Individual-level Characteristics Research Articles

Migrant selection and sorting during the Great American Drought

America’s worst drought spanned the 1930s, coinciding with the most extensive environmental migration in United States history. Nearly 100 years later, we know little about who moved and who stayed. This paper studies heterogeneity in migration from drought by relating migration decisions recorded in the 1940 census to county drought conditions. Drought increased migration primarily for individuals with a 12th-grade education or higher. Drought migrants, both women and men, left rural and urban locations and most often relocated to rural destinations. These findings highlight the importance of individual-level characteristics for adaptation to climate shocks, challenge the perception that rural-to-urban is the dominant environmental migrant channel, and document the central importance of drought for internal migration during the 1930s.

Hazardous alcohol consumption in slow- and fast-privatized Russian industrial towns

Hazardous drinking, defined as the consumption of homemade, unofficially made alcohol and non-beverages, is prevalent and accounts for a high proportion of alcohol-related deaths in Russia. Individual-level characteristics are important explanations of hazardous drinking, but they are unlikely to explain spatial variation in this type of alcohol consumption. Areas that attracted insufficient attention in the research of hazardous drinking are the legacy of industrialization and the speed of economic reforms, mainly through the privatization policy of major enterprises in the 1990s. Applying mixed-effects logistic regressions to a unique dataset from 30 industrial towns in the European part of Russia, we find that in addition to individual-level characteristics such as gender, age, marital status, education, social isolation, labor market status, and material deprivation, the types of towns where informants’ relatives resided such as industrial structure and speed of privatization also accounted for the variance in hazardous alcohol consumption among both male and female populations of the analyzed towns.

Spatial prevalence and associated risk factors of childhood diarrhea: A cross-sectional study in rural Indian context

Diarrheal disease is the most prevalent health issue among children under the age of five, contributing to a staggering toll of approximately 525,000 deaths worldwide each year. Despite its devastating consequences, it is essential to note that this disease is both preventable and treatable. The present study aims to investigate the prevalence of childhood (under 5 years) diarrheal disease in rural parts of India and identify relevant factors associated with diarrheal disease, encompassing maternal, household, environmental, and individual-level characteristics. In addition, the study highlights the spatial distribution of diarrheal disease across India, identifying hot-spot and cold-spot regions, along with the associated risk factors. The study utilized secondary data extracted from the fifth round of the National Family Health Survey, conducted in India between 2019 and 2021 (N=161,368). Binary logistic regression analysis and spatial clustering techniques, specifically utilizing Global Moran’s I, were employed to achieve the study’s objectives. The findings revealed that diarrhea cases are more prevalent among the poor, rural, and children of Muslim families, as well as among those residing in environments lacking hygienic or adequate sanitation facilities and in households without floors, walls, or roofs built with concrete material. The highest prevalence is observed in the eastern and western parts of India. Necessary interventions are required to reduce diarrhea cases among children under the age of five in rural Indian areas. Given the vector-borne nature of this disease, public awareness of the importance of maintaining fresh and clean drinking water facilities is imperative.

An updated analysis of safety climate and downstream outcomes in two convenience samples of U.S. fire departments (FOCUS 1.0 and 2.0 survey waves)

