Statistical Areas Research Articles

50 years of comprehensive state-wide data on pregnancy termination in South Australia: a retrospective, population-based, cohort study.

Termination of pregnancy is a common procedure, accessed globally, irrespective of logistical and legal barriers. We aimed to document changes in procedural characteristics and demographic factors over time in South Australia, as well as to examine how key sociodemographic variables affect gestational age at pregnancy termination. South Australian legislation has required mandatory collection of data related to pregnancy termination since 1970. We conducted a retrospective population-based cohort study of all pregnancy terminations in the state from 1970 to 2020. The primary outcome was gestational age at termination of pregnancy. Linear regression was used to examine socioeconomic characteristics that could confer disadvantage in access to pregnancy termination, with postcodes coded according to the 2016 Australian Statistical Geographical Classification-Remote Area system and the Index of Relative Socio-Economic Advantage and Disadvantage (IRSAD) from the Australian Bureau of Statistics 2018. Between Jan 1, 1970, and Dec 31, 2020, 225 440 pregnancy terminations were performed at a median of 8 weeks' gestation (IQR 7-10). Most pregnant individuals (median age 24 years [IQR 20-31]) resided in urban areas (n=166 311 [77·9%]) and received surgical pregnancy termination (n=287 345 [78·4%]). Major complication rates remained low over time; however, major complications occurred more frequently at higher gestational ages. Gestational age at pregnancy termination decreased over time (from 9 weeks [IQR 8-11] in years 1970-75 to 7 weeks [6-9] in years 2016-20) and was strongly correlated with being a teenager (ie, aged <20 years), rurality, and IRSAD score, with individuals at greater levels of socioeconomic disadvantage undergoing pregnancy termination at consistently higher gestational ages. Over time, there have been many changes in the demographics of women and the method used for pregnancy termination. Even in a high-income country with legal and publicly funded access to pregnancy termination, there are significant differences in the gestational age at which pregnancy termination occurs, which vary with socioeconomic disadvantage. Understanding changes in the demographics of those undergoing this procedure and the influence of various indicators of socioeconomic disadvantage is vital for optimal service provision. None.

Ambient particulate matter and frequency of outpatient visits for chronic rhinosinusitis in the United States.

Emerging evidence has underscored the harmful effects of air pollution on the upper airway. We investigated the relationship between ambient particulate matter (PM) level and the frequency of outpatient visits for chronic rhinosinusitis (CRS). We conducted an ecological cohort study of US adults enrolled in The Merative MarketScan outpatient database from 2007 to 2020. For each geographical subunit (core-based statistical area [CBSA]), we calculated the annual rate of CRS-related outpatient visits per 1000 well-patient checkup visits (CRS-OV). Using data from the Environmental Protection Agency's Air Quality System, we mapped the rolling statistical average of daily PM2.5 and PM10 over the preceding year onto each CBSA×year combination. We employed multivariable negative binomial regression modeling to estimate the association between PM levels and subsequent CRS-OV. Across 3933 observations (CBSA×year combinations), encompassing ∼4 billion visits, the median CRS-OV was 164 (interquartile range 110-267). The mean PM2.5 level was 8.9µg/m3 (SD 2.6) and the mean PM10 level was 20.2µg/m3 (SD 7.2). Adjusting for patient demographics and respiratory comorbidities, a compounded rise in subsequent CRS-OV was observed with increasing PM levels. Each µg/m3 rise in PM2.5 independently predicted a 10% increase in CRS-OV (adjusted incidence rate ratio [aIRR]) 1.10, 95% confidence interval [CI] 1.08-1.13) and each µg/m3 rise in PM10 independently predicted a 3% increase in CRS-OV (aIRR 1.03, 95% CI 1.02-1.04). Elevated ambient PM2.5 and PM10 levels are associated with a subsequent compounded increase in the frequency of CRS-OV, with PM2.5 predicting a more pronounced rise compared to PM10.

