Creating small-area deprivation indices: a guide for stages and options

Development of Composite Measures of Neighborhood Economic Factors for Use in Spinal Cord Injury Outcomes Studies: A Spinal Cord Injury Model Systems Database Study

OBJECTIVE

To develop composite measures of neighborhood economic factors for use with the national Spinal Cord Injury Model Systems (SCIMSs) database in cross-sectional and longitudinal investigations of the social determinants of health.

DESIGN

Secondary data analysis of administrative data from the 2009, 2014, and 2019 American Community Survey (ACS) 5-year estimates and survey data collected for the SCIMS database.

SETTING

Community.

PARTICIPANTS

The validity of the neighborhood economic measures developed from the ACS data was tested with a sample of SCIMS participants who completed a follow-up interview between 2017 and 2021 (N=8,130). The predictive validity of the neighborhood measures was assessed with a subsample of cases with complete data on the outcome and covariate measures (N=6,457).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

A binary measure of self-rated health status (1=poor/fair health; 0=good/very good/excellent).

RESULTS

A combination of panel review and data reduction techniques yielded 2 distinct measuring neighborhood socioeconomic status (SES) and neighborhood socioeconomic disadvantage that were validated using 3 waves of ACS data and the SCIMS data. The odds of reporting poor health were lower among people living in moderate- and high-SES neighborhoods and highest among people living in moderately and highly disadvantaged neighborhoods. The negative association between neighborhood SES and poor health was fully attenuated by differences in participants' individual demographic and economic characteristics whereas the positive association between neighborhood disadvantage and poor health persisted after adjusting for individual differences.

CONCLUSIONS

The two composite measures of neighborhood economic factors developed by this study are robust in samples from different periods of time and valid for use with the SCIMS database. Future investigations conducting surveillance of the needs of the SCI population using this resource may consider using these measures to assess the effect of the social determinants of health in outcomes after SCI.

Socioenvironmental sugar promotion and geographical inequalities in dental health of 5-year-old children in England

OBJECTIVES

To investigate the relationship between socioenvironmental sugar promotion and geographical inequalities in the prevalence of dental caries amongst 5-year-olds living across small areas within England.

METHODS

Ecological data from the National Dental Epidemiology Programme (NDEP) 2018-2019, comprising information on the percentage of 5-year-olds with tooth decay (≥1 teeth that are decayed into dentine, missing due to decay, or filled), and untreated tooth decay (≥1 decayed but untreated teeth), in lower-tier local authorities (LAs) of England. These were analysed for association with a newly developed Index of Sugar-Promoting Environments Affecting Child Dental Health (ISPE-ACDH). The index quantifies sugar-promoting determinants within a child's environment and provides standardized scores for the index, and its component domains that is, neighbourhood-, school- and family-environment, with the highest scores representing the highest levels of sugar promotion in lower-tier LAs (N = 317) of England. Linear regressions, including unadjusted models separately using index and each domain, and models adjusted for domains were built for each dental outcome.

RESULTS

Participants lived across 272 of 317 lower-tier LAs measured within the index. The average percentage of children with tooth decay and untreated tooth decay was 22.5 (SD: 8.5) and 19.6 (SD: 8.3), respectively. The mean index score was (0.1 [SD: 1.01]). Mean domain scores were: neighbourhood (0.02 [SD: 1.03]), school (0.1 [SD: 1.0]), and family (0.1 [SD: 0.9]). Unadjusted linear regressions indicated that the LA-level percentage of children with tooth decay increased by 5.04, 3.71, 4.78 and 5.24 with increased scores of the index, and neighbourhood, school and family domains, respectively. An additional model, adjusted for domains, showed that this increased percentage predicted by neighbourhood domain attenuated to 1.37, and by family domain it increased to 6.33. Furthermore, unadjusted models indicated that the LA-level percentage of children with untreated tooth decay increased by 4.72, 3.42, 4.45 and 4.97 with increased scores of the index, and neighbourhood, school, and family domains, respectively. The model, adjusted for domains, showed that this increased percentage predicted by neighbourhood domain attenuated to 1.24 and by family domain rose to 6.47. School-domain was not significantly associated with either outcome in adjusted models.

CONCLUSIONS

This study reveals that socioenvironmental sugar promotion, particularly within neighbourhood- and family-environments, may contribute to geographical inequalities in dental caries in children. Further research involving data on individual-level dental outcomes and confounders is required.

Socioeconomic status in adulthood of children with and without a history of seizures: A retrospective cohort study

OBJECTIVE

Compare adulthood socioeconomic status for children with and without a history of seizures.

METHODS

Retrospective cohort study using Aberdeen Children of the Nineteen Fifties (ACONF) data comprising children born 1950-1956 attending primary school 1962-1964, with follow-up data collected in 2001. Adulthood socioeconomic status was based on registrar general measure of occupational social class and categorised as high or low. We adjusted for potentially confounding variables including childhood socioeconomic status, behavioural issues (Rutter A/B scores), biological sex, school test scores, educational attainment, parental engagement with education, peer-status in school, and alcohol use in adulthood. A multivariate binary logistic regression was performed to estimate the adjusted association between children with a history of seizures of any type (for example febrile seizures, or provoked seizures of any other etiology or seizures in the context of epilepsy) or severity and adult socioeconomic status. Multiple imputation using the Monte-Carlo-Markov-Chain method accounted for missing data.

RESULTS

Pooled estimates (N = 2,208) comparing children with a history of seizures (n = 81) and children without a history of seizures (n = 2,127) found no differences between these cohorts in terms of adulthood socioeconomic status in both unadjusted (Odds Ratio (OR) 1.45 [95 % CI 0.71-2.96], p = 0.31) and adjusted (1.02 [0.46, 2.24], p = 0.96) analyses. Compared to males, females were at increased odds of having a lower socioeconomic status in adulthood (1.56 [1.13-2.17], p = 0.01).Compared to those with low educational attainment, those with moderate (0.32 [0.21, 0.48], p < 0.001) and high (0.12 [0.07, 0.20], p < 0.001) educational attainment were at reduced odds of having a lower socioeconomic status in adulthood.

CONCLUSION

Cognitive problems in childhood (using educational attainment and scores on primary school tests proxy markers for cognition) rather than a history of seizures per se, were associated with lower SES in a population of adults born 1950-56 in Aberdeen. This relationship may be different depending on the time in history and nation/region of study. Given the changes in health, education and social support in the management of children with seizures over time, it would be of interest to investigate outcomes in a contemporary cohort. Such studies should ideally have validated diagnoses of seizures, details on seizure characteristics such as seizure type and severity, and a large sample size using national data.

Area-level Measures of the Social Environment: Operationalization, Pitfalls, and Ways Forward

People's mental health is intertwined with the social environment in which they reside. This chapter explores approaches for quantifying the area-level social environment, focusing specifically on socioeconomic deprivation and social fragmentation. We discuss census data and administrative units, egocentric and ecometric approaches, neighborhood audits, social media data, and street view-based assessments. We close the chapter by discussing possible paths forward from associations between social environments and health to establishing causality, including longitudinal research designs and time-series social environmental indices.

Trends in socioeconomic inequalities in cause-specific premature mortality in Belgium, 1998-2019

BACKGROUND

Higher levels of socioeconomic deprivation have been consistently associated with increased risk of premature mortality, but a detailed analysis by causes of death is lacking in Belgium. We aim to investigate the association between area deprivation and all-cause and cause-specific premature mortality in Belgium over the period 1998-2019.

METHODS

We used the 2001 and 2011 Belgian Indices of Multiple Deprivation to assign statistical sectors, the smallest geographical units in the country, into deprivation deciles. All-cause and cause-specific premature mortality rates, population attributable fraction, and potential years of life lost due to inequality were estimated by period, sex, and deprivation deciles.

RESULTS

Men and women living in the most deprived areas were 1.96 and 1.78 times more likely to die prematurely compared to those living in the least deprived areas over the period under study (1998-2019). About 28% of all premature deaths could be attributed to socioeconomic inequality and about 30% of potential years of life lost would be averted if the whole population of Belgium faced the premature mortality rates of the least deprived areas.

