromes, often originating in childhood and adolescence. Obesity, immigration, social isolation, psychiatric comorbidities, sleep deprivation and opiate use, among others, stood out as target characteristics for further research.

User characteristics and preferences for micromobility use in first- and last-mile journeys in Dublin, Ireland

Micromobility and public transport integration is an area of urban mobility that has been increasingly gaining attention and importance. The necessity to provide improvements to public transport services and their spatial reach in order to reduce the number and volume of car trips in cities is irrefutable. The integration of micromobility and public transport is often presented as a valuable strategy to improve door-to-door connectivity and coverage of existing public transport services, via provision of key first- and last-mile or access-egress connections. The aim of this study was to understand and model the propensity of commuters in County Dublin to use micromobility (private and shared) in the first- and last-miles of their public transport journeys. A stated preference experiment was conducted with a focus on these specific segments of the journey and a mixed logit model was estimated to identify user characteristics and preferences based on socio-economic factors, mobility patterns and attitudes towards and previous experience of shared mobility. The results show that, in this particular case study, respondents with a strong walking preference were less likely to be interested in private micromobility and sharing services for the first- and last-mile. A common assumption that walking is a last resort, and only chosen when there are no other options available was not confirmed by the findings of this study. Moreover, competition between the modes showed that private bicycles are the preferred mode after walking, followed by private e-scooters, while shared modes were less likely to be chosen over walking, and shared e-scooters less so than shared bicycles. Overall, the findings suggest that a small percentage of the sample – mainly young (<35 years old) and male respondents with interest in and experience of e-scooters – were interested in using private and shared e-scooters for the first- and last-mile, but that the majority of the respondents were likely to choose walking, albeit with longer travel times overall.

Multi-scale analysis of urban forests and socioeconomic patterns in a desert city, Phoenix, Arizona

Understanding the relationship between various socioeconomic factors and urban forest structure is essential for directing resources to ensure equitable distribution of green space. Through a case study of a desert city, i.e., Phoenix, AZ, this study provides a novel application of Multiscale Geographically Weighted Regression (MGWR) in which we explore the spatially variable relationships between a wide array of socioeconomic indicators and urban forest attributes. Through the computation of various scales of influence for different explanatory variables, MGWR enhances our analysis’s precision and stresses the association between socioeconomic status and urban forest structure at local and regional scales. Our results indicate that although there has been a pattern of green inequality where minority and low-income communities have less access to urban forests, education levels were mostly insignificant based on the MGWR results. In some instances, higher incomes are negatively correlated with tree canopy coverage. Additionally, the stem density model outperformed the canopy coverage model in terms of prediction accuracy. This research adds a new dimension to urban forestry literature and emphasizes the value of customized urban planning strategies and the environmental justice implications of urban forestry, particularly in arid environments.

Socioeconomic and demographic factors predictive of same day access utilization in outpatient radiation oncology.

Access to medical care extends to not only the timely and appropriate receipt of services but also addresses inclusivity and underlying determinants of health. Given that patients from disadvantaged backgrounds have been shown to be more likely to experience delays in care, a same day access scheduling initiative was proposed to address this equity issue. Therefore, this study aims to evaluate our experience, focusing on identifying socioeconomic and demographic patterns of same day access utilization. From March 2021 to January 2023, all patients referred for new consultation to a tertiary care-based radiation oncology department were offered same day appointments as part of a prospective pilot initiative. Descriptive statistics were used to identify factors predictive of utilization. On multivariate analysis, patient characteristics independently associated with higher odds of same day access utilization included low-income status ([OR]=3.70, 95% CI (1.47-6.14)) and Black or Latino race ([OR]=4.05, 95% CI: 1.72-9.11). While we were unable to acquire data on actual clinical outcomes for patients opting for same day appointments, the enthusiasm for this program was obvious. Patients from disadvantaged backgrounds and vulnerable segments of the population were more likely to elect for same day appointments. Implications on health equity are discussed. Patient-centered approaches to overcome barriers of access can potentially help ensure that care is equitable. Our findings, representing the first published data analyzing a longitudinal experience with same day appointments in oncology, strongly suggest that certain disadvantaged populations may benefit more from access initiatives.