BackgroundThe Fire service Organizational Culture of Safety (FOCUS) survey is an assessment tool comprised of psychometrically validated metrics of safety climate, safety behavior, and downstream outcomes (organizational and injury) that are specific to the U.S. fire and rescue service.MethodsThis analysis consists of a descriptive summary of two independent survey waves (FOCUS 1.0 and 2.0). The fire departments included in these survey waves were from convenience sampling (n1.0 = 275; n2.0 = 170). In addition to department level characteristics, we examined individual level characteristics for firefighters and EMS providers in participating departments (n1.0 = 22,719; n2.0 = 16,882). We conducted regression analyses to examine the associations between safety climate and safety behaviors, organizational outcomes, and safety outcomes. All analyses were stratified by organization type (career, volunteer).ResultsOur analysis indicated that a majority of respondents were males (90.7%FOCUS 1.0; 90.4%FOCUS 2.0), non-officers (68.4%FOCUS 1.0; 66.4%FOCUS 2.0), and non-Hispanic Whites (70.8%FOCUS 1.0; 69.5%FOCUS 2.0). For both samples there was a higher prevalence of injuries among individuals in career departments (nFOCUS 1.0 = 3778 [17.5%]; nFOCUS 2.0 = 3072 [18.7%]) than volunteer departments (nFOCUS 1.0 = 103 [8.8%]; nFOCUS 2.0 = 34 [7.4%]). We observed an approximate 10-point difference between the mean scores of Management Commitment to Safety for career and volunteer departments in both samples. We observed associations for two organizational outcomes, Safety Behavior and Job Satisfaction, with Management Commitment to Safety and Supervisor Support for Safety overall and when stratified by organization type. We observed a decrease in the odds of injuries associated with a one-unit increase in Management Commitment to Safety (OR1.0 overall: 0.98, 95% CI 0.97–0.99; OR2.0 volunteer: 0.90, 95% CI 0.85–0.95) and Supervisor Support for Safety (OR1.0 overall: 0.95, 95% CI 0.93–0.97; OR1.0 career: 0.95, 95% CI 0.92–0.98).ConclusionsFrom our current study, and a prior analysis of a geographically stratified random sample of U.S. fire departments, we identified that from all the organizational outcomes, job satisfaction was most consistently associated with FOCUS safety climate. Further, firefighters in our samples consistently rated Supervisor Support for Safety higher than Management Commitment to Safety. Future interventions should support fire departments in improving their departmental Management Commitment to Safety and maintaining their Supervisor for Safety.

Why do citizens support algorithmic government?

Abstract As governments increasingly adopt algorithms and artificial intelligence (AAI), we still know comparatively little about citizens’ support for algorithmic government. In this paper, we analyze how many and what kind of reasons for government use of AAI citizens support. We use a sample of 17,000 respondents from 16 OECD countries and find that opinions on algorithmic government are divided. A narrow majority of people (55.6%) support a majority of reasons for using algorithmic government, and this is relatively consistent across countries. Results from multilevel models suggest that most of the cross-country variation is explained by individual-level characteristics, including age, education, gender, and income. Older and more educated respondents are more accepting of algorithmic government, while female and low-income respondents are less supportive. Finally, we classify the reasons for using algorithmic government into two types, “fairness” and “efficiency,” and find that support for them varies based on individuals’ political attitudes.

Analyzing the influence of university support and entrepreneurial culture on solar entrepreneurial intentions among Indian students

The involvement of students in entrepreneurial activities related to solar energy has the potential to have a major impact on expanding energy access. Nevertheless, there is an absence of research on the dynamics and knowledge of solar entrepreneurship, specifically from an Indian perspective. The main aim of this study is to highlight the significance of solar energy entrepreneurship and its capacity to motivate and encourage university students towards entrepreneurship, thereby fostering sustainable development. The study intends to analyze several individual-level characteristics that influence university learners' intentions to engage in solar entrepreneurship. The goal of this study is to shed light on how students' career choices in the field of solar entrepreneurship are influenced by their personal passion, self-efficacy, personality traits, and social norms. A stratified random selection technique was employed to gather a representative sample of 279 university students who are currently enrolled in both public and private universities situated in the northwest region of India. The study employed partial least squares structural equation modeling (PLS-SEM) to examine the relationship between individual-level factors and entrepreneurial attitude. The analysis indicated that all individual-level factors, except entrepreneurial passion, were significant predictors of entrepreneurial attitude. Furthermore, the findings demonstrated a positive association between student entrepreneurial attitudes and solar entrepreneurial intentions. The research also examines the managerial implications, limitations, and future scope of the research.

What explains regional variation in privately provided out-of-area residential placement costs for people with intellectual disability in Ireland?