CONCLUSION

Premature mortality rates have declined over time, but inequality has increased due to a faster pace of decrease in the least deprived areas compared to the most deprived areas. As the causes of death related to poor lifestyle choices contribute the most to the inequality gap, more effective, country-level interventions should be put in place to target segments of the population living in the most deprived areas as they are facing disproportionately high risks of dying.

Neighborhood characteristics and HIV treatment outcomes: A scoping review

Recognizing challenges faced by people living with HIV is vital for improving their HIV treatment outcomes. While individual-level interventions play a crucial role, community factors can shape the impact of individual interventions on treatment outcomes. Understanding neighborhood characteristics' association with HIV treatment outcomes is crucial for optimizing effectiveness. This review aims to summarize the research scope on the association between neighborhood characteristics and HIV treatment outcomes. The databases PubMed, CINAHL (EBSCOhost), Embase (Elsevier), and PsychINFO (EBSCOhost) were searched from the start of each database to Nov 21, 2022. Screening was performed by three independent reviewers. Full-text publications of all study design meeting inclusion criteria were included in the review. There were no language or geographical limitations. Conference proceedings, abstract only, and opinion reports were excluded from the review. The search yielded 7,822 publications, 35 of which met the criteria for inclusion in the review. Studies assessed the relationship between neighborhood-level disadvantage (n = 24), composition and interaction (n = 17), social-economic status (n = 18), deprivation (n = 16), disorder (n = 8), and rural-urban status (n = 7) and HIV treatment outcomes. The relationship between all neighborhood characteristics and HIV treatment outcomes was not consistent across studies. Only 7 studies found deprivation had a negative association with HIV treatment outcomes; 6 found that areas with specific racial/ethnic densities were associated with poor HIV treatment outcomes, and 5 showed that disorder was associated with poor HIV treatment outcomes. Three studies showed that rural residence was associated with improved HIV treatment outcomes. There were inconsistent findings regarding the association between neighborhood characteristics and HIV treatment outcomes. While the impact of neighborhood characteristics on disease outcomes is highly recognized, there is a paucity of standardized definitions and metrics for community characteristics to support a robust assessment of this hypothesis. Comparative studies that define and assess how specific neighborhood indicators independently or jointly affect HIV treatment outcomes are highly needed.

Construction and validation of the area level deprivation index for health research: A methodological study based on Nepal Demographic and Health Survey

Area-level factors may partly explain the heterogeneity in risk factors and disease distribution. Yet, there are a limited number of studies that focus on the development and validation of the area level construct and are primarily from high-income countries. The main objective of the study is to provide a methodological approach to construct and validate the area level construct, the Area Level Deprivation Index in low resource setting. A total of 14652 individuals from 11,203 households within 383 clusters (or areas) were selected from 2016-Nepal Demographic and Health survey. The index development involved sequential steps that included identification and screening of variables, variable reduction and extraction of the factors, and assessment of reliability and validity. Variables that could explain the underlying latent structure of area-level deprivation were selected from the dataset. These variables included: housing structure, household assets, and availability and accessibility of physical infrastructures such as roads, health care facilities, nearby towns, and geographic terrain. Initially, 26-variables were selected for the index development. A unifactorial model with 15-variables had the best fit to represent the underlying structure for area-level deprivation evidencing strong internal consistency (Cronbach's alpha = 0.93). Standardized scores for index ranged from 58.0 to 140.0, with higher scores signifying greater area-level deprivation. The newly constructed index showed relatively strong criterion validity with multi-dimensional poverty index (Pearson's correlation coefficient = 0.77) and relatively strong construct validity (Comparative Fit Index = 0.96; Tucker-Lewis Index = 0.94; standardized root mean square residual = 0.05; Root mean square error of approximation = 0.079). The factor structure was relatively consistent across different administrative regions. Area level deprivation index was constructed, and its validity and reliability was assessed. The index provides an opportunity to explore the area-level influence on disease outcome and health disparity.

Changes in neighborhood physical and social environments matter for change in mental health: Longitudinal evidence from Dutch panel data

Numerous neighborhood environments have been recognized to affect mental health, but only a few longitudinal studies investigated these associations jointly and whether different population groups are affected differently. We used three-wave panel data of 2699 adults between 2010 and 2016 in the Netherlands to assess the associations between changes in neighborhood physical and social environments and mental health changes. Further, we assessed possible effect modification of gender and income. Mental health was measured using the Mental Health Inventory. Time-varying exposure to green space, blue space, population density, air pollution, socioeconomic deprivation, and social fragmentation were assigned based on individuals' neighborhood histories. Fixed-effect regressions were conducted to assess within-person associations between single and multiple exposures on mental health for the entire sample and stratified by gender and income. Our single-exposure models showed that increases in blue space were significantly associated with mental health improvements, while increases in fine particulate matter (PM2.5) resulted in declines in mental health. These associations were not attenuated in the multi-exposure model. We observed no significant associations for the remaining environments. Stratification analyses showed that females' mental health further declined as PM2.5 concentrations increased compared to males. Increasing levels of socioeconomic deprivation were associated with further declines in mental health among the less well-off compared with higher-income earners. Our longitudinal findings suggested that neighborhood physical and social environment changes were associated with mental health changes. Future research is required to establish the underlying pathways.

Association between hospitalizations for sensitive conditions and quality of primary care

OBJECTIVE

To analyze the association between municipal rates of ambulatory care sensitive conditions (ACSC) hospitalization and the quality of primary health care (PHC), socioeconomic, and demographic variables and those related to local characteristics of the health system from 2010 to 2019.

METHOD

Ecological time series study in Brazilian municipalities analyzing the correlation of ACSC hospitalization rates with PHC quality measured by the three cycles of the Primary Care Access and Program for improving primary care access and quality (PMAQ-AB). The study included municipalities whose teams participated in 80% or more of at least two PMAQ-AB cycles. The correlation between standardized ACSC hospitalization rates and PHC quality and other variables was analyzed. Spearman's test was used between the response variable and numerical explanatory variables. Generalized equations estimation was used as a multivariate model associating ACSC hospitalization rates with the other variables over the years.

RESULTS

A total of 3,500 municipalities were included in the models. The quality of PHC (PMAQ-AB score) showed an inverse association with the variation in ACSC hospitalization rates. Hospitalization rates fell by -2% per year every ten-point increase in the PMAQ-AB score, adjusted by the remaining variables. A one-unit increase in the beds per 1,000 inhabitants variable had an impact of approximately +6.4% on ACSC hospitalization rates. Regarding population size, larger municipalities had lower ACSC hospitalization rates. Increased PHC coverage and lower socioeconomic inequality were also associated with the reduction in hospitalizations.

CONCLUSIONS

The reduction in ACSC hospitalization rates over time was associated with an increase in the quality of PHC. It was also associated with a reduction in the number of hospital beds and municipalities with better socioeconomic indicators.

Prevalence of adult smokers in Brazilian capitals according to socioeconomic deprivation

OBJECTIVE

To estimate the prevalence of adult smokers in the 26 capitals and the Federal District according to the Brazilian Deprivation Index (Índice Brasileiro de Privação - IBP).

METHODS

Dataset on smoking were obtained from the Surveillance of Risk and Protective Factors for Noncommunicable Diseases by Survey (Vigitel) system for the 26 capitals and the Federal District, in the period from 2010 to 2013. The IBP classifies the census sectors according to indicators such as: income less than ½ minimum wage, illiterate population and without sanitary sewage. In the North and Northeast regions, the census sectors were grouped into four categories (low, medium, high and very high deprivation) and in the South, Southeast and Midwest regions into three (low, medium and high deprivation). Prevalence estimates of adult smokers were obtained using the indirect estimation method in small areas. To calculate the prevalence ratios, Poisson models are used.

RESULTS

The positive association between prevalence and deprivation of census sector categories was found in 16 (59.3%) of the 27 cities. In nine (33.3%) cities, the sectors with the greatest deprivation had a higher prevalence of smokers when compared to those with the least deprivation, and in two (7.4%) there were no differences. In Aracaju, Belém, Fortaleza, João Pessoa, Macapá and Salvador, the prevalence of adult smokers was three times higher in the group of sectors with greater deprivation compared to those with less deprivation.

CONCLUSION

Sectors with greater social deprivation had a higher prevalence of smoking, compared with less deprivation, pointing to social inequalities.

