scholarly journals Health disparities by neighborhood socioeconomic status and the role of spatial spillovers

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
L H Dekker ◽  
R H Rijnks ◽  
J O Mierau
Keyword(s):  
Socioeconomic Status ◽  
Health Disparities ◽  
Health Outcomes ◽  
Population Health ◽  
Policy Design ◽  
Spillover Effect ◽  
Additional Increase ◽  
Spatial Spillovers ◽  
Socioeconomic Characteristics ◽  
Deprived Neighborhoods

Abstract Background The contextual determinants of population health disparities across neighborhoods with similar socioeconomic characteristics are not well understood. We aimed to estimate subjective and objective population health measures within and between neighborhoods with similar socioeconomic status (NSES) scores, and the (in)direct potential of a spillover effect of NSES of adjacent neighborhoods. Methods Based on whole-population neighborhood data we determined the percentage of inhabitants with good/very good self-assessed health (SAH) and with at least one chronic disease (CD) in 11,521 neighborhoods with on average 1,470 inhabitants. Neighborhoods were classified by the quintile of a composite NSES score. Spatial lag of X models was applied by including neighborhood cross-sectional units on population density, the percentage of inhabitants aged 65 and over, and the NSES of adjacent neighborhoods by constructing a spatial weights matrix. Results Substantial population health disparities in SAH and CD both between neighborhoods with different and similar NSES scores were observed, with the largest SAH variance in the lowest NSES group. These differences were only partially explained by neighborhood characteristics. Neighborhoods adjacent to higher SES neighborhoods showed a higher SAH and a lower prevalence of CD, adjusted for other explanatory variables. When NSES in the first decile would be increased to the NSES of the quintile median, the direct effect on SAH would increase by 5.6% in the lowest NSES group. Spillovers would lead to an additional increase of 1.7% in all NSES groups. Conclusions Population health differs substantially among neighborhoods with similar socioeconomic characteristics, which can partially be explained by a socioeconomic spillover effect. The mechanisms behind these spillovers need further study, but may already provide interesting leads to policy design aimed at improving population health outcomes of deprived neighborhoods. Key messages Neighborhood population health is partially affected by a SES spillover effect. This study provides interesting leads to policy design aimed at improving health outcomes of deprived neighborhoods.

scholarly journals The Health Potential of Neighborhoods: A Population-Wide Study in The Netherlands

2020 ◽  
Author(s):  
Louise H. Dekker ◽  
Richard H Rijnks ◽  
Jochen O. Mierau
Keyword(s):  
Health Disparities ◽  
The Netherlands ◽  
Population Health ◽  
Policy Design ◽  
Spillover Effect ◽  
Population Data ◽  
Spillover Effects ◽  
Neighborhood Density ◽  
Spatial Spillovers ◽  
Socioeconomic Characteristics

Abstract Background: While differences in population health across neighborhoods with different socioeconomic characteristics are well documented, health disparities across neighborhoods with similar socioeconomic characteristics are less well understood. Studying the determinants of variation of health among neighborhoods with similar socio-economic characteristics is pivotal for gaining insight into where health potential lies. We aimed to estimate population health inequalities, both within and between neighborhoods with similar socio-economic status, and assessed the association of neighborhood characteristics and socio-economic spillover effects from adjacent neighborhoods. Methods: Based on whole-population data from the Netherlands we determined the percentage of inhabitants with good/very good self-assessed health (SAH) as well as the percentage of inhabitants with at least one chronic disease (CD) in 11,504 neighborhoods. Neighborhoods were classified by quintiles of a composite NSES score. Spatial models were estimated by including the spatially weighted NSES of adjacent neighborhoods. Results: Substantial population health disparities in SAH and CD both within and between neighborhoods NSES quintiles were observed, with the largest SAH variance in the lowest NSES group. These differences were partially explained by neighborhood density and the percentage of inhabitants ≥65 years old. Neighborhoods adjacent to higher SES neighborhoods showed a higher SAH and a lower prevalence of CD, adjusted for other explanatory variables. Policy simulations indicate how modest changes in NSES among groups of neighborhoods with similar socio-economic characteristics can contribute to population health, partially due to spatial spillovers. Conclusion: Population health differs substantially among neighborhoods with similar socioeconomic characteristics, which can partially be explained by a spatial socio-economic spillover effect. This provides interesting leads to policy design aimed at improving population health outcomes of deprived neighborhoods focusing on health potential.

