Health Care Utilization Variables Research Articles

A study to assess the health care utilization and the activities of the daily living status amongst the geriatric population residing in urban and rural field practice areas of a medical college in Tamil Nadu

A study to assess the health care utilization and the activities of the daily living status amongst the geriatric population residing in urban and rural field practice areas of a medical college in Tamil Nadu

SARS-CoV-2 pandemic effect on pediatric asthma health care utilization in an urban hospital

The "September epidemic" is a well-described phenomenon of increased pediatric asthma-related health care utilization from August to September each year. The coronavirus disease 2019 (COVID-19) pandemic has brought about significant changes in health care utilization, warranting an investigation into its impact on the September epidemic. Our aim was to identify the impact of COVID-19 in asthma-related health care utilization, specifically in the September epidemic. Our study involved a retrospective analysis of data from a Children's Hospital in New York City. We compared the change in asthma-related health care utilization during August and September 2020 with the average change in utilization during the same period in 2017-2019 and 2021-2022. Stratified analyses based on age and sex were conducted by using chi-square tests to determine variations in health care utilization. During September 2020, there was a marked reduction in emergency department (ED) visits related to asthma, with only a 6% rise from the preceding month. This stands in contrast to the observed increases from 89% to 193% in the other years studied (P< .05 for all). This pattern was seen in both sexes and in children under 13 years old (P< .05). No significant variation was found for those older than 13 years (P> .05). Despite an overall reduction in health care utilization over the first year of the COVID-19 pandemic, the decline in ED visits related to asthma during the September epidemic was significantly more pronounced. These results suggest that there may be remediable risk factors contributing to the September epidemic that can be used to guide future interventions for managing pediatric asthma.

Home-Based Pediatric Hospice and Palliative Care Provider Visits: Effects on Healthcare Utilization

ObjectiveThis hypothesis-generating study sought to assess the impact of home-based hospice and palliative care (HBHPC) provider home visits (HV) on healthcare utilization. Study designRetrospective review of individuals ages 1 month to 21 years receiving an in-person HBHPC provider (MD/DO or APN) home visit through two HBHPC programs in the Midwest from 1/1/2013 through 12/31/2018. Descriptive statistics were calculated for healthcare utilization variables. Paired t-test or Wilcoxon signed-rank test compared the changes in healthcare utilization the year prior to and year after initial provider home visits. ResultsThe cohort included 195 individuals (49% female), with diagnoses comprised of 49% neurologic, 30% congenital chromosomal, 11% oncologic, 7% cardiac, and 3% other. After implementation of HBHPC services, these patients showed reductions in median [IQR] number of ICU days (before HV 12 [4, 37] after HV 0 [0, 8], p<0.001); inpatient admissions (before HV 1 [1, 3] after HV 1 [0, 2], p=0.005); and number of inpatient days (before HV 5 [1, 19] after HV 2 [0, 8], p=0.009). There was an increase in clinically relevant phone calls (CRPC) to the HBHPC team (before HV 1 [0, 4] vs after HV 4 [1, 7], p<0.001) and calls to the HBHPC team before emergency department visits (before HV 0 [0, 0] vs after HV 1 [1, 2], p<0.001). ConclusionHBHPC provider home visits were associated with decreased inpatient admissions, hospital days, and ICU days, and increased CRPC and phone calls prior to emergency department visit. These findings indicate that HBHPC HV may contribute to decreased inpatient utilization and increased utilization of the HBHPC team.

Understanding regional variation in euthanasia using geomedical frameworks: a critical ethical reflection

Considerable geographical variation in the uptake of euthanasia has been reported: some municipalities in the Netherlands have a 25 times higher euthanasia rate than other municipalities. Current ‘geomedical’ frameworks for interpreting practice variation in health care utilization seem inadequately tailored to understand regional variation in morally controversial procedures such as euthanasia. The aim of this conceptual article is threefold: i) to add relevant medical ethical principles to current frameworks; ii) to provide a four-step ethical-geomedical model for the interpretation of geographical differences in the utilization of health care in general and for ethically controversial treatments in specific; iii) to gain better understanding of the existing geographical variation in the incidence of euthanasia by using this framework in our analysis.