Home Mortgage Lending and Neighborhood Mental Health: A Spatial Econometric Analysis of 18 U.S. Metropolitan Statistical Areas.

This study investigates the relationship between home mortgages and neighborhood mental health across the 18 largest metropolitan statistical areas (MSAs) in the United States. Home mortgages, a primary avenue to homeownership, contribute to housing security and stability. Moreover, their issuance reflects local investment and potential improvements in the built environment, hypothesized to positively influence community mental well-being. Using census tract-level data from multiple sources, we employed a spatial econometric approach, specifically spatial error modeling, to account for spatial dependency and estimate the association between home mortgage lending (2011 to 2020) and the prevalence of self-reported poor mental health in 2020. Our findings indicate a statistically significant negative association between mortgage issuance and self-reported poor mental health across all 18 MSAs, suggesting that increased mortgage lending is associated with improved neighborhood mental health. Comparisons between standard linear models and spatial error models highlight the influence of unmeasured, spatially correlated factors on neighborhood mental health outcomes. This study underscores mortgage lending as a crucial factor in community well-being and emphasizes the necessity of addressing spatial dependency in neighborhood health studies for accurate estimations. The findings offer valuable insights for researchers and policymakers aiming to enhance community mental health and address health disparities through informed housing policies.

Measuring Geographic Opportunity for Higher Education in US Metropolitan Areas

AbstractResearch demonstrates that students who live closer to postsecondary institutions are more likely to attend college. Yet many communities offer few or no postsecondary options. In this study, we expand on prior research on the geographic distribution of postsecondary opportunity by developing a finer-grained measure that accounts for both the variety and density of college options in each metropolitan statistical area (MSA). Our estimates use several decay functions to measure the distance-weighted characteristics of institutions, allowing us to cross-validate different approaches and identify the most appropriate model to inform future analyses of geographic opportunity. The results of the cross-validation indicate that both the type of geographic measure used and the penalty in the inverse distance weighting function result in substantively different model fit. We find that each of the measures used in a spatial model—enrollment size, tuition, and net price—has a positive and statistically significant relationship with postsecondary enrollment at the MSA level, indicating the continuing importance of geographic opportunity for college attendance.

Underlying Dimensions of Racial Residential Segregation and Police-Caused Homicide of Blacks: A Cross-Sectional Analysis of Core Based Statistical Areas

This work builds upon previous research to assess the associations between underlying dimensions of racial residential segregation and police-caused homicide of Black civilians. Previous scholars have observed that much of the existing work on residential segregation and police-caused homicide has used one measure of segregation, most commonly the dissimilarity index, although recent work has expanded the focus to include multiple indicators of segregation. Our research extends this research by computing multiple measures of racial residential segregation for a sample of U.S. Core Based Statistical Areas in 2010 and factor analyzing them to identify underlying dimensions. Measures of the resulting dimensions are merged with the Mapping Police Violence data on police-caused homicide. The analyses reaffirm and extend previous conclusions about the importance of spatial relationships within residential locations for understanding contemporary police-caused homicide, while raising questions about the underlying theoretical mechanisms.

Identifying measures for understanding and addressing county-level disparities in adolescent HPV vaccination coverage in North Carolina.

HPV vaccination coverage is characterized by geographic disparities in the US, with national studies finding lower coverage in rural versus nonrural areas. To direct quality improvement efforts in North Carolina, we sought to understand how different rurality measures characterize these disparities. We used separate negative binomial regression models to test associations between 5 dichotomized county-level rurality measures and HPV vaccination coverage (≥1 dose) among North Carolina adolescents, ages 11-12 (n=326,345). Rurality measures were derived from: Office of Management and Budget's Metropolitan Statistical Areas, Rural-Urban Continuum Codes, Index of Relative Rurality, US Census Bureau classifications, and North Carolina Rural Center classifications. Models controlled for Social Vulnerability Index (SVI) percentile and rate of pediatricians per county. Vaccination data came from the North Carolina Immunization Registry. HPV vaccination coverage was 29% across North Carolina's 100 counties (range: 13%, 46%). Agreement between rurality measures ranged from 54% to 93% of counties. In adjusted analyses, none of the 5 rurality measures correlated with HPV vaccination coverage, but higher SVI and higher rate of pediatricians were positively associated with coverage (p< 0.01). Exploratory moderation analyses suggested regional variation in the relationship between rurality and coverage, with a positive association in one region, a negative association in one region, and no association in four regions. County-level rurality measures did not identify disparities in HPV vaccination coverage in North Carolina. Measures related to social vulnerability and access to pediatricians may be better suited for understanding and addressing the state's substantial county-level vaccination disparities.

