Rural Hospitals Experienced More Patient Volume Variability Than Urban Hospitals During The COVID-19 Pandemic, 2020-21.

Fluctuations in patient volume during the COVID-19 pandemic may have been particularly concerning for rural hospitals. We examined hospital discharge data from the Healthcare Cost and Utilization Project State Inpatient Databases to compare data from the COVID-19 pandemic period (March 8, 2020-December 31, 2021) with data from the prepandemic period (January 1, 2017-March 7, 2020). Changes in average daily medical volume at rural hospitals showed a dose-response relationship with community COVID-19 burden, ranging from a 13.2 percent decrease in patient volume in periods of low transmission to a 16.5 percent increase in volume in periods of high transmission. Overall, about 35 percent of rural hospitals experienced fluctuations exceeding 20 percent (in either direction) in average daily total volume, in contrast to only 13 percent of urban hospitals experiencing similar magnitudes of changes. Rural hospitals with a large change in average daily volume were more likely to be smaller, government-owned, and critical access hospitals and to have significantly lower operating margins. Our findings suggest that rural hospitals may have been more vulnerable operationally and financially to volume shifts during the pandemic, which warrants attention because of the potential impact on these hospitals' long-term sustainability.

Mortality for Time-Sensitive Conditions at Urban vs Rural Hospitals During the COVID-19 Pandemic.

Importance: COVID-19 pandemic-related disruptions to the health care system may have resulted in increased mortality for patients with time-sensitive conditions. Objective: To examine whether in-hospital mortality in hospitalizations not related to COVID-19 (non-COVID-19 stays) for time-sensitive conditions changed during the pandemic and how it varied by hospital urban vs rural location. Design, Setting, and Participants: This cohort study was an interrupted time-series analysis to assess in-hospital mortality during the COVID-19 pandemic (March 8, 2020, to December 31, 2021) compared with the prepandemic period (January 1, 2017, to March 7, 2020) overall, by month, and by community COVID-19 transmission level for adult discharges from 3813 US hospitals in the State Inpatient Databases for the Healthcare Cost and Utilization Project. Exposure: The COVID-19 pandemic. Main Outcomes and Measures: The main outcome measure was in-hospital mortality among non-COVID-19 stays for 6 time-sensitive medical conditions: acute myocardial infarction, hip fracture, gastrointestinal hemorrhage, pneumonia, sepsis, and stroke. Entropy weights were used to align patient characteristics in the 2 time periods by age, sex, and comorbidities. Results: There were 18 601 925 hospitalizations; 50.3% of patients were male, 38.5% were aged 18 to 64 years, 45.0% were aged 65 to 84 years, and 16.4% were 85 years or older for the selected time-sensitive medical conditions from 2017 through 2021. The odds of in-hospital mortality for sepsis increased 27% from the prepandemic to the pandemic periods at urban hospitals (odds ratio [OR], 1.27; 95% CI, 1.25-1.29) and 35% at rural hospitals (OR, 1.35; 95% CI, 1.30-1.40). In-hospital mortality for pneumonia had similar increases at urban (OR, 1.48; 95% CI, 1.42-1.54) and rural (OR, 1.46; 95% CI, 1.36-1.57) hospitals. Increases in mortality for these 2 conditions showed a dose-response association with the community COVID-19 level (low vs high COVID-19 burden) for both rural (sepsis: 22% vs 54%; pneumonia: 30% vs 66%) and urban (sepsis: 16% vs 28%; pneumonia: 34% vs 61%) hospitals. The odds of mortality for acute myocardial infarction increased 9% (OR, 1.09; 95% CI, 1.06-1.12) at urban hospitals and was responsive to the community COVID-19 level. There were significant increases in mortality for hip fracture at rural hospitals (OR, 1.32; 95% CI, 1.14-1.53) and for gastrointestinal hemorrhage at urban hospitals (OR, 1.15; 95% CI, 1.09-1.21). No significant change was found in mortality for stroke overall. Conclusions and Relevance: In this cohort study, in-hospital mortality for time-sensitive conditions increased during the COVID-19 pandemic. Mobilizing strategies tailored to the different needs of urban and rural hospitals may help reduce the likelihood of excess deaths during future public health crises.

Medicare Advantage in rural areas: implications for hospital sustainability.