Impact of Obesity on Efficacy, Safety, and Expansion Kinetics of Chimeric Antigen Receptor-T (CAR T) Therapy in Patients with Relapsed or Refractory Large B Cell Lymphoma (LBCL)

Introduction: Obesity causes low-grade inflammation and immune escape, where T-cells lose their ability to infiltrate tumors. However, such T-cell exhaustion, characterized by increased expression of inhibitory checkpoint receptors, has led to better responses to immune checkpoint blockade therapy in patients with higher body mass index (BMI) (Wang Nature Medicine 2019). Previous studies have shown no significant association of obesity with safety or efficacy of Chimeric Antigen Receptor T-cell (CAR T) therapy in hematologic malignancies (Faruqi Blood Advances 2022). However, data on the impact of BMI on outcomes in LBCL and CAR T expansion kinetics is limited. We conducted a single-site study to investigate the effect of obesity on efficacy and safety outcomes, healthcare utilization, and CAR T expansion in patients with relapsed/refractory LBCL treated with Axicabtagene Ciloleucel(axi-cel). Methods: 189 patients with relapsed/refractory LBCL who were consecutively treated with axi-cel from 12/2017 to 5/2022 were included in the study. BMI was calculated on the day of apheresis. Patients were then divided into three BMI categories based on CDC guidelines: underweight/normal (≤24.99kg/m 2), overweight (&amp;gt;25kg/m 2 and ≤29.99kg/m 2), and obese (&amp;gt;30kg/m 2). Response was assessed using the Lugano 2014 criteria, and toxicities via the CTCAE version 5.0 and ASTCT grading. CAR T expansion was measured by real-time flow cytometry with anti-idiotype-FMC63 conjugated to Dylight 650. Areas under the curve (AUCs) were calculated for CAR T expansion in the 28 days following infusion using the linear trapezoidal method on raw values, and results were transformed using a base-10 logarithm. Continuous outcomes were compared using a Kruskal-Wallis test, categorical outcomes using Chi-square tests, and time-to-event outcomes using the Kaplan-Meier estimator and the log-rank test. Statistical significance was determined at p &amp;lt; 0.05. All analyses were conducted in R 4.3.0. Results: Out of 189 patients, 46% (n=87) were in the underweight/normal group, 29% (n=56) in the overweight, and 24% (n=46) were obese. Baseline demographics and clinical characteristics between groups were similar except for gender ( p=0.009). Complete response (CR) and overall response (ORR) including partial response (PR) and CR were compared among these cohorts. Neither complete response nor overall response was found to be significantly different amongst the BMI categories for their best response within 12 months of CAR T infusion ( p=0.9886 and 0.802, respectively). Median progression-free survival (PFS) was 14.6 months (IQR 4.93, 44.81) for the underweight/normal weight cohort, 14.89 months (IQR 5.82, 40.07) for the overweight cohort, and 28.37 months (IQR 3.88, NA) for obese; but was not statistically significant ( p=0.8004) [Figure 1]. Median overall survival (OS) was 44.81 months (IQR 18.18, NA) for the underweight/normal weight cohort and 40.07 months (IQR 23.18, NA) for the overweight category. This was not achieved for the obese category, and no statistical difference was found ( p=0.3478). When cytokine release syndrome (CRS) or immune cell-associated neurologic toxicity syndrome (ICANS) events were compared among these groups, there was no significant difference in either overall incidence or when graded as severe (›3), based on BMI category. Similarly, no significant differences were found in healthcare utilization variables, including length of hospital stay, ICU admissions, tocilizumab, anakinra or steroid use. Differences in day 7 and day 14 CAR T expansion were not statistically significant ( p=0.754 and 0.355, respectively). For AUC calculations, there was no statistically significant difference amongst the BMI categories (CD4: p=0.8284, CD8: p=0.8695, CD4 + CD8: p=0.8820) [Figure 2]. Conclusion: Contrary to the effect of obesity on response to therapies like checkpoint inhibitors we demonstrate that BMI does not significantly impact patients treated with CAR T. Weight has been central to chemotherapy dosing for lymphoma, but despite weight-based dosing for axi-cel, correlative studies on CAR T expansion kinetics did not indicate any significant difference. These findings suggest that obesity may not substantially influence the outcomes or dosing considerations of CAR T therapy, supporting the use of CAR T therapy as an effective treatment option for lymphoma patients, regardless of their BMI.