Traffic Congestion and Safety: Mixed Effects on Total and Fatal Crashes

This paper examines the effects of traffic congestion on total crashes, fatal or serious injury (FSI) crashes, and fatal-only crashes in peak periods using a zone-level safety analysis in Greater Melbourne. Bayesian mixed-effect negative binomial models are employed to investigate the relationship between a congestion index and the frequency of total and FSI crashes. In addition, Bayesian mixed-effect binary logistic models are adopted to explore the association between the congestion index and the likelihood of having fatal crashes in Statistical Area Level 2 (SA2) zones. Modelling results indicate that traffic congestion tends to increase total crashes in both the AM and PM peak periods and FSI crashes in the AM peak period. In contrast, traffic congestion tends to decrease the likelihood of having fatal crashes at both the AM and PM peaks. These findings suggest that many policies to reduce traffic congestion may also enhance road safety by lowering the overall number of crashes. However, it is crucial to incorporate careful speed management within these policies to reduce the risk of fatal crashes effectively.

Design of a Location-Based Case-Control Study of Built Environment Risk Factors for Pedestrian Fatalities in the U.S.

The location-based case-control design is a useful approach for studies where the exposures of interest are aspects of the environment around the location of a health event such as a pedestrian fatality. In this design locations are the unit of analysis and an enumerated cohort of locations are followed through time for the health events of interest and a case-control study of locations is nested within the cohort. Locations where events occurred (case-locations) are compared to matched locations where these events did not occur (control-locations). We describe the application of this design to the issue of pedestrian fatalities using a cohort of 9,612,698 intersections, 17,737,728 road segments, and 222,318 entrance/exit ramp segments that existed in 2017 across all 384 U.S. Metropolitan Statistical Areas. This cohort of locations was followed up from Jan 1, 2017 to Dec 31, 2018 for pedestrian fatalities using the National Highway Traffic Safety Administration Fatality Analysis Reporting System. In total, 10,587 fatalities were identified as having occurred on cohort locations and 21,174 matched control locations were selected using incidence density sampling. Geographic information systems, spatially linked administrative data sets and virtual neighborhood audits via Google Street View are underway to characterize study locations.

It’s not (only) personal, it’s business: personal bankruptcy exemptions and business credit

Abstract In the USA, state-level exemptions determine the amount of property that individuals can protect from creditor liquidation during the debt settlement process. We exploit within-metropolitan statistical area variation in personal bankruptcy exemptions created by state borders and a stacked regression approach to identify the spillover effects of these laws on business credit extended to small firms. Subsequent to exemption increases, we find a reduction of 1–2 percent in originations of business credit. The effect is strongest for the smallest firms, which are more financially constrained. We provide household-level evidence that both business debt and personal debt decline for borrowers whose home equity becomes covered by the exemption, suggesting an overall decrease in credit availability for small businesses. As a result, increases in exemptions lead to fewer small establishments and lower employment, especially in industries dependent on external finance, suggesting that negative real economic effects occur via a credit market channel.