Time trends and social inequalities in infant and young child feeding practices: national estimates from Brazil's Food and Nutrition Surveillance System, 2008-2019

OBJECTIVE

To describe the time trends and socio-economic inequalities in infant and young child feeding practices in accordance with the Brazilian deprivation index (BDI).

DESIGN

This time-series study analysed the prevalence of multiple breast-feeding and complementary feeding indicators based on data from the Brazilian Food and Nutrition Surveillance System, 2008-2019. Prais-Winsten regression models were used to analyse time trends. Annual percent change (APC) and 95 % CI were calculated.

SETTING

Primary health care services, Brazil.

PARTICIPANTS

Totally, 911 735 Brazilian children under 2 years old.

RESULTS

Breast-feeding and complementary feeding practices differed between the extreme BDI quintiles. Overall, the results were more favourable in the municipalities with less deprivation (Q1). Improvements in some complementary feeding indicators were observed over time and evidenced such disparities: minimum dietary diversity (Q1: Δ 47·8-52·2 %, APC + 1·44, P = 0·006), minimum acceptable diet (Q1: Δ 34·5-40·5 %, APC + 5·17, P = 0·004) and consumption of meat and/or eggs (Q1: Δ 59·7-80·3 %, APC + 6·26, P < 0·001; and Q5: Δ 65·7-70·7 %, APC + 2·20, P = 0·041). Stable trends in exclusive breast-feeding and decreasing trends in the consumption of sweetened drinks and ultra-processed foods were also observed regardless the level of the deprivation.

CONCLUSIONS

Improvements in some complementary food indicators were observed over time. However, the improvements were not equally distributed among the BDI quintiles, with children from the municipalities with less deprivation benefiting the most.

Measuring small-area level deprivation in Belgium: The Belgian Index of Multiple Deprivation

BACKGROUND

In the past, deprivation has been mostly captured through simple and univariate measures such as low income or poor educational attainment in research on health and social inequalities in Belgium. This paper presents a shift towards a more complex, multidimensional measure of deprivation at the aggregate level and describes the development of the first Belgian Indices of Multiple Deprivation (BIMDs) for the years 2001 and 2011.

METHODS

The BIMDs are constructed at the level of the smallest administrative unit in Belgium, the statistical sector. They are a combination of six domains of deprivation: income, employment, education, housing, crime and health. Each domain is built on a suite of relevant indicators representing individuals that suffer from a certain deprivation in an area. The indicators are combined to create the domain deprivation scores, and these scores are then weighted to create the overall BIMDs scores. The domain and BIMDs scores can be ranked and assigned to deciles from 1 (the most deprived) to 10 (the least deprived).

RESULTS

We show geographical variations in the distribution of the most and least deprived statistical sectors in terms of individual domains and overall BIMDs, and we identify hotspots of deprivation. The majority of the most deprived statistical sectors are located in Wallonia, whereas most of the least deprived statistical sectors are in Flanders.

CONCLUSION

The BIMDs offer a new tool for researches and policy makers for analyzing patterns of deprivation and identifying areas that would benefit from special initiatives and programs.

Neighbourhood socioeconomic disadvantage and psychiatric disorders among refugees: a population-based, quasi-experimental study in Denmark

PURPOSE

Refugees are vulnerable to psychiatric disorders because of risk factors linked to migration. Limited evidence exist on the impact of the neighbourhood in which refugee resettle. We examined whether resettling in a socioeconomically disadvantaged neighbourhood increased refugees' risk of psychiatric disorders.

METHODS

This register-based cohort study included 42,067 adults aged 18 years and older who came to Denmark as refugees during 1986-1998. Resettlement policies in those years assigned refugees in a quasi-random fashion to neighbourhoods across the country. A neighbourhood disadvantage index was constructed using neighbourhood-level data on income, education, unemployment, and welfare receipt. Main outcomes were psychiatric diagnoses and psychiatric medication usage ascertained from nationwide patient and prescription drug registers, with up to 30-year follow-up. Associations of neighbourhood disadvantage with post-migration risk of psychiatric disorders were examined using Cox proportional hazards and linear probability models adjusted for individual, family, and municipality characteristics.

RESULTS

The cumulative risk of psychiatric diagnoses and medication was 13.7% and 46.1%, respectively. Refugees' risk of psychiatric diagnoses and psychiatric medication usage was higher among individuals assigned to high-disadvantage compared with low-disadvantage neighbourhoods in analyses including fixed effects for assigned municipality (psychiatric diagnoses: hazard ratio (HR) = 1.14, 95% CI 1.04, 1.25; psychiatric medication: HR = 1.05, 95% CI 1.00, 1.11). Consistent results were found using linear probability models. Results for diagnostic categories and subclasses of medications suggested that the associations were driven by neurotic and stress-related disorders and use of anxiolytic medications.

CONCLUSION

Resettlement in highly disadvantaged neighbourhoods was associated with an increase in refugees' risk of psychiatric disorders, suggesting that targeted placement of newly arrived refugees could benefit refugee mental health. The results contribute quasi-experimental evidence to support links between neighbourhood characteristics and health.

Measuring disadvantage: A systematic comparison of United States small-area disadvantage indices

Extensive evidence demonstrates the effects of area-based disadvantage on a variety of life outcomes, such as increased mortality and low economic mobility. Despite these well-established patterns, disadvantage, often measured using composite indices, is inconsistently operationalized across studies. To address this issue, we systematically compared 5 U.S. disadvantage indices at the county-level on their relationships to 24 diverse life outcomes related to mortality, physical health, mental health, subjective well-being, and social capital from heterogeneous data sources. We further examined which domains of disadvantage are most important when creating these indices. Of the five indices examined, the Area Deprivation Index (ADI) and Child Opportunity Index 2.0 (COI) were most related to a diverse set of life outcomes, particularly physical health. Within each index, variables from the domains of education and employment were most important in relationships with life outcomes. Disadvantage indices are being used in real-world policy and resource allocation decisions; an index's generalizability across diverse life outcomes, and the domains of disadvantage which constitute the index, should be considered when guiding such decisions.

How well does the Scottish Index of Multiple Deprivation identify income and employment deprived individuals across the urban-rural spectrum and between local authorities?

BACKGROUND

Area-based indices of deprivation are used to identify populations at need, to inform service planning and policy, to rank populations for monitoring trends in inequalities, and to evaluate the impacts of interventions. There is scepticism of the utility of area deprivation indices in rural areas because of the spatial heterogeneity of their populations.

OBJECTIVE

To compare the sensitivity of the Scottish Index of Multiple Deprivation (SIMD) for detecting income and employment deprived individuals by urban-rural classification and across local authorities.

STUDY DESIGN

Descriptive analysis of cross-sectional data.

METHODS

Data from the 2020 Scottish Index of Multiple Deprivation (SIMD) were used to calculate the number and percentage of income and employment deprived people missed within each of the six-fold urban-rural classification strata and each local authority using areas ranked by the national SIMD, within local authority rankings, and within urban-rural strata rankings, for deprivation thresholds between the 5% most deprived areas and the 30% most deprived areas. The Slope Index of Inequality (SII) and Relative Index of Inequality (RII) were calculated within local authorities and urban-rural classification strata to estimate the concentration of deprivation within ranked data zones.

RESULTS

The number and percentage of income and employment deprived people is higher in urban than rural areas. However, using the national, local authority, and within urban-rural classification strata rankings of SIMD, and under all deprivation thresholds (from the 5%-30% most deprived areas), the percentage of income and employment deprived people missed by targeting the most deprived areas within urban-rural strata is higher in more remote and rural areas, and in island local authorities. The absolute number of income and employment deprived individuals is greater in urban areas across rankings and thresholds.

CONCLUSION

The SIMD misses a higher percentage of income and employment deprived people in remote, rural and island areas across deprivation thresholds and irrespective of whether national, local or within urban-rural classification strata are used. However, the absolute number of people missed is higher in urban areas.

The Swiss neighbourhood index of socioeconomic position: update and re-validation

BACKGROUND

The widely used Swiss neighbourhood index of socioeconomic position (Swiss-SEP 1) was based on data from the 2000 national census on rent, household head education and occupation, and crowding. It may now be out of date.