Expenditure on residential placements for people with intellectual disability (ID) in Ireland is considerable and expected to increase. Despite this, there is limited evidence on the factors driving variation in privately provided 'out-of-area' residential placement costs, including across Community Health Organisations (CHOs)/regions. This is important to help inform the delivery of services at best value. We analyse unit cost data from 2019 for a sample of 278 high-cost publicly funded privately provided out-of-area residential placements for people with ID in Ireland. We undertake univariate analysis of the relationship between costs and a wide range of variables using t-tests and one-way analysis of variance. We employ multivariable regression analysis to examine how raw differentials in unit costs across regions can be accounted for by individual-level characteristics. We estimate average unit costs of €264170 per annum in our sample. The univariate analysis shows considerable variation in costs across a range of personal, disability, psychiatry/psychological, forensic issues, behaviour and supports and plans related variables. We also find wide variation in average unit costs across CHOs/regions (F=4.58, P<0.001), ranging from €213380 to €331880. The multivariable analysis shows that regional differences remain even after accounting for a wide range of individual characteristics that influence costs. Our analysis shows that while the majority of differences in costs across regions can be explained, there is potential for cost savings in the provision of high-cost publicly funded out-of-area residential placements in Ireland. Overall this can help to develop and implement a more sustainable disability residential funding model in a context of rising demand for services. It also has potential implications for the approach to procurement of services.

Comparing Individual and Community-level Characteristics of People with Ground Beef-associated Salmonellosis and Other Ground Beef Eaters: A Case-control Analysis

Salmonella is estimated to be the leading bacterial cause of U.S. domestically acquired foodborne illness. Large outbreaks of Salmonella attributed to ground beef have been reported in recent years. The demographic and sociodemographic characteristics of infected individuals linked to these outbreaks are poorly understood. We employed a retrospective case-control design; case-patients were people with laboratory-confirmed Salmonella infections linked to ground beef-associated outbreaks between 2012 and 2019, and controls were respondents to the 2018–2019 FoodNet Population Survey who reported eating ground beef and denied recent gastrointestinal illness. We used county-level CDC/ATSDR Social Vulnerability Index (SVI) to compare case-patient and controls. Case-patient status was regressed on county-level social vulnerability and individual-level demographic characteristics. We identified 376 case-patients and 1,321 controls in the FoodNet sites. Being a case-patient was associated with increased overall county-level social vulnerability (OR: 1.21 [95% CI: 1.07–1.36]) and socioeconomic vulnerability (OR: 1.24 [1.05–1.47]) when adjusted for individual-level demographics. Case-patient status was not strongly associated with the other SVI themes of household composition and disability, minority status and language, and housing type and transportation. Data on individual-level factors such as income, poverty, unemployment, and education could facilitate further analyses to understand this relationship.

Regional crises and European fiscal preferences: how regional Covid-19, economic downturn, and migration shape support for EU risk sharing

ABSTRACT Economic suffering prompted by the Covid-19 pandemic, coming on the heels of earlier 2008 global-financial and 2015 migration crises, revived debate on citizen support for European fiscal integration policies. Such support can be expected to reflect not only individual-level characteristics but also the extent of crisis exposure in subnational regional contexts where individuals live and work. Unfortunately, existing studies of public support have said little about such regional contexts. This study hence explores how regional-level experience with ‘polycrisis’ affects support for EU fiscal capacities, combining regional-level crisis measures with a 2020 survey experiment on European citizens’ preferences towards fiscal capacity instruments in 5 European countries (DE, ES, FR, IT, NL). This allows tests of whether individual support for various European fiscal capacities reflect regional differences in covid suffering, growth losses after the 2008 global financial crisis, and migration spikes from the 2015 migration crisis. We expect and find that citizens in regions more heavily impacted by the pandemic, financial crisis, and (albeit less so) migration crisis – measured separately and as a composite – tend to more readily support European fiscal integration capacity that is redistributive between countries, financed through progressive taxation, refrains from budgetary conditionality, and is lenient towards reform non-compliance.

