Health Services Research最新文献

Objective: To evaluate if state death investigation systems affect the reporting of suicides, particularly when comparing medical examiners to coroners.

Data sources and study setting: We used restricted-access state mortality data from National Vital Statistics System between the years 1959 to 2016. These data were matched with state-level changes in death investigation systems reported by the Centers for Disease Control and Prevention database on the Public Health Law Program: Coroner/ME Laws.

Study design: We used difference-in-differences and event study methods for the analysis. We estimated the relative per capita changes in suicides, accidental deaths, and homicides when comparing coroner-only states with other death investigation types. Sub-analyses estimated differences by sex, race, and if coroners were required to receive training.

Data collection/extraction methods: Not Applicable.

Principal findings: Coroners-only states underreported suicides by 17.4% (p < 0.05) and performed 20.4% (p < 0.05) fewer autopsies compared to states with county coroners and a state medical examiner. This pattern is consistent by sex and race. Required coroner training did not affect death determination significantly.

Conclusion: Coroners-only states underreported suicides compared to states with county coroners and a state medical examiner. The disparity in the use of autopsies is a potential mechanism for underreporting of suicides by coroners. If all coroners-only states adopted a state medical examiner, suicide reporting would increase by 2243-3100 deaths in the United States annually.

Objective: To examine racial inequities in low-risk and high-risk (or "medically appropriate") cesarean delivery rates in New Jersey during the era surrounding the United States cesarean surge and peak.

Study setting and design: This retrospective repeated cross-sectional study examined the universe of childbirth hospitalizations in New Jersey from January 1, 2000 through September 30, 2015. We estimate the likelihood of cesarean delivery by maternal race and ethnicity, with mixed-level logistic regression models, stratified by cesarean risk level designated by the Society of Maternal Fetal Medicine (SMFM).

Data sources and analytic sample: We used all-payer hospital discharge data from the Healthcare Cost and Utilization Project's State Inpatient Discharge Database and linked this data to the American Hospital Association Annual Survey. ZIP-code Tabulation Area (ZCTA)-level racialized economic segregation index data were from the 2007-2011 American Community Survey. We identified 1,604,976 statewide childbirth hospitalizations using International Classification of Diseases-9-CM (ICD-9) diagnosis and procedure codes and Diagnosis-Related Group codes, and created an indicator of cesarean delivery using ICD-9 codes.

Principal findings: Among low-risk deliveries, Black patients, particularly those in the age group of 35-39 years, had higher predicted probabilities of giving birth via cesarean than White people in the same age categories (Black-adjusted predicted probability = 24.0%; vs. White-adjusted predicted probability = 17.3%). Among high-risk deliveries, Black patients aged 35 to 39 years had a lower predicted probability (by 2.7 percentage points) of giving birth via cesarean compared with their White counterparts.

Conclusions: This study uncovered a lack of medically appropriate cesarean delivery for Black patients, with low-risk Black patients at higher odds of cesarean delivery and high-risk Black patients at lower odds of cesarean than their White counterparts. The significant Black-White inequities highlight the need to address misalignment of evidence-based cesarean delivery practice in the efforts to improve maternal health equity. Quality metrics that track whether cesareans are provided when medically needed may contribute to clinical and policy efforts to prevent disproportionate maternal morbidity and mortality among Black patients.

Objective: To compare theoretical strengths and limitations of common immortal time adjustment methods, propose a new approach using multiple imputation (MI), and provide practical guidance for using MI in precision medicine evaluations centered on a real-world case study.

Study setting and design: Methods comparison, guidance, and real-world case study based on previous literature. We compared landmark analysis, time-distribution matching, time-dependent analysis, and our proposed MI application. Guidance for MI spanned (1) selecting the imputation method; (2) specifying and applying the imputation model; and (3) conducting comparative analysis and pooling estimates. Our case study used a matched cohort design to evaluate overall survival benefits of whole-genome and transcriptome analysis, a precision medicine technology, compared to usual care for advanced cancers, and applied both time-distribution matching and MI. Bootstrap simulation characterized imputation sensitivity to varying data missingness and sample sizes.

Data sources and analytic sample: Case study used population-based administrative data and single-arm precision medicine program data from British Columbia, Canada for the study period 2012 to 2015.

Principal findings: While each method described can reduce immortal time bias, MI offers theoretical advantages. Compared to alternative approaches, MI minimizes information loss and better characterizes statistical uncertainty about the true length of the immortal time period, avoiding false precision. Additionally, MI explicitly considers the impacts of patient characteristics on immortal time distributions, with inclusion criteria and follow-up period definitions that do not inadvertently risk biasing evaluations. In the real-world case study, survival analysis results did not substantively differ across MI and time distribution matching, but standard errors based on MI were higher for all point estimates. Mean imputed immortal time was stable across simulations.