Differences in healthcare service utilization in patients with polypharmacy according to their risk level by adjusted morbidity groups: a population-based cross-sectional study

BackgroundPatients with polypharmacy suffer from complex medical conditions involving a large healthcare burden. This study aimed to describe the characteristics and utilization of primary care (PC) and hospital care (HC) and factors associated in chronic patients with polypharmacy, stratifying by adjusted morbidity groups (AMG) risk level, sex and age, and comparing with non-polypharmacy.MethodsCross-sectional study conducted in a Spanish basic healthcare area. Studied patients were those over 18 years with chronic diseases identified by the AMG tool from Madrid electronic clinical record, which was the data source. Sociodemographic, sociofunctional, clinical and healthcare utilization variables were described and compared by risk level, sex, age and having or not polypharmacy. Factors associated with healthcare utilization in polypharmacy patients were determined by a negative binomial regression model.ResultsIn the area studied, 61.3% patients had chronic diseases, of which 16.9% had polypharmacy vs. 83.1% without polypharmacy. Patients with polypharmacy (vs. non-polypharmacy) mean age was 82.7 (vs. 52.7), 68.9% (vs. 60.7%) were women, and 22.0% (vs. 1.2%) high risk. Their average number of chronic diseases was 4.8 (vs. 2.2), and 95.6% (vs. 56.9%) had multimorbidity. Their mean number of annual healthcare contacts was 30.3 (vs. 10.5), 25.9 (vs. 8.8) with PC and 4.4 (vs. 1.7) with HC. Factors associated with a greater PC utilization in patients with polypharmacy were elevated complexity, high risk level and dysrhythmia. Variables associated with a higher HC utilization were also increased complexity and high risk, in addition to male sex, being in palliative care, having a primary caregiver, suffering from neoplasia (specifically lymphoma or leukaemia) and arthritis, whereas older age and immobilization were negatively associated.ConclusionsPolypharmacy population compared to non-polypharmacy was characterized by a more advanced age, predominance of women, high-risk, complexity, numerous comorbidities, dependency and remarkable healthcare utilization. These findings could help healthcare policy makers to optimize the distribution of resources and professionals within PC and HC systems, aiming for the improvement of polypharmacy management and rational use of medicines while reducing costs attributed to healthcare utilization by these patients.

Abstract 17960: Lipoprotein(a) Testing Frequency and Predictors in a Multiethnic Health System

Introduction: Lipoprotein(a) [Lp(a)] is a causal risk factor for atherosclerotic cardiovascular disease (ASCVD). Contemporary practice guidelines recommend incorporating Lp(a) testing into routine clinical practice. We examined the prevalence of Lp(a) testing and characteristics that predicted which individuals underwent testing. Methods: This was a retrospective cohort study. Eligible individuals were patients of a large Northern Californian health system with 2 or more primary care visits between the years 2010 to 2021. Incident density matching was used to select controls matched with a case if a visit date was within ±6 months of the index date of initial Lp(a) test. Prevalence of Lp(a) testing was determined by year. Conditional logistic regression was used to assess the relationship between Lp(a) testing and sociodemographic, clinical, and healthcare utilization variables. Results: Of the 1,484,410 individuals in the cohort, 12,506 (0.84%) had their Lp(a) level checked. The median Lp(a) level was 35 mg/dL and 5,598 (37.78%) of those who underwent testing had a Lp(a) level &gt; 50mg/dL. Among those with Lp(a) testing, there was a higher proportion of Asian individuals and a lower proportion of non-Hispanic Black and Hispanic individuals. In adjusted-analyses, those with a history of ASCVD had over twice the odds of undergoing testing (OR = 2.11 (95% CI = 1.98, 2.26)). South Asian individuals were three times more likely to have undergone Lp(a) testing as compared to non-Hispanic White individuals (OR = 3.25, (95% CI = 3.06, 3.46)), while those identified as non-Hispanic Black and Hispanic were significantly less likely to have undergone Lp(a) testing (OR = 0.7, (95% CI = 0.62,0.8) and 0.62 (95% CI = 0.57, 0.67), respectively). Conclusions: This study examined the real-world clinical testing patterns of Lp(a) over a decade within a large, diverse health system. Expanding access to Lp(a) testing may help reduce disparities within ASCVD risk assessment and treatment.

