Medical Care最新文献

Objectives: To provide population utility norms from the Health Utilities Index Mark 3 (HUI3) for the province of Quebec, Canada.

Methods: This study used data from the Care Trajectories Enriched Data (TorSaDE) cohort, which combines data from the Canadian Community Health Survey (CCHS) and the Quebec Provincial Insurance Board [Régie de l'assurance maladie du Quebec (RAMQ)]. The CCHS is a multiround health-related survey conducted by Statistics Canada since 2007. For each round spanning over 2 years, respondents were randomly selected and completed an online questionnaire. Quebec data for the HUI3 were available in the CCHS for rounds 2007, 2009, and 2013. The RAMQ database is an administrative database that contains information on health care services use and medical diagnostics. HUI3 scores were stratified by sociodemographic variables, as well as by self-reported health problems in the CCHS and by medical diagnostics from the RAMQ. Medical diagnostics were retrieved for the CCHS completion year and the year before and identifiable with the ICD-9 code in the RAMQ database.

Results: A total of 55,656 individuals were considered in this analysis. The mean (95% CI) and the median interquartile range of HUI3 were respectively 0.919 (0.918-0.919) and 0.973 (0.905-1) for the entire population. Individuals with lower scores were females, those aged 75 and over, divorced or widowed, unemployed during the last 12 months, less educated, or with a lower annual household income. Individuals born abroad and with normal weight of body mass index had higher utility scores. HUI3 score decreased with the number of diagnosed diseases from 0.946 (0.946-0946) for individuals without diagnosed disease to 0.682 (0.678-0.686) for individuals diagnosed with up to 18 diseases. Regardless of the number of diagnosed diseases in the RAMQ database, individuals who self-reported suffering from a single health problem presented a significantly lower HUI3 ranging from 0.944 (0.943-0.944) for Asthma to 0.789 (0.782-0.796) for Alzheimer compared with 0.956 (0.956-0.957) for individuals with no reported health problems. The same pattern was observed when considering individuals regardless of the diagnosed and self-reported diseases.

Conclusion: Utility score norms for HUI3 were produced in the general population of Quebec. Significant differences among various health problems were identified and norms can be used to compare populations in studies that do not have a control group.

Introduction: Individual-level social risk factors have a significant impact on health. Social risks can be documented in the electronic health record using ICD-10 diagnosis codes (the "Z codes"). This study aims to summarize the literature on using Z codes to document social risks.

Methods: A scoping review was conducted using the PubMed, Medline, CINAHL, and Web of Science databases for papers published before June 2024. Studies were included if they were published in English in peer-reviewed journals and reported a Z code utilization rate with data from the United States.

Results: Thirty-two articles were included in the review. In studies based on patient-level data, patient counts ranged from 558 patients to 204 million, and the Z code utilization rate ranged from 0.4% to 17.6%, with a median of 1.2%. In studies that examined encounter-level data, sample sizes ranged from 19,000 to 2.1 billion encounters, and the Z code utilization rate ranged from 0.1% to 3.7%, with a median of 1.4%. The most reported Z codes were Z59 (housing and economic circumstances), Z63 (primary support group), and Z62 (upbringing). Patients with Z codes were more likely to be younger, male, non-White, seeking care in an urban teaching facility, and have higher health care costs and utilizations.

Discussion: The use of Z codes to document social risks is low. However, the research interest in Z codes is growing, and a better understanding of Z code use is beneficial for developing strategies to increase social risk documentation, with the goal of improving health outcomes.

Background: Community-acquired acute kidney injury (CA-AKI) occurs outside of the hospital and is the most common form of AKI. CA-AKI is not well understood, which hinders efforts to prevent, identify, and manage CA-AKI.

Objective: Examine 30-day outcomes following CA-AKI using national administrative and lab data from the Veterans Health Administration (VA).

Research design: Retrospective cohort study.

Subjects: VA primary care patients with recorded outpatient serum creatinine (SCr) with observed CA-AKI (cases) and a standardized mortality ratio propensity-weighted 5% comparator sample without observed CA-AKI in 2013-2017.

Measures: CA-AKI was defined as a ≥1.5-fold relative increase in outpatient SCr or inpatient SCr (≤24 h from admission) from a reference outpatient SCr ≤12 months prior. Outcomes were 30-day mortality and hospitalization and were assessed in separate weighted Cox regression models.

Results: Among 220,777 CA-AKI events and 492,539 comparators without observed CA-AKI, CA-AKI was associated with a higher risk of 30-day all-cause mortality [hazard ratio (HR)=4.17, 95% CI: 3.74, 4.63] and hospitalization (HR=1.82, 95% CI: 1.74, 1.90) versus comparator. Risks increased with severity (mortality HR=3.02, 7.67, and 12.22 for AKI stages 1-3, respectively). Outpatient CA-AKI was associated with a high risk of mortality (HR=2.04, 95% CI: 1.83, 2.28) and even higher for inpatient CA-AKI, present [≤24 h from admission (HR=11.32, 95% CI: 10.16, 12.61)].