Conclusions: Precision medicine evaluations must employ immortal time adjustment methods for unbiased, decision-grade real-world evidence generation. MI is a promising solution to the challenge of immortal time bias.

Objective: To generate evidence regarding the offensive (customer acquisition) versus defensive (customer retention) motivation for pharmaceutical manufacturer coupons.

Data sources and study setting: Retail prescriptions from IQVIA's Formulary Impact Analyzer data between 2017 and 2019.

Study design: Ordinary least squares regression models with person, therapeutic class, drug, and time-fixed effects to measure the association between switching medications and coupon usage as well as the association between patient out-of-pocket spending and switching to a drug and using a coupon. To study switching type heterogeneity, reanalysis of associations for any type of switch, generic-brand switches, and brand-brand switches. Reestimation of baseline analyses for sodium-glucose cotransporter-2 inhibitors, anticoagulants, and inhaled corticosteroids/long-acting beta2-agonists to assess heterogeneity by drug class and market maturity.

Data collection: 1,167,132 privately insured patients that utilized at least one coupon between 2017 and 2019 for one or more prescriptions.

Principal findings: Coupon usage was associated with a 1.0 percentage point reduction in any kind of drug switch in the full sample and by 0.65-2.9 percentage points for the drug class subgroups. However, these estimates are governed by market dynamics; the probability of switching increased by 40% on the first coupon usage before declining by more than 50% on subsequent coupons. Switching after the first coupon use may be explained by systematic savings implied by coupon use; we find coupons reduced patient out-of-pocket spending by $45.00 (i.e., the majority of patient out-of-pocket costs). In subgroup analyses, coupon savings were $6.43 larger than average for anticoagulants, characterized by the highest levels of brand and generic competition among the considered therapeutic classes.

Conclusions: Pharmaceutical manufacturers may be using coupons to acquire customers and then build brand loyalty, especially in markets with more generic competition. Antitrust authorities and other regulators should scrutinize the impact of coupons on market competitiveness and drug spending.

Objective: To compare morbidity burden captured from multimorbidity indices and aggregated measures of clinically meaningful categories captured in primary care community-based health center (CBHC) patients.

Data sources and study setting: Electronic health records of patients seen in 2019 in OCHIN's national network of CBHCs serving patients in rural and underserved communities.

Study design: Age-stratified analyses comparing the most common conditions captured by the Charlson, Elixhauser, and Multimorbidity Weighted (MWI) indices, and Classification Software Refined (CCSR) and Chronic Condition Indicator (CCI) algorithms.

Data collection/extraction methods: Active ICD-10 conditions on patients' problem list in 2019.

Principal findings: Approximately 35%-56% of patients with at least one condition are not captured by the Charlson, Elixhauser, and MWI indices. When stratified by age, this range broadens to 9%-90% with higher percentages in younger patients. The CCSR and CCI reflect a broader range of acute and chronic conditions prevalent among CBHC patients.

Conclusion: Three commonly used indices to capture morbidity burden reflect conditions most prevalent among older adults, but do not capture those on problem lists for younger CBHC patients. An index with an expanded range of care conditions is needed to understand the complex care provided to primary care populations across the lifespan.

Objective: To understand whether and how primary care providers and staff elicit patients' past experiences of healthcare discrimination when providing care.

Data sources/study setting: Twenty qualitative semi-structured interviews were conducted with healthcare staff in primary care roles to inform future interventions to integrate data about past experiences of healthcare discrimination into clinical care.

Study design: Qualitative study.

Data collection/extraction methods: Data were collected via semi-structured qualitative interviews between December 2018 and January 2019, with health care staff in primary care roles at a hospital-based clinic within an urban safety-net health system that serves a patient population with significant racial, ethnic, and linguistic diversity.

Principal findings: Providers did not routinely, or in a structured way, elicit information about past experiences of healthcare discrimination. Some providers believed that information about healthcare discrimination experiences could allow them to be more aware of and responsive to their patients' needs and to establish more trusting relationships. Others did not deem it appropriate or useful to elicit such information and were concerned about challenges in collecting and effectively using such data.

Conclusions: While providers see value in eliciting past experiences of discrimination, directly and systematically discussing such experiences with patients during a primary care encounter is challenging for them. Collecting this information in primary care settings will likely require implementation of multilevel systematic data collection strategies. Findings presented here can help identify clinic-level opportunities to do so.