Inpatient Discharge Data Research Articles

The role of prescribed controlled substance acquisition as potential triggers of opioid overdose: A case-crossover study

Background: The role of prescribed opioids and benzodiazepines as risk factors for opioid overdose are well established, however, their role as potential ‘triggers’ of opioid overdose has not been formally investigated. Objective: The objective of this study was to evaluate the temporal relationship between controlled substance acquisition and opioid overdose utilizing a case-crossover design. Methods: This study utilized Arkansas statewide data between 2014 and 2020. Prescription Drug Monitoring Program (PDMP) data were used to assess controlled substance acquisition and fatal and non-fatal opioid overdose were assessed using linked death certificate, inpatient discharge, and emergency department (ED) data. All persons residing in Arkansas who experienced an opioid overdose or had ≥ 1 Arkansas PDMP prescription fill(s) were included. Controlled substance characteristics were described in the 7 days prior to overdose and compared to the controlled substance characteristics in 11 weekly (7-day) control windows prior to overdose. Binary controlled substance variables indicating presence or absence of: any controlled substance, opioid, benzodiazepine, stimulant, sedative, carisoprodol, opioid and benzodiazepine, and opioid and benzodiazepine and carisoprodol were created. Additionally, total morphine milliequivalents were calculated for each time window. Conditional logistic regression models were estimated and adjusted odds ratios for each controlled substance characteristic after accounting for other controlled substance, and prior overdose, and clinical characteristics derived from ED and inpatient data are reported. Results: A total of 2,818,135 individuals with ≥1 Arkansas PDMP record(s) (45.10 % male; 39.94 mean age) were included, of which 28,670 (1.02 %) experienced ≥1 opioid overdose. There was a significant association between opioid overdose and the acquisition of a controlled substance (OR=1.785; p < 0.001), opioid (OR=1.992; p < 0.001), benzodiazepine (OR=1.379; p < 0.001), carisoprodol (OR=1.744; p < 0.001), opioid and benzodiazepine (OR=2.203; p < 0.001), and opioid and benzodiazepine and carisoprodol (OR=2.503; p < 0.001), in the 7 days prior to an opioid overdose event. Conclusion: Controlled substance prescription acquisition, particularly opioids in combination with carisoprodol and/or benzodiazepines, are potential triggers of opioid overdose.

Emergency department utilization among adults with epilepsy: A multi-state cross-sectional analysis, 2010–2019

ObjectiveWe described patterns and trends in ED use among adults with epilepsy in the United States. MethodsUtilizing inpatient and ED discharge data from seven states, we conducted a cross-sectional analysis to identify adult ED visits diagnosed with epilepsy or seizures from 2010 to 2019. Using ED visit counts and estimates of state-level epilepsy prevalence, we calculated ED visit rates overall and by payer, condition, and year. ResultsOur data captured 304,935 ED visits with epilepsy as a primary or secondary diagnosis in 2019. Across the seven states, visit rates ranged between 366 and 726 per 1000 and were higher than rates for adults without epilepsy in all states but one. ED visit rates were highest among Medicare and Medicaid beneficiaries (vs commercial or self-pay). Adults with epilepsy were more likely to be admitted as inpatients. Visits for nervous system disorders were 6.3–8.2 times higher among people with epilepsy, and visits for mental health conditions were 1.2–2.6 times higher. Increases in ED visit rates from 2010 to 2019 among people with epilepsy exceeded increases among adults without by 6.0–27.3 percentage points. ConclusionAdults with epilepsy visit the ED frequently and visit rates have been increasing over time. These results underscore the importance of identifying factors contributing to ED use and designing tailored interventions to improve ambulatory care quality.

Performance of health care service area definitions for capturing variation in inpatient care and social determinants of health.

To quantify the degree to which health care service area (HCSA) definitions captured hospitalizations and heterogeneity in social determinants of health (SDOH). Geospatial data from the Centers for Medicare and Medicaid Services, the Census Bureau, and the Dartmouth Institute. Drive-time isochrones from MapBox. Area Deprivation Index (ADI) data. 2017 inpatient discharge data from Arizona, Florida, Iowa, Maryland, Nebraska, New Jersey, New York, and Wisconsin, State Emergency Department Databases and State Inpatient Databases, Healthcare Cost and Utilization Project, Agency for Healthcare Research and Quality; and Fee-For-Service Medicare data in 48 states. Cross-sectional, descriptive analysis. The capture rate was the percentage of inpatient discharges occurring in the same HCSA as the hospital. We compared capture rates for each HCSA definition for different populations and by hospital type. We measured SDOH heterogeneity using the coefficient of variation of the ADI among ZIP codes within each HCSA. HCSA definitions captured a wide range of inpatient discharges, ranging from 20% to 50% for Public Use Microdata Areas (PUMAs) to 93%-97% for Metropolitan Statistical Areas (MSAs). Three-quarters of inpatient discharges were from facilities within the same county as the patient's residential ZIP code, while nearly two-thirds were within the same Hospital Service Area. From the hospital perspective, 74.7% of inpatient discharges originated from within a 30-min drive and 90.1% within a 60-min drive. Capture rates were the lowest for teaching hospitals. PUMAs and drive-time-based HCSAs encompassed more homogenous populations while MSAs, Commuting Zones, and Hospital Referral Regions captured the most variation. The proportion of hospital discharges captured by each HCSA varied, with MSAs capturing the highest proportion of discharges and PUMAs capturing the lowest. Additionally, researchers face a trade-off between capture rate and population homogeneity when deciding which HCSA to use.