Conclusions: In a national cohort of Veterans, CA-AKI was associated with a 2-fold increased risk of hospitalization and a 3-11-fold risk of mortality. Improving identification and management is critical to mitigate adverse outcomes of CA-AKI.

Objectives: This study aims to evaluate rates of public insurance participation among the different psychotherapist professions as well as among psychiatrists. In addition, it seeks to assess individual and contextual factors that are associated with public insurance participation.

Background: Historically, Medicaid- and Medicare-insured individuals have faced unique barriers to access to mental health professionals. Because prior literature has focused on psychiatrists, little is currently known of public insurance participation rates among psychotherapists-even though they constitute the bulk of the mental health workforce.

Methods: A retrospective analysis of Medicaid and Medicare participation among a census of all Georgia psychologists, licensed clinical social workers, licensed professional counselors, marriage and family therapists, as well as psychiatrists, using their complete licensing rosters as of November 2023 (N = 21,260).

Results: Findings show that 82.7% of psychotherapists did not accept any public insurance. This rate was 58.8% among psychiatrists. Among Georgia-licensed clinicians located outside of the state, likely to practice through telehealth, insurance acceptance was substantially lower than their in-state peers', suggesting that telehealth may have limited reach among publicly-insured patients. Psychotherapists' different professions, as well as factors such as urbanicity, hospital setting, practice size, and individual tenure length, were strongly associated with the likelihood of insurance participation.

Conclusion: Psychotherapists' low rates of participation in public insurance programs and meaningful variation between professions underscore that policies to better Medicaid and Medicare beneficiaries' access to mental health treatment must consider psychotherapists' unique practice patterns and implement interventions informed by them.

Objective: To test the impact of virtual care usage on quality metrics used for performance measurement.

Background: Virtual care improves access to primary care; however, the quality of care must not be adversely impacted by its use.

Methods: This is a mixed-design etiologic study using data from patients receiving primary care in a large, regional health system from January 2020 through December 2021. Eligible patients had at least one primary care contact. Eligible physicians had 10 or more patient contacts. The quartile of virtual visits per physician per month is calculated as the percentage of total visits conducted by phone or video (Q1 is the lowest). Six metrics used for value-based reimbursement were chosen for modeling with generalized linear mixed models.

Results: The data included 200,090 patients of 683 physicians in 42 clinics over 24 months. Virtual care usage peaked in April 2020 at 78% and then stabilized at 18%. The blood pressure metric was met in 66% (95% CI: 63%-69%) of physician months in Q1 and 65% (95% CI: 63%-68%) in Q4 (P = 0.003). The hemoglobin A1c metric was met in 73% (95% CI: 70%-76%) of physician months in Q1 and 72% (95% CI: 69%-75%) in Q4, not a significant difference. Breast cancer screening completion and colon cancer screening completion did not differ across virtual care quartiles. Medicare annual wellness visits were completed in 55% (95% CI: 50%-60%) of Q1 physician months and 54% in each of Q2, Q3, and Q4 (P < 0.0001).

Conclusions: Some quality metrics were modestly impacted by high virtual primary care usage; the absolute differences in rates were small. This may provide reassurance to physicians and their health systems that telemedicine use may not adversely impact quality metrics.

Objectives: We examined how individual-level turnover among Veterans Health Administration primary care providers (PCPs) from fiscal years 2017 to 2021 was associated with health care system-level burnout and turnover intent.

Background: Burnout among PCPs has been well documented in recent studies, but less is known about the potential relationship between burnout and turnover.

Methods: We identified a national cohort of 6444 PCPs (physicians, nurse practitioners, and physician assistants) in 129 Veterans Health Administration health care systems in the first quarter of fiscal year 2017 and tracked their employment status for 20 quarters. PCP employment data on turnover were linked to annual health care system-level employee survey data on burnout, turnover intent, and other covariates. We performed logistic regression to estimate the impact of health care system-level burnout and turnover intent on individual PCP turnover, controlling for individual and health care system-level covariates and adjusting for clustering at the health care system level.

Results: Median health care system-level burnout ranged from 42.5% to 52.0% annually, and turnover among PCPs ranged from 6.3% to 8.4% (mean = 7.0%; SD = 0.9%). Separation from employment was higher among employees at health care systems with the highest burnout (odds ratio =1.14; 95% CI = 1.01-1.29) and turnover intent (OR = 1.18; 95% CI = 1.03-1.35).

Conclusions: PCPs in health care systems with high burnout are more likely to separate from employment. Policymakers and administrators seeking to improve retention should consider system-level interventions to address organizational drivers of burnout.

Study design: Retrospective cohort study.

Objective: To characterize variation in dexamethasone and remdesivir use over time among hospitals.

Background: Little is known about hospital-level variation in COVID-19 drug treatments in a large and diverse network in the United States.