Factors that facilitate consumer uptake of sustainable dietary patterns in Western countries: a scoping review

ObjectivesThis scoping review aims to describe factors that facilitate consumer-level transitions to more environmentally sustainable diets.MethodsUsing scoping review methods, four databases were searched for articles published in English examining facilitators to consuming an environmentally sustainable diet and focused on consumers, using data collected in Western countries, and were published between 2012 and 2022. Researchers extracted study characteristics and factors influencing adoption or uptake of sustainable foods or dietary patterns. Using this data, researchers conducted a thematic analysis to determine five main themes describing leverage points (modifiable) for dietary transitions.ResultsResults are reported per PRISMA guidelines: 21 studies were included with data from the U.K., U.S., Australia, and Europe. The results of this review indicate that values, knowledge, marketing, consumer-product relationships, and support networks, along with their respective subthemes, may be central drivers of consumer adoption of sustainable dietary patterns. Consumers are more likely to purchase and consume products which are familiar and appealing and align with their values. Cost, lack of knowledge, and lack of social support act as barriers to dietary change to more sustainable food choices. Income, education, ethnicity, sex, and employment were common individual-level characteristics identified as influential over likelihood of adopting environmentally sustainable dietary patterns. Individual-level characteristics create nuances in both likelihood to adopt, and the experience of barriers to adopting, sustainable dietary patterns.ConclusionKnowledge of leverage points and individual-level nuances is useful in informing strategies to facilitate transitions to more sustainable diets.

Modeling health and well-being measures using ZIP code spatial neighborhood patterns

Individual-level assessment of health and well-being permits analysis of community well-being and health risk evaluations across several dimensions of health. It also enables comparison and rankings of reported health and well-being for large geographical areas such as states, metropolitan areas, and counties. However, there is large variation in reported well-being within such large spatial units underscoring the importance of analyzing well-being at more granular levels, such as ZIP codes. In this paper, we address this problem by modeling well-being data to generate ZIP code tabulation area (ZCTA)-level rankings through spatially informed statistical modeling. We build regression models for individual-level overall well-being index and scores from five subscales (Physical, Financial, Social, Community, Purpose) using individual-level demographic characteristics as predictors while including a ZCTA-level spatial effect. The ZCTA neighborhood information is incorporated by using a graph Laplacian matrix; this enables estimation of the effect of a ZCTA on well-being using individual-level data from that ZCTA as well as by borrowing information from neighboring ZCTAs. We deploy our model on well-being data for the U.S. states of Massachusetts and Georgia. We find that our model can capture the effects of demographic features while also offering spatial effect estimates for all ZCTAs, including ones with no observations, under certain conditions. These spatial effect estimates provide community health and well-being rankings of ZCTAs, and our method can be deployed more generally to model other outcomes that are spatially dependent as well as data from other states or groups of states.

Program of All-Inclusive Care for the Elderly: an untapped setting for research to advance pain care in older persons.

The Program of All-Inclusive Care for the Elderly (PACE) is a community-based care model in the United States that provides comprehensive health and social services to frail, nursing home-eligible adults aged 55 years and older. PACE organizations aim to support adequate pain control in their participants, yet few evidence-based pain interventions have been adopted or integrated into this setting. This article provides a roadmap for researchers who are interested in collaborating with PACE organizations to embed and evaluate evidence-based pain tools and interventions. We situate our discussion within the Consolidated Framework for Implementation Research (CFIR), a meta-theoretical framework that considers multi-level influences to implementation and evaluation of evidence-based programs. Within each CFIR domain, we identify key factors informed by our own work that merit consideration by research teams and PACE collaborators. Inner setting components pertain to the organizational culture of each PACE organization, the type and quality of electronic health record data, and availability of staff to assist with data abstraction. Outer setting components include external policies and regulations by the National PACE Association and audits conducted by the Centers for Medicare and Medicaid Services, which have implications for research participant recruitment and enrollment. Individual-level characteristics of PACE organization leaders include their receptivity toward new innovations and perceived ability to implement them. Forming and sustaining research-PACE partnerships to deliver evidence-based pain interventions pain will require attention to multi-level factors that may influence future uptake and provides a way to improve the health and well-being of patients served by these programs.