Cost of Cardiovascular Disease Event and Cardiovascular Disease Treatment-Related Complication Hospitalizations in the United States.

Cardiovascular disease (CVD) is among the costliest conditions in the United States, and cost-effectiveness analyses can be used to assess economic impact and prioritize CVD treatments. We aimed to develop standardized, nationally representative CVD events and selected possible CVD treatment-related complication hospitalization costs for use in cost-effectiveness analyses. Nationally representative costs were derived using publicly available inpatient hospital discharge data from the 2012-2018 National Inpatient Sample. Events were identified using the principal International Classification of Diseases, Ninth Revision and International Classification of Diseases, Tenth Revision codes. Facility charges were converted to costs using charge-to-cost ratios, and total costs were estimated by applying a published professional fee ratio. All costs are reported in 2021 US dollars. Mean costs were estimated for events overall and stratified by age, sex, and survival status at discharge. Annual costs to the US health care system were estimated by multiplying the mean annual number of events by the mean total cost per discharge. The annual mean number of hospital discharges among CVD events was the highest for heart failure (1 087 000 per year) and cerebrovascular disease (800 600 per year). The mean cost per hospital discharge was the highest for peripheral vascular disease ($33 700 [95% CI, $33 300-$34 000]) and ventricular tachycardia/ventricular fibrillation ($32 500 [95% CI, $32 100-$32 900]). Hospitalizations contributing the most to annual US health care costs were heart failure ($19 500 [95% CI, $19 300-$19 800] million) and acute myocardial infarction ($18 300, [95% CI, $18 200-$18 500] million). Acute kidney injury was the most frequent possible treatment complication (515 000 per year), and bradycardia had the highest mean hospitalization costs ($17 400 [95% CI, $17 200-$17 500]). The hospitalization cost estimates and statistical code reported in the current study have the potential to increase transparency and comparability of cost-effectiveness analyses for CVD in the United States.

Unsupervised clustering analysis of trauma/non-trauma centers using hospital features including surgical care.

Injuries are a leading cause of death in the United States. Trauma systems aim to ensure all injured patients receive appropriate care. Hospitals that participate in a trauma system, trauma centers (TCs), are designated with different levels according to guidelines that dictate access to medical and research resources but not specific surgical care. This study aimed to identify patterns of injury care that distinguish different TCs and hospitals without trauma designation, non-trauma centers (non-TCs). We extracted hospital-level features from the state inpatient hospital discharge data in Washington state, including all TCs and non-TCs, in 2016. We provided summary statistics and tested the differences of each feature across the TC/non-TC levels. We then conducted 3 sets of unsupervised clustering analyses using the Partition Around Medoids method to determine which hospitals had similar features. Set 1 and 2 included hospital surgical care (volume or distribution) features and other features (e.g., the average age of patients, payer mix, etc.). Set 3 explored surgical care without additional features. The clusters only partially aligned with the TC designations. Set 1 found the volume and variation of surgical care distinguished the hospitals, while in Set 2 orthopedic procedures and other features such as age, social vulnerability indices, and payer types drove the clusters. Set 3 results showed that procedure volume rather than the relative proportions of procedures aligned more, though not completely, with TC designation. Unsupervised machine learning identified surgical care delivery patterns that explained variation beyond level designation. This research provides insights into how systems leaders could optimize the level allocation for TCs/non-TCs in a mature trauma system by better understanding the distribution of care in the system.