METHODS

We created a new index (Swiss-SEP 2) based on the 2012-2015 yearly micro censuses that have replaced the decennial house-to-house census in Switzerland since 2010. We used principal component analysis on neighbourhood-aggregated variables and standardised the index. We also created a hybrid version (Swiss-SEP 3), with updated values for neighbourhoods centred on buildings constructed after the year 2000 and original values for the remaining neighbourhoods.

RESULTS

A total of 1.54 million neighbourhoods were included. With all three indices, the mean yearly equivalised household income increased from around 52,000 to 90,000 CHF from the lowest to the highest index decile. Analyses of mortality were based on 33.6 million person-years of follow-up. The age- and sex-adjusted hazard ratios of all-cause mortality comparing areas in the lowest Swiss-SEP decile with areas of the highest decile were 1.39 (95% confidence interval [CI] 1.36-1.41), 1.31 (1.29-1.33) and 1.34 (1.32-1.37) using the old, new and hybrid indices, respectively.

DISCUSSION

The Swiss-SEP indices capture area-based SEP at a high resolution and allow the study of SEP when individual-level SEP data are missing or area-level effects are of interest. The hybrid version (Swiss-SEP 3) maintains high spatial resolution while adding information on new neighbourhoods. The index will continue to be useful for Switzerland's epidemiological and public health research.

Is area-level socioeconomic deprivation associated with mortality due to circulatory system diseases in Poland?

BACKGROUND

Socioeconomic deprivation (SED) is known to influence cardiovascular health. However, studies analyzing the relationship between deprivation and circulatory system diseases (CSD) in Central and Eastern Europe are limited. This study aimed to assess the relationship between SED and mortality due to CSD at a population level in 66 sub-regions of Poland.

METHODS

The 2010-2014 data regarding mortality and SED components were obtained from the Central Statistical Office. An area-based SED index was calculated based on the higher education rates, employment structure, wages, unemployment, and poverty. The dynamics of changes in mortality due to CSD was expressed by the number of deaths prevented or postponed (DPP) in terciles of the SED index. The associations between the mortality from CSD and SED index were analyzed using multivariate Poisson regression models and generalized estimating equations.

RESULTS

Among men, the percentage of DPP in 2014 was 13.1% for CSD, 23.4% for ischemic heart disease (IHD), and 21.4% for cerebrovascular diseases (CeVD). In the case of women, the proportion of DPP was 12.8, 25.6, and 21.6%, respectively. More deprived sub-regions experienced a greater decrease in CSD-related mortality than less deprived sub-regions. The disparity in mortality reduction between more deprived and less deprived sub-regions was even more pronounced for women. After adjusting for smoking prevalence, average BMI, population density, and changes in mortality over time, it was found that the SED index over the 2010-2014 time period was significantly associated with CSD- and IHD-related mortality for men (respectively 5.3 and 19.5% expected mortality increase per 1-unit increase of SED index), and with IHD- and CeVD-related mortality for women (respectively 30.3 and 23.0% expected mortality increase per 1-unit increase of SED index).

CONCLUSIONS

Significant differences in mortality changes due to CSD in Poland could be observed in relation to socioeconomic deprivation, resulting in reduced health inequalities. To reduce CSD mortality, more comprehensive preventive measures, including approaches addressing the socioeconomic factors, mainly poverty, education and employment, are needed, particularly in less urbanized areas.

Inequalities in mortality associated with housing conditions in Belgium between 1991 and 2020

BACKGROUND

Poor housing conditions have been associated with increased mortality. Our objective is to investigate the association between housing inequality and increased mortality in Belgium and to estimate the number of deaths that could be prevented if the population of the whole country faced the mortality rates experienced in areas that are least deprived in terms of housing.

METHODS

We used individual-level mortality data extracted from the National Register in Belgium and relative to deaths that occurred between Jan. 1, 1991, and Dec. 31, 2020. Spatial and time-specific housing deprivation indices (1991, 2001, and 2011) were created at the level of the smallest geographical unit in Belgium, with these units assigned into deciles from the most to the least deprived. We calculated mortality associated with housing inequality as the difference between observed and expected deaths by applying mortality rates of the least deprived decile to other deciles. We also used standard life table calculations to estimate the potential years of life lost due housing inequality.

RESULTS

Up to 18.5% (95% CI 17.7-19.3) of all deaths between 1991 and 2020 may be associated with housing inequality, corresponding to 584,875 deaths. Over time, life expectancy at birth increased for the most and least deprived deciles by about 3.5 years. The gap in life expectancy between the two deciles remained high, on average 4.6 years. Life expectancy in Belgium would increase by approximately 3 years if all deciles had the mortality rates of the least deprived decile.

CONCLUSIONS

Thousands of deaths in Belgium could be avoided if all Belgian neighborhoods had the mortality rates of the least deprived areas in terms of housing. Hotspots of housing inequalities need to be located and targeted with tailored public actions.

Household food insecurity risk indices for English neighbourhoods: Measures to support local policy decisions

BACKGROUND

In England, the responsibility to address food insecurity lies with local government, yet the prevalence of this social inequality is unknown in small subnational areas. In 2018 an index of small-area household food insecurity risk was developed and utilised by public and third sector organisations to target interventions; this measure needed updating to better support decisions in different settings, such as urban and rural areas where pressures on food security differ.

METHODS

We held interviews with stakeholders (n = 14) and completed a scoping review to identify appropriate variables to create an updated risk measure. We then sourced a range of open access secondary data to develop an indices of food insecurity risk in English neighbourhoods. Following a process of data transformation and normalisation, we tested combinations of variables and identified the most appropriate data to reflect household food insecurity risk in urban and rural areas.

RESULTS

Eight variables, reflecting both household circumstances and local service availability, were separated into two domains with equal weighting for a new index, the Complex Index, and a subset of these to make up the Simple Index. Within the Complex Index, the Compositional Domain includes population characteristics while the Structural Domain reflects small area access to resources such as grocery stores. The Compositional Domain correlated well with free school meal eligibility (rs = 0.705) and prevalence of childhood obesity (rs = 0.641). This domain was the preferred measure for use in most areas when shared with stakeholders, and when assessed alongside other configurations of the variables. Areas of highest risk were most often located in the North of England.

CONCLUSION

We recommend the use of the Compositional Domain for all areas, with inclusion of the Structural Domain in rural areas where locational disadvantage makes it more difficult to access resources. These measures can aid local policy makers and planners when allocating resources and interventions to support households who may experience food insecurity.

The Canadian Environmental Quality Index (Can-EQI): Development and calculation of an index to assess spatial variation of environmental quality in Canada's 30 largest cities

BACKGROUND

Multiple characteristics of the urban environment have been shown to influence population health and health-related behaviours, though the distribution and combined effects of these characteristics on health is less understood. A composite measure of multiple environmental conditions would allow for comparisons among different urban areas; however, this measure is not available in Canada.

OBJECTIVES

To develop an index of environmental quality for Canada's largest urban areas and to assess the influence of population size on index values.

METHODS

We conducted a systematic search of potential datasets and consulted with experts to refine and select datasets for inclusion. We identified and selected nine datasets across five domains (outdoor air pollution, natural environments, built environments, radiation, and climate/weather). Datasets were chosen based on known impacts on human health across the life course, complete geographic coverage of the cities of interest, and temporal alignment with the 2016 Canadian census. Each dataset was then summarized into dissemination areas (DAs). The Canadian Environmental Quality Index (Can-EQI) was created by summing decile ranks of each variable based on hypothesized relationships to health outcomes.

RESULTS

We selected 30 cities with a population of more than 100,000 people which included 28,026 DAs and captured approximately 55% of the total Canadian population. Can-EQI scores ranged from 21.1 to 88.9 out of 100, and in Canada's largest cities were 10.2 (95% CI: -10.7, -9.7) points lower than the smallest cities. Mapping the Can-EQI revealed high geographic variability within and between cities.

DISCUSSION

Our work demonstrates a valuable methodology for exploring variations in environmental conditions in Canada's largest urban areas and provides a means for exploring the role of environmental factors in explaining urban health inequalities and disparities. Additionally, the Can-EQI may be of value to municipal planners and decision makers considering the allocation of investments to improve urban conditions.