Suburban Blues: Urbanicity, healthcare service mix and spatial patterns of admissions for depression

Abstract Objectives Medical practice variation is a key target for policy makers aiming to improve the efficiency of healthcare delivery. Studies point to strong regional variation in healthcare utilization in Austria which seems to be a by-product of institutional rules and healthcare service mix rather than variance in epidemiology. Methods We use a set of routine healthcare data on hospital admissions for depressive episodes (ICD-10 F320-F329) of adult Austrian patients from 2009 to 2014 on the municipality level to explore spatial patterns of supply-induced demand by nearby hospitals (“Roemer's law”) in hospital admission rates. The dataset covers 99,844 hospital episodes of 69,555 patients in 2,114 municipalities. We estimate a random effects spatial autoregressive model to regress log hospital admission rates on hospital supply and urbanicity as proxies for the municipalities’ healthcare service mix alongside a set of relevant socio-economic/-demographic characteristics. Preliminary results Hospital admission rates per 1,000 inhabitants in the population for depressive episodes vary strongly between municipalities (min=0, max=37.7, mean=1.9, SD = 1.8). We find higher admission rates in the population of suburban municipalities (0.10, 95%-CI: 0.58 - 0.13) and municipalities with hospitals (0.07, 95%-CI: 0.01 - 0.1), regardless of hospital type and size. The spatial structure suggests positive spill-over effects on the hospital admission rates between neighbouring municipalities. Conclusions Our results suggest that high admission rates are a seemingly suburban phenomenon and that healthcare service mix is a driving factor for admission rates. Though it is not clear whether the regional variation in medical practice reflects under- or oversupply of hospital care, promoting timely access to primary care and early-stage treatments may reduce the burden of avoidable hospitalizations for patients and public budgets, and close a gap of unmet need for care of vulnerable populations. Key messages • Hospital admission rates for depression of adults per 1,000 inhabitants in the population for depressive episodes vary strongly between municipalities. • Hospital supply and urbanicity are major driving factors of the spatial patterns in regional varation of hospital admissions.

Urban/rural Patterns of Healthcare Utilization among People with Rheumatoid Arthritis and Osteoarthritis in a large U.S. patient registry.

Rural residence has been associated with health disparities in rheumatic diseases and other chronic conditions in the United States. This study aimed to determine if a relationship exists between geographic residence and healthcare utilization outcomes for people with rheumatoid arthritis (RA) and osteoarthritis (OA) in a U.S.-wide rheumatic disease registry. Participants were in FORWARD, The National Databank for Rheumatic Diseases, a U.S.-wide rheumatic disease longitudinal cohort completing questionnaires between 1999 and 2019. Healthcare utilization variables (i.e., medical visits and diagnostic tests) from six-month questionnaires were analyzed by geographic categories (small rural/isolated, large rural, and urban). Double selection LASSO with Poisson regression was used to assess the best model when examining the association between healthcare utilization variables and geographic residence. Among 37,802 participants with RA, urban residents were more likely than small rural residents to utilize in-person healthcare by most measures including physician visits and diagnostic tests. Urban residents reported more rheumatologist visits [IRR=1.22 (95% CI 1.18-1.27)] but fewer primary care visits [IRR=0.90 (95% CI 0.85-0.94)]. Among 8,248 participants with OA, urban residents were also more likely than rural residents to report healthcare utilization by most measures. Individuals residing in urban areas were more likely than those in rural areas to report in-person healthcare utilization. Specifically, urban residents with RA were more likely to report rheumatologist visits, but less likely to report primary care visits. Less disparity existed in OA healthcare utilization, though an urban-rural disparity still existed by most measures.

Regional Variation in Health Care Utilization Among Adults With Inadequate Cardiovascular Health in the USA.

Background Prior evidence of region-level differences in health outcomes and specialized healthcare services in the US poses questions of whether there are differences in utilization of healthcare that may account for regional differences in healthcare outcomes. This study aimed to examine regional differences in healthcare utilization for individuals with poor cardiovascular health (CVH) compared to those with ideal/intermediate CVH. Methods In this cross-sectional analytical study, two 3-year periods (2008-2010 and 2018-2020) were pooled and analyzed using multivariate Poisson's regression of region on counts of healthcare utilization, while controlling for relevant covariates. The interaction of the non-southern regions with recent years was to reveal how the regional dispersion in healthcare usage was changing over time for the non-southern regions compared to the south. Results The results showed significant regional variation in healthcare usage for individuals with poor CVH, with lower health utilization rates observed primarily in southern states, consistent with higher rates of coronary heart disease in those regions. The impact of a unit improvement on CVH score was to reduce the level of healthcare utilization by 15.7% ([95% CI, 15 - 17%; p < 0.001]) for individuals with poor CVH and 19.1% ([95% CI, 19 - 20%; p < 0.001]) for the intermediate and ideal subgroups, with the Northeast exhibiting the highest level of healthcare usage. Conclusion Our results suggest that there is a need for public health interventions to reduce regional disparities in access to healthcare for the people at greatest risk of cardiovascular events by considering individual factors as well as the broader regional and policy contexts where these people live.