Research Methodology to Examine Disparities in Communication Disorders in Underrepresented and Vulnerable Populations

2021 ◽  
pp. 151-179
Author(s):  
Molly Jacobs ◽  
Charles Ellis
Keyword(s):  
Socioeconomic Status ◽  
Health Disparities ◽  
Health Outcomes ◽  
Vulnerable Populations ◽  
Communication Disorders ◽  
Causal Pathways ◽  
Research Designs ◽  
Race Ethnicity ◽  
Conducting Research ◽  
Research Frameworks

The existence of disparities in health has gained national attention. While disparities in communication disorders undoubtedly exist, little research has documented these disparities. Disparities may occur across categories such as race/ethnicity, age, sex/gender, geographic, and socioeconomic status. In order to heighten awareness of existing disparities in the field of communication sciences and disorders (CSD), this chapter focuses on designing and conducting research to identify and explain disparities among population subgroups. The chapter consists of seven sections: 1) Challenges in Defining Variables for Measuring Health Disparities, 2) Other Data Considerations, 3) Thinking Beyond the Traditionally Measured Sociodemographic Variables, 4) Causal Pathways Between Social Determinants and Health Outcomes, 5) Research Designs, 6) Research Frameworks, and 7) Theories of Contextual Factors. The goal of this chapter is to offer information that assist CSD researchers in systematically identifying, analyzing, and addressing health disparities in CSD.

scholarly journals The economic burden of health disparities related to socioeconomic status in Israel

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Eran Politzer ◽  
Amir Shmueli ◽  
Shlomit Avni
Keyword(s):  
Socioeconomic Status ◽  
Health Disparities ◽  
Health Outcomes ◽  
Health Inequalities ◽  
Low Income ◽  
Health Care Expenditures ◽  
Low Ses ◽  
Disability Benefits ◽  
Excess Morbidity ◽  
Product Loss

Abstract Background Low socioeconomic status (SES) is often associated with excess morbidity and premature mortality. Such health disparities claim a steep economic cost: Possibly-preventable poor health outcomes harm societal welfare, impair the domestic product, and increase health care expenditures. We estimate the economic costs of health inequalities associated with socioeconomic status in Israel. Methods The monetary cost of health inequalities is estimated relative to a counterfactual with a more equal outcome, in which the submedian SES group achieves the average health outcome of the above-median group. We use three SES measures: the socioeceonmic ranking of localities, individuals’ income, and individuals’ education level. We examine costs related to the often-worse health outcomes in submedian SES groups, mainly: The welfare and product loss from excess mortality, the product loss from excess morbidity among workers and working-age adults, the costs of excess medical care provided, and the excess government expenditure on disability benefits. We use data from the Central Bureau of Statistics’ (CBS) surveys and socio-health profile of localities, from the National Insurance Institute, from the Ministry of Health, and from the Israel Tax Authority. All costs are adjusted to 2014 terms. Results The annual welfare loss due to higher mortality in socioeconomically submedian localities is estimated at about 1.1–3.1 billion USD. Excess absenteeism and joblessness occasioned by illness among low-income and poorly educated workers are associated with 1.4 billion USD in lost product every year. Low SES is associated with overuse of inpatient care and underuse of community care, with a net annual cost of about 80 million USD a year. The government bears additional cost of 450 million USD a year, mainly due to extra outlays for disability benefits. We estimate the total cost of the estimated health disparities at a sum equal to 0.7–1.6% of Israel’s GDP. Conclusions Our estimates underline the substantial economic impact of SES-related health disparities in Israel. The descriptive evidence presented in this paper highlights possible benefits to the economy from policies that will improve health outcomes of low SES groups.

Regional characteristics of the gender employment gap: A spatio-temporal approach

2021 ◽  
pp. 103530462110232
Author(s):  
Jorge Chica-Olmo ◽  
Marina Checa-Olivas ◽  
Fernando Lopez-Castellano
Keyword(s):  
Gender Gap ◽  
Spillover Effect ◽  
Jel Codes ◽  
Labour Relations ◽  
Socioeconomic Characteristics ◽  
Regional Characteristics ◽  
Spatial Approach ◽  
Employment Gap ◽  
Diffusion Mechanisms ◽  
Spatio Temporal

There is a substantial body of research that recognises the importance of analysing regional characteristics in employment and labour relations that occur in a given geographical context. However, this phenomenon has been scarcely studied from a spatial approach. This article uses a spatio-temporal panel data model to examine the spatial interactions between the gender employment gap and, some labour and socioeconomic characteristics of 727 municipalities of Andalusia, Spain, for the period 2012–2016. The results show that due to spatial diffusion mechanisms, a spatial spillover effect occurs in both the gender gap in employment and in some of the labour and socioeconomic characteristics considered. These findings may be extended to other geographic areas and can be of use for the implementation of regional policies aimed at narrowing the gender employment gap. JEL Codes: R10, J16, E24

scholarly journals Health Disparities among adults cared for at an urban cystic fibrosis program