Use Of Area-Based Socioeconomic Deprivation Indices: A Scoping Review And Qualitative Analysis

There is considerable interest among researchers, clinicians, and policy makers in understanding the impact of place on health. In this scoping review and qualitative analysis, we sought to assess area-level socioeconomic deprivation indices used in public health and health outcomes research in the US. We conducted a systematic scoping review to identify area-level socioeconomic deprivation indices commonly used in the US since 2015. We then qualitatively compared the indices based on the input-variable domains, data sources, index creation characteristics, index accessibility, the geography over which the index is applied, and the nature of the output measure or measures. We identified fifteen commonly used indices of area-level socioeconomic deprivation. There were notable differences in the characteristics of each index, particularly in how they define socioeconomic deprivation based on input-variable domains, the geography over which they are applied, and their output measures. These characteristics can help guide future index selection and application in clinical care, research, and policy decisions.

German Index of Socioeconomic Deprivation (GISD): Revision, update and applications

Background

Regional deprivation indices enable researchers to analyse associations between socioeconomic disadvantages and health outcomes even if the health data of interest does not include information on the individuals' socioeconomic position. This article introduces the recent revision of the German Index of Socioeconomic Deprivation (GISD) and presents associations with life expectancy as well as age-standardised cardiovascular mortality rates and cancer incidences as applications.

Methods

The GISD measures the level of socioeconomic deprivation using administrative data of education, employment, and income situations at the district and municipality level from the INKAR database. The indicators are weighted via principal component analyses. The regional distribution is depicted cartographically, regional level associations with health outcomes are presented.

Results

The principal component analysis indicates medium to high correlations of the indicators with the index subdimensions. Correlation analyses show that in districts with the lowest deprivation, the average life expectancy of men is approximately six years longer (up to three years longer for women) than for those from districts with the highest deprivation. A similar social gradient is observed for cardiovascular mortality and lung cancer incidence.

Conclusions

The GISD provides a valuable tool to analyse socioeconomic inequalities in health conditions, diseases, and their determinants at the regional level.

Association of socioeconomic deprivation with life expectancy and all-cause mortality in Spain, 2011–2013

Life tables summarise a population’s mortality experience during a time period. Sex- and age-specific life tables are needed to compute various cancer survival measures. However, mortality rates vary according to socioeconomic status. We present sex- and age-specific life tables based on socioeconomic status at the census tract level in Spain during 2011–2013 that will allow estimating cancer relative survival estimates and life expectancy measures by socioeconomic status. Population and mortality data were obtained from the Spanish Statistical Office. Socioeconomic level was measured using the Spanish Deprivation Index by census tract. We produced sex- and age-specific life expectancies at birth by quintiles of deprivation, and life tables by census tract and province. Life expectancy at birth was higher among women than among men. Women and men in the most deprived census tracts in Spain lived 3.2 and 3.8 years less than their counterparts in the least deprived areas. A higher life expectancy in the northern regions of Spain was discovered. Life expectancy was higher in provincial capitals than in rural areas. We found a significant life expectancy gap and geographical variation by sex and socioeconomic status in Spain. The gap was more pronounced among men than among women. Understanding the association between life expectancy and socioeconomic status could help in developing appropriate public health programs. Furthermore, the life tables we produced are needed to estimate cancer specific survival measures by socioeconomic status. Therefore, they are important for cancer control in Spain.

Classification of Deprivation Indices That Applied to Detect Health Inequality: A Scoping Review

INTRODUCTION

Many studies around the world are undertaken to establish the association between deprivation and public health indicators. Both separate indicators (e.g., income, education, occupation, public security and social support) and complex models (indices) include several indicators. Deprivation indices are actively used in public health since the mid 1980s. There is currently no clear classification of indices.

METHODS

In the current review, data related to deprivation indices are combined and analyzed in order to create a taxonomy of indices based on the results obtained. The search was carried out using two bibliographic databases. After conducting a full-text review of the articles and searching and adding relevant articles from the bibliography, and articles that were already known to the authors, sixty studies describing the use of sixty deprivation indices in seventeen countries were included in the narrative synthesis, resulting in development of a taxonomy of indices. When creating the taxonomy, an integrative approach was used that allows integrating new classes and sub-classes in the event that new information appears.

RESULTS

In the review, 68% (41/60) of indices were classified as socio-economic, 7% (4/60) of indices as material deprivation, 5% (3/60) of indices as environmental deprivation and 20% (12/60) as multidimensional indices.

CONCLUSIONS

The data stimulates the use of a competent approach, and will help researchers and public health specialist in resolving conflicts or inconsistencies that arise during the construction and use of indices.

Area-level socioeconomic deprivation, non-national residency, and Covid-19 incidence: A longitudinal spatiotemporal analysis in Germany

Background

Socioeconomic conditions affect the dynamics of the Covid-19 pandemic. We analysed the association between area-level socioeconomic deprivation, proportion of non-nationals, and incidence of Covid-19 infections in Germany.

Methods

Using linked nationally representative data at the level of 401 German districts from three waves of infection (January-2020 to May-2021), we fitted Bayesian spatiotemporal models to assess the association between socioeconomic deprivation, and proportion of non-nationals with Covid-19 incidence, controlling for age, sex, vaccination coverage, settlement structure, and spatial and temporal effects. We estimated risk ratios (RR) and corresponding 95% credible intervals (95% CrI). We further examined the deprivation domains (education, income, occupation), interactions between deprivation, sex and the proportion of non-nationals, and explored potential pathways from deprivation to Covid-19 incidence.

Findings

Covid-19 incidence risk was 15% higher (RR=1·15, 95%-CrI=1·06-1·24) in areas classified with the highest deprivation quintile (Q5) compared to the least deprived areas (Q1). Medium-low (Q2), medium (Q3), and medium-high (Q4) deprived districts showed 6% (1·06, 1·00-1·12), 8% (1·08, 1·01-1·15), and 5% (1·05, 0·98-1·13) higher risk, respectively, compared to the least deprived. Districts with higher proportion of non-nationals showed higher incidence risk compared to districts with lowest proportion, but the association weakened across the three waves. During the first wave, an inverse association was observed with highest incidence risk in least deprived areas (Q1). Deprivation interacted with sex, but not with the proportion of non-nationals.

Interpretation

Socioeconomic deprivation, and proportion of non-nationals are independently associated with the incidence of Covid-19. Regional planning of non-pharmaceutical interventions and vaccination strategies would benefit from consideration of area-level deprivation and non-national residency.

Funding

The study was funded by the German Ministry of Health (ZMV I 1 - 25 20 COR 410).

Is limited access to obstetric services associated with adverse birth outcomes? A cross-sectional study of Korean national birth data

OBJECTIVES

The geographical disparity in the access to essential obstetric services is a public health issue in many countries. We explored the association between timely access to obstetric services and the individual risk of adverse birth outcomes.

DESIGN

Repeated cross-sectional design.

SETTING

South Korean national birth data linked with a medical service provision database.

PARTICIPANTS

1 842 718 singleton livebirths from 2014 to 2018.

PRIMARY OUTCOME MEASURES

Preterm birth (PTB), post-term birth, low birth weight (LBW) and macrosomia.

RESULTS

In the study population, 9.3% of mothers lived in districts where the Time Relevance Index (TRI) was as low as the first quartile (40.6%). Overall PTB and post-term birth rates were 5.0% and 0.1%, respectively. Among term livebirths, LBW and macrosomia occurred in 1.0% and 3.3%, respectively. When the TRI is lower, representing less access to obstetric care, the risk of macrosomia was higher (adjusted OR=1.15, 95% CI 1.11 to 1.20 for Q1 compared with Q4). Similarly, PTB is more likely to occur when TRI is lower (1.05, 95% CI 1.00 to 1.10 for Q1; 1.03, 95% CI 1.01 to 1.05 for Q2). There were some inverse associations between TRI and post-term birth (0.80, 95% CI 0.71 to 0.91, for Q2; 0.84, 95% CI 0.76 to 0.93, for Q3).

CONCLUSIONS

We observed less accessibility to obstetric service is associated with higher risks of macrosomia and PTB. This finding supports the role of obstetric service accessibility in the individual risk of adverse birth outcomes.