Assessing heat vulnerability and multidimensional inequity: Lessons from indexing the performance of Australian capital cities

Increased extreme heat, driven by rapid urbanisation and climate change, has caused increasing heat-related deaths, and posed significant threats to vulnerable populations. Studies have proposed many heat vulnerability indices, but most lack comprehensiveness in study design and validation. Furthermore, a significant gap remains for an analysis of inequity in heat vulnerability assessment research, a crucial component for promoting thermal equity. This paper introduces a Comprehensive Heat Vulnerability Index (CHVI) for Australia's capital cities, using socio-demographic, health, and environmental indicators at the finest statistical level, the Statistical Area 1 (SA1) scale, and employs the Gini coefficient to analyse inequity in heat vulnerability. Key findings include: (a) Heat vulnerability exposure decreases from urban to rural areas, with sensitivity exhibiting similar trends but more pronounced regional variations; (b) Adaptive capacity increases as exposure decreases, though this pattern is not consistent in city centres and certain regions; (c) The CHVI effectively measures heat vulnerability, demonstrating strong correlations with Pearson Correlation Coefficients of 0.68 and 0.92; (d) The distribution of heat vulnerability aligns with exposure trends but shows greater regional diversity, and city centres tend to have lower vulnerability levels; (e) Thermal inequity among SA1s is influenced by factors such as the Indigenous population, unemployment rates, English proficiency, and public transportation usage; (f) There is a slight variation in these factors of thermal inequity across each capital city. The findings provide a comprehensive understanding of heat vulnerability and thermal inequity in the Australian capital cities that could aid in informing climate resilience and adaptation strategies and policies for sustainable development.

Abstract B139: Redlining bias and progression of chronic health conditions in survivors of childhood cancer: A report from the St. Jude lifetime cohort study

Abstract Background/Aims: Racial health disparities have been linked to racial and residential segregation. Redlining bias is a special form of racial and residential segregation involving the systematic denial of mortgages, insurance, and other financial services to racial/ethnic minorities. This study examined associations between redlining bias and the progression of chronic health conditions (CHCs) in adult survivors of childhood cancer. Methods: We included 1,670 adult survivors of childhood cancer from the St. Jude Lifetime Cohort Study who had undergone two or more clinical assessments. Over 200 individual CHCs were graded using a modified CTCAE version and aggregated to determine the global CHC burden. CHC progression from baseline to follow-up evaluation was classified as either worsening over time (from Grade 2 to 3-5 or from Grade 3 to 4-5) or new-onset (from Grades 0-1 to 2-5). Survivors' home addresses at baseline (2007-2020) were linked to contemporary redlining bias data (2010-2017) created by Beyer et al. (J Clin Oncol, 2021). Redlining bias was defined as the odds ratio of a mortgage application denial for properties in a local area versus all metropolitan statistical areas and further classified as high (≥2.0), moderate (1.0-1.9), low (0.5-0.9), or least bias (&amp;lt;0.5). Multivariable logistic regression tested associations of risk for worsening/new-onset CHCs (vs. no progression) with redlining bias (high vs. moderate/low/least bias), adjusting for age, sex, treatment factors, personal socioeconomic status (SES; education and health insurance), and lifestyle factors. Joint effects of redlining bias and personal SES on CHC progression were also evaluated. Results: Survivors’ mean±SD age at baseline was 31.7±8.3 years; 17.5% lived in high and 19.5% in moderate redlining neighborhoods. The bivariate analysis found that living in a neighborhood classified as having high redlining bias (vs. moderate/low/least bias) increased the risk of progression of the global burden of CHCs (OR=1.32, 95% CI: 1.02-1.70). Multivariable analysis found significant associations of redlining bias with the risk of worsening/new-onset of individual CHCs such as cardiomyopathy (OR=1.63, 95% CI: 1.07-2.48), peripheral motor neuropathy (OR=1.56, 95% CI: 1.00-2.44), asthma (OR=1.85, 95% CI: 1.04-3.32), and restrictive pulmonary deficit (OR=1.79, 95% CI: 1.05-3.04). Considering joint effects, survivors enrolled in public insurance programs and living in a high redlining area had a higher risk of progression of the global burden of CHCs (OR=1.73, 95% CI: 1.05-2.87) and of worsening/new-onset in peripheral motor neuropathy (OR=3.18, 95% CI: 1.57-6.42) and restrictive pulmonary deficit (OR=5.29, 95% CI: 2.46-11.37) CHCs. Conclusions: The significant associations of redlining bias with the progression of CHCs among adult survivors of childhood cancer suggest an urgent need for interventions (typically social or healthcare policies) targeting neighborhoods with high discriminatory exposures to prevent CHC burden in this population. Citation Format: Jaesung Choi, Madeline R. Horan, Nickhill Bhakta, Deo Kumar Srivastava, Kirsten K. Ness, Gregory T. Armstrong, Melissa M. Hudson, I-Chan Huang. Redlining bias and progression of chronic health conditions in survivors of childhood cancer: A report from the St. Jude lifetime cohort study [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B139.