Sports participation moderates the risk of family-specific negative life events on alcohol use among adolescents: Evidence from the longitudinal MyLife study

Negative life events (NLE) have been associated with increased alcohol use (AU) during adolescence. However, whether this risk association may be modified by leisure activities such as sports participation (SP) remains poorly understood. This study examined whether accumulated family-specific NLE in particular were associated with greater AU, and if so, whether SP moderated this association to reduce AU among high-NLE adolescents.We examined five annual assessments from a nationwide cohort of 3,422 Norwegian adolescents (13–15 year-olds; 55.3 % girls at baseline) who participated in the MyLife study. At each assessment, adolescents reported their AU on the Alcohol Use Disorders Identification Test-Concise (AUDIT-C), the number of family-specific NLE in the past 12 months, SP days in the past 30 days, and multiple sociodemographic and individual-level characteristics (covariates). Changes over time in AU as a function of NLE, SP, and their interaction (NLExSP) were examined with a set of partially nested growth curve models.AU increased non-linearly over time in all models. The fully adjusted best-fitting model showed significant NLExSP interactions (estimate = -0.013, 95% CI [-0.02, −0.006]), such that the initial AUDIT-C scores were lower for high-NLE adolescents with high SP and greater for high-NLE adolescents with low SP. Further, linear increases in AU over time were marginally steeper for high-NLE adolescents with high SP (NLExSPxTime estimate = 0.034, 95% CI [-0.0002, 0.007]). Thus, SP appeared to have a protective role in reducing AU for high-NLE youth primarily during middle school years. Prevention efforts thus may utilize organized sports for youth facing family-specific NLE as a resource early on.

Computational limits to the legibility of the imaged human brain

Our knowledge of the organisation of the human brain at the population-level is yet to translate into power to predict functional differences at the individual-level, limiting clinical applications and casting doubt on the generalisability of inferred mechanisms. It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal.Here we comprehensively investigate the resolvability of such patterns with data and compute at unprecedented scale. Across 23 810 unique participants from UK Biobank, we systematically evaluate the predictability of 25 individual biological characteristics, from all available combinations of structural and functional neuroimaging data. Over 4526 GPU*hours of computation, we train, optimize, and evaluate out-of-sample 700 individual predictive models, including fully-connected feed-forward neural networks of demographic, psychological, serological, chronic disease, and functional connectivity characteristics, and both uni- and multi-modal 3D convolutional neural network models of macro- and micro-structural brain imaging.We find a marked discrepancy between the high predictability of sex (balanced accuracy 99.7%), age (mean absolute error 2.048 years, R2 0.859), and weight (mean absolute error 2.609Kg, R2 0.625), for which we set new state-of-the-art performance, and the surprisingly low predictability of other characteristics. Neither structural nor functional imaging predicted an individual's psychology better than the coincidence of common chronic disease (p < 0.05). Serology predicted chronic disease (p < 0.05) and was best predicted by it (p < 0.001), followed by structural neuroimaging (p < 0.05).Our findings suggest either more informative imaging or more powerful models will be needed to decipher individual level characteristics from the human brain. We make our models and code openly available.

Gendered division of paid, unpaid, and total work in India: who bears more burden?

ABSTRACT This study examines the gendered division of paid, unpaid, and total work in contemporary India. We explore this division of work through an analysis of India’s first large-scale time use survey, conducted in 2019. Our findings reveal that the distribution of paid, unpaid, and total work is highly gender-biased. We also found that rural women bear relatively more burden of total work than urban women; whereas, urban men bear relatively more burden of total work than rural men. We observed striking gender differences in the paid, unpaid, and total work burden across the key household and individual-level characteristics of age, marital status, presence of children, income status and employment status. Compared to all other categories, married employed women belonging to the working age cohort (15–59) bear the highest burden of total work in India, and hence are left with the least available free time.

Improving state medicolegal death investigation: Does system level reform improve public health data concerning drug-related mortality?

In the United States, the governance of unnatural death certification varies greatly by state. Although cross-sectional research has linked mortality data quality with variation in medicolegal death investigation systems across states-especially with regards to drug-related deaths-this relationship has not be sufficiently tested using longitudinal data. This research assesses the impact of system governance reform on the quality of drug mortality data by assessing the impact of transitioning from a coroner system to a medical examiner system on data quality. The research finds no evidence that system-level reform is associated with improved drug-related mortality data quality. These findings suggest that alternative methods should be examined for improving public health data concerning drug-related mortality. These likely include focusing on individual-level characteristics and practices of officials and offices, rather than system-level variables.