Feasibility of the Implementation of LESS-CHRON in Clinical Practice: A Pilot Intervention Study in Older Patients With Multimorbidity.

Potentially inappropriate medication refers to the prescription of drugs whose risks outweigh the benefits. There are different pharmacotherapeutic optimization strategies to detect and avoid potentially inappropriate medications (PIMs), namely deprescription. The List of Evidence-Based Deprescribing for Chronic Patients (LESS-CHRON) criteria were designed as a tool to systematize the deprescribing process. LESS-CHRON has established itself as one of the most suitable to be applied in older (≥65 years) multimorbid patients. However, it has not been applied to these patients, to measure the impact on their treatment. For this reason, a pilot study was conducted to analyze the feasibility of implementing this tool in a care pathway. A pre-post quasi-experimental study was conducted. Older outpatients with multimorbidity from the Internal Medicine Unit of a benchmark Hospital were included. The main variable was feasibility in clinical practice, understood as the likelihood that the deprescribing intervention recommended by the pharmacist would be applied to the patient. Success rate, therapeutic, and anticholinergic burden, and other variables related to health care utilization were analyzed. A total of 95 deprescribing reports were prepared. Forty-three were evaluated by the physician who assessed the recommendations made by pharmacists. This translates into an implementation feasibility of 45.3%. The application of LESS-CHRON identified 92 PIMs. The acceptance rate was 76.7% and after 3 months 82.7% of the stopped drugs remained deprescribed. A reduction in anticholinergic burden and enhanced adherence was achieved. However, no improvement was found in clinical or health care utilization variables. The implementation of the tool in a care pathway is feasible. The intervention has achieved great acceptance and deprescribing has been successful in a not insignificant percentage. Future studies with a larger sample size are necessary to obtain more robust results in clinical and health care utilization variables.

Impact of COVID-19 on Dental Care Utilization and Oral Health Conditions in the United States

We aim to understand the impact of the COVID-19 on health care utilization and oral health conditions of patients at federally qualified health centers (FQHCs), where patients are disproportionately low income, publicly insured, or uninsured. Using deidentified electronic health records of patients at FQHCs in the United States from January 2019 through December 2020 (n = 431,509), variations in health care utilization since the COVID-19 outbreak were observed by procedure types and patient characteristics. Changes in dental utilization and oral health conditions were characterized using mixed-effect negative binomial and logistic regression models. Dental utilization decreased more drastically than medical utilization during shelter-in-place periods in 2020 and rebounded more slowly after the reopening. Greater demands for oral surgery and teledentistry and less demands for preventive services were observed in 2020. As compared to 2019, patients experienced more psychological stress-related dental conditions with odds ratios of 1.52 (95% confidence interval [CI], 1.31-1.76) for uninsured, 1.48 (95% CI, 1.07-2.02) for Medicaid enrollees, and 2.38 (95% CI, 1.68-3.40) for private insurance beneficiaries. As a result of COVID-19, patients received more invasive dental procedures due to delayed treatment and experienced a higher risk of psychological stress-related dental conditions. Continued support for statewide policies to expand access to oral health care and oral health promotion strategies for the vulnerable populations would be encouraged. Our study describes the impact of COVID-19 on dental care use and oral health conditions at Federally Qualified Health Centers, targeted to provide care for some of the most vulnerable populations in the United States. The results of this retrospective cohort study can be used by clinicians and policymakers on understanding the clinical needs of the vulnerable populations after the pandemic. It highlights the need for continued support to expand access to oral health care and oral health promotion to these populations.

Differences in chronic pain care receipt among veterans from differing racialized groups and the impact of rural versus urban residence.