Daily Temperatures and Child Hospital Admissions in Aotearoa New Zealand: Case Time Series Analysis.

The influence of global climate change on temperature-related health outcomes among vulnerable populations, particularly young children, is underexplored. Using a case time series design, we analysed 647,000 hospital admissions of children aged under five years old in New Zealand, born between 2000 and 2019. We explored the relationship between daily maximum temperatures and hospital admissions across 2139 statistical areas. We used quasi-Poisson distributed lag non-linear models to account for the delayed effects of temperature over a 0-21-day window. We identified broad ICD code categories associated with heat before combining these for the main analyses. We conducted stratified analyses by ethnicity, sex, and residency, and tested for interactions with long-term temperature, socioeconomic position, and housing tenure. We found J-shaped temperature-response curves with increased risks of hospital admission above 24.1 °C, with greater sensitivity among Māori, Pacific, and Asian compared to European children. Spatial-temporal analysis from 2013-2019 showed rising attributable fractions (AFs) of admissions associated with increasing temperatures, especially in eastern coastal and densely populated areas. Interactive maps were created to allow policymakers to prioritise interventions. Findings emphasize the need for child-specific and location-specific climate change adaptation policies, particularly for socioeconomically disadvantaged groups.

Outcomes by Race and Ethnicity Following a Medicare Bundled Payment Program for Joint Replacement

The Comprehensive Care for Joint Replacement (CJR) model, a traditional Medicare bundled payment program for lower-extremity joint replacement, is associated with care for patients outside traditional Medicare. Whether CJR model outcomes have differed by patient race or ethnicity outside of traditional Medicare is unclear. To evaluate outcomes associated with the CJR model among Hispanic patients not enrolled in traditional Medicare. This cohort study used hospitalization data from California's Patient Discharge Dataset for all patients who underwent lower-extremity joint replacement in California between January 1, 2014, and December 31, 2017. In California, 3 metropolitan statistical areas (MSAs) were randomly selected to participate in CJR in April 2016. Hospitals not participating in other Medicare Alternative Payment Models were included in the treated group if they were in these 3 MSAs and in the control group if they were in the remaining 23 MSAs. The data analysis was performed between October 1 and December 31, 2023. Comprehensive Care for Joint Replacement program implementation. The main outcomes were hospital length of stay and home discharge rates by race and ethnicity. Home discharge status included self-care, the use of home health services, and hospice care at home. Event study, difference-in-differences, and triple differences models were used to estimate differential changes in health care service use by race and ethnicity for patients in the treated MSAs compared with the control MSAs before vs after CJR implementation. Of 309 834 hospitalizations (patient mean [SD] age, 68.3 [11.3] years; 60.6% women; 14.8% Hispanic; 72.4% non-Hispanic White), 48.0% were in treated MSAs and 52.0% in control MSAs. The CJR program was associated with an increase in home discharge rates for patients without traditional Medicare coverage; however, the increase differed by patient race and ethnicity. The increase was 0.05 (95% CI, 0.02-0.08) percentage points higher for Hispanic patients with Medicare Advantage and 0.03 (95% CI, 0.01-0.04) percentage points higher for Hispanic patients without Medicare compared with their non-Hispanic White counterparts. This cohort study shows that CJR program outcomes differed by race and ethnicity for patients covered outside traditional Medicare, with home discharge rates increasing more for Hispanic compared with non-Hispanic White patients. These findings suggest the importance of considering differential outcomes of Medicare payment policies for racial and ethnic minority patient populations beyond the initially targeted groups.