OBJECTIVES: A growing number of Medicare beneficiaries in rural areas are enrolled in Medicare Advantage plans, which negotiate hospital reimbursement. This study examined the association between Medicare Advantage penetration levels in rural areas and hospital financial distress and closure. STUDY DESIGN: This retrospective cohort study followed rural general acute care hospitals open in 2008 through 2019 or until closure using Healthcare Cost and Utilization Project State Inpatient Databases for 14 states. METHODS: The primary independent variables were the percentage of Medicare Advantage stays out of total Medicare stays at the hospital and the percentage of Medicare Advantage beneficiaries out of total beneficiaries in the hospital's county. Financial distress was defined using the Altman Z score, where values less than or equal to 1.1 indicate financial distress and values greater than 2.8 indicate stability. The Z score was examined as a continuous outcome in hospital and county fixed-effects models. Risk of closure was examined using Cox proportional hazard models adjusted for hospital and market factors. RESULTS: Rural hospital Medicare Advantage penetration grew from 6.5% in 2008 to 20.6% in 2019. A 1-percentage point increase in hospital penetration was associated with an increase in financial stability of 0.04 units on the Altman Z score (95% CI, 0.00-0.08; P = .03) and a 4% reduction in risk of closure (HR, 0.96; 95% CI, 0.92-1.00; P = .04). Results were consistent when measuring Medicare Advantage penetration at the county level. CONCLUSIONS: Our findings counter the notion that Medicare Advantage plans financially hurt rural hospitals because they pay less generously than traditional Medicare.

Preventable tragedies: findings from the #NotAnAccident index of unintentional shootings by children.

BACKGROUND: Between 2015 and 2021, 3,498 Americans died from unintentional gun injuries, including 713 children 17 years and younger. Roughly 30 million American children live in homes with firearms, many of which are loaded and unlocked. This study assesses the scope of unintentional shootings by children 17 and younger in the US and the relationship between these shootings and state-level secure storage laws. METHODS: Demographic and injury data of both perpetrators and victims of unintentional shootings by children 17 and younger in the US from 1/1/2015-12/31/2021 were extracted from the #NotAnAccident Index. The #NotAnAccident Index contains media-report data, which is systematically flagged through Google Alerts. We describe characteristics of incidents and examine incident rates over time. The association between state-level secure storage laws and rates of unintentional shootings by children is assessed in multivariate negative binomial regression models. RESULTS: 2,448 unintentional shootings by children resulted in 926 deaths and 1,603 nonfatal gun injuries over a period of seven years. Most perpetrators (81%) and victims (76%) were male. The mean age was 10.0 (SD 5.5) for shooters and 10.9 (SD 8.1) for victims. Children were as likely to shoot themselves (49%) as they were to shoot others (47%). The majority of victims were under 18 years old (91%). Shootings most often occurred in or around homes (71%) and with handguns (53%). From March to December 2020, coinciding with the COVID-19 pandemic, incidents increased 24% over the same period in 2019, which was driven largely by an increase among shooters ages 0-5. Depending on the type of law, rates of unintentional shootings by children were 24% to 72% lower in states with secure storage laws, compared to states without such laws. CONCLUSIONS: Unintentional shootings by children are on the rise, particularly among children 0-5 years old, but are preventable tragedies. Our results show that secure firearm storage policies are strongly correlated with lower rates of unintentional shootings by children. Firearm storage policies, practices, and education efforts are needed to ensure guns are kept secured and inaccessible to children.

Effects of the COVID-19 early pandemic on delivery outcomes among women with and without COVID-19 at birth.

BACKGROUND: The COVID-19 pandemic may influence delivery outcomes through direct effects of infection or indirect effects of disruptions in prenatal care. We examined early pandemic-related changes in birth outcomes for pregnant women with and without a COVID-19 diagnosis at delivery. METHODS: We compared four delivery outcomes-preterm delivery (PTD), severe maternal morbidity (SMM), stillbirth, and cesarean birth-between 2017 and 2019 (prepandemic) and between April and December 2020 (early-pandemic) using interrupted time series models on 11.8 million deliveries, stratified by COVID-19 infection status at birth with entropy weighting for historical controls, from the Healthcare Cost and Utilization Project across 43 states and the District of Columbia. RESULTS: Relative to 2017-2019, women without COVID-19 at delivery in 2020 had lower odds of PTD (OR = 0.93; 95% CI = 0.92-0.94) and SMM (OR = 0.88; 95% CI = 0.85-0.91) but increased odds of stillbirth (OR = 1.04; 95% CI = 1.01-1.08). Absolute effects were small across race/ethnicity groups. Deliveries with COVID-19 had an excess of each outcome, by factors of 1.07-1.46 for outcomes except SMM at 4.21. The effect for SMM was more pronounced for Asian/Pacific Islander non-Hispanic (API; OR = 10.51; 95% CI = 5.49-20.14) and Hispanic (OR = 5.09; 95% CI = 4.29-6.03) pregnant women than for White non-Hispanic (OR = 3.28; 95% CI = 2.65-4.06) women. DISCUSSION: Decreasing rates of PTD and SMM and increasing rates of stillbirth among deliveries without COVID-19 were small but suggest indirect effects of the pandemic on maternal outcomes. Among pregnant women with COVID-19 at delivery, adverse effects, particularly SMM for API and Hispanic women, underscore the importance of addressing health disparities.