Population-Level Exposure to PM2.5, NO2, Greenness (NDVI), Accessible Greenspace, Road Noise, and Rail Noise in England

Air pollution, greenspace and noise are interrelated environmental factors with the potential to influence human health outcomes. Research has measured these exposures in diverse ways across the globe, but no study has yet performed a country-wide analysis of air pollution, greenspace, and noise in England. This study examined cross-sectional PM2.5, NO2, greenness, accessible greenspace, road noise, and rail noise exposure data at all residential postcodes in England (n = 1,227,681). Restricted cubic spline models were fitted between each environmental exposure and a measure of socioeconomic status, the Index of Multiple Deprivation (IMD) rank. Population-weighted exposures by IMD deciles, urbanicity, and region were subsequently estimated. Restricted cubic spline models were also fitted between greenness and each other environmental exposure in the study. The results show some evidence of inequalities in exposure to air pollutants, greenspace, and noise across England. Notably, there is a socioeconomic gradient in greenness, NO2, PM2.5, and road noise in London. In addition, NO2, PM2.5, and road noise exposure decrease as greenness increases in urban areas. Concerningly, almost all air pollution estimates in our study exceed international health guidelines. Further research is needed to elucidate the socioeconomic patterns and health impacts of air pollution, greenspace, and noise over time.

Spatiotemporal modeling of COVID-19 spread: unveiling socioeconomic disparities and patterns, across social classes in the urban population of Kermanshah, Iran.

Presenting ongoing outbreaks and the potential for their spread to nearby neighborhoods and social classes may offer a deeper understanding, enable a more efficient reaction to outbreaks, and enable a comprehensive understanding of intricate details for strategic response planning. Hence, this study explored the spatiotemporal spread of COVID-19 outbreaks and prioritization of the risk areas among social classes in the Kermanshah metropolis. In this cross-sectional study, the data of 58.951 COVID-19-infected patients were analyzed. In 2020, out of 24.849 infected patients, 10.423 were females, 14,426 were males, and in 2021, 15.714 were females, and 18,388 were males. To categorize social classes (working, middle, and upper), we utilized economic, social, cultural, and physical indicators. Our analysis utilized Arc/GIS 10.6 software along with statistical tests, including standard distance (SD), mean center (MC), standard deviational ellipse (SDE), and Moran's I. The results revealed that the average epicenter of the disease shifted from the city center in 2020-2021 to the eastern part of the city in 2021. The results related to the SD of the disease showed that more than 70% of the patients were concentrated in this area of the city. The SD of COVID-19 in 2020 compared to 2021 also indicated an increased spread throughout the city. Moran's I test and the hotspot test results showed the emergence of a clustered pattern of the disease. In the Kermanshah metropolis, 58,951 COVID-19 cases were recorded, with 55.76% males and 44.24% females. Social class distribution showed 28.86% upper class, 55.95% middle class, and 15.19% working class. A higher disease prevalence among both males and females in the upper class compared to others. Our study designed a spatiotemporal disease spread model, specifically tailored for a densely populated urban area. This model allows for the observation of how COVID-19 propagates both spatially and temporally, offering a deeper understanding of outbreak dynamics in different neighborhoods and social classes of the city.

Socioeconomic and demographic patterning of family uptake of a paediatric electronic patient portal innovation.