Abstract A137: Drive time to physicians and outcomes for bladder cancer

Abstract Introduction: Poor geographic access to cancer care providers can delay treatment and limit access to high quality care. Previous research has shown that for patients with bladder cancer, time to cystectomy and receipt of neoadjuvant chemotherapy are associated with improved survival. Travel burden may be an important barrier to timely guideline-concordant treatment and is particularly relevant for residents of rural areas, which are often medically underserved. This study examines the association between drive time to medical oncologists and urologists and clinical outcomes for muscle-invasive bladder cancer (MIBC) patients. Data and Methods: We used information on physician location from the Centers for Medicare and Medicaid Services and patient-level data from the 2010-2016 Pennsylvania Cancer Registry linked to Pennsylvania Healthcare Cost Containment Council (PHC4) inpatient discharge data through 2018. We examined patients in Pennsylvania with a first lifetime bladder cancer diagnosis who underwent radical cystectomy at a non-federal short-term general hospital identified in PHC4. Outcomes examined were surgery within 90 days, receipt of neoadjuvant chemotherapy, 90-day mortality and 90-day readmission. We estimated multivariable linear regression models at the patient level. Our primary exposures were the natural log number of minutes from the patient’s home census tract centroid to the nearest medical oncologist and the nearest urologist. We controlled for patient age, sex, race/ethnicity, tumor size, tumor node involvement, and Elixhauser comorbidity index. We included a control for whether the patient received neoadjuvant chemotherapy in models estimating the association between drive times and cystectomy within 90 days. We also controlled for area-level characteristics, including ZIP code median and area deprivation index quartile. Standard errors were clustered at the county level.Results: The study included 954 patients with MIBC, 1,520 medical oncologists, and 976 urologists in Pennsylvania. Mean (SD) drive time to the nearest oncologist was 17.1 (11.4) minutes and median drive time was 14.4 minutes. Mean drive time to the nearest urologist was 13.9 (9.2) minutes and median drive time was 11.7 minutes. A 30-minute increase in drive time to the nearest urologist was associated with a 12.5 percentage point lower likelihood of undergoing cystectomy within 90 days (95% CI: -24.3 to -0.6). A 30-minute increase in drive time to the nearest oncologist was associated with an 11.9 percentage point lower likelihood of receiving neoadjuvant chemotherapy (95% CI: -23.4 to -0.4). Drive time was not significantly associated with 90-day mortality or readmission within 90 days. Conclusions: Drive time to both oncologists and urologists is associated with timely receipt of guideline-recommended care for patients with MIBC. Understanding the impact of geographic access to medical oncologists and urologists on clinical outcomes for patients with MIBC can inform providers and policymakers in addressing geographic equity in cancer care. Citation Format: Sruthi Muluk, Coleman Drake, Zhaojun Sun, Manisha Bhattacharya, Bruce Jacobs, Lindsay M. Sabik. Drive time to physicians and outcomes for bladder cancer [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr A137.

Effect of chronic disease on racial difference in COVID-19-associated hospitalization among cancer patients.

Research indicates that Black cancer patients have higher rates of COVID-19 hospitalization than their White counterparts. However, the extent to which chronic diseases contribute to racial disparities remains uncertain. We aimed to quantify the effect of chronic diseases on racial disparity in COVID-19-associated hospitalization among cancer patients. We linked Louisiana Tumor Registry's data with statewide COVID-19 data and hospital in-patient discharge data to identify patients diagnosed with cancer in 2015-2019 who tested positive for COVID-19 in 2020 and those with COVID-19-associated hospitalization. Multivariable logistic regression and mediation methods based on linear structural equations were employed to assess the effects of the number of chronic diseases (0, 1-2, ≥3) and individual chronic diseases. Of 6381 cancer patients who tested positive for COVID-19, 31.6% were non-Hispanic Black cancer patients. Compared with non-Hispanic White cancer patients, non-Hispanic Black cancer patients had a higher prevalence of chronic diseases (79.5% vs 66.0%) and higher COVID-19-associated hospitalization (27.2% vs 17.2%). The odds of COVID-19-associated hospitalization were 80% higher for non-Hispanic Black cancer patients than non-Hispanic White cancer patients (odds ratio = 1.80, 95% confidence interval = 1.59 to 2.04). After adjusting for age, sex, insurance, poverty, obesity, and cancer type, number of chronic diseases explained 37.8% of the racial disparity in COVID-19-associated hospitalization, and hypertension, diabetes, and chronic renal disease were the top 3 chronic diseases explaining 9.6%, 8.9%, and 7.3% of the racial disparity, respectively. Chronic diseases played a substantial role in the racial disparity in COVID-19-associated hospitalization among cancer patients, especially hypertension, diabetes, and renal disease. Understanding and addressing the root causes are crucial for targeted interventions, policies, and health-care strategies to reduce racial disparity.