Immune globulin usage trends in commercially insured and Medicare populations, 2009-2019.

BACKGROUND: Longitudinal patterns of immune globulins (IG) use have not been described in large populations. Understanding IG usage is important given potential supply limitations impacting individuals for whom IG is the sole life-saving/health-preserving therapy. The study describes US IG utilization patterns from 2009 to 2019. STUDY DESIGN AND METHODS: Using IBM MarketScan commercial and Medicare claims data, we examined four metrics overall and by condition-specific categories during 2009-2019: (1) IG administrations per 100,000 person-years, (2) IG recipients per 100,000 enrollees, (3) average annual administrations per recipient, and (4) average annual dose per recipient. RESULTS: In the commercial and Medicare populations respectively: IG administrations per 100,000 person-years increased by 120% (213-470) and 144% (692-1693); IG recipients per 100,000 enrollees grew by 71% (24-42) and 102% (89-179); average annual administrations per recipient rose by 28% (8-10) and 19% (8-9); and average annual dose (grams) per recipient increased by 29% (384-497) and 34% (317-426). IG administrations associated with immunodeficiency (per 100,000 person-years) increased by 154% (from 127 to 321) and 176% (from 365 to 1007). Autoimmune and neurologic conditions were associated with higher annual average administrations and dose than other conditions. DISCUSSION: IG use increased, coinciding with a growth in the IG recipient population in the United States. Several conditions contributed to the trend, with the largest increase observed among immunodeficient individuals. Future investigations should assess changes in the demand for IVIG by disease state or indication and consider treatment effectiveness.

Background rates of adverse events of special interest for COVID-19 vaccine safety monitoring in the United States, 2019-2020.

BACKGROUND: The U.S. Food and Drug Administration (FDA) Biologics Effectiveness and Safety (BEST) Initiative conducts active surveillance of adverse events of special interest (AESI) after COVID-19 vaccination. Historical incidence rates (IRs) of AESI are comparators to evaluate safety. METHODS: We estimated IRs of 17 AESI in six administrative claims databases from January 1, 2019, to December 11, 2020: Medicare claims for adults ≥ 65 years and commercial claims (Blue Health Intelligence®, CVS Health, HealthCore Integrated Research Database, IBM® MarketScan® Commercial Database, Optum pre-adjudicated claims) for adults < 65 years. IRs were estimated by sex, age, race/ethnicity (Medicare), and nursing home residency (Medicare) in 2019 and for specific periods in 2020. RESULTS: The study included >100 million enrollees annually. In 2019, rates of most AESI increased with age. However, compared with commercially insured adults, Medicare enrollees had lower IRs of anaphylaxis (11 vs 12-19 per 100,000 person-years), appendicitis (80 vs 117-155), and narcolepsy (38 vs 41-53). Rates were higher in males than females for most AESI across databases and varied by race/ethnicity and nursing home status (Medicare). Acute myocardial infarction (Medicare) and anaphylaxis (all databases) IRs varied by season. IRs of most AESI were lower during March-May 2020 compared with March-May 2019 but returned to pre-pandemic levels after May 2020. However, rates of Bell's palsy, Guillain-Barré syndrome, narcolepsy, and hemorrhagic/non-hemorrhagic stroke remained lower in multiple databases after May 2020, whereas some AESI (e.g., disseminated intravascular coagulation) exhibited higher rates after May 2020 compared with 2019. CONCLUSION: AESI background rates varied by database and demographics and fluctuated in March-December 2020, but most returned to pre-pandemic levels after May 2020. It is critical to standardize demographics and consider seasonal and other trends when comparing historical rates with post-vaccination AESI rates in the same database to evaluate COVID-19 vaccine safety.

Assessment of performance characteristics of COVID-19 ICD-10-CM diagnosis code U07.1 using SARS-CoV-2 nucleic acid amplification test results.