Patient portals allowing access to electronic health care records and services can inform and empower but may widen existing sociodemographic inequities. We aimed to describe associations between activation of a paediatric patient portal and patient race/ethnicity, socioeconomic status and markers of previous engagement with health care. A retrospective single site cross-sectional study was undertaken to examine patient portal adoption amongst families of children receiving care for chronic or complex disorders within the United Kingdom. Descriptive and multivariable regression analysis was undertaken to describe associations between predictors (Race/Ethnicity, age, socio-economic deprivation status based on family residence, and previous non-attendance to outpatient consultations) and outcome. A sample of 3687 children, representative of the diverse 'real world' patient population, was identified. Of these 37% (1364) were from a White British background, 71% (2631) had English as the primary family spoken language (PSL), 14% (532) lived in areas of high deprivation, and 17% (643) had high (>33%) rates of non-attendance. The families of 73% (2682) had activated the portal. In adjusted analyses, English as a PSL (adjusted odds ratio [aOR] 1.58, 95% confidence interval 1.29-1.95) and multi-morbidity (aOR 1.26, 1.22-1.30) was positively associated with portal activation, whilst families from British Black African backgrounds (aOR 0.68, 0.50-0.93), and those with high rates of non-attendance (aOR 0.48, 0.40-0.58) were less likely to use the portal. Family race/ethnicity and previous low engagement with health care services are potentially key drivers of widening inequity in access to health care following the implementation of patient portals, a digital health innovation intended to inform and empower. Health care providers should be aware that innovative human-driven engagement approaches, targeted towards previously underserved communities, are needed to ensure equitable access to high quality patient-centred care.

Socioeconomic patterns in indoor environment in Denmark: cross-sectional studies from 2000 and 2021

ObjectivesTo examine changes and socioeconomic patterns in indicators of a poor indoor environment in 2000 and 2021. Study designCross-sectional data from the Danish Health and Morbidity Survey in 2000 and 2021. MethodsThe study included 27,068 participants. Indicators of indoor environment (annoyances from mould, temperature, draught, traffic and neighbour noise, and presence of water damage) were obtained from questionnaires (2021) and partly by interview (2000). Socioeconomic status included home ownership, educational level, and household income. The degree of social inequality in the indoor environment was estimated using the concentration index of inequality. ResultsThe prevalence of annoyances due to draught, temperature, and noise increased significantly from 2000 to 2021 (e.g., temperature 5.9%–25.1%, odds ratio (OR) 6.72, 95% confidence interval (CI) 6.12–7.38), whereas the prevalence of water damage decreased (17.7%–13.8%, OR 0.85, 95% CI 0.76–0.96). No difference was seen in annoyances due to mould (3.1% in 2000 and 2.5% in 2021, OR 0.90, 95% CI 0.69–1.17). Social inequality was present for thermal conditions, annoyances due to noise and presence of water damage when assessed by income but not by educational level. Conditions were more prevalent among individuals with low income in both 2000 and 2021. ConclusionsThe proportion of individuals reporting a poor indoor environment due to thermal conditions and noise increased in the period 2000–2021. Social inequality was observed in all indicators of a poor indoor environment for household income, whereas the inequality was less pronounced when assessed by educational level.

Early adulthood socioeconomic trajectories contribute to inequalities in adult diet quality, independent of childhood and adulthood socioeconomic position

BackgroundDiet is an important risk factor for cardiovascular disease and shows well-established socioeconomic patterning among adults. However, less clear is how socioeconomic inequalities in diet develop across the life course....

Housing Market Segmentation as a Driver of Urban Micro-Segregation? An In-Depth Analysis of Two Viennese Districts