Ability of municipality-level deprivation indices to capture social inequalities in perinatal health in France: A nationwide study using preterm birth and small for gestational age to illustrate their relevance

BACKGROUND

Evidence-based policy-making to reduce perinatal health inequalities requires an accurate measure of social disparities. We aimed to evaluate the relevance of two municipality-level deprivation indices (DIs), the French-Deprivation-Index (FDep) and the French-European-Deprivation-Index (FEDI) in perinatal health through two key perinatal outcomes: preterm birth (PTB) and small-for-gestational-age (SGA).

METHODS

We used two data sources: The French National Perinatal Surveys (NPS) and the French national health data system (SNDS). Using the former, we compared the gradients of the associations between individual socioeconomic characteristics (educational level and income) and "PTB and SGA" and associations between municipality-level DIs (Q1:least deprived; Q5:most deprived) and "PTB and SGA". Using the SNDS, we then studied the association between each component of the two DIs (census data, 2015) and "PTB and SGA". Adjusted odds ratios (aOR) were estimated using multilevel logistic regression with random intercept at the municipality level.

RESULTS

In the NPS (N = 26,238), PTB and SGA were associated with two individual socioeconomic characteristics: maternal educational level (≤ lower secondary school vs. ≥ Bachelor's degree or equivalent, PTB: aOR = 1.43 [1.22-1.68], SGA: (1.31 [1.61-1.49]) and household income (< 1000 € vs. ≥ 3000 €, PTB: 1.55 [1.25-1.92], SGA: 1.69 [1.45-1.98]). For both FDep and FEDI, PTB and SGA were more frequent in deprived municipalities (Q5: 7.8% vs. Q1: 6.3% and 9.0% vs. 5.9% for PTB, respectively, and 12.0% vs. 10.3% and 11.9% vs. 10.2% for SGA, respectively). However, after adjustment, neither FDep nor FEDI showed a significant gradient with PTB or SGA. In the SNDS (N = 726,497), no FDep component, and only three FEDI components were significantly associated (specifically, the % of the population with ≤ lower secondary level of education with both outcomes (PTB: 1.5 [1.15-1.96]); SGA: 1.25 [1.03-1.51]), the % of overcrowded (i.e., > 1 person per room) houses (1.63 [1.15-2.32]) with PTB only, and unskilled farm workers with SGA only (1.52 [1.29-1.79]).

CONCLUSION

Some components of FDep and FEDI were less relevant than others for capturing ecological inequalities in PTB and SGA. Results varied for each DI and perinatal outcome studied. These findings highlight the importance of testing DI relevance prior to examining perinatal health inequalities, and suggest the need to develop DIs that are suitable for pregnant women. .

Inequalities in Life Expectancy Across North Carolina: A Spatial Analysis of the Social Determinants of Health and the Index of Concentration at Extremes

Health inequalities are characterized by spatial patterns of social, economic, and political factors. Life expectancy (LE) is a commonly used indicator of overall population health and health inequalities that allows for comparison across different spatial and temporal regions. The objective of this study was to examine geographic inequalities in LE across North Carolina census tracts by comparing the performance of 2 popular geospatial health indices: Social Determinants of Health (SDoH) and the Index of Concentration at Extremes (ICE). A principal components analysis (PCA) was used to address multicollinearity among variables and aggregate data into components to examine SDoH, while the ICE was constructed using the simple subtraction of geospatial variables. Spatial regression models were employed to compare both indices in relation to LE to evaluate their predictability for population health. For individual SDoH and ICE components, poverty and income had the strongest positive correlation with LE. However, the common spatial techniques of adding PCA components together for a final SDoH aggregate measure resulted in a poor relationship with LE. Results indicated that both metrics can be used to determine spatial patterns of inequities in LE and that the ICE metric has similar success to the more computationally complex SDoH metric. Public health practitioners may find the ICE metric's high predictability matched with lower data requirements to be more feasible to implement in population health monitoring.

Targeting population groups with heavier burden of hepatocellular carcinoma incidence: a nationwide descriptive epidemiological study in Sweden

Contemporary European studies examining associations between socioeconomic status and hepatocellular carcinoma (HCC) incidence are scarce. We aimed to target population groups with a heavier burden of HCC by assessing associations of individual‐level sociodemographic variables and neighbourhood deprivation with all‐stage and stage‐specific HCC incidence rates (IR). Patient and population data stratified by calendar year (2012‐2018), sex, age (5‐year groups), household income (low, medium and high), country of birth (Nordic, non‐Nordic) and neighbourhood deprivation (national quintiles Q1‐Q5) were retrieved from Swedish registers. HCC stages were defined by Barcelona Clinic Liver Cancer stages 0 to A (early‐stage) and B to D (late‐stage). IR (per 100 000 person‐years) were estimated by Poisson regression models. Men had four times higher IR than women. IRs increased markedly with lower household income as well as with neighbourhood deprivation. Seven times higher IR was observed among people with a low household income living in the most deprived neighbourhoods (IR 3.90, 95% confidence interval [CI] 3.28‐4.64) compared to people with a high household income living in the least deprived neighbourhoods (IR 0.58, 95% CI 0.46‐0.74). The gradient across income categories was more pronounced for late‐stage than early‐stage HCC. IR reached 30 (per 100 000 person‐years) for people in the age span 60 to 79 years with low income and 20 for 60 to 79 year old people living in the most deprived neighbourhoods (regardless of income). Men with low household income and/or living in the most deprived neighbourhoods might be considered as primary targets in studies evaluating the cost‐effectiveness of screening for early‐stage HCC detection.

Discovering Associations between Social Determinants and Health Outcomes: Merging Knowledge Graphs from Literature and Electronic Health Data

Social Determinants of Health (SDoH) are an increasingly important part of the broader research and public health efforts in understanding individuals' physical and mental well-being. Despite this, non-clinical factors affecting health are poorly recorded in electronic health databases and techniques to study how SDoH might relate to population outcomes are lacking. This paper proposes an approach to systematically identify and quantify associations between SDoH and health-related outcomes in a specific cohort of people by (1) leveraging published evidence from literature to build a knowledge graph of health and social factor associations and (2) analysing a large dataset of claims and medical records where those associations may be found. This work demonstrates how the proposed approach could be used to generate hypotheses and inform further research on SDoH in a data-driven manner.

Can an Ecological Index of Deprivation Be Used at the Country Level? The Case of the French Version of the European Deprivation Index (F-EDI)

Most ecological indices of deprivation are constructed from census data at the national level, which raises questions about the relevance of their use, and their comparability across a country. We aimed to determine whether a national index can account for deprivation regardless of location characteristics. In Metropolitan France, 43,853 residential census block groups (IRIS) were divided into eight area types based on quality of life. We calculated score deprivation for each IRIS using the French version of the European Deprivation Index (F-EDI). We decomposed the score by calculating the contribution of each of its components by area type, and we assessed the impact of removing each component and recalculating the weights on the identification of deprived IRIS. The set of components most contributing to the score changed according to the area type, but the identification of deprived IRIS remained stable regardless of the component removed for recalculating the score. Not all components of the F-EDI are markers of deprivation according to location characteristics, but the multidimensional nature of the index ensures its robustness. Further research is needed to examine the limitations of using these indices depending on the purpose of the study, particularly in relation to the geographical grid used to calculate deprivation scores.

The Regeneration of Urban Blue Spaces: A Public Health Intervention? Reviewing the Evidence

Research in recent years has demonstrated that urban surface waters (“urban blue spaces”) can provide beneficial effects on human health and wellbeing. Despite blue spaces prevailing on urban development agendas across the world, little investigation has been done whether and how the regeneration of such spaces is used as a (community-based) public health intervention. Therefore, a review was conducted to analyze urban blue space regeneration projects in terms of their significance for public health. Results show that the regeneration of urban blue spaces displays a diversity of intervention types and follows certain development trends seen in general urban regeneration: Similarities mainly arise in relation to objectives (multi-dimensional goals with increasing focus on environmental sustainability and economic interests), stakeholders (shift to multi-actor governance with a rise of partnerships and community participation), and funding (prevalence of mixed financial schemes and increasing reliance on external funding sources). Although threefold public health effects have been noted across the projects (i. behavioral changes toward healthier lifestyles, ii. healthier urban environments, iii. health policy changes), results of this review indicate that the potential to use urban blue regeneration as a community-based health intervention has yet to be realized.