The Food and Drug Administration's Biologics Effectiveness and Safety Initiative conducts active surveillance to protect public health during the coronavirus disease 2019 (COVID-19) pandemic. This study evaluated performance of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis code U07.1 in identifying COVID-19 cases in claims compared with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleic acid amplification test results in linked electronic health records (EHRs). Care episodes in three populations were defined using COVID-19-related diagnoses (population 1), SARS-CoV-2 nucleic acid amplification test procedures (population 2), and all-cause hospitalizations (population 3) in two linked claims-EHR databases: IBM® MarketScan® Explorys® Claims-EMR Data Set (commercial) and OneFlorida Data Trust linked Medicaid-EHR. Positive and negative predictive values were calculated. Respectively, populations 1, 2, and 3 included 26,686, 26,095, and 2,564 episodes (commercial) and 29,117, 23,412, and 9,629 episodes (Florida Medicaid). The positive predictive value was >80% and the negative predictive value was >95% in each population, with the highest positive predictive value in population 3 (commercial: 91.9%; Medicaid: 93.1%). Findings did not vary substantially by patient age. Positive predictive values in populations 1 and 2 fluctuated during April-June 2020. They then stabilized in the commercial but not the Medicaid population. Negative predictive values were consistent over time in all populations and databases. Our findings indicate that U07.1 has high performance in identifying COVID-19 cases and noncases in claims databases. Performance may vary across populations and periods.

Risk of Closure Among Independent and Multihospital-Affiliated Rural Hospitals.

Importance: The increase in rural hospital closures has strained access to inpatient care in rural communities. It is important to understand the association between hospital system affiliation and access to care in these communities to inform policy on this issue. Objective: To examine the association between affiliation and rural hospital closure. Design Setting and Participants: This cohort study used survival models with a time-dependent variable for affiliation vs independent status to assess risk of closure among a national cohort of US rural hospitals from January 2007 through December 2019. Data analysis was conducted from March to October 2021. Hospital affiliations were identified from the American Hospital Association Annual Survey and Irving Levin Associates and closures from the University of North Carolina Sheps Center (Chapel Hill). Additional covariates came from the Healthcare Cost and Utilization Project State Inpatient Databases and other national sources. Exposures: Affiliation with another hospital or multihospital health system. Main Outcomes and Measures: Closure was the main outcome. The models included hospital, market, and utilization characteristics and were stratified by financial distress in 2007. Results: Among 2237 rural hospitals operating in 2007, 140 (6.3%) had closed by 2019. The proportion of rural hospitals that were independent decreased from 68.9% in 2007 to 47.0% in 2019; the proportion that were affiliated increased from 31.1% to 46.7%. Among financially distressed hospitals in 2007, affiliation was associated with lower risk of closure compared with being independent (adjusted hazard ratio [aHR], 0.49; 95% CI, 0.26-0.92). Conversely, among hospitals that were financially stable in 2007, affiliation was associated with higher risk of closure compared with being independent (aHR, 2.36; 95% CI, 1.20-4.62). For-profit ownership was also strongly associated with closure for hospitals that were financially stable in 2007 (aHR, 4.08; 95% CI, 1.86-8.97). Conclusions and Relevance: The results of this cohort study suggest that affiliations may be associated with lower risk of closure for some rural hospitals in financial distress. However, among initially financially stable hospitals, an increased risk of closure for hospitals associated with affiliation and proprietary ownership raises concerns about the association of affiliation with closures in some circumstances. Policy interventions to stabilize inpatient care in rural areas should account for these findings.

Contributors to disparities in postpartum readmission rates between safety-net and non-safety-net hospitals: A decomposition analysis.

