area deprivation
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Author(s):  
Veena Shankaran ◽  
Li Li ◽  
Catherine Fedorenko ◽  
Hayley Sanchez ◽  
Yuxian Du ◽  
...  

PURPOSE Although financial toxicity is a growing cancer survivorship issue, no studies have used credit data to estimate the relative risk of financial hardship in patients with cancer versus individuals without cancer. We conducted a population-based retrospective matched cohort study using credit reports to investigate the impact of a cancer diagnosis on the risk of adverse financial events (AFEs). METHODS Western Washington SEER cancer registry (cases) and voter registry (controls) records from 2013 to 2018 were linked to quarterly credit records from TransUnion. Controls were age-, sex-, and zip code–matched to cancer cases and assigned an index date corresponding to the case's diagnosis date. Cases and controls experiencing past-due credit card payments and any of the following AFEs at 24 months from diagnosis or index were compared, using two-sample z tests: third-party collections, charge-offs, tax liens, delinquent mortgage payments, foreclosures, and repossessions. Multivariate logistic regression models were used to evaluate the association of cancer diagnosis with AFEs and past-due credit payments. RESULTS A total of 190,722 individuals (63,574 cases and 127,148 controls, mean age 66 years) were included. AFEs (4.3% v 2.4%, P < .0001) and past-due credit payments (2.6% v 1.9%, P < .0001) were more common in cases than in controls. After adjusting for age, sex, average baseline credit line, area deprivation index, and index/diagnosis year, patients with cancer had a higher risk of AFEs (odds ratio 1.71; 95% CI, 1.61 to 1.81; P < .0001) and past-due credit payments (odds ratio 1.28; 95% CI, 1.19 to 1.37; P < .0001) than controls. CONCLUSION Patients with cancer were at significantly increased risk of experiencing AFEs and past-due credit card payments relative to controls. Studies are needed to investigate the impact of these events on treatment decisions, quality of life, and clinical outcomes.


2022 ◽  
Vol 226 (1) ◽  
pp. S38-S39
Author(s):  
Francis M. Hacker ◽  
Jaclyn M. Phillips ◽  
Lara S. Lemon ◽  
Aislin DeFilippo ◽  
Hyagriv Simhan

Author(s):  
Ian Thomas ◽  
Peter Mackie

IntroductionPrior research into the prevalence of SARS-CoV-2 infection amongst people experiencing homelessness (PEH) largely relates to people in communal forms of temporary accommodation in contexts where this type of accommodation remained a major part of the response to homelessness during the COVID-19 pandemic. Little is known about the prevalence of SARS-CoV-2 amongst PEH more broadly, and in a policy and practice context that favoured self-contained accommodation, such as Wales, UK. ObjectiveDescribe the prevalence of SARS-CoV-2 amongst PEH in Wales, UK, using routinely collected administrative data from the Secure Anonymised Information Linkage Databank. MethodsRoutinely collected data were used to identify PEH in Wales between 1st March 2020 and 1st March 2021. Using SARS-CoV-2 pathology testing data, prevalence rates were generated for PEH and three comparator groups: (1) the not-homeless population; (2) a cohort `exact matched' for age, sex, local authority and area deprivation; and (3) a matched comparison group created using these same variables and Propensity Score Matching (PSM). Three logistic regressions were run on samples containing each of the comparator groups to explore the effect of experiencing homelessness on testing positive for SARS-CoV-2. ResultsThe prevalence of SARS-CoV-2 infection amongst PEH was 5.0%, compared to the not-homeless population at 5.6%. For the exact matched and PSM match comparator groups, prevalence was 6.9% and 6.7%, respectively. Logistic regression found that SARS-CoV-2 infection was 0.9 times less likely amongst PEH compared to people not experiencing homelessness from the general population. The odds of SARS-CoV-2 infection for PEH was 0.75 and 0.73 where the `not-homeless' comparators were from the exact match and PSM samples, respectively. ConclusionOur analysis revealed that a year into the COVID-19 pandemic, the prevalence of SARS-CoV-2 amongst PEH in Wales was lower than the general population. A policy response to homelessness that moved away from communal accommodation may be partly responsible for the reduced SAR-CoV-2 infection amongst PEH.