Assessing the effect of environmental and socio-economic factors on skin melanoma incidence: an island-wide spatial study in Gran Canaria (Spain), 2007-2018

INTRODUCTION

Skin melanoma incidence has risen in the last decades becoming a major public health problem in many regions of the world. Geographic variation of rates is not well understood.

PURPOSE

To assess the spatial distribution of skin melanoma in Gran Canaria Island (Canary Islands, Spain) and to evaluate the role of environmental, socio-economic, and demographic factors in this distribution.

METHODS

We performed a small-area study with disease mapping at the census-tract level (CT) in Gran Canaria between 2007 and 2018. After testing for spatial autocorrelation, we integrated individual-level health data with census-based demographic and socio-economic indicators, and satellite-based environmental data. Finally, we assessed the role of demographic, socio-economic and environmental factors on skin melanoma incidence using a Bayesian analytical framework, with options for non-spatial and spatial random effects.

RESULTS

1058 patients were diagnosed with invasive skin melanoma in the study period and geolocated to a CT (number of CT in Gran Canaria = 565). We found evidence of global spatial autocorrelation in skin melanoma incidence (Moran's I = 0.09, pseudo p-value = 0.001). A few hotspots were detected, fundamentally in urban northern tracts. A radial pattern of high values was also observed in selected ravines with historical isolation. Multivariable conditional autoregressive models identified urbanicity, percent of females, and a high socio-economic status as risk factors for disease. Solar radiation did not show a significant role.

CONCLUSION

Urbanicity and a high socio-economic status were identified as the main risk factors for skin melanoma. These associations might reflect differential melanoma susceptibilities or be explained by health inequalities in detection. This study also uncovered high-risk areas in particular ravines. Future targeted research in these regions might help better understand the role of genetic and toxic factors in melanoma pathogenesis.

Neighborhood socioeconomic deprivation and psychiatric medication purchases. Different neighborhood delineations, different results? A nationwide register-based multilevel study

Previous studies of neighborhood socioeconomic deprivation and mental health treatment have shown mixed results. Multiple reviews have highlighted that the definition and measurement of neighborhoods can lead to different results, providing one explanation for these mixed findings. This study compares the use of micro-areas created using an automated redistricting algorithm and divided by physical barriers with the use of two administrative units, Danish parishes and postal codes. The geographical data are linked to Danish register data of the Danish population from age 16 in 2017, N = 4,347,001, to measure the association between the purchase of psychiatric medication and neighborhood socioeconomic deprivation using logistic multilevel models. Neighborhood socioeconomic deprivation is associated with a slightly increased probability of redeeming prescriptions for psychiatric medication after controlling for individual sociodemographic composition. However, this association was present only for micro-areas and not for parishes or postal codes. The results indicate that neighborhood effects on psychiatric medication purchases are affected by the neighborhood delineations used and that future studies should carefully consider how neighborhoods are defined and measured.

Measuring health-related social deprivation in small areas: development of an index and examination of its association with cancer mortality

BACKGROUND

The small-area deprivation indices are varied across countries due to different social context and data availability. Due to lack of chronic disease-related social deprivation index (SDI) in Hong Kong, China, this study aimed to develop a new SDI and examine its association with cancer mortality.

METHODS

A total of 14 socio-economic variables of 154 large Tertiary Planning Unit groups (LTPUGs) in Hong Kong were obtained from 2016 population by-census. LTPUG-specific all-cause and chronic condition-related mortality and chronic condition inpatient episodes were calculated as health outcomes. Association of socio-economic variables with health outcomes was estimated for variable selection. Candidates for SDI were constructed with selected socio-economic variables and tested for criterion validity using health outcomes. Ecological association between the selected SDI and cancer mortality were examined using zero-inflated negative binomial regression.

RESULTS

A chronic disease-related SDI constructed by six area-level socio-economic variables was selected based on its criterion validity with health outcomes in Hong Kong. It was found that social deprivation was associated with higher cancer mortality during 2011-2016 (most deprived areas: incidence relative risk [IRR] = 1.40, 95% confidence interval [CI]: 1.27-1.55; second most deprived areas: IRR = 1.34, 95%CI: 1.21-1.48; least deprived areas as reference), and the cancer mortality gap became larger in more recent years. Excess cancer death related to social deprivation was found to have increased through 2011-2016.

CONCLUSIONS

Our newly developed SDI is a valid and routinely available measurement of social deprivation in small areas and is useful in resource allocation and policy-making for public health purpose in communities. There is a potential large improvement in cancer mortality by offering relevant policies and interventions to reduce health-related deprivation. Further studies can be done to design strategies to reduce the expanding health inequalities between more and less deprived areas.

Center variation in long-term outcomes for socioeconomically deprived children

Neighborhood socioeconomic deprivation is associated with adverse outcomes after pediatric liver transplant. We sought to determine if this relationship varies by transplant center. Using SRTR, we included patients <18 years transplanted 2008-2013 (N = 2804). We matched patient ZIP codes to a deprivation index (range [0,1]; higher values indicate increased socioeconomic deprivation). A center-level patient-mix deprivation index was defined by the distribution of patient-level deprivation. Centers (n = 66) were classified as high or low deprivation if their patient-mix deprivation index was above or below the median across centers. Center quality was classified as low or high graft failure if graft survival rates were better or worse than the overall 10-year graft survival rate. Primary outcome was patient-level graft survival. We used random-effect Cox models to evaluate center-level covariates on graft failure. We modeled center quality using stratified Cox models. In multivariate analysis, each 0.1 increase in the patient-mix deprivation index was associated with increased hazard of graft failure (HR 1.32; 95%CI: 1.05, 1.66). When stratified by center quality, patient-mix deprivation was no longer significant (HR 1.07, 95%CI: 0.89, 1.28). Some transplant centers care for predominantly high deprivation children and maintain excellent outcomes. Revealing and replicating these centers' practice patterns should enable more equitable outcomes.

How does vulnerability to COVID-19 vary between communities in England? Developing a Small Area Vulnerability Index (SAVI)

BACKGROUND

During the initial wave of the COVID-19 epidemic in England, several population characteristics were associated with increased risk of mortality-including, age, ethnicity, income deprivation, care home residence and housing conditions. In order to target control measures and plan for future waves of the epidemic, public health agencies need to understand how these vulnerabilities are distributed across and clustered within communities.

METHODS

We performed a cross-sectional ecological analysis across 6789 small areas in England. We assessed the association between COVID-19 mortality in each area and five vulnerability measures relating to ethnicity, poverty, prevalence of long-term health conditions, living in care homes and living in overcrowded housing. Estimates from multivariable Poisson regression models were used to derive a Small Area Vulnerability Index.

RESULTS

Four vulnerability measures were independently associated with age-adjusted COVID-19 mortality. Each SD increase in the proportion of the population (1) living in care homes, (2) admitted to hospital in the past 5 years for a long-term health condition, (3) from an ethnic minority background and (4) living in overcrowded housing was associated with a 28%, 19% 8% and 11% increase in age-adjusted COVID-19 mortality rate, respectively.

CONCLUSION

Vulnerability to COVID-19 was noticeably higher in the North West, West Midlands and North East regions, with high levels of vulnerability clustered in some communities. Our analysis indicates the communities who will be most at risk from a second wave of the pandemic.

A comparison of small-area deprivation indicators for public-health surveillance in Sweden

AIMS

The aims of this study were to construct a small-area index of multiple deprivation (IMD) from single deprivation indicators (SDIs) and to compare the explanatory power of the IMD and SDIs with regard to mortality. We considered a small-area division of Sweden consisting of 5985 DeSO (Demografiska statistikområden), each with a population size between 653 and 4243 at the end of 2018.

METHODS

Four SDIs were provided by open-source data: (a) the proportion of inhabitants with a low economic standard; (b) the proportion of inhabitants aged 25-64 years with ⩽12 years of schooling; (c) the proportion of inhabitants aged 16-64 years who were not in paid employment; and (d) the proportion of inhabitants who lived in a rented apartment/house. A four-indicator IMD was constructed using factor analysis. As a validation, the IMD and SDIs were compared by exploring their DeSO-level associations with spatially smoothed death rates, with robustness checks of associations across different small-area contexts defined by degree of urbanisation and distribution of immigrants from non-Western countries.

