scholarly journals Impact of Neighborhood Socioeconomic Disadvantage on Staffing Hours in US Nursing Homes

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 979-980
Author(s):  
Jason Falvey ◽  
Erinn Hade ◽  
Steven Friedman ◽  
Rebecca Deng ◽  
Jasmine Travers
Keyword(s):  
Nursing Homes ◽  
Fixed Effects ◽  
Socioeconomic Disadvantage ◽  
Housing Quality ◽  
Area Deprivation ◽  
Block Group ◽  
Socioeconomically Disadvantaged ◽  
Census Block ◽  
Census Block Group ◽  
Disadvantaged Neighborhoods

Abstract Severe socioeconomic disadvantage in neighborhoods where nursing homes (NH) are located may be an important contributor to disparities in resident quality of care. Disadvantaged neighborhoods may have undesirable attributes (e.g., poor public transit) that make it challenging to recruit and retain qualified staff. Lower NH staffing could subsequently leave residents vulnerable to adverse events. Thus, the purpose of this study was to evaluate whether NHs located in socioeconomically disadvantaged neighborhoods had lower healthcare provider staffing levels. We linked publicly available NH data geocoded at the Census block-group level with the Area Deprivation Index, a measure of neighborhood socioeconomic factors including poverty, employment, and housing quality (percentiles: 1-100). Consistent with prior literature on threshold effects of neighborhood poverty on outcomes, we characterized NHs as being located in a disadvantaged neighborhood if the census-block group ADI score was ≥85/100. We used generalized estimating equations clustered at the county level with fixed effects for state and rural location to evaluate relationships between ADI score and staffing. NHs located in socioeconomically disadvantaged neighborhoods had 12.1% lower levels of staffing for registered nurses (mean: 5.8 fewer hours/100 resident-days, 95% CI: 4.4-7.1 hours), 1.2% lower for certified nursing assistants (2.9 fewer hours/100 resident days; 95% CI 0.6-5.1 hours), 20% lower for physical therapists (1.4 fewer hours/100 resident-days; 95% CI 1.1-1.8 hours), and 19% lower for occupational therapists (1.3 fewer hours/100 resident-days; 95% CI 1.0-1.6 hours). These findings highlight disparities that could be targeted with policy interventions focused on recruiting and retaining staff in socioeconomically disadvantaged neighborhoods.

scholarly journals Neighborhood Socioeconomic Status in Relation to All-cause, Cancer, and Cardiovascular Mortality in the Black Women’s Health Study

2016 ◽  
Vol 26 (2) ◽  
pp. 157 ◽  
Author(s):  
Traci N. Bethea ◽  
Julie R. Palmer ◽  
Lynn Rosenberg ◽  
Yvette C. Cozier
Keyword(s):  
Socioeconomic Status ◽  
Black Women ◽  
Cardiovascular Mortality ◽  
Health Study ◽  
Block Group ◽  
Census Block ◽  
Census Block Group ◽  
Neighborhood Socioeconomic Status ◽  
Us Census ◽  
Neighborhood Ses

<p><strong>Background</strong>: Neighborhood socioeconomic status (SES) is associated with adverse health outcomes, but longitudinal data among Black Americans, who tend to live in more deprived neighborhoods, is lacking. <br />Objectives: We prospectively assessed the relation of neighborhood SES to mortality in the Black Women’s Health Study.</p><p><strong>Design</strong>: A prospective cohort of 59,000 Black women was followed from 1995-2011. Participant addresses were geocoded and US Census block group was identified. Neighborhood SES was measured by a score based on US Census block group data for six indicators of income, education and<br />wealth.</p><p><strong>Main outcome measures:</strong> Deaths were identified through the National Death Index. Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% CIs with control for covariates.</p><p><strong>Results</strong>: Based on 2,598 deaths during 1995-2011, lower neighborhood SES was associated with increased all-cause and cancer mortality irrespective of individual education: among those with 16 or more years of education, HRs for lowest relative to highest neighborhood SES quartile were 1.42 (95% CI 1.18-1.71) for all-cause and 1.54 (95% CI 1.14-2.07) for cancer mortality. Neighborhood SES was associated with cardiovascular mortality among lesseducated women.</p><p><strong>Conclusions</strong>: Lower neighborhood SES is associated with greater risk of mortality among Black women. The presence of the association even among women with high levels of education suggests that individual<br />SES may not overcome the unfavorable influence of neighborhood deprivation. <em>Ethn Dis</em>. 2016;26(2):157-164; doi:10.18865/<br />ed.26.2.157</p>

scholarly journals 4537 Association between Geographic Socioeconomic Disadvantage and Incidence of Total Hip Replacement Surgery