BACKGROUND: Safety-net hospitals (SNHs) treat more maternal patients with risk factors for postpartum readmission. OBJECTIVE: To assess how patient, hospital, and community characteristics explain the SNH/non-SNH disparity in postpartum readmission rates. DESIGN: A linear probability model assessed covariates associated with postpartum readmissions. Oaxaca-Blinder decomposition estimates quantified the contribution of covariates to the SNH/non-SNH disparity in postpartum readmission rates. SETTING: Healthcare Cost and Utilization Project 2016-2018 State Inpatient Databases from 25 states. PARTICIPANTS: 3.5 million maternal delivery stays. MEASUREMENTS: The outcome was inpatient readmission within 42 days of delivery. SNHs had a share of Medicaid/uninsured stays in the top quartile. A range of patient, hospital, and community characteristics was considered as covariates. RESULTS: The unadjusted postpartum readmission rate was 4.2 per 1000 index deliveries higher at SNHs than at non-SNHs (19.1 vs. 14.9, p < .001). Adjustment reduced the risk difference to 0.65 per 1000 (95% confidence interval [CI]: -0.14, 1.44). Patient (66%), hospital (14%), and community (4%) characteristics explained 84% of the disparity. The single largest contributors to the disparity were race/ethnicity (20%), hypertension (12%), hospital preterm delivery rate (10%), and preterm delivery (7%). Collectively, patient comorbidities explained 31% of the disparity. CONCLUSION: Higher postpartum readmission rates at SNHs versus non-SNHs were largely due to differences in the patient mix rather than hospital factors. Hospital initiatives are needed to reduce the risk of postpartum readmissions among SNH patients. Improving factors that contribute to the disparity, including underlying health conditions and health inequities associated with race, requires enduring investments in public health.

Access To Obstetric, Behavioral Health, And Surgical Inpatient Services After Hospital Mergers In Rural Areas.

Despite rural hospitals' central role in their communities, they are increasingly in financial distress and may merge with other hospitals or health systems, potentially reducing service lines that are less profitable or duplicative of services that the acquirer also offers. Using hospital discharge data from thirty-two Healthcare Cost and Utilization Project State Inpatient Databases from the period 2007-18, we examined the influence of rural hospital mergers on changes to inpatient service lines at hospitals and within their catchment areas. We found that merged hospitals were more likely than independent hospitals to eliminate maternal/neonatal and surgical care. Whereas the number of mental/substance use disorder-related stays decreased or remained stable at merged hospitals and within their catchment areas, it increased for unaffiliated hospitals and their catchment areas, indicating a potential unmet need in the communities of rural hospitals postmerger. Although a merger could salvage a hospital's sustainability, it also could reduce service lines and responsiveness to community needs.

Quality of Care Before and After Mergers and Acquisitions of Rural Hospitals.

Importance: Rural hospitals are increasingly merging with other hospitals. The associations of hospital mergers with quality of care need further investigation. Objectives: To examine changes in quality of care for patients at rural hospitals that merged compared with those that remained independent. Design, Setting, and Participants: In this case-control study, mergers at community nonrehabilitation hospitals in Federal Office of Rural Health Policy-eligible zip codes during 2009 to 2016 in 32 states were identified from Irving Levin Associates and the American Hospital Association Annual Survey. Outcomes for inpatient stays for select conditions and elective procedures were derived from the Healthcare Cost and Utilization Project State Inpatient Databases. Difference-in-differences linear probability models were used to assess premerger to postmerger changes in outcomes for patients discharged from merged vs comparison hospitals that remained independent. Data were analyzed from February to December 2020. Exposures: Hospital mergers. Main Outcomes and Measures: The main outcome was in-hospital mortality among patients admitted for acute myocardial infarction (AMI), heart failure, stroke, gastrointestinal hemorrhage, hip fracture, or pneumonia, as well as complications during stays for elective surgeries. Results: A total of 172 merged hospitals and 266 comparison hospitals were analyzed. After matching, baseline patient characteristics were similar for 303 747 medical stays and 175 970 surgical stays at merged hospitals and 461 092 medical stays and 278 070 surgical stays at comparison hospitals. In-hospital mortality among AMI stays decreased from premerger to postmerger at merged hospitals (9.4% to 5.0%) and comparison hospitals (7.9% to 6.3%). Adjusting for patient, hospital, and community characteristics, the decrease in in-hospital mortality among AMI stays 1 year postmerger was 1.755 (95% CI, -2.825 to -0.685) percentage points greater at merged hospitals than at comparison hospitals (P < .001). This finding held up to 4 years postmerger (DID, -2.039 [95% CI, -3.388 to -0.691] percentage points; P = .003). Greater premerger to postmerger decreases in mortality at merged vs comparison hospitals were also observed at 5 years postmerger among stays for heart failure (DID, -0.756 [95% CI, -1.448 to -0.064] percentage points; P = .03), stroke (DID, -1.667 [95% CI, -3.050 to -0.283] percentage points; P = .02), and pneumonia (DID, -0.862 [95% CI, -1.681 to -0.042] percentage points; P = .04). Conclusions and Relevance: These findings suggest that rural hospital mergers were associated with better mortality outcomes for AMI and several other conditions. This finding is important to enhancing rural health care and reducing urban-rural disparities in quality of care.

