National Hospitalization Trends and the Role of Preventable Hospitalizations among Centenarians in the United States (2000–2009)

Increases in life expectancy mean that an unprecedented number of individuals are reaching centenarian status, often with complex health concerns. We analyzed nationally representative hospital admissions data (200–2009) from the National Inpatient Study (NIS) for 52,618 centenarians (aged 100–115 years, mean age 101.4). We predicted length of stay (LOS) via negative binomial models and total inflation adjusted costs via fixed effects regression analysis informed by descriptive data. We also identified hospitalizations due to ambulatory care-sensitive conditions defined by AHRQ Prevention Quality Indicators. Mean LOS decreased from 6.1 to 5.1 days, while over the same time period the mean total adjusted charges rose from USD 13,373 to USD 25,026 in 2009 dollars. Black, Hispanic, Asian, or other race centenarians had higher cost stays compared to White, but only Black and Hispanic centenarians had significantly greater mean length of stay. Comorbidities predicted greater length of stay and higher costs. Centenarians admitted on weekends had higher costs but shorter length of stay. In total, 29.4% of total costs were due to potentially preventable hospitalizations for total charges (2000–2009) of USD 341.8M in 2009 dollars. Centenarian hospitalizations cost significantly more than hospitalization for any other group of elderly in the U.S.

Hearing Loss and Preventable Hospitalizations

Nearly half of all adults over the age of 60 years have hearing loss. Recent research suggests adults with hearing loss experience increased health care expenditures and hospitalization. However, little is known about whether these are preventable hospitalizations which may indicate poorer healthcare system engagement. In this cross-sectional analysis, we examined data from combined 2016-2018 Medicare Current Beneficiary Survey (MCBS) datasets. Participants are asked to describe their self-perceived trouble hearing. Preventable hospitalizations were defined and generated from administrative claims files based on the Agency for Healthcare Research and Quality identified conditions that should be manageable in ambulatory care settings. Multivariate regression models adjusted for demographic/socioeconomic characteristics and general health determinants were used to explore the association between trouble hearing and outcomes. The combined 2016-2018 MCBS administrative claims files included 18,814 participant-years, 49.8% reported no trouble hearing, 43.4% reported a little trouble and 6.8% a lot of trouble hearing, respectively. A higher proportion of those with a lot of trouble hearing (6.8%) experienced at least one preventable hospitalization compared to those with a little trouble hearing (3.4%) and no trouble hearing (2.5%). In a fully adjusted logistic regression model, hearing loss was associated with 1.35 times the odds of experiencing at least one preventable hospitalization per year (OR=1.35; 95% CI=1.03-1.77). Medicare beneficiaries with hearing loss experience higher rates of preventable hospitalizations. This may be due to avoidance of care due to communication barriers. Further work is needed to understand underlying reasons and whether addressing hearing loss modifies the observed association.

Examining the Risk of Potentially Preventable Hospitalization in Adults With Congenital and Acquired Disabilities

Introduction Adults with congenital (cerebral palsy/spina bifida (CP/SB)) or acquired disabilities (spinal cord injury (SCI) or multiple sclerosis (MS)) are more likely than those without disability to develop medical complications. Little is known about potentially preventable hospitalizations (PPH) among adults with disabilities. PPHs are preventable if a patient had timely access to care. Our objective was to estimate PPH risk for each of the aforementioned disabilities. Methods We used private payer claims data from 2007-2017 to identify adults (18+) with diagnoses of CP/SB (n=10,617), SCI (n=5,173), and MS (n=6,198). Adults without these disabilities were included as controls. We propensity score matched individuals for age and sex. Logistic regression models with repeated measures were subsequently applied, adjusting for age, sex, race/ethnicity, health indicators, U.S. census divisions, and socioeconomic variables. Odd ratios (OR) were compared over 4-years of follow up. Results Adults with CP/SB, SCI, and MS had higher odds of any PPH compared with adults without disability [CP/SB: (OR=4.10; 95% CI: 2.31-7.31); SCI: (OR=1.67; 95% CI: 1.21-2.32); and MS: (OR=1.48; 95% CI: 1.00-2.25)]. Use of preventative services reduced the PPH risk. For example, wellness visit reduced the odds of PPH by almost half [CP/SB: (OR=0.52; 95% CI: 0.41-0.67); SCI: (OR=0.57; 95% CI: 0.45-0.71); and MS: (OR=0.53; 95% CI: 0.40-0.66)]. Conclusions Adults with disabilities are at greater odds of PPH compared to adults without disabilities. Clinical guidelines for use of preventative care for adults living with disabilities need to be accordingly updated.

Preventable Hospitalizations in Adults With Alzheimer's Disease and Related Dementias: United States, 2016–2018

Alzheimer's disease and related dementias (ADRD) are a significant public health burden. Preventing hospitalizations in adults with ADRD is a public health priority. Data from the 2016–2018 Healthcare Cost Utilization Project National Inpatient Sample, an all-payer representative sample of US hospitalizations, were used to describe potentially preventable hospitalizations in adults ≥45 years with ADRD using International Classification of Disease, Tenth Edition, Clinical Modification (ICD-10-CM) codes. Definitions for principal or any-listed ICD-10-CM codes from the Agency for Healthcare Research and Quality defined potentially preventable hospitalizations where admissions might have been avoided by appropriate outpatient primary care management. Of discharges in adults ≥45 years with a potentially preventable hospitalization diagnosis, 11.4% (N=389,155) had a diagnosis of ADRD listed in any position. Of those discharges with ADRD, a significantly higher proportion (82.6%) with diagnosis related to potentially preventable hospitalizations were aged ≥75 years compared to 78.9% without potentially preventable hospitalizations. Additionally, of those with ADRD and potentially preventable hospitalization diagnoses, a higher proportion died in the hospital (5.7%) compared to those without potentially preventable hospitalization diagnoses (3.4%). The most common potentially preventable hospitalization diagnoses among adults with ADRD were related to sepsis (34.0%), injuries (20.8%), urinary tract infections (14.2%), and heart failure (12.7%). Measures focusing on preventing injuries as well as identifying early signs and symptoms of potentially preventable hospitalizations like urinary tract infections and sepsis in adults with ADRD could reduce the number of preventable hospitalizations in this population.