2020 ◽  
Vol 4 (s1) ◽  
pp. 80-80
Author(s):  
Rafa Rahman ◽  
Joseph K. Canner ◽  
Elliot R. Haut ◽  
Casey J. Humbyrd
Keyword(s):  
Incidence Rate ◽  
Total Hip Replacement ◽  
Race And Ethnicity ◽  
Hip Replacement ◽  
Socioeconomic Disadvantage ◽  
Area Deprivation ◽  
Socioeconomically Disadvantaged ◽  
Cross Sectional ◽  
Total Hip ◽  
Replacement Surgery

OBJECTIVES/GOALS: Total hip replacement (THR) improves function for those with arthritis, but not all patients have equal access to this elective procedure. To better geographically target healthcare resources, we explored whether geographic socioeconomic disadvantage is associated with incidence of elective THR. METHODS/STUDY POPULATION: We performed a cross-sectional analysis of data in the state of Maryland from 2013-2019. We categorized 5-digit zipcodes into national quartiles of socioeconomic disadvantage using the Area Deprivation Index (ADI). For each zipcode, we calculated the THR incidence rate using Maryland Health Services Cost Review Commission (HSCRC) inpatient and outpatient data in those age 65 years and older. We included only elective THRs. We analyzed the association between a zipcode’s disadvantage quartile and THR incidence rate using multivariate linear regression, correcting for differences across zipcodes in gender, race, and ethnicity distributions, and distance to the nearest hospital performing THRs. RESULTS/ANTICIPATED RESULTS: We analyzed 414 zipcodes with overall average THR rate of 370.8 per 100,000 persons >65yo per year. Relative to zipcodes in the least socioeconomically disadvantaged quartile, those in the second-least disadvantaged had 82.2 fewer THRs per 100,000 persons >65yo per year, those in the second-most disadvantaged had 144.2 fewer, and those in the most disadvantaged had 207.4 fewer (all p65yo per year, those in the second-most disadvantaged had 136.2 fewer, and those in the most disadvantaged had 182.9 fewer (all p <.05). DISCUSSION/SIGNIFICANCE OF IMPACT: More socioeconomically disadvantaged areas have significantly lower rates of elective THR, independent of differences in demographics and hospital proximity. These findings show how disparities can affect access and outcomes, and should inform targeting of community-level education and intervention.

Impact of Socioeconomic Status & Race/Ethnicity On Survival in Diffuse Large B-Cell Lymphoma (DLBCL): A Population-Based Study.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 1954-1954
Author(s):  
James M. Foran ◽  
Laura A. McClure ◽  
Christina A. Clarke ◽  
Theresa H. M. Keegan
Keyword(s):  
Socioeconomic Status ◽  
B Cell Lymphoma ◽  
Population Based ◽  
Block Group ◽  
Census Block ◽  
Census Block Group ◽  
Risk Of Death ◽  
Race Ethnicity ◽  
The Impact ◽  
Neighborhood Ses

Abstract Abstract 1954 Poster Board I-977 Introduction: Despite advances in treatment and a well-characterized prognostic index, significant heterogeneity remains in DLBCL survival. Preliminary data suggest a potential survival disparity based on race/ethnicity or socioeconomic status (SES). To evaluate the impact of these and other variables on survival we performed an analysis in the ethnically diverse population-based California Cancer Registry (CCR). We utilized Neighborhood SES, an index of 7 census measures of education, income, occupation & cost of living, based on the residential census-block group at diagnosis. Each census-block group comprises ∼1500 residents. Neighborhood SES has been shown to be significantly associated with survival after Follicular Lymphoma (JCO 27:3044, 2009). Methods: All pts with DLBCL (ICD-O-3 codes 9680 & 9684) diagnosed from Jan 1988 to Dec 2007 and reported to CCR were included in the analysis, including n=16,892 diagnosed from 1988-2000, and n=11,916 from 2001-2007 (total study pop'n =28,808). HIV/AIDS pts were excluded, as were n=63 with Mediastinal LBCL & n=10 with primary effusion lymphoma. The mean age was 63 yrs, and the cohort was 53% male. Between time periods, there was a relative increase in Hispanic pts [15.4% (1988-2000) to 20.8% (2001-2007), p<0.001], and a 4% increase in advanced stage from 42% (1988-2000) to 46% (2001-2007) (p<0.001). Neighborhood SES was stratified into quintiles from lowest (SES-1) to highest (SES-5), the pt distribution was: SES-1, 14%; SES-2, 18%; SES-3, 21%; SES-4, 23%; and SES-5, 24%. To evaluate the impact of prognostic factors (particularly diagnosis period, SES, and race/ethnicity) on overall survival (OS) & disease-specific survival (DSS) we used Cox proportional hazards regression to calculate hazard ratios (HR) for death with 95% CI's. Multivariate regression models included variables significant at p<0.15 in univariate models or with a priori hypotheses for inclusion. Results are presented by stage at diagnosis [Localized/Regional (LocReg) vs. Advanced (ADV)]. Results: There was a significant improvement in OS in patients diagnosed after 2001 for both LocReg (HR 0.87, 95%CI 0.82-0.91, p<0.001) and ADV stage (HR 0.69, 95%CI 0.66-0.72, p<0.001), which correlates with the introduction of rituximab into therapy for DLBCL. As expected, age >60 years was associated with a significantly worse OS for LocReg (HR 3.06, 95%CI 2.90-3.24) and ADV stage (2.02, 95%CI 1.93-2.12). Females also had significantly better OS compared with males (Loc-Reg - HR 0.90, 95%CI 0.86-0.94; ADV - HR 0.89, 95%CI 0.85-0.93). There was no significant impact of race/ethnicity on survival with the exception of non-Hispanic Asian/Pacific Islanders (NH A/PI) with ADV stage, for whom OS was significantly inferior compared with whites (HR 1.18, 95%CI 1.09-1.27, p<0.001). Compared with the highest quintile (SES-5), there was a significant effect of lower neighborhood SES on OS and DSS (see Table). Conclusion: There has been a significant improvement in survival after DLBCL since 2001, but patients in the lowest SES-1 quintile have a 34% higher risk of death from any cause and 20% higher risk for death from lymphoma than those in the highest SES-5. In this model, race/ethnicity did not have a significant impact on survival with the exception of NH A/PI with ADV stage. Studies to understand and address these socioeconomic disparities are urgently required in order to extend the improvements in DLBCL survival more effectively. Disclosures: Foran: Genentech: Honoraria, Research Funding.