Validating Claims-Based Algorithms Determining Pregnancy Outcomes and Gestational Age Using a Linked Claims-Electronic Medical Record Database.

INTRODUCTION: Pregnancy outcome identification and precise estimates of gestational age (GA) are critical in drug safety studies of pregnant women. Validated pregnancy outcome algorithms based on the International Classification of Diseases, Tenth Revision, Clinical Modification/Procedure Coding System (ICD-10-CM/PCS) have not previously been published. METHODS: We developed algorithms to classify pregnancy outcomes and estimate GA using ICD-10-CM/PCS and service codes on claims in the 2016-2018 IBM® MarketScan® Explorys® Claims-EMR Data Set and compared the results with ob-gyn adjudication of electronic medical records (EMRs). Obstetric services were grouped into episodes using hierarchical and spacing requirements. GA was based on evidence with the highest clinical accuracy. Among pregnancies with obstetric EMRs, 100 full-term live births (FTBs), 100 preterm live births (PTBs), 100 spontaneous abortions (SAs), and 24 stillbirths were selected for review. Physicians adjudicated cases using Global Alignment of Immunization safety Assessment in pregnancy (GAIA) definitions applied to structured EMRs. RESULTS: The claims-based algorithms identified 34,204 pregnancies, of which 9.9% had obstetric EMRs. Of sampled pregnancies, 92 FTBs, 93 PTBs, 75 SAs, and 24 stillbirths were adjudicated. Among these pregnancies, the percent agreement was 97.8%, 62.4%, 100.0%, and 70.8% for FTBs, PTBs, SAs, and stillbirths, respectively. The percent agreement on GA within 7 and 28 days, respectively, was 85.9% and 100.0% for FTBs, 81.7% and 98.9% for PTBs, 61.3% and 94.7% for SAs, and 66.7% and 79.2% for stillbirths. CONCLUSIONS: The pregnancy outcome algorithms had high agreement with physician adjudication of EMRs and may inform post-market maternal safety surveillance.

Vaccine exposure during pregnancy among privately and publicly insured women in the United States, 2016-2018.

BACKGROUND: Vaccine use during pregnancy affects maternal and infant health. Many women do not receive vaccines recommended during pregnancy; conversely, inadvertent exposure to vaccines contraindicated or not recommended during pregnancy may occur. We assessed exposure to two recommended vaccines and two vaccines not recommended during pregnancy among privately and Medicaid-insured women in the United States. METHODS: This study includes a retrospective cohort of pregnancies in women aged 12-55 years resulting in live birth, spontaneous abortion, or stillbirth identified in the IBM® MarketScan® Commercial, Blue Health Intelligence® (BHI®) Commercial, and IBM MarketScan Multi-State Medicaid Databases from August 1, 2016, to December 31, 2018. Gestational age at vaccination was determined using a validated algorithm. We examined vaccines (1) recommended by the Centers for Disease Control and Prevention Advisory Committee on Immunization Practices (ACIP) (tetanus, diphtheria, and acellular pertussis [Tdap]; inactivated influenza) and (2) not recommended (human papillomavirus [HPV]) or contraindicated (measles, mumps, and rubella [MMR]). RESULTS: We identified 496,771 (MarketScan Commercial), 858,961 (BHI), and 289,573 (MarketScan Medicaid) pregnancies (approximately 75% aged 20-34 years). Across these three databases, 52.1%, 50.3%, and 31.3% of pregnancies, respectively, received Tdap, most often at a gestational age of 28 weeks, and influenza vaccination occurred in 32.1%, 30.8%, and 18.0% of pregnancies, respectively. HPV vaccination occurred in < 0.2% of pregnancies, mostly in the first trimester among women aged 12-19 years, and MMR was administered in < 0.1% of pregnancies. Use of other contraindicated vaccines per ACIP (e.g., varicella, live attenuated influenza) was rare. CONCLUSION: Maternal vaccination with ACIP-recommended vaccines was suboptimal among privately and Medicaid-insured patients, with lower vaccination coverage among Medicaid-insured pregnancies than their privately insured counterparts. Inadvertent exposure to contraindicated vaccines during pregnancy was rare. This study evaluated only vaccinations reimbursed among insured populations and may have limited generalizability to uninsured populations.

Price elasticity of demand for buprenorphine/naloxone prescriptions.