Abstract 841: Fine particulate matter exposure during breast cancer treatment among older women associated with increased mortality

Abstract Background: Fine Particulate Matter (PM2.5) is associated with higher mortality among people with chronic conditions. Breast cancer (BC) survivors may be particularly vulnerable during cancer treatment. We assessed the association between PM2.5 during 9-months following BC diagnosis and all-cause and BC-mortality using policy relevant PM2.5 cut-offs. Methods: Using SEER-Medicare linked with a high-resolution model-based PM2.5 by residential zipcode (2007-2016), we assembled a cohort of women ≥66 years with incident stage I-IV BC. We assessed PM2.5 exposure, categorized as &amp;lt;8 µg/m3 (low), 8-12 µg/m3 (moderate) and &amp;gt;12 µg/m3 (high—above air quality standard), and types of cancer treatments (surgery [SX], chemotherapy [CTX], radiation therapy [RTX]) during a 9-month landmark period. We identified all-cause and BC death within 5 years of diagnosis. We fit multivariable Cox proportional hazard models to assess associations with PM2.5 exposure adjusting for individual-level characteristics (demographics, tumor characteristics, comorbidities), neighborhood SES, SEER state, and long-term PM2.5 exposure. We also evaluated the association between PM2.5 exposure and mortality by treatment types (SX, SX + RTX, SX + CTX, SX + RTX + CTX, and RTX and/or CTX). We used multiple imputation to account for missing variables (race, ER status, HER2 status, grade). Results: Among 72,798 women (mean age 75 years, 83% white, 56% stage I, 32% II, 9.2% III, 2.9% IV), 41%, 32%, 13%, 8.4%, and 5.1% received SX + RTX, SX only, SX + Chemo + RTX, SX + Chemo, and CTX and/or RTX. The mean PM2.5 level was 8.8 µg/m3 [36%, 52% and 11% were exposed to &amp;lt;8, 8-12, and &amp;gt;12, respectively]. We observed 12,163 all-cause and 4,729 BC deaths during follow-up. Compared to those exposed to low levels, higher levels of PM2.5 exposure were associated with increased all-cause mortality for both moderate PM2.5 [HR 1.03 (95%CI:1.00-1.05)] and high PM2.5 [HR 1.09 (95%CI: 1.06-1.14)] but not BC-specific mortality. Among treatment subgroups, PM2.5 was associated with higher all-cause mortality in SX+RTX for moderate [HR 1.24 (95%CI:1.18-1.32)] and high PM2.5 [HR 1.36 (95%CI: 1.25-1.48)] and SX+RTX+CTX for high PM2.5 [HR 1.33 (95%CI: 1.17-1.51)]. There were no association between moderate/high PM2.5 with all-cause mortality in SX only and SX+CTX. Among the SX+RTX subgroup, BC-specific mortality was elevated for both moderate [HR 1.50 (95%CI:1.35-1.66)] and high PM2.5 [HR 1.81 (95%CI: 1.55-2.12)]. Conclusion: Even below EPA regulatory thresholds (PM2.5 &amp;lt;12 µg/m3), we observed increased risk of death in BC survivors who had high PM2.5 exposure during treatment period. The elevated mortality primarily occurred among women who received RTX and was present for all-cause and BC-specific mortality. The potential interaction between RTX and PM2.5 exposure, even after adjusting for tumor subtype and stage, warrants further evaluation. Citation Format: Benjamin Bates, Rachel Nethery, Melanie Rua, Hari Iyer, Elisa Bandera, Soko Setoguchi. Fine particulate matter exposure during breast cancer treatment among older women associated with increased mortality [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 841.

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

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. 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. 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. 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.

Tree-Based Machine Learning to Identify Predictors of Psoriasis Incidence at the Neighborhood Level: A Populational Study from Quebec, Canada.