The current study aimed to identify differences in Veterans Affairs (VA) chronic pain care for Black, Asian, and Hispanic Americans, compared to non-Hispanic White Americans, and examine the intersection of race and rurality. Using national administrative data, all veterans who presented to the VA for chronic pain in 2018 were included. Demographic and comorbidity variables were built from 2018 data and health care utilization variables from 2019 data. Multivariate log-binomial regression models examined differences between racialized groups, and interactions with rural/urban residence, for each health care utilization variable. The full cohort included 2,135,216 veterans with chronic pain. There were no differences between racialized groups in pain-related primary care visits. Black Americans were less likely to receive pain clinic visits (aRR = 0.87, CI: 0.86-0.88). Rurality further decreased the likelihood of Black Americans visiting a pain clinic. Black, Hispanic, and Asian Americans were more likely to receive pain-related physical therapy visits relative to White Americans. Black and Hispanic Americans were more likely to present to emergency/urgent care for chronic pain. While there were no differences in pain-related primary care visits, the decreased likelihood of pain clinic visits and increased use of emergency department/urgent care among Black Americans could indicate inadequate management of chronic pain. Tailored strategies are needed to provide equitable care that meets the needs of patients from racialized groups while accounting for systemic and cultural factors.

Geographical Variations in Healthcare Utilization among Pregnant Women in Nigeria: A Case of Anti-Tetanus Injection Utilization

Geographical Variations in Healthcare Utilization among Pregnant Women in Nigeria: A Case of Anti-Tetanus Injection Utilization

Advancing an Algorithm for the Identification of Patients with High Data-Continuity in Electronic Health Records.

Identifying high data-continuity patients in an electronic health record (EHR) system may facilitate selecting cohorts with a lower degree of variable misclassification and promote study validity. We updated a previously developed algorithm for identifying patients with high EHR data-completeness by adding demographic and health utilization factors to improve adaptability to networks serving patients of diverse backgrounds. We also expanded the algorithm to accommodate data in the ICD-10 era. We used Medicare claims linked with EHR data to identify individuals aged ≥65 years. EHR-continuity was defined as the proportion of encounters captured in EHR data relative to claims. We compared the model with additional demographic factors and their interaction terms with other predictors with the original algorithm and assessed the performance by area under the ROC curve (AUC) and net reclassification index (NRI). The study cohort consisted of 264,099 subjects. The updated prediction model had an AUC of 0.93 in the validation set. Compared to the previous model, the new model had an NRI of 37.4% (p<0.001) for EHR-continuity classification. Interaction terms between demographic variables and other predictors did not improve the performance. Patients within the top 20% of predicted EHR-continuity had four times less misclassification of key variables compared to the remaining population. Adding demographic and healthcare utilization variables significantly improved the model performance. Patients with high predicted EHR-continuity had less misclassification of study variables compared to the remaining population in both ICD-9 and 10 eras.

Predicting incident heart failure among patients with type 2 diabetes mellitus: The DM-CURE risk score.

Early identification and prediction of incident heart failure (HF) is important because of severe morbidity and mortality. This study aimed to predict onset of HF among patients with diabetes. A time-varying Cox model was derived from ACCORD clinical trial to predict the risk of incident HF, defined by hospitalization for HF (HHF). External validation was performed on patient-level data from the Harmony Outcome trial and Chronic Renal Insufficiency Cohort (CRIC) study. The model was transformed into an integer-based scoring algorithm for 10-year risk evaluation. A stepwise algorithm identified and selected predictors from demographic characteristics, physical examination, laboratory results, medical history, medication and health care utilization, to develop a risk prediction model. The main outcome was incident HF, defined by HHF. The C statistic and Brier score were used to assess model performance. In total, 9649 patients with diabetes free of HF were used, with median follow-up of 4 years and 299 incident hospitalization of HF events. The model identified several predictors for the 10-year HF incidence risk score 'DM-CURE': socio-Demographic [education, age at type 2 diabetes (T2DM) diagnosis], Metabolic (glycated haemoglobin, systolic blood pressure, body mass index, high-density lipoproteins), diabetes-related Complications (myocardial infarction, revascularization, cardiovascular medications, neuropathy, hypertension duration, albuminuria, urine albumin-to-creatinine ratio, End Stage Kidney Disease), and health care Utilization (all-cause hospitalization, emergency room visits) for Risk Evaluation. Among them, the strongest impact factors for future HF were age at T2DM diagnosis, health care utilization and cardiovascular disease-related variables. The model showed good discrimination (C statistic: 0.838, 95% CI: 0.821-0.855) and calibration (Brier score: 0.006, 95% CI: 0.006-0.007) in the ACCORD data and good performance in the validation data (Harmony: C statistic: 0.881, 95% CI: 0.863-0.899; CRIC: C statistic: 0.813, 95% CI: 0.794-0.833). The 10-year risk of incident HF increased in a graded fashion, from ≤1% in quintile 1 (score ≤14), 1%-5% in quintile 2 (score 15-23), 5%-10% in quintile 3 (score 24-27), 10%-20% in quintile 4 (score 28-33) and ≥20% in quintile 5 (score >33). The DM-CURE model and score were useful for population risk stratification of incident HHF among patients with T2DM and can be easily applied in clinical practice.