Assessing hospital differences in low-risk cesarean delivery metrics in Florida

Unnecessary cesarean deliveries lead to increased maternal and neonatal morbidities and mortalities. In 2020, Florida had a cesarean delivery rate of 35.9%, the third highest in the nation. An effective quality improvement strategy to reduce overall cesarean delivery rates is to decrease primary cesarean deliveries in low-risk births (nulliparous, term, singleton, vertex). Of note, 3 nationally accepted hospital measures of low-risk cesarean delivery rates include the nulliparous, term, singleton, vertex; Joint Commission; and Society for Maternal-Fetal Medicine metrics. Comparing metrics is necessary because accurate and timely measurement is essential to support multihospital quality improvement efforts to reduce low-risk cesarean delivery rates and improve the quality of maternal care. This study aimed to assess differences in hospital low-risk cesarean delivery rates in Florida using 5 different metrics of low-risk cesarean delivery rate based on (1) risk methodology, nulliparous, term, singleton, vertex; Joint Commission; and Society for Maternal-Fetal Medicine metrics, and (2) data source, linked birth certificate and hospital discharge records and hospital discharge records only. This was a population-based study of live Florida births from 2016 to 2019 to compare 5 approaches to calculating low-risk cesarean delivery rates. Analyses were performed using linked birth certificate data and inpatient hospital discharge data. The 5 low-risk cesarean delivery measures were defined as follows: nulliparous, term, singleton, vertex birth certificate; Joint Commission-linked used Joint Commission exclusions; Society for Maternal-Fetal Medicine-linked used Society for Maternal-Fetal Medicine exclusions; Joint Commission hospital discharge with Joint Commission exclusions; and Society for Maternal-Fetal Medicine hospital discharge with Society for Maternal-Fetal Medicine exclusions. Nulliparous, term, singleton, vertex birth certificate was based on data from birth certificates and not using linked hospital discharge data. Designated as nulliparous, term, singleton, vertex, it does not exclude other high-risk conditions. The second and third measures (Joint Commission-linked used Joint Commission exclusions and Society for Maternal-Fetal Medicine-linked used Society for Maternal-Fetal Medicine exclusions) use data elements from the full-linked dataset to designate nulliparous, term, singleton, vertex and excluded several high-risk conditions. The last 2 measures (Joint Commission hospital discharge with Joint Commission exclusions; and Society for Maternal-Fetal Medicine hospital discharge with Society for Maternal-Fetal Medicine exclusions) were based on data from hospital discharge data only and not using linked birth certificate data. These measures generally reflect term, singleton, and vertex because parity could not be assessed adequately on hospital discharge data. Hospital differences between these 5 measures were calculated overall and by neonatal intensive care unit level. Overall, the median of hospital low-risk cesarean rates decreased across the measures, from NTSV-BC 30.7%, to Joint Commission linked 29.1%, and Society for Maternal Fetal Medicine hospital discharge 29.2% with a large decrease to Joint Commission hospital discharge 19.4% and Society for Maternal Fetal Medicine hospital discharge 18.1%. A similar trend was seen by neonatal intensive care unit level. For each of the measures, level II had the highest median low-risk cesarean rates (nulliparous. term, singleton, vertex birth certificate) 32.7%, Joint Commission linked (31.4%), Society for Maternal Fetal Medicine linked: 31.1%, Society for Maternal Fetal Medicine hospital discharge 19.3%), except for level III Joint Commission hospital discharge (20.0%). A comparison of the median number of low-risk births overall and by neonatal intensive care unit level showed a decreasing number across the linked and hospital discharge measures. Again, a wide gap in low-risk cesarean delivery rates was identified between linked measures and hospital discharge measures. However, this gap narrowed as hospital rates increased. Quality monitoring of low-risk cesarean delivery rates measured by the nulliparous, term, singleton, vertex metric using the birth certificate was fairly accurate and provided timely assessment for use by Florida hospitals. The nulliparous, term, singleton, vertex birth certificate rates were comparable with low-risk metrics using the linked data source. Overall, metrics used within the same data source had similar rates, with the Society for Maternal-Fetal Medicine metric having the lowest rates. Across data sources, metrics using hospital discharge data only resulted in substantially underestimated rates because of the inclusion of multiparous women and should be interpreted with caution.

Inflammatory Bowel Disease in Appalachian Kentucky: An Investigation of Outcomes and Health Care Utilization.

Rural residence has been associated with a lower incidence of inflammatory bowel disease (IBD) but higher health care utilization and worse outcomes. Socioeconomic status is intrinsically tied to both IBD incidence and outcomes. Inflammatory bowel disease outcomes have not been investigated in Appalachia: a rural, economically distressed region rife with risk factors for both increased incidence and unfavorable outcomes. Hospital inpatient discharge and outpatient services databases were utilized to assess outcomes in patients diagnosed with either Crohn's disease (CD) or ulcerative colitis (UC) in Kentucky. Encounters were classified by patient residence in Appalachian or non-Appalachian counties. Data were reported as crude and age-adjusted rates of visits per 100,000 population per year collected in 2016 to 2019. National inpatient discharge data from 2019, stratified by rural and urban classification codes, were utilized to compare Kentucky to national trends. Crude and age-adjusted rates of inpatient, emergency department and outpatient encounters were higher in the Appalachian cohort for all 4 years observed. Appalachian inpatient encounters are more frequently associated with a surgical procedure (Appalachian, 676, 24.7% vs non-Appalachian, 1408, 22.2%; P = .0091). In 2019, the Kentucky Appalachian cohort had significantly higher crude and age-adjusted rates of inpatient discharges for all IBD diagnoses compared with national rural and nonrural populations (crude 55.2; 95% CI, 50.9-59.5; age-adjusted 56.7; 95% CI, 52.1-61.3). There is disproportionately higher IBD health care utilization in Appalachian Kentucky compared with all cohorts, including the national rural population. There is a need for aggressive investigation into root causes of these disparate outcomes and identification of barriers to appropriate IBD care.