Social disorganization and police arrest trajectories

2021 ◽  
pp. 0032258X2110321
Author(s):  
Ivan T Wong ◽  
John L Worrall
Keyword(s):  
Social Disorganization ◽  
Economic Status ◽  
Time Varying ◽  
Block Group ◽  
Group Level ◽  
Census Block ◽  
Cross Sectional ◽  
Census Block Group ◽  
Police Decision Making ◽  
Arrest Activity

Prior police decision-making research is limited by (1) its emphasis on individual and organizational predictors and (2) cross-sectional designs, which fail to account for the time-varying aspects of police activities and the factors explaining them. Using group-based trajectory modeling, this study tested the ability of social disorganization theory to explain arrest activity at the Census block group level in Dallas, Texas. Social disorganization variables helped predict certain arrest trajectories, but not all of them. Specifically, socio-economic status was significant in low and medium arrest trajectory groups. An interaction between racial heterogeneity and family disruption was also significant in the medium arrest trajectory group. Theoretical implications are discussed.

scholarly journals Modeling Pediatric Body Mass Index and Neighborhood Environment at Different Spatial Scales

2018 ◽  
Vol 15 (3) ◽  
pp. 473 ◽  
Author(s):  
Lauren Grant ◽  
Chris Gennings ◽  
Edmond Wickham ◽  
Derek Chapman ◽  
Shumei Sun ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Census Tract ◽  
Spatial Scales ◽  
Block Group ◽  
Group Level ◽  
Census Block ◽  
Census Block Group ◽  
Multiple Spatial Scales ◽  
Census Tract Level ◽  
Selection Of

In public health research, it has been well established that geographic location plays an important role in influencing health outcomes. In recent years, there has been an increased emphasis on the impact of neighborhood or contextual factors as potential risk factors for childhood obesity. Some neighborhood factors relevant to childhood obesity include access to food sources, access to recreational facilities, neighborhood safety, and socioeconomic status (SES) variables. It is common for neighborhood or area-level variables to be available at multiple spatial scales (SS) or geographic units, such as the census block group and census tract, and selection of the spatial scale for area-level variables can be considered as a model selection problem. In this paper, we model the variation in body mass index (BMI) in a study of pediatric patients of the Virginia Commonwealth University (VCU) Medical Center, while considering the selection of spatial scale for a set of neighborhood-level variables available at multiple spatial scales using four recently proposed spatial scale selection algorithms: SS forward stepwise regression, SS incremental forward stagewise regression, SS least angle regression (LARS), and SS lasso. For pediatric BMI, we found evidence of significant positive associations with visit age and black race at the individual level, percent Hispanic white at the census block group level, percent Hispanic black at the census tract level, and percent vacant housing at the census tract level. We also found significant negative associations with population density at the census tract level, median household income at the census tract level, percent renter at the census tract level, and exercise equipment expenditures at the census block group level. The SS algorithms selected covariates at different spatial scales, producing better goodness-of-fit in comparison to traditional models, where all area-level covariates were modeled at the same scale. These findings underscore the importance of considering spatial scale when performing model selection.

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