Geographical incidence of testosterone supplementation in total hip and knee arthroplasty

IntroductionPrescription testosterone usage in the United States declined after 2013 following reports of its association with myocardial infarction and stroke. However, more recently there has been a documented increase in testosterone prescriptions. Recently, testosterone levels have also been hypothesized to increase infection risk in patients undergoing elective shoulder arthroplasty. Furthermore, testosterone may increase the risk of venous thromboembolism. These complications are perioperative concerns for total hip and knee arthroplasties (THA/TKA). Therefore, the purpose of our study is to identify trends in the incidence of testosterone prescriptions in patients who underwent THA/TKA with respect to geographical population data. MethodsWe retrospectively reviewed 40,711 primary THAs and 50,893 primary TKAs performed in males between 1/1/2016 and 12/31/2021 using a commercial claims database. Records were reviewed for demographics, geographical location, and supplemental testosterone prescriptions within 1 year prior to surgery. Patient Metropolitan Statistical Area (MSA) was assessed with respect to United States Census Population Data. ResultsWe identified 91,604 males who underwent primary THA (n = 40,711) or TKA (n = 50,893). For THA/TKA, patients who were younger had a higher likelihood of having a supplemental testosterone prescription (OR = 0.99, 95 % CI [0.99–1.00], p < 0.001). TKA patients (2,507, 4.9 %) had a higher rate of testosterone prescriptions than THA patients overall (1,413, 3.4 %), (OR = 1.44 95 % CI [1.35, 1.54], p < 0.001) as well as within each year.For THA and TKA patients, patients in the Midwest (OR = 1.61, p < 0.001), South (OR = 3.04, p < 0.001), and West (OR = 2.28, p < 0.001) regions all had higher testosterone prescription rates than the Northeast. Patients living in a city (MSA population ≥200,000) were more likely to be prescribed testosterone (OR = 1.20, p = 0.002). ConclusionSurgeons conducting TKA/THA should be aware that younger patients, those in higher population areas, and those in the Midwest, South, and West regions are more likely to be prescribed testosterone than those in the Northeast.

Firearm Homicide Mortality is Linked to Food Insecurity in Major US Metropolitan Cities.

Gun violence disproportionately affects metropolitan areas of the United States (US). There is limited information regarding the influence of social determinants of health, such as food insecurity (FI) on firearm homicide mortality (FHM) in major metropolitan cities in the US. We sought to examine the relationship between FI and FHM. This was a cross-sectional analysis examining the largest 51 US major metropolitan statistical areas (MSAs) using data from 2018. Demographic data, markers of social inequities, and firearm homicide data were obtained from the US Census Bureau, US Department of Education, and the Frey and Brookings Institute. Food insecurity prevalence was obtained from Feeding America. Spearman ρ and linear regression were performed. Using Spearman rho analysis, higher FI (r = 0.55, P < 0.001) was associated with FHM. Other variables associated with FHM included percent Black/African American (AA) (r = 0.77, P < 0.001), poverty rate (r = 0.53, P < 0.001), and percent of children living in single parent households (r = 0.58, P < 0.001). In linear regression analyses, FI was associated with increased FHM, with 1.3 additional FHM events for each unit increase in FI (β = 1.33, 95% CI 0.27-2.39, P = 0.02). The percent of a population that is Black/AA was also associated with FHM, with more than 4 additional cases for each 1% increase in the population (β = 4.32, 95% CI 3.26-5.38, P < 0.001). Food insecurity may influence FHM in major US metropolitan cities. Community- and hospital-based programs that target FI may help combat the gun violence epidemic and decrease gun violence.