Abstract P272: Spatial and Temporal Trends of Infective Endocarditis Hospitalizations in Kentucky, 2008-2018

Background: Infective endocarditis (IE) hospitalization rates have increased significantly in the last decade, primarily due to the opioid abuse epidemic. A North Carolina study showed an increase in intravenous drug use infective endocarditis (IDU-IE) from 0.9 to 10.9/100,000 during 2007-2017. In 2016, Kentucky ranked among the top ten states with the highest opioid-related overdose deaths. Limited data about IE trends are available for Kentucky. Objective: To examine county-level spatial and temporal trends in IE rates in Kentucky and identify significant high-rate clusters. Methods: We used the Kentucky hospital inpatient discharge data (2008-2018). We included hospitalizations for patients 18 years and older who are Kentucky residents. All IE admissions were identified using ICD-9 and ICD-10 diagnosis codes (421.0, 421.1, 421.9, 424.90, 424.91, 424.99, 112.81, I33.0, I39, I33.9, I38, A3282, and B376). IDU-IE were identified using additional ICD codes for injected illicit drug use. We used US Census data to calculate yearly hospitalization rates of IE /100,000 residents. Joinpoint regression was used to calculate annual percent change (APC). We conducted spatiotemporal cluster analysis at the county level using a Poisson model in SaTScan. Results: Throughout the study period, there were 17,787 IE hospital admissions, including 3,577 IDU-IE. Medicaid patients were more often in the IDU-IE group rather than the non-IDU-IE (71% versus 15%). The IDU-IE hospitalization rate increased from 1.2 to 27.3 / 100,000 in 2008 to 2018 (Figure). From 2013 to 2018, APC for IDU-IE hospitalizations was 71.3, (95% CI: 47.8, 98.5). The spatiotemporal scan statistics identified five statistically significant clusters. The largest cluster occurred during 2015-2018 and included 34 counties out of 120 Kentucky counties, all located in the Appalachian region of Eastern Kentucky (RR= 2.1, p&lt;0.00001). Conclusion: Our study showed increased rates of IE hospitalizations due to an increase in IDU-IE, especially in Eastern Kentucky.

Association of the Affordable Care Act Medicaid Expansion With Secondary Overtriage among Young Adult Trauma Patients

BackgroundPrevious work has shown that the Affordable Care Act (ACA) Medicaid expansion decreased the uninsured rate and improved some trauma outcomes among young adult trauma patients, but no studies have investigated the impact of ACA Medicaid expansion on secondary overtriage, namely the unnecessary transfer of non-severely injured patients to tertiary trauma centers. MethodsStatewide hospital inpatient and emergency department discharge data from two Medicaid expansion and one non-expansion state were used to compare changes in insurance coverage and secondary overtriage among trauma patients aged 19-44 y transferred into a level I or II trauma center before (2011-2013) to after (2014-quarter 3, 2015) Medicaid expansion. Difference-in-difference (DD) analyses were used to compare changes overall, by race/ethnicity, and by ZIP code-level median income quartiles. ResultsMedicaid expansion was associated with a decrease in the proportion of patients uninsured (DD: -4.3 percentage points; 95% confidence interval (CI): -7.4 to -1.2), an increase in the proportion of patients insured by Medicaid (DD: 8.2; 95% CI: 5.0 to 11.3), but no difference in the proportion of patients who experienced secondary overtriage (DD: -1.5; 95% CI: -4.8 to 1.8). There were no differences by race/ethnicity or community income level in the association of Medicaid expansion with secondary overtriage. ConclusionsIn the first 2 y after ACA Medicaid expansion, insurance coverage increased but secondary overtriage rates were unchanged among young adult trauma patients transferred to level I or II trauma centers.

Association of emergency department crowding with inpatient outcomes.

To examine the association of higher emergency department (ED) census with inpatient outcomes on the day of discharge (inpatient length of stay, in-hospital mortality, ED revisits, and readmissions). All-payer ED and inpatient discharge data and hospital characteristics data from all non-federal, general, and acute care hospitals in the state of California from October 1, 2015 to December 31, 2017. In retrospective data analysis, we examined whether ED census was associated with inpatient outcomes for all inpatients, including those not admitted through the ED. The main predictor variable was ED census on day of discharge, categorized based on hospital year and day of week. Separate linear regression models with robust SEs and hospital fixed effects examined the association of ED census on inpatient outcomes (length of stay, 3-day ED revisit, 30-day all-cause readmission, in-hospital mortality), controlling for patient and visit-level factors. We stratified analyses by whether admission was elective or unscheduled. Inpatient discharges in non-federal, general medical hospitals with EDs. We examined 5,784,253 discharges. The adjusted model showed that, compared to when the ED was below the median, higher ED census on the day of discharge was associated with longer inpatient length of stay, lower readmissions, and higher in-hospital mortality (90th percentile for length of stay: +0.8% [95% confidence interval, CI: +0.6% to +1.1%]; readmissions: -0.59 percentage points [or -5.6%] [95% CI: -0.0071 to -0.0048]; mortality: +0.14 percentage points [or +5.4%] [95% CI: +0.0009 to +0.0018]). [Correction added on 18 November 2022, after first online publication: '[odds rato, OR -5.6%]' and '[OR +5.4%]' of the preceding sentence have been corrected to '[or -5.6%]' and '[or +5.4%]', respectively, in this version.] Results for length of stay were primarily driven by patients with elective admissions, while results for readmissions and in-hospital mortality were primarily driven by patients with unscheduled admissions. This study suggests that ED crowding may affect inpatients throughout the hospital, even patients who are already admitted to the hospital.