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Emily DiMango ◽  
Kaitlyn Simpson ◽  
Elizabeth Menten ◽  
Claire Keating ◽  
Weijia Fan ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
New York ◽  
New York City ◽  
Health Disparities ◽  
Pulmonary Function ◽  
Health Outcomes ◽  
York City ◽  
Median Income ◽  
Cross Sectional ◽  
Caucasian Patients

Abstract Background Evidence is conflicting regarding differential health outcomes in racial and ethnic minorities with cystic fibrosis (CF), a rare genetic disease affecting approximately 28,000 Americans. We performed a cross-sectional analysis of health outcomes in Black/Latinx patients compared with non-Hispanic Caucasian patients cared for in a CF center in New York City. Adult patients enrolled in the CF Foundation Patient Registry at the Columbia University Adult CF Program and seen at least once during 2019 were included. Health metrics were compared between Black/Latinx and non-Hispanic Caucasian patients. Results 262 patients were eligible. 39 patients (15%) identified as Black/Latinx or non-Hispanic Caucasian. Descriptive statistics are reported with mean (standard deviation). Current age was 35.9 (13.3) years for non-Hispanic Caucasian and 32.0 (9.3) years for Black/Latinx patients (p = 0.087). Age of diagnosis did not differ between groups; 9.56 (15.96) years versus 11.59 (15.8) years for non-Hispanic Caucasian versus Black/Latinx respectively (p = 0.464). Pulmonary function, measured as mean forced expiratory volume in one second (FEV1) was 70.6 (22.5) percent predicted in non-Hispanic Caucasian versus 59.50 (27.9) percent predicted in Black/Latinx patients (p = 0.010). Number of visits to the CF clinic were similar between groups. When controlled for age, gender, co-morbidities, median income, and insurance status, there was a continued association between minority status and lower FEV1. Conclusions Minorities with CF have significantly lower pulmonary function, the major marker of survival, than non-Hispanic Caucasians, even when controlled for a variety of demographic and socioeconomic factors that are known to affect health status in CF. Significant health disparities based on race and ethnicity exist at a single CF center in New York City, despite apparent similarities in access to guideline based care at an accredited CF Center. This data confirms the importance of design of culturally appropriate preventative and management strategies to better understand how to direct interventions to this vulnerable population with a rare disease.

scholarly journals Understanding the influence of socioeconomic status on the association between combinations of lifestyle factors and adverse health outcomes: a systematic review protocol

BMJ Open ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. e042212
Author(s):  
Hamish Foster ◽  
Peter Polz ◽  
Frances Mair ◽  
Jason Gill ◽  
Catherine A O'Donnell
Keyword(s):  
Systematic Review ◽  
Socioeconomic Status ◽  
Health Outcomes ◽  
Meta Analysis ◽  
Lifestyle Factors ◽  
Lifestyle Factor ◽  
Unhealthy Lifestyle ◽  
Adverse Health ◽  
Adverse Health Outcomes ◽  
The Impact

IntroductionCombinations of unhealthy lifestyle factors are strongly associated with mortality, cardiovascular disease (CVD) and cancer. It is unclear how socioeconomic status (SES) affects those associations. Lower SES groups may be disproportionately vulnerable to the effects of unhealthy lifestyle factors compared with higher SES groups via interactions with other factors associated with low SES (eg, stress) or via accelerated biological ageing. This systematic review aims to synthesise studies that examine how SES moderates the association between lifestyle factor combinations and adverse health outcomes. Greater understanding of how lifestyle risk varies across socioeconomic spectra could reduce adverse health by (1) identifying novel high-risk groups or targets for future interventions and (2) informing research, policy and interventions that aim to support healthy lifestyles in socioeconomically deprived communities.Methods and analysisThree databases will be searched (PubMed, EMBASE, CINAHL) from inception to March 2020. Reference lists, citations and grey literature will also be searched. Inclusion criteria are: (1) prospective cohort studies; (2) investigations of two key exposures: (a) lifestyle factor combinations of at least three lifestyle factors (eg, smoking, physical activity and diet) and (b) SES (eg, income, education or poverty index); (3) an assessment of the impact of SES on the association between combinations of unhealthy lifestyle factors and health outcomes; (4) at least one outcome from—mortality (all cause, CVD and cancer), CVD or cancer incidence. Two independent reviewers will screen titles, abstracts and full texts of included studies. Data extraction will focus on cohort characteristics, exposures, direction and magnitude of SES effects, methods and quality (via Newcastle-Ottawa Scale). If appropriate, a meta-analysis, pooling the effects of SES, will be performed. Alternatively, a synthesis without meta-analysis will be conducted.Ethics and disseminationEthical approval is not required. Results will be disseminated via peer-reviewed publication, professional networks, social media and conference presentations.PROSPERO registration numberCRD42020172588.

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