2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 60-60
Author(s):  
Jarrod Dalton ◽  
Elizabeth Pfoh ◽  
Kristen Berg ◽  
Douglas Gunzler ◽  
Lyla Mourany ◽  
...  

Abstract The prevalence of Alzheimer’s disease (AD) is anticipated to increase drastically. Neighborhood socioeconomic position (SEP) has been related to multiple processes of health. Understanding whether SEP is related to AD can inform who is at greatest risk of developing this disease. We analyzed electronic medical records of 394892 patients from the two largest health systems in Northeast Ohio to evaluate the relationship between Ohio Area Deprivation Index quintiles (defined at the census tract level) and hazard for a composite outcome of AD diagnosis or primary AD death. We included residents of Cuyahoga and neighboring counties, and used the first outpatient visit beyond age 60 occurring between 2005 and 2015 as study baseline. Outcome data were censored at the earlier of a) the beginning of any 3-year time period without visits or b) non-AD death. We estimated a Cox proportional hazards regression model, adjusting ADI quintile effects for the interaction between age at baseline, sex and race as well as birth year. We used quadratic terms for continuous predictors. After adjusting for these factors, ADI quintile was significantly related (χ2 = 83.0 on 4 d.f.; p &lt; 0.0001) to the composite time-to-event outcome. Compared to the lowest-deprivation quintile, ADI quintiles 4 (adjusted hazard ratio [95% confidence interval]: 1.18 [1.10, 1.26]) and 5 (1.37 [1.28, 1.47]) had significantly higher hazard for the composite outcome. In conclusion, neighborhood deprivation may be a risk factor for AD independent of demographic factors. Preventive efforts should target individuals living in neighborhoods with high levels of deprivation.


2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 979-980
Author(s):  
Jason Falvey ◽  
Erinn Hade ◽  
Steven Friedman ◽  
Rebecca Deng ◽  
Jasmine Travers

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.


BMJ Open ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. e052646
Author(s):  
Sharmani Barnard ◽  
Paul Fryers ◽  
Justine Fitzpatrick ◽  
Sebastian Fox ◽  
Zachary Waller ◽  
...  

ObjectivesTo examine magnitude of the impact of the COVID-19 pandemic on inequalities in premature mortality in England by deprivation and ethnicity.DesignA statistical model to estimate increased mortality in population subgroups during the COVID-19 pandemic by comparing observed with expected mortality in each group based on trends over the previous 5 years.SettingInformation on deaths registered in England since 2015 was used, including age, sex, area of residence and cause of death. Ethnicity was obtained from Hospital Episode Statistics records linked to death data.ParticipantsPopulation study of England, including all 569 824 deaths from all causes registered between 21 March 2020 and 26 February 2021.Main outcome measuresExcess mortality in each subgroup over and above the number expected based on trends in mortality in that group over the previous 5 years.ResultsThe gradient in excess mortality by area deprivation was greater in the under 75s (the most deprived areas had 1.25 times as many deaths as expected, least deprived 1.14) than in all ages (most deprived had 1.24 times as many deaths as expected, least deprived 1.20). Among the black and Asian groups, all area deprivation quintiles had significantly larger excesses than white groups in the most deprived quintiles and there were no clear gradients across quintiles. Among the white group, only those in the most deprived quintile had more excess deaths than deaths directly involving COVID-19.ConclusionThe COVID-19 pandemic has widened inequalities in premature mortality by area deprivation. Among those under 75, the direct and indirect effects of the pandemic on deaths have disproportionately impacted ethnic minority groups irrespective of area deprivation, and the white group the most deprived areas. Statistics limited to deaths directly involving COVID-19 understate the pandemic’s impact on inequalities by area deprivation and ethnic group at younger ages.