Association of Patient Characteristics With Postoperative Mortality in Children Undergoing Tonsillectomy in 5 US States

The rate of postoperative death in children undergoing tonsillectomy is uncertain. Mortality rates are not separately available for children at increased risk of complications, including young children (aged <3 y) and those with sleep-disordered breathing or complex chronic conditions. To estimate postoperative mortality following tonsillectomy in US children, both overall and in relation to recognized risk factors for complications. Retrospective cohort study based on longitudinal analysis of linked records in state ambulatory surgery, inpatient, and emergency department discharge data sets distributed by the Healthcare Cost and Utilization Project for 5 states covering 2005 to 2017. Participants included 504 262 persons younger than 21 years for whom discharge records were available to link outpatient or inpatient tonsillectomy with at least 90 days of follow-up. Tonsillectomy with or without adenoidectomy. Postoperative death within 30 days or during a surgical stay lasting more than 30 days. Modified Poisson regression with sample weighting was used to estimate postoperative mortality per 100 000 operations, both overall and in relation to age group, sleep-disordered breathing, and complex chronic conditions. The 504 262 children in the cohort underwent a total of 505 182 tonsillectomies (median [IQR] patient age, 7 [4-12] years; 50.6% females), of which 10.1% were performed in young children, 28.9% in those with sleep-disordered breathing, and 2.8% in those with complex chronic conditions. There were 36 linked postoperative deaths, which occurred a median (IQR) of 4.5 (2-20.5) days after surgical admission, and most of which (19/36 [53%]) occurred after surgical discharge. The unadjusted mortality rate was 7.04 (95% CI, 4.97-9.98) deaths per 100 000 operations. In multivariable models, neither age younger than 3 years nor sleep-disordered breathing was significantly associated with mortality, but children with complex chronic conditions had significantly higher mortality (16 deaths/14 299 operations) than children without these conditions (20 deaths/490 883 operations) (117.22 vs 3.87 deaths per 100 000 operations; adjusted rate difference, 113.55 [95% CI, 51.45-175.64] deaths per 100 000 operations; adjusted rate ratio, 29.39 [95% CI, 13.37-64.62]). Children with complex chronic conditions accounted for 2.8% of tonsillectomies but 44% of postoperative deaths. Most deaths associated with complex chronic conditions occurred in children with neurologic/neuromuscular or congenital/genetic disorders. Among children undergoing tonsillectomy, the rate of postoperative death was 7 per 100 000 operations overall and 117 per 100 000 operations among children with complex chronic conditions. These findings may inform decision-making for pediatric tonsillectomy.

Maltreatment Related Hospitalizations Among Children Ages 17 Years and Younger: New York State, 2011-2013.

Child maltreatment is an important societal and public health problem. However, there are limited data on the epidemiology of maltreatment related hospitalizations. The objective of this study was to describe maltreatment related hospitalizations among children ages 17 and younger in New York State (NYS). Using 2011-2013 statewide planning and research cooperative system (SPARCS) inpatient hospital discharge data, maltreatment related hospitalizations among children ages 17years and younger were identified using international classification of diseases, ninth revision, clinical modification codes for diagnoses and external cause of injury. Distributions of demographic and inpatient care characteristics were compared between hospitalizations for maltreatment and those for other causes, and between different types of maltreatment, using chi-square tests (for categorical variables) and t-tests (for continuous variables). During 2011-2013, a total of 853 maltreatment related hospitalizations among 836 children ages 17years and younger were documented in NYS SPARCS. Infants (children < 1) had the highest rates of hospitalization. Overall, physical abuse was the most prevalent maltreatment type reported. This is the first study in NYS to describe the epidemiology of child maltreatment hospitalizations; it establishes a statewide baseline for this public health and societal issue.

Development and validation of a knowledge-based score to predict Fried's frailty phenotype across multiple settings using one-year hospital discharge data: The electronic frailty score.