Diabetes Screening and Monitoring Among Older Mexican-Origin Populations in the U.S.

The purpose of the study is to examine diabetes screening and monitoring among Latino individuals as compared with non-Latino White individuals and to better understand how we can use neighborhood data to address diabetes care inequities. This is a retrospective observational study linked with neighborhood-level Latino subgroup data obtained from the American Community Survey. We used generalized estimating equation negative binomial and logistic regression models adjusted for patient-level covariates to compare annual rates of glycated hemoglobin (HbA1c) monitoring for those with diabetes and odds of HbA1c screening for those without diabetes by ethnicity and among Latinos living in neighborhoods with low (0.0-22.0%), medium (22.0-55.7%), and high (55.7-98.0%) population percent of Mexican origin. Latino individuals with diabetes had 18% higher rates of HbA1c testing than non-Latino White individuals with diabetes (adjusted rate ratio [aRR] 1.18 [95% CI 1.07-1.29]), and Latinos without diabetes had 25% higher odds of screening (adjusted odds ratio 1.25 [95% CI 1.15-1.36]) than non-Latino White individuals without diabetes. In the analyses in which neighborhood-level percent Mexican population was the main independent variable, all Latinos without diabetes had higher odds of HbA1c screening compared with non-Latino White individuals, yet only those living in low percent Mexican-origin neighborhoods had increased monitoring rates (aRR 1.31 [95% CI 1.15-1.49]). These findings reveal novel variation in health care utilization according to Latino subgroup neighborhood characteristics and could inform the delivery of diabetes care for a growing and increasingly diverse Latino patient population. Clinicians and researchers whose work focuses on diabetes care should take steps to improve equity in diabetes and prevent inequity in treatment.

Impact of “Defensive Medicine” on the Costs of Diabetes and Associated Conditions

Geographic variation in health care spending is typically attributed to differences in patient health status and provider practice patterns. While medicolegal considerations (i.e., "defensive medicine") anecdotally impact health care spending, this phenomenon is difficult to measure. The purpose of this study was to explore the association between the medicolegal environment and Medicare costs for diabetes and associated conditions of interest to vascular surgeons. Specifically, we hypothesized that an adverse medicolegal environment is associated with higher per capita Medicare costs for diabetic patients. Medicare data including the most recent (2018) Medicare Geographic Variation Public Use Files and Chronic Conditions Data Files were linked to National Practitioner Data Bank files from the preceding 5years (2013-2017), in addition to the US census data and American Medical Association workforce statistics. The state-level medicolegal environment was characterized by K-means clustering across a panel of metrics related to malpractice payment magnitude and prevalence. Per capita Medicare spending for diabetes was compared across 5 distinct medicolegal environments. Costs were standardized and risk-adjusted to account for known geographic variation in health care costs and patient population. Analysis of variance was applied to unadjusted data, followed by multivariate regression modeling. Readmission rates, per capita imaging studies, per capita tests, per capita procedures, and lower extremity amputation rates were compared between the least litigious quintile from the K-means clustering and the 2 most litigious quintiles. The median unadjusted Medicare per capita expenditure on diabetic patients was $15,963 ($14,885-$17,673), ranging from $13,762 (Iowa) to $21,865 (D.C.). A 1.6-fold variation persisted after payment standardization. Cluster analysis based on malpractice-related variables yields 5 distinct medicolegal environments, based on litigation frequency and malpractice payment amounts. Per capita spending on diabetes varied, ranging from $15,799 in states with low payments and infrequent litigation to $18,838 in states with the most adverse medicolegal environment (P<0.05). After cost standardization and risk adjustment with multiple linear regression, malpractice claim prevalence (per 100 physicians) remained an independent predictor of states with the highest diabetes mellitus spending (P=0.022). Moreover, diabetic patients in states with adverse medicolegal environments had more procedures, imaging studies, and readmissions (P<0.05 for all) but did not have significant differences in amputation rates compared to less litigious states. An adverse medicolegal environment is independently associated with higher health care costs but does not result in improved outcome (i.e. amputation rate) for diabetic Medicare beneficiaries. Across states, a 1% increase in lawsuits/100 physicians was associated with a >10% increase in risk-adjusted standardized per capita costs. These findings demonstrate the potential contribution of "defensive medicine" to variation in health care utilization and spending in a population of interest to vascular surgeons.