Methods: We selected individuals hospitalized with COVID-19 across 163 hospitals between February 23, 2020 and October 31, 2021 from using the HCA CHARGE, an electronic health record repository from a network of community health care facilities in the United States. We quantified receipt of dexamethasone, remdesivir, and combined use of dexamethasone and remdesivir during the hospital stay. We used 2-level logistic regression models to determine the intraclass correlation coefficient (ICC) at the hospital level, adjusting for patient and hospital characteristics. The ICC shows the proportion of total variation in drug use accounted for by hospitals.

Results: Among 161,667 individuals hospitalized with COVID-19, 73.0% were treated with dexamethasone, 49.1% with remdesivir, and 45.0% with both dexamethasone and remdesivir. The proportion of variation in dexamethasone use was 12.7% (adjusted ICC: 0.127), 8.5% for remdesivir, and 11.3% for combined drug use, indicating low interhospital variation. In the fully adjusted models, between-facility variation in dexamethasone use declined from 34.1% in February-March 2020 to 11.3% in January-March 2021 and then increased to 17.3% in July-October 2021. The variation in remdesivir use remained relatively stable during the study period.

Conclusions: During the first 2 years of the pandemic, there was relatively consistent use of dexamethasone and remdesivir across the hospitals examined. Consistent adoption and implementation of treatment guidelines across the hospitals examined may have led to a decrease in variation in drug usage over time.

Background: Plan of Care of Visits (POCV), including the patient, nurse, and hospital provider were implemented across an integrated health system to improve provider-patient communication during hospitalization and patient outcomes.

Objectives: To assess POCV adoption after implementation, patient characteristics assosites were classified as teachsites were classified as teachsites were classified as teachsites were classified as ciated with POCV completion, and association of POCV with 30-day readmissions.

Methods: This retrospective cohort study utilized electronic medical record (EMR) data of 237,430 adult patients discharged to home from 11 hospitals from January 2020 to December 2022. POCV completion was a discrete EMR variable. POCV adoption was estimated monthly by hospital as proportion of patients with at least 1 POCV during hospitalization, with variation among hospitals measured using the Variance Partition Coefficient (VPC). Multivariable logistic regressions assessed factors associated with POCV completion and POCV association with 30-day readmission.

Results: POCV adoption increased from 69% to 94% (2020-2022) and varied by 50% across hospitals (VPC 0.50, 95% CI: 0.29-0.70). Odds of a discharge-day POCV were lower among older patients (≥65 vs. 18-34 y, OR 0.81, CI: 0.79-0.83), and higher among female (OR 1.06; CI: 1.04-1.07), Asian (vs. White, OR 1.13; CI: 1.06-1.21), Hispanic (OR 1.09; CI: 1.05-1.13), and surgical patients (vs. medical, OR 1.33; CI: 1.30-1.35). Patients completing discharge-day POCV had lower 30-day readmission odds (2022 OR 0.76, CI: 0.73-0.79). Patients with POCV on ≥75% of hospital days had similar readmission odds trends.

Conclusions: POCV implementation was successful, and POCV completion was associated with fewer 30-day readmissions. Future work should focus on increasing POCV adoption while reducing hospital variation.

Background: Home-based primary care (HBPC) is an important care delivery model for high-need older adults. Currently, target patient populations vary across HBPC programs, hindering expansion and large-scale evaluation.

Objectives: Develop and validate criteria that identify appropriate HBPC target populations.

Research design: A modified Delphi process was used to achieve expert consensus on criteria for identifying HBPC target populations. All criteria were defined and validated using linked data from Medicare claims and the National Health and Aging Trends Study (NHATS) (cohort n=21,727). Construct validation involved assessing demographics and health outcomes/expenditures for selected criteria.

Subjects: Delphi panelists (n=29) represented diverse professional perspectives. Criteria were validated on community-dwelling Medicare beneficiaries (age ≥70) enrolled in NHATS.

Measures: Criteria were selected via Delphi questionnaires. For construct validation, sociodemographic characteristics of Medicare beneficiaries were self-reported in NHATS, and annual health care expenditures and mortality were obtained via linked Medicare claims.

Results: Panelists proposed an algorithm of criteria for HBPC target populations that included indicators for serious illness, functional impairment, and social isolation. The algorithm's Delphi-selected criteria applied to 16.8% of Medicare beneficiaries. These HBPC target populations had higher annual health care costs [Med (IQR): $10,851 (3316, 31,556) vs. $2830 (913, 9574)] and higher 12-month mortality [15% (95% CI: 14, 17) vs. 5% (95% CI: 4, 5)] compared with the total validation cohort.

Conclusions: We developed and validated an algorithm to define target populations for HBPC, which suggests a need for increased HBPC availability. By enabling objective identification of unmet demands for HBPC access or resources, this algorithm can foster robust evaluation and equitable expansion of HBPC.