SummaryBackgroundMost claims-based frailty instruments have been designed for group stratification of older populations according to the risk of adverse health outcomes and not frailty itself. We aimed to develop and validate a tool based on one-year hospital discharge data for stratification on Fried's frailty phenotype (FP).MethodsWe used a three-stage development/validation approach. First, we created a clinical knowledge-driven electronic frailty score (eFS) calculated as the number of deficient organs/systems among 18 critical ones identified from the International Statistical Classification of Diseases and Related Problems, 10th Revision (ICD-10) diagnoses coded in the year before FP assessment. Second, for eFS development and internal validation, we linked individual records from the Lc65+ cohort database to inpatient discharge data from Lausanne University Hospital (CHUV) for the period 2004-2015. The development/internal validation sample included community-dwelling, non-institutionalised residents of Lausanne (Switzerland) recruited in the Lc65+ cohort in three waves (2004, 2009, and 2014), aged 65-70 years at enrolment, and hospitalised at the CHUV at least once in the year preceding the FP assessment. Using this sample, we selected the best performing model for predicting the dichotomised FP, with the eFS or ICD-10-based variables as predictors. Third, we conducted an external validation using 2016 Swiss nationwide hospital discharge data and compared the performance of the eFS model in predicting 13 adverse outcomes to three models relying on well-designed and validated claims-based scores (Claims-based Frailty Index, Hospital Frailty Risk Score, Dr Foster Global Frailty Score).FindingsIn the development/internal validation sample (n = 469), 14·3% of participants (n = 67) were frail. Among 34 models tested, the best-subsets logistic regression model with four predictors (age and sex at FP assessment, time since last hospital discharge, eFS) performed best in predicting the dichotomised FP (area under the curve=0·71; F1 score=0·39) and one-year adverse health outcomes. On the external validation sample (n = 54,815; 153 acute care hospitals), the eFS model demonstrated a similar performance to the three other claims-based scoring models. According to the eFS model, the external validation sample showed an estimated prevalence of 56·8% (n = 31,135) of frail older inpatients at admission.InterpretationThe eFS model is an inexpensive, transportable and valid tool allowing reliable group stratification and individual prioritisation for comprehensive frailty assessment and may be applied to both hospitalised and community-dwelling older adults.FundingThe study received no external funding.

Aortic dissection and ruptures in adult congenital heart disease in Texas from 2009 to 2019.

Acute thoracic aortic dissection and rupture (TADR) has an incidence of 5-7 per 100 000-person years. Today, most children with congenital heart disease (CHD) survive to become adults with congenital heart disease (ACHD). This study evaluates TADR in patients with ACHD in a large, hospitalized patient population over 11 years to evaluate the incidence, risk factors and outcomes associated with TADR. This was a retrospective review of the Texas Inpatient Discharge Data Set from 1 January 2009 to 31 December 2019. All non-trauma discharges of patients ≥18 years were included. ACHD discharges were identified by International Classification of Diseases, 9th edition (ICD-9)/10 diagnosis codes. TADR were identified using 2 definitions: TADR1 is an ICD-9/10 code for TADR, and TADR2 is TADR1 with an ICD-9/10 procedure code for aortic intervention. Descriptive, univariate and logistic regression statistics were used. A total of 22 154 664 eligible discharges were identified, of which 12 584 (0.06%) were TADR1 and a subgroup of 5699 (0.03%) were TADR2. CHD was more prevalent in TADR1 (0.2% vs 0.05%; P < 0.001) and TADR2 (0.3% vs 0.04%; P < 0.001). Adjusting for known TADR risk factors, CHD had an odds ratio of 1.69 (95% confidence interval: 1.09-2.63; P = 0.020) for TADR1 and an odds ratio of 1.69 (95% confidence interval: 0.99-2.88; P = 0.056) for TADR2. No in-hospital deaths were found in patients with CHD with TADR. ACHD discharges had a higher frequency of TADR versus the general population (0.9-1.2 vs 0.3-0.6 per 1000 discharges). There is an indication that CHD confers an increased adjusted odds of TADR. As the ACHD population continues to grow in number as well as age, it will be important to continue to assess the risk of TADR from CHD and how traditional risk factors impact this risk.

Impact of diabetes and modifiable risk factors on pancreatic cancer survival in a population-based study after adjusting for clinical factors.

Our study aimed to examine the impact of diabetes, smoking and BMI on pancreatic cancer survival in a population-based setting by adjusting both sociodemographic and clinical factors and measuring their attributable risk. Data on pancreatic adenocarcinoma patients diagnosed in 2011-2017 were acquired from the Louisiana Tumor Registry. Diabetes, smoking, height, and weight were abstracted from medical records and linked with Hospital Inpatient Discharge Data to enhance the completeness of the diabetes data. The Cox regression model was used to assess effect sizes of diabetes, smoking, and BMI on cancer-specific survival and survival rate. The partial population attributable risk was employed to measure the attributable risk of these risk factors. Of the 3,200 eligible patients, 34.6% were diabetics, 23.9% were current smokers, and 52.3% had BMI ≥ 25kg/m2. After adjusting for sociodemographic and clinical factors, diabetic patients had an increased cancer-specific death risk of 15% (95% CI, 1.06-1.25), 36% (95% CI, 1.19-1.44) for current smokers, and 24% (95% CI, 1.00-1.54) for patients with a BMI ≥ 40 when compared to their counterparts. Diabetic current smokers had significantly lower 2- and 3-year adjusted cancer-specific survival rates, 13.1% and 10.5%, respectively. By eliminating diabetes and modifiable risk factors, an estimated 16.6% (95% CI, 6.9%-25.9%) of the cancer-specific deaths could be avoided during a nine-year observational period between 2011 and 2019. Diabetes and smoking contributed substantially to the reduction of pancreatic cancer survival even after controlling for sociodemographic and clinical factors; however, BMI ≥ 35 was observed to increase risk of mortality among stage III-IV patients only.