Variation in the utilization of medical devices across Germany, Italy, and the Netherlands: A multilevel approach.

Variation in healthcare utilization has been discussed extensively, with many studies showing that variation exists, but fewer studies investigating the underlying factors. In our study, we used a logistic multilevel-model at the patient, hospital, and regional levels to investigate (i) the levels to which variation could be attributed and (ii) the hospital and regional factors associated with treatment decisions. To do so, we used hospital discharge records for the years 2012-2016 in Germany and Italy and for 2014-2016 in the Netherlands combined with hospital and regional characteristics in nine case studies. We used a theoretical framework to categorize these case studies into effective, preference-sensitive, and supply-sensitive care. Our results suggest that most variation in the treatment decision can be attributed to the hospital level (e.g., case volume), whereas only a minor part is explained by regional characteristics. Italy had the highest share attributable to the regional level, whereas the Netherlands had the lowest. We observed less variation for procedures in the effective-care category compared to the preference- and supply-sensitive categories. Although our results were heterogeneous, we identified patterns in line with the theoretical framework for treatment categories, underlining the need to address variation differently depending on the category in question.

Association of household net worth with healthcare utilization in 90-day window after radical cystectomy using real-world data.

444 Background: Radical cystectomy (RC) remains the gold standard for muscle invasive bladder cancer yet confers significant healthcare costs. Prior work on the impact of costs commonly relied on comparisons by insurance status (yes/no) and income, a partial proxy for net worth, ie one’s net economic standing or value of financial assets minus liabilities. Few have examined the relationship between net worth and encounter type. Methods: We identified commercially insured Black, White, Asian, and Hispanic patients who underwent radical cystectomy between January 1, 2007 to April 10, 2021 in The OptumLabs Data Warehouse, a longitudinal, real-world data asset with de-identified administrative claims and electronic health record data. Demographics and costs included household net worth, health plan costs, out of pocket costs, and total healthcare costs accrued from index admission to 90 days after. Multivariable logistic regression models were generated for each encounter type [acute inpatient, emergency department (ED), outpatient, and office visit]. Odds ratios (OR) are presented with 95% confidence intervals (CI) and p-values. A p value of &lt;0.05 was deemed statistically significant. Results: The study cohort was comprised of 141,903 patients (2.4% were Black, 11.4% Hispanic, and 8.1% Asian). Household net worth categories were near evenly distributed amongst the study cohort. Acute inpatient encounters harbored the greatest health plan (mean $24,642.80, SD $57,218.41) and out of pocket costs (mean $1,428.24, SD $2108). Office visits conferred the lowest health plan costs (mean $1,126.78, SD $4,119.12) while ED visits had the lowest out of pocket costs (mean $181.88; SD $399.65). Black patients harbored increased odds of an acute inpatient encounter (HR 1.22, 95% CI 1.16-1.29) and ED encounter (HR 1.20, 95% CI 1.14-1.27) while Asian (HR 0.76, 95% CI 0.69-0.85, p&lt;0.001) and Hispanic (HR 0.74, 95% CI 0.69-0.78, p&lt;0.001) patients had lower odds of an outpatient encounter. Increasing household net worth was associated with decreasing odds of acute inpatient or ED encounters, and greater odds of an office visit. Conclusions: Our study is the largest cohort of commercially insured patients to examine variations in healthcare utilization by net worth in the 90 days after radical cystectomy. Those with lower net worth harbored greater risk of an acute inpatient encounter (and higher medical costs) while those with the greater net worth had greater odds of office visits (and lower costs). Greater financial flexibility (i.e., net worth) continues to confer differences in healthcare utilization (and lower healthcare costs), even within a commercially insured population. Household net worth provides a more comprehensive assessment of patients’ financial flexibility than income alone and functions as proxy for healthcare access and privilege that could not be directly measured.