High-cost high-need patients in Medicaid: segmenting the population eligible for a national complex case management program

Background High-cost high-need patients are typically defined by risk or cost thresholds which aggregate clinically diverse subgroups into a single ‘high-need high-cost’ designation. Programs have had limited success in reducing utilization or improving quality of care for high-cost high-need Medicaid patients, which may be due to the underlying clinical heterogeneity of patients meeting high-cost high-need designations. Methods Our objective was to segment a population of high-cost high-need Medicaid patients (N = 676,161) eligible for a national complex case management program between January 2012 and May 2015 to disaggregate clinically diverse subgroups. Patients were eligible if they were in the top 5 % of annual spending among UnitedHealthcare Medicaid beneficiaries. We used k-means cluster analysis, identified clusters using an information-theoretic approach, and named clusters using the patients’ pattern of acute and chronic conditions. We assessed one-year overall and preventable hospitalizations, overall and preventable emergency department (ED) visits, and cluster stability. Results Six clusters were identified which varied by utilization and stability. The characteristic condition patterns were: 1) pregnancy complications, 2) behavioral health, 3) relatively few conditions, 4) cardio-metabolic disease, and complex illness with relatively 5) low or 6) high resource use. The patients varied by cluster by average ED visits (2.3–11.3), hospitalizations (0.3–2.0), and cluster stability (32–91%). Conclusions We concluded that disaggregating subgroups of high-cost high-need patients in a large multi-state Medicaid sample identified clinically distinct clusters of patients who may have unique clinical needs. Segmenting previously identified high-cost high-need populations thus may be a necessary strategy to improve the effectiveness of complex case management programs in Medicaid.

Comparison of deep learning with traditional models to predict preventable acute care use and spending among heart failure patients

Recent health reforms have created incentives for cardiologists and accountable care organizations to participate in value-based care models for heart failure (HF). Accurate risk stratification of HF patients is critical to efficiently deploy interventions aimed at reducing preventable utilization. The goal of this paper was to compare deep learning approaches with traditional logistic regression (LR) to predict preventable utilization among HF patients. We conducted a prognostic study using data on 93,260 HF patients continuously enrolled for 2-years in a large U.S. commercial insurer to develop and validate prediction models for three outcomes of interest: preventable hospitalizations, preventable emergency department (ED) visits, and preventable costs. Patients were split into training, validation, and testing samples. Outcomes were modeled using traditional and enhanced LR and compared to gradient boosting model and deep learning models using sequential and non-sequential inputs. Evaluation metrics included precision (positive predictive value) at k, cost capture, and Area Under the Receiver operating characteristic (AUROC). Deep learning models consistently outperformed LR for all three outcomes with respect to the chosen evaluation metrics. Precision at 1% for preventable hospitalizations was 43% for deep learning compared to 30% for enhanced LR. Precision at 1% for preventable ED visits was 39% for deep learning compared to 33% for enhanced LR. For preventable cost, cost capture at 1% was 30% for sequential deep learning, compared to 18% for enhanced LR. The highest AUROCs for deep learning were 0.778, 0.681 and 0.727, respectively. These results offer a promising approach to identify patients for targeted interventions. FUNDunding Acknowledgement Type of funding sources: Private company. Main funding source(s): internally funded by Diagnostic Robotics Inc.

Exploration of Preventable Hospitalizations for Colorectal Cancer with the National Cancer Control Program in Taiwan

Background: Causing more than 40,000 deaths each year, cancer is one of the leading causes of mortality and preventable hospitalizations (PH) in Taiwan. To reduce the incidence and severity of cancer, the National Cancer Control Program (NCCP) includes screening for various types of cancer. A cohort study was conducted to explore the long-term trends in PH/person-years following NCCP intervention from 1997 to 2013. Methods: Trend analysis was carried out for long-term hospitalization. The Poisson regression model was used to compare PH/person-years before (1997–2004) and after intervention (2005–2013), and to explore the impact of policy intervention. Results: The policy response reduced 26% for the risk of hospitalization; in terms of comorbidity, each additional point increased the risk of hospitalization by 2.15 times. The risk of hospitalization doubled for each 10-year increase but was not statistically significant. Trend analysis validates changes in the number of hospitalizations/person-years in 2005. Conclusions: PH is adopted as an indicator for monitoring primary care quality, providing governments with a useful reference for which to gauge the adequacy, accessibility, and quality of health care. Differences in PH rates between rural and urban areas can also be used as a reference for achieving equitable distribution of medical resources.

Association between multimorbidity and fear of falling: a cross-sectional study with hospitalized older adults / Associação entre multimorbidade e medo de cair: um estudo transversal com idosos hospitalizados

The aim of this study was to analyze the association between multimorbidity and fear of falling among hospitalized older adults. This is a quantitative study with an analytical sectional design, conducted with 83 individuals aged 60 years or older, hospitalized for falls in a referral hospital for emergency treatment in Brazil. Data collection took place through the application of a questionnaire containing socioeconomic and health information