Hospital Geographic Location and Unexpected Complications in term Newborns in Florida.

Birth trauma rates in term of neonates is a quality measure used by the Joint Commission. In the United States birth trauma rates occurs at a rate of 37 per 1000 live births and are on the decline. However, this decline has been significantly lower among term neonates born in rural facilities. There is a critical lack of evidence toward the influence geographical risk factors has on birth trauma rates for neonatal patients. We sought to measure rural community and hospital characteristics associated with birth trauma. A retrospective longitudinal study design was used to examine inpatient medical discharge data across 103 hospitals of neonates at birth from 2013 to 2018. Discharge data was linked to the American Hospital Association annual survey. We used a multi-level mixed effect model to investigate the relationship between individual and hospital-level attributes associated with increased risk of birth trauma among neonatal patients. We found that rural hospitals were 3.99 times (p < 0.001) more likely to experience higher birth trauma than urban hospitals. Medium sized hospitals were 2.11 times (p < 0.001) more likely to experience higher birth trauma. Hospitals who indicate having a safety culture were more likely (p < 0.05) to have high rates of birth trauma. Neonates born at rural hospitals, were more likely to experience a birth-related injury. Policy strategies focusing on improving health care quality in rural areas are critical to mitigating this increased risk of birth trauma. Further research is required to assess how physician characteristics may impact birth trauma rates.

The Impact of Smoke-Free Air Laws and Conventional Cigarette Taxes on Cardiovascular Hospitalizations.

Smoke-free air legislation and conventional cigarette taxes have long been used to reduce smoking initiation, prevalence, and conventional cigarette consumption. However, the extent to which these policies affect population health across a range of diagnoses and age groups remains less well understood. Analyses use 2005-2014 hospital inpatient discharge data from up to 40 US states to estimate the effects of smoke-free air laws and conventional cigarette taxes on cardiovascular hospitalizations among working age and older adults. An increase in the percent of a county's population covered by smoke-free air laws yielded a significant decline of 2.4% (Relative risk [RR]: 0.976, 95% confidence interval [95% CI]: 0.954, 0.997) in acute cerebrovascular disease hospitalizations among older adults. Moreover, significant declines of 2.0% (RR: 0.980, 95% CI: 0.967, 0.994) and 2.8% (RR: 0.972, 95% CI: 0.949, 0.996) in acute cerebrovascular disease were observed among older adults in the first year and subsequent years after smoke-free air legislation was implemented, respectively. Conventional cigarette taxes did not yield a significant change in acute cerebrovascular disease hospitalizations, nor did either tobacco control policy lead to a significant decline in acute myocardial infarction hospitalizations. Smoke-free air laws play an important role in reducing adult cardiovascular hospitalizations. These findings confirm existing research on acute cerebrovascular disease outcomes, as well as the modest effects on acute myocardial infarction hospitalizations observed in state- and national-level analyses. Current research at the local level finds smoke-free air laws yield 40% declines in acute myocardial infarction hospitalizations and 29% declines in acute cerebrovascular disease. State- and national-level analyses find smaller effects of smoke-free air laws, and largely omit analyses of working age adults. Existing research likely suffers from omitted variable bias, including state-level tobacco control funding and local-level conventional cigarette taxes. Using adult hospitalization data from up to 40 states, this study confirms existing evidence at the national and state level, and provides new evidence that smoke-free air laws significantly reduce acute cerebrovascular disease hospitalizations among older adults.

Unwarranted Variation in Healthcare: Impacts of Test-Order Practice Variation on Care-Delivery Cost

Motivated by payment reform model policies that aim to reduce practice variation, this study explores whether and how lower variations in clinical practice relate to hospital resource usage. Specifically, we focus on practice variation in test-ordering (e.g., laboratory and radiology tests) stage and use the statistical process control as a theoretical lens to hypothesize its negative impacts on subsequent care-delivery costs. We define practice variation as all variation not resulting from patient mix, and construct a measure using inpatient discharge data for each patient cohort having an identical medical condition. The high underuse practice variation in test-ordering implies that a hospital is more likely to fail to order proper tests for some patients. Thus, such a hospital may spend extra efforts in care-delivery stages to alleviate potential adverse effects of the test underuse. We also consider the intervening effects of two quality initiatives on the relationship: process quality (i.e., how well a hospital adheres to evidence-based medical guidelines to diagnose and treat patients) and experiential quality (i.e., external perceptions of care quality from a patient’s perspective). Based on a comprehensive six-year inpatient data from New York and Florida states, we find that higher underuse variation in the test-ordering lead to higher care-delivery cost. Interestingly, this phenomenon is even stronger when a hospital provides a higher-quality patient experience because such a hospital tends to provide more responsive care, which is often resource-intensive. Therefore, hospitals may be improperly rewarded for quality improvements if practice variation is ignored, implying that incentives and penalties for hospital operations should be designed to account for such effects. We discuss our findings and policy implications.