RESULTS

The constructed IMD and SDI1 performed essentially equally and outperformed SDI2, SDI3 and SDI4. Associations between IMD/SDI1 and the spatially smoothed death rates were most pronounced within the age range 60-79 years, showing 5-8% lowered rates among those categorised in the least deprived quintiles of IMD and SDI1, respectively, and 7-9% elevated rates among those categorised in the most deprived quintiles. These associations were consistent within each small-area context.

CONCLUSIONS

We suggest prioritisation of SDI1, that is, a DeSO-level deprivation indicator based on open-access data on economic standard, for public-health surveillance in Sweden.

Auto-Regressive Integrated Moving Average Model (ARIMA): conceptual and methodological aspects and applicability in infant mortality

Abstract This reflective theoretical article, aims to discuss conceptual and methodological aspects about the applications of time series modeling, in particular, the Integrated Auto-regressive Moving Average model and its applicability in infant mortality. This modeling makes it possible to predict future values using past data, outlining and estimating possible scenarios of the health event, highlighting its magnitude. Due to the persistence of infant mortality as a public health problem, the applicability of this method is useful in the timely and systematic management of child health indicators, in addition to being a method with low operating cost, which in contexts of cost reduction in public healthcare services, becomes a potential management tool. However, there are still gaps in the use of statistical methods in the decision-making and policy-making process in public healthcare, such as the modeling in question. These are methodological (robust statistics), institutional (outdated information systems) and cultural obstacles (devaluation of the data produced, mainly at the local level).

How does area-level deprivation depress an individual's self-rated health and life satisfaction? Evidence from a nationwide population-based survey in Japan

BACKGROUND

Area-level deprivation is well known to have an adverse impact on mortality, morbidity, or other specific health outcomes. This study examined how area-level deprivation may affect self-rated health (SRH) and life satisfaction (LS), an issue that is largely understudied.

METHODS

We used individual-level data obtained from a nationwide population-based internet survey conducted between 2019 and 2020, as well as municipality-level data obtained from a Japanese government database (N = 12,461 living in 366 municipalities). We developed multilevel regression models to explain an individual's SRH and LS scores using four alternative measures of municipality-level deprivation, controlling for individual-level deprivation and covariates. We also examined how health behavior and interactions with others mediated the impact of area-level deprivation on SRH and LS.

RESULTS

Participants in highly deprived municipalities tended to report poorer SRH and lower LS. For example, when living in municipalities falling in the highest tertile of municipality-level deprivation as measured by the z-scoring method, SRH and LS scores worsened by a standard deviation of 0.05 (p < 0.05) when compared with those living in municipalities falling in the lowest tertile of deprivation. In addition, health behavior mediated between 17.6 and 33.1% of the impact of municipality-level deprivation on SRH and LS, depending on model specifications.

CONCLUSION

Results showed that area-level deprivation modestly decreased an individual's general health conditions and subjective well-being, underscoring the need for public health policies to improve area-level socioeconomic conditions.

Reporting of social deprivation in musculoskeletal trials: An analysis of 402 randomised controlled trials

BACKGROUND

Social deprivation is broadly defined as the restriction of access an individual has to social or cultural interactions due to poverty, discrimination or other disadvantages. While social deprivation is a widely acknowledged determinant of outcome in musculoskeletal conditions, it remains unclear how this is considered in the conduct and interpretation of musculoskeletal trials.

AIM

To determine the frequently to which measures of social deprivation are reported in trials recruiting people with musculoskeletal diseases.

MATERIALS AND METHODS

We conducted a Pubmed search of randomised controlled trials published between 01 January 2019 and 01 June 2020. We included full-text papers of trials recruiting people with musculoskeletal diseases, irrespective of intervention type or origin. We extracted data relating to trial characteristics, setting, trial design, funding source and musculoskeletal disease. We extracted data on any reported social deprivation index or measure of social deprivation based on internationally adopted indicators. We analysed data descriptively to summarise the reporting of each social deprivation index and measure of social deprivation within trials.

RESULTS

From 2133 potentially eligible citations, 402 were eligible. Mean age of participants was 51.7 years; 63% were female. Trials most frequently recruited people with spinal pain (24.6%) or osteoarthritis (10.0%). Two trials (0.5%) reported social deprivation indices/scores. When assessed by discrete measures of social deprivation, 164 trials (40.8%) reported one or more social deprivation measures. The most commonly reported measures were morbidity (20.2%), employment status (17.7%) and educational attainment (15.5%). Race (6.7%), ethnicity (6.2%) and annual salary (1.3%) were infrequently reported. One trial (0.3%) presented subgroup results by social deprivation measures.

DISCUSSION AND CONCLUSION

Social deprivation is inconsistently reported in musculoskeletal trials. Trialists should report baseline measures of social deprivation in trial reports and aid generalisability to target population, and to examine whether social deprivation might modify treatment effects of interventions for musculoskeletal conditions.

Construction of an area-deprivation index for 2869 counties in China: a census-based approach

BACKGROUND

A paucity of data has made it challenging to construct a deprivation index at the lowest administrative, or county, level in China. An index is required to guide health equity monitoring and resource allocation to regions of greatest need. This study used China's 2010 census data to construct a county-level area-deprivation index (CADI).

METHODS

Data for 2869 counties from China's 2010 census were used to generate a CADI. Eleven indicators across four domains of deprivation were selected for principal component analysis with standardisation of the first principal component. Sensitivity analysis was used to test whether the population size and weighting method affected the index's robustness. Deprived counties identified by the CADI were then compared with China's official list of poverty-stricken counties.

RESULTS

The first principal component explained 60.38% of the total variation in the deprivation indicators. The CADI ranged from the least deprived value of -2.71 to the most deprived value of 2.92, with SD of 1. The CADI was found to be robust against county-level population size and different weighting methods. When compared with the official list of poverty-stricken counties in China, the deprived counties identified by the CADI were found to be even more deprived.

CONCLUSION

Constructing a robust area-deprivation index for China at the county level based on population census data is feasible. The CADI is a potential policy tool to identify China's most deprived areas. In the future, it may support health equity monitoring and comparison at the national and subnational levels.

Disease mapping of early- and late-stage cancer to monitor inequalities in early detection: a study of cutaneous malignant melanoma

We consider disease mapping of early- and late-stage cancer, in order to identify and monitor inequalities in early detection. Our method is demonstrated by mapping cancer incidence at high geographical resolution using data on 10,302 cutaneous malignant melanoma (CMM) cases within the 3.7 million population of South-West Sweden. The cases were geocoded into small-areas, each with a population size between 600 and 2600 and accessible socio-demographic data. Using the disease mapping application Rapid Inquiry Facility (RIF) 4.0, we produced regional maps to visualise spatial variations in stage I, II and III-IV CMM incidences, complemented by local maps to explore the variations within two urban areas. Pronounced spatial disparities in stage I CMM incidence were revealed by the regional and local maps. Stage I CMM incidence was markedly higher in wealthier small-areas, in particular within each urban area. A twofold higher stage I incidence was observed, on average, in the wealthiest small-areas (upper quintile) than in the poorest small-areas (lower quintile). We identified in the regional map of stage III-IV CMM two clusters of higher or lower than expected late-stage incidences which were quite distinct from those identified for stage I. In conclusion, our analysis of CMM incidences supported the use of this method of cancer stage incidence mapping for revealing geographical and socio-demographic disparities in cancer detection.

A diabetes risk index for small areas in England

UK and international studies point to significant area variation in diabetes risk, and summary indices of diabetic risk are potentially of value in effective targeting of health interventions and healthcare resources. This paper aims to develop a summary measure of the diabetic risk environment which can act as an index for targeting health care resources. The diabetes risk index is for 6791 English small areas (which provide entire coverage of England) and has advantages in incorporating evidence from both diabetes outcomes and area risk factors, and in including spatial correlation in its construction. The analysis underlying the risk index shows that area socio-economic status, social fragmentation and south Asian ethnic concentration are all positive risk factors for diabetes risk. However, urban-rural and regional differences in risk intersect with these socio-demographic influences.