Gestational and postnatal exposure to wildfire smoke and prolonged use of respiratory medications in early life

As wildfire frequency and severity increases, smoke exposures will cause increasingly more adverse respiratory effects. While acute respiratory effects of smoke exposure have been documented in children, longer term sequelae are largely unstudied. Our objective here was to examine the association between gestational and postnatal exposure to wildfire smoke and prolonged use of prescription medication for respiratory conditions in early childhood. Using Merative MarketScan claims data, we created cohorts of term children born in western states between 1 January 2010-31 December 2014 followed for at least three years. Using NOAA Hazard Mapping System data, we determined the average number of days a week that >25% of the population in a metropolitan statistical area (MSA) was covered by smoke within each exposure period. The exposure periods were defined by trimester and two 12 week postnatal periods. Medication use was based on respiratory indication (upper respiratory, lower respiratory, or any respiratory condition) and categorized into outcomes of prolonged use (⩾30 d use) (PU) and multiple prolonged uses (at least two prolonged uses) (MPU). We used logistic regression models with random intercepts for MSAs adjusted for child sex, birth season, and birth year. Associations differed by exposure period and respiratory outcome, with elevated risk of MPU of lower respiratory medications following exposure in the third trimester and the first 12 postnatal weeks (RR 1.15, 95% CI 0.98, 1.35; RR 1.21, 95% CI 1.05, 1.40, respectively). Exposure in the third trimester was associated with an increase in MPU of any respiratory among males infants only (male RR 1.22, 95% CI 1.00, 1.50; female RR 0.93, 95% CI 0.66, 1.31). Through novel use of prescription claims data, this work identifies critical developmental windows in the 3rd trimester and first 12 postnatal weeks during which environmental inhalational disaster events may impact longer-term respiratory health.

An innovative linked Electronic Health Record derived data ecosystem for understanding the complexities of health and ageing.

ObjectiveWe describe the creation of an Electronic Health Record (EHR) derived linked data platform within the Australian National Centre for Healthy Ageing (NCHA). ApproachOur approach involved establishing a core set of EHR data suitable for research from the sole public health provider within a defined geographic region. Relevant items were selected based on published literature and consensus processes and curated within a specialised research data warehouse. Curation involved: data validation, quality assessment, internal linkage between episodes and data harmonisation/merging. An AI supported natural language processing pipeline was implemented to extract text-based data. Approvals were obtained from government agencies to link commonwealth claims data (primary care, medication, aged care, death) and state administrative hospital data, for all residents aged ≥60 years. Publicly available datasets were scoped for inclusion and collaborations established to incorporate local council data. ResultsA core of 113 curated items for &gt;900,000 people over 10 years have been established across 4 hospitals and 10 community/outpatient services. Linked state and commonwealth data were obtained for 179,089 residents aged &gt;60 years (Jan 2010-May 2021, linkage accuracy 98.4%). Environmental (greenery, air pollution, walkability) and Census data have been incorporated for each neighbourhood (Statistical Area 1). Data access, extraction and release has been tested through over 30 use-case projects in: dementia, residential aged care, medication use, homelessness, environmental impacts on health and ageing and health service redesign. ImplicationsThe NCHA Data Platform provides an international exemplar for the use of linked EHR data in advancing population health research.