The concept of segregation analyses the unequal distribution of social groups between neighbourhoods. It rests on two assumptions: that of homogeneous neighbourhoods and of a market liberal housing system. Both assumptions are applicable the context of American cities, but they display severe limitations when applied to the European context. Vienna’s housing market is particularly highly segmented, not only throughout the city as a whole but also within neighbourhoods. In the densely built-up area, residential buildings of different segments with different underlying rent regulations and entry barriers can be found side by side. Therefore, buildings are expected to show varying tenant and owner structures, which undermines the idea of a homogeneous neighbourhood. Against this background, we analyse at the micro scale small neighbourhoods defined by 100 m grid cells in a case study of two inner-city Viennese districts (districts 6 and 7) characterised by a particularly vivid housing-transformation and commodification dynamic. Using a novel and fine-grained dataset combining building information with the socio-economic data of households, we investigate the patterns and dynamics of income inequality and income segregation, as well as the relationship between housing market segments and socio-economic patterns. As data comprise two cross-sections for the years 2011 and 2020/21, changes in the neighbourhoods during the house-price boom period are also considered. This leads us to ask the question: How do housing market segmentation and its related changes affect income inequality and segregation at the micro scale? Our analysis delivers two main results: Firstly, we show the existence of marked social variation and related dynamics at the micro scale, even within a small urban area. Secondly, we show that the spatial distribution of housing market segments has a strong impact on income inequality in the neighbourhood.

Farmland Abandonment and Afforestation—Socioeconomic and Biophysical Patterns of Land Use Change at the Municipal Level in Galicia, Northwest Spain

Farmland Abandonment and Afforestation—Socioeconomic and Biophysical Patterns of Land Use Change at the Municipal Level in Galicia, Northwest Spain

Spatial and Socioeconomic Patterns of Mental Health and Healthcare Utilization in Cologne, Germany.

Children and adolescents are significantly tied to their family's socioeconomic position and living environment. Neighbourhood and the living environment have been identified as potential risk factors for mental disorders in this age group. The aim of the study was to investigate the distribution of mental and behavioural disorders (prevalence) and the provision of mental health services for children and adolescents aged 0-19 years in the city of Cologne. In particular, the study aimed to examine the association of these factors with area deprivation and the availability of mental health services covered by statutory health insurance. Finally, possible spatial variations in these aspects were analysed. Claims data of children and adolescents aged 0 to 19 years included in four statutory health insurance of the year 2021 were analysed. A deprivation index using data on the level of the ZIP code area was calculated. Analyses were carried out descriptively, using ordinary least squares (OLS) and geographically weighted regression (GWR). The prevalence of mental and behavioural disorders in children and adolescents varied across ZIP code areas, with higher rates in the northern, southern, and eastern parts of the city. The results indicated that the use of services by male children and adolescents with a prevalent diagnosis of mental and behavioural disorders was higher in areas with a higher density of healthcare providers. However, prevalence was on the whole lower in areas with a higher density of healthcare providers. In addition, the density of health care providers was higher in the city centre with comparatively lower deprivation. These results indicate inadequate access to care for children and young people outside the city centre. However, due to the heterogeneity of the population in these areas, this study provides only preliminary insights. Data with a finer geographic resolution are needed for further research in order to analyse the association further.

Use of Socio-economic, Climatic, and Land use Land Cover Patterns in Solid Waste Forecasting with Integrated Gradient LSTNet Based Model in Lomé, Togo

ABSTRACT Few studies have explored recurrent neural network models in municipal solid waste (MSW) generation forecasting regarding the inefficiency of conventional and probabilistic tools. This study aimed to develop a new approach of Integrated Gradient Long- and Short-term Time series Network (LSTNet+IG)-based models, assess the baseline neural network models, and provide features influences explanation on MSW generation by using socio-economic, climatic, and Land Use Land Cover (LULC) patterns. Hence, the Random Forest Regressor and Gradient Boosting Regressor methods were used for feature selection, and the IG component was incorporated to LSTNet model for feature influence explanation alongside baseline models implementation. Moreover, the metrics such as root relative square error (RSE) and relative absolute error (RAE) were used to evaluate models’ performance. As a result, the LSTNet+IG outperforms others with the lowest RSE (0.9727) and RAE (0.9108). Moreover, five key influencing features were found, namely the number of households and institutions, mean relative humidity, electricity consumption, built area, and mean temperature. Hence, the socio-economic, climatic, and LULC features in MSW generation forecasting have shown to be important in decision-making for effective MSW management. Therefore, the LSTNet+IG model adoption by stakeholders could help to anticipate and mitigate MSW mismanagement through better planning.