Psoriasis is a major global health burden affecting ~ 60 million people worldwide. Existing studies on psoriasis focused on individual-level health behaviors (e.g. diet, alcohol consumption, smoking, exercise) and characteristics as drivers of psoriasis risk. However, it is increasingly recognized that health behavior arises in the context of larger social, cultural, economic and environmental determinants of health. We aimed to identify the top risk factors that significantly impact the incidence of psoriasis at the neighborhood level using populational data from the province of Quebec (Canada) and advanced tree-based machine learning (ML) techniques. Adult psoriasis patients were identified using International Classification of Disease (ICD)-9/10 codes from Quebec (Canada) populational databases for years 1997-2015. Data on environmental and socioeconomic factors 1year prior to psoriasis onset were obtained from the Canadian Urban Environment Health Consortium (CANUE) and Statistics Canada (StatCan) and were input as predictors into the gradient boosting ML. Model performance was evaluated using the area under the curve (AUC). Parsimonious models and partial dependence plots were determined to assess directionality of the relationship. The incidence of psoriasis varied geographically from 1.6 to 325.6/100,000 person-years in Quebec. The parsimonious model (top 9 predictors) had an AUC of 0.77 to predict high psoriasis incidence. Amongst top predictors, ultraviolet (UV) radiation, maximum daily temperature, proportion of females, soil moisture, urbanization, and distance to expressways had a negative association with psoriasis incidence. Nighttime light brightness had a positive association, whereas social and material deprivation indices suggested a higher psoriasis incidence in the middle socioeconomic class neighborhoods. This is the first study to highlight highly variable psoriasis incidence rates on a jurisdictional level and suggests that living environment, notably climate, vegetation, urbanization and neighborhood socioeconomic characteristics may have an association with psoriasis incidence.

Development and validation of an intrinsic capacity score in the UK Biobank study

BackgroundIn 2015, the World Health Organization introduced the concept of intrinsic capacity (IC) to define the individual-level characteristics that enable an older person to be and do the things they value. This study developed an intrinsic capacity score for UK Biobank study participants and validated its use as a tool for health outcome prediction, understanding healthy aging trajectories, and genetic research. MethodsOur analysis included data from 45,208 UK biobank participants who had a complete record of the ten variables included in the analysis. Factor adequacy was tested using Kaiser–Meyer–Olkin, Barthelt's, and the determinant of matrix tests, and the number of factors was determined by the parallel analysis method. Exploratory and confirmatory factor analyses were employed to determine the structure and dimensionality of indicators. Finally, the intrinsic capacity score was generated, and its construct and predictive validities as well as reliability were assessed. ResultsThe factor analysis identified a multidimensional construct comprising one general factor (intrinsic capacity) and five specific factors (locomotor, vitality, cognitive, psychological, and sensory). The bifactor structure showed a better fit (comparative fit index = 0.995, Tucker Lewis index = 0.976, root mean square error of approximation = 0.025, root mean square residual = 0.009) than the conventional five-factor structure. The intrinsic capacity score generated using the bifactor confirmatory factor analysis has good construct validity, as demonstrated by an inverse association with age (lower intrinsic capacity in older age; (β) =−0.035 (95%CI: −0.036, −0.034)), frailty (lower intrinsic capacity score in prefrail participants, β = −0.104 (95%CI: (−0.114, −0.094)) and frail participants, β = −0.227 (95%CI: −0.267, −0.186) than robust participants), and comorbidity (a lower intrinsic capacity score associated with increased Charlson's comorbidity index, β =−0.019 (95%CI: −0.022, −0.015)). The intrinsic capacity score also predicted comorbidity (a one-unit increase in baseline intrinsic capacity score led to a lower Charlson's comorbidity index, β = 0.147 (95%CI: −0.173, −0.121)) and mortality (a one-unit increase in baseline intrinsic capacity score led to 25 % lower risk of death, odds ratio = 0.75(95%CI: 0.663, 0.848)). ConclusionThe bifactor structure showed a better fit in all goodness of fit tests. The intrinsic capacity construct has strong structural, construct, and predictive validities and is a promising tool for monitoring aging trajectories.