Improving Electronic Health Record (EHR) data linkages for identifying Aged Care Residents through publicly available data

IntroductionAccurate identification of Aged Care residents accessing hospital services is challenging due to multiple hospital Electronic Health Record (EHR) systems and entry points resulting in inconsistent addresses and coding. Improving accuracy and completeness of addresses is crucial for cohort identification and data linkage. ObjectiveDevelop methods to validate cross-sectoral data linkages to improve identification of aged care residents within EHR systems. ApproachAmong individuals aged ≥65 years (or ≥50 identifying as Aboriginal or Torres Strait Islander) between January 2013 to December 2023 within our Data Platform, we first used data items mandated by the Department of Health to identify aged care residency. Data quality was enhanced using a residential address index derived from Australian Geocoded National Address File (G-NAF). We evaluated sensitivity, specificity, and Positive Predictive Values (PPVs) of recorded addresses against the Australian Institute of Health and Welfare (AIHW) aged care services list. ResultsAmong 409,677 addresses extracted, 55.4% (raw) had sufficient data for validation. This increased to 89.9% (enhanced) with G-NAF. Among 79,028 admitted and 88,876 emergency patients, 22.7% and 18.4% respectively were aged care residents. We compared performance of raw vs enhanced comparisons were: sensitivity (admission: 34.1% vs 73.8%), (emergency: 42.6% vs 66.2%); specificity (admission: 96.2% vs 90.9%), emergency (96.8% vs 95.2%); Positive Predictive Value (admission: 61.3% vs 58.8%), (emergency: 64.2% vs 65.3%). Conclusions and ImplicationsOur approach significantly increased sensitivity without sacrificing specificity, enhancing Aged Care resident identification in EHR data. It also improved linkage with external datasets and Statistical Area Level 1 (SA1) mapping.

Detainer Requests Issued by ICE and Fair/Poor Self-Rated Health among Latines in the U.S., 2017–2020

AbstractU.S. Immigration and Customs Enforcement (ICE) issues detainer requests to local law enforcement agencies to hold detainees suspected of being undocumented immigrants until they can be transferred into ICE custody. We examined the association between area-level detainer requests and self-rated health among Latine adults. We linked health data from Latine adults included in the 2017–2020 Behavioral Risk Factor Surveillance System (n = 69,386) to detainer requests per 1,000 non-citizens in core-based statistical areas, (n = 152 across 49 states). We fit logistic regression models of self-rated fair/poor health on detainer requests, adjusted for individual- and area-level confounders. In adjusted analyses, we found that Latine adults living in areas with the highest quartile of requests had 24% higher odds of fair/poor health (OR 1.24, 95% CI = 1.05,1.47) relative to those in the lowest quartile. Local law enforcement agencies should limit cooperation with federal immigrant agencies to protect the health of Latine communities.

Rural-Urban Disparities in the Uptake of New Diabetes Medications

OBJECTIVE This study assessed rural-urban differences in the uptake and use of glucagon-like peptide 1 (GLP-1) receptor agonists, dipeptidyl peptidase 4 (DPP-4) inhibitors, and sodium–glucose cotransporter 2 (SGLT2) inhibitors among U.S. adults with diabetes. RESEARCH DESIGN AND METHODS We calculated person-level annual total and out-of-pocket (OOP) expenditures for new, other, and all diabetes medications in the Medical Expenditure Panel Survey. We defined newer diabetes medications as GLP-1 receptor agonists, DPP-4 inhibitors, and SGLT2 inhibitors. The primary outcome was whether a person received a new diabetes medication during the year, and secondary outcomes were medication expenditures. The key independent variable was metropolitan statistical area (MSA) status. Logistic regression was used to estimate use rates of new diabetes medications by MSA status, and a two-part model was used to estimate individual-level annual total and OOP expenditures on new, other, and all diabetes medications. RESULTS We observed no significant difference (adjusted odds ratio 0.943, P = 0.37) in newer diabetes medication use. Individuals with diabetes in non-MSA areas were more likely to have spending (probit coefficient 0.058, P = 0.06) and to spend more on other diabetes medications (combined marginal effect $103.13, P = 0.09), although this result was not statistically significant. This imbalance increased from $81.33 (P = 0.09) in 2003–2006 to $136.66 (P = 0.08) in 2017–2020. CONCLUSIONS Rural-urban diabetes outcome disparities are not likely to be the result of differences in the uptake of GLP-1 receptor agonist, DPP-4 inhibitor, and SGLT2 inhibitor medications.