2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 918-918
Author(s):  
Kellia Hansmann ◽  
Amy Kind ◽  
Ryan Powell

Abstract Medicare’s Hospital Readmissions Reduction Program (HRRP) places disproportionate penalties on hospitals serving populations with complex medical and social needs. Without measures to identify the social need intensity of populations cared for by these hospitals, the HRRP cannot account for these risk factors, leading to burdensome penalties that may inadvertently hinder the ability of such hospitals to care for vulnerable populations. The objective of this study is to characterize the social need intensity of US hospital acute care populations. Using the Area Deprivation Index (ADI), a validated measure that ranks neighborhood socioeconomic disadvantage based on income, employment, housing, and education factors, we determined an “Area Deprivation Share” (ADS) for hospitals with 25 or more discharges using 100% of national Medicare claims data from 2013-2014. Hospital ADS is the proportion of qualifying discharges residing in the most disadvantaged neighborhoods (ADI ≥ 80th percentile) out of all qualifying discharges during the study period. Of 4,603 hospitals, median ADS was 17% (Interquartile Range: 6% - 34%). Hospitals in the highest quintile of ADS (39% to 100%), were more frequently located in small towns or isolated rural areas (52.6%, comparted to 24.2% in lower quintiles) and served a higher percentage of Black patients (19.0%, comparted to 9.7% in lower quintiles). ADS is a potential tool to inform future Medicare policy decisions. Additional research will inform how hospitals target care processes to meet the needs of older adults with complex social needs. Further study can also explore overlapping disadvantage domains of socioeconomic status, race, and rurality.


2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 1012-1013
Author(s):  
Ali Vaeli Zadeh ◽  
Fei Tang ◽  
Carlos Gomez ◽  
Luci Leykum ◽  
Orna Intrator ◽  
...  

Abstract Using predictive analytic modeling, the Veterans Affairs (VA) Geriatrics and Extended Care Data Analysis Center (GECDAC) identified vulnerable “High-Need High-Risk” (HNHR) Veterans, as requiring more support and services. We sought to identify variables linked with utilization of our outpatient HNHR C4 clinic offering Comprehensive Geriatric Assessment, Care Planning, Care Coordination, and Co-management". Of 724 HNHR Veterans contacted, 531 were reached and invited to participate; 193 were not reached, 326 were reached but declined the C4 clinic, 205 attended the clinic. We compared these groups. Independent variables were organized using Anderson’s behavioral model into predisposing (age, gender, race, ethnicity), enabling (drive time, service eligibility, Area Deprivation Index, marital status), and need factors (mental health cognitive condition, ambulatory care sensitive conditions, NOSOS, JFI, CAN, etc.). C4 enrollment acceptance was the outcome. Results showed that compared to patients who declined, HNHR veterans who attended C4 clinic had more chronic health conditions(p&lt;0.01), more service eligibility(p=0.01), more driving time to the closest VA clinic(p=0.01), and more were married (p=0.01). Patients who declined C4 clinic might have greater barriers to care access. Accessing needed healthcare among HNHR older adults maybe impacted more by enabling factors that allow the individual to seek care if needed and are the resources that may facilitate access to services, rather than need factors, which include individuals' perceptions of their health and functional state, and healthcare needs assessed by professionals. More social and intermediary determinants of health should be incorporated as enabling factors into models striving to understand drivers of healthcare use.


2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 917-917
Author(s):  
Dextiny McCain ◽  
Adrienne Aiken Morgan ◽  
Regina Wright

Abstract Previous research suggests depressive symptoms and loneliness are increasingly prevalent among older adults living in lower-income neighborhoods. The purpose of this study was to examine the extent to which neighborhood socioeconomic status (SES) was associated with depressive symptoms and loneliness among a sample of older adults from the Healthy Heart and Mind Study (N = 165; mean age = 68.48 (SD = 6.26); 66.7% women; 40.6% African American). It was hypothesized that older adults living in neighborhoods with greater socioeconomic disadvantage would report more depressive symptoms and loneliness than those residing in neighborhoods with less socioeconomic disadvantage. Depression was assessed with the Beck Depression Inventory-II (BDI-II), and loneliness was assessed using the Revised University of California, Los Angeles (UCLA) Loneliness scale. Neighborhood SES was measured with the Area Deprivation Index (ADI), which allows rankings of neighborhoods by SES disadvantage both statewide and nationally. After controlling for demographic variables (age, sex, and race), linear regression analyses showed that greater neighborhood SES disadvantage was associated with higher depression scores (β = -.094; p = .041) and higher loneliness scores (β = -.258; p = .003). These findings highlight the importance of neighborhood context on mental health in older adults, as underserved populations are more likely to experience declines in mental health under strenuous circumstances. Future research should investigate the impact of neighborhood SES on mental health in aging adults.


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