Although there have been supply-side efforts in response to the opioid crisis (e.g., prescription drug monitoring programs), little information exists on demand-side approaches related to patient cost sharing that may affect utilization of and adherence to pharmacotherapy by individuals with opioid use disorder. Among individuals who had initiated pharmacotherapy, we estimated the price elasticity of demand of prescription fills of buprenorphine/naloxone, a common pharmacotherapy drug, overall and by patient characteristics. Using the IBM MarketScan® Commercial Claims and Encounters Database for individuals with employer-sponsored private health insurance coverage, we examined the relationship between cost sharing and the number of buprenorphine/naloxone prescription fills using enrollee-level longitudinal fixed effects models. Cost sharing was expressed as a price index for each employer-plan. By including enrollee-level fixed effects, the identification of the effect of interest comes from longitudinal variation in prices across multiple time points for each enrollee. Overall, the demand for buprenorphine/naloxone was price inelastic (p = 0.191). However, some subgroups were responsive to price. A doubling of price was associated with a decrease in fills by 3.0% for enrollees aged 45-64 years (p = 0.029); 5.7% for those in rural areas (p = 0.033); 5.8% for residents of the South (p ≤0.001); and 3.0% for those enrolled in an HMO (p = 0.004). Insurers should consider the effects on these groups before increasing beneficiary out-of-pocket costs for pharmacotherapy and efforts to increase adherence should consider that price may be a barrier for some subgroups with OUD.

Comparison of 3 Safety-Net Hospital Definitions and Association With Hospital Characteristics.

Importance: No consensus exists on how to define safety-net hospitals (SNHs) for research or policy decision-making. Identifying which types of hospitals are classified as SNHs under different definitions is key to assessing policies that affect SNH funding. Objective: To examine characteristics of SNHs as classified under 3 common definitions. Design, Setting, and Participants: This cross-sectional analysis includes noncritical-access hospitals in the Healthcare Cost and Utilization Project State Inpatient Databases from 47 US states for fiscal year 2015, linked to the Centers for Medicare & Medicaid Services Hospital Cost Reports and to the American Hospital Association Annual Survey. Data were analyzed from March 1 through September 30, 2018. Exposures: Hospital characteristics including organizational characteristics, scope of services provided, and financial attributes. Main Outcomes and Measures: Definitions of SNH based on Medicaid and Medicare Supplemental Security Income inpatient days historically used to determine Medicare Disproportionate Share Hospital (DSH) payments; Medicaid and uninsured caseload; and uncompensated care costs. For each measure, SNHs were defined as those within the top quartile for each state. Results: The 2066 hospitals in this study were distributed across the Northeast (340 [16.5%]), Midwest (587 [28.4%]), South (790 [38.2%]), and West (349 [16.9%]). Concordance between definitions was low; 269 hospitals (13.0%) or fewer were identified as SNHs under any 2 definitions. Uncompensated care captured smaller (200 of 523 [38.2%]) and more rural (65 of 523 [12.4%]) SNHs, whereas DSH index and Medicaid and uncompensated caseload identified SNHs that were larger (264 of 518 [51.0%] and 158 of 487 [32.4%], respectively) and teaching facilities (337 of 518 [65.1%] and 229 of 487 [47.0%], respectively) that provided more essential services than non-SNHs. Uncompensated care also distinguished remarkable financial differences between SNHs and non-SNHs. Under the uncompensated care definition, median (interquartile range [IQR]) bad debt ($27.1 [$15.5-$44.3] vs $12.8 [$6.7-$21.6] per $1000 of operating expenses; P < .001) and charity care ($19.9 [$9.3-$34.1] vs $9.1 [$4.0-$18.7] per $1000 of operating expenses) were twice as high and median (IQR) unreimbursed costs ($32.6 [$12.4-$55.4] vs $23.6 [$9.0-$42.7] per $1000 of operating expenses; P < .001) were 38% higher for SNHs than for non-SNHs. Safety-net hospitals defined by uncompensated care burden had lower median (IQR) total (4.7% [0%-9.9%] vs 5.8% [1.2%-11.2%]; P = .003) and operating (0.3% [-8.0% to 7.2%] vs 2.3% [-3.9% to 8.9%]; P < .001) margins than their non-SNH counterparts, whereas differences between SNH and non-SNH profit margins generally were not statistically significant under the other 2 definitions. Conclusions and Relevance: Different SNH definitions identify hospitals with different characteristics and financial conditions. The new DSH formula, which accounts for uncompensated care, may lead to redistributed payments across hospitals. Our results may inform which types of hospitals will experience funding changes as DSH payment policies evolve.

Shifts in Medicaid and Uninsured Payer Mix at Safety-Net and Non-Safety-Net Hospitals During the Great Recession.

EXECUTIVE SUMMARY: There has been ongoing concern regarding the viability of safety-net hospitals (SNHs), which care for vulnerable populations. The authors examined payer mix at SNHs and non-SNHs during a period covering the Great Recession using data from the 2006 to 2012 Healthcare Cost and Utilization Project State Inpatient Databases from 38 states. The number of privately insured stays decreased at both SNHs and non-SNHs. Non-SNHs increasingly served Medicaid-enrolled and uninsured patients; in SNHs, the number of Medicaid stays decreased and uninsured stays remained stable. These study findings suggest that SNHs were losing Medicaid-enrolled patients relative to non-SNHs before the Medicaid expansion under the Affordable Care Act (ACA). Postexpansion, Medicaid stays will likely increase for both SNHs and non-SNHs, but the increase at SNHs may not be as large as expected if competition increases. Because hospital stays with private insurance and Medicaid help SNHs offset uncompensated care, a lower-than-expected increase could affect SNHs' ability to care for the remaining uninsured population. Continued monitoring is needed once post-ACA data become available.

Early Impact of the Affordable Care Act Coverage Expansion on Safety-Net Hospital Inpatient Payer Mix and Market Shares.

OBJECTIVE: To examine the impact of the Affordable Care Act's coverage expansion on safety-net hospitals (SNHs). STUDY SETTING: Nine Medicaid expansion states. STUDY DESIGN: Differences-in-differences (DID) models compare payer-specific pre-post changes in inpatient stays of adults aged 19-64 years at SNHs and non-SNHs. DATA COLLECTION METHODS: 2013-2014 Healthcare Cost and Utilization Project State Inpatient Databases. PRINCIPAL FINDINGS: On average per quarter postexpansion, SNHs and non-SNHs experienced similar relative decreases in uninsured stays (DID = -2.2 percent, p = .916). Non-SNHs experienced a greater percentage increase in Medicaid stays than did SNHs (DID = 13.8 percent, p = .041). For SNHs, the average decrease in uninsured stays (-146) was similar to the increase in Medicaid stays (153); privately insured stays were stable. For non-SNHs, the decrease in uninsured (-63) plus privately insured (-33) stays was similar to the increase in Medicaid stays (105). SNHs and non-SNHs experienced a similar absolute increase in Medicaid, uninsured, and privately insured stays combined (DID = -16, p = .162). CONCLUSIONS: Postexpansion, non-SNHs experienced a greater percentage increase in Medicaid stays than did SNHs, which may reflect patients choosing non-SNHs over SNHs or a crowd-out of private insurance. More research is needed to understand these trends.

Returns to Emergency Department, Observation, or Inpatient Care Within 30 Days After Hospitalization in 4 States, 2009 and 2010 Versus 2013 and 2014.

BACKGROUND: Nationally, readmissions have declined for acute myocardial infarction (AMI) and heart failure (HF) and risen slightly for pneumonia, but less is known about returns to the hospital for observation stays and emergency department (ED) visits. OBJECTIVE: To describe trends in rates of 30-day, all-cause, unplanned returns to the hospital, including returns for observation stays and ED visits. DESIGN: By using Healthcare Cost and Utilization Project data, we compared 210,007 index hospitalizations in 2009 and 2010 with 212,833 matched hospitalizations in 2013 and 2014. SETTING: Two hundred and one hospitals in Georgia, Nebraska, South Carolina, and Tennessee. PATIENTS: Adults with private insurance, Medicaid, or no insurance and seniors with Medicare who were hospitalized for AMI, HF, and pneumonia. MEASUREMENTS: Thirty-day hospital return rates for inpatient, observation, and ED visits. RESULTS: Return rates remained stable among adults with private insurance (15.1% vs 15.3%; P = 0.45) and declined modestly among seniors with Medicare (25.3% vs 25.0%; P = 0.04). Increases in observation and ED visits coincided with declines in readmissions (8.9% vs 8.2% for private insurance and 18.3% vs 16.9% for Medicare, both P ≤ 0.001). Return rates rose among patients with Medicaid (31.0% vs 32.1%; P = 0.04) and the uninsured (18.8% vs 20.1%; P = 0.004). Readmissions remained stable (18.7% for Medicaid and 9.5% for uninsured patients, both P > 0.75) while observation and ED visits increased. CONCLUSIONS: Total returns to the hospital are stable or rising, likely because of growth in observation and ED visits. Hospitalists' efforts to improve the quality and value of hospital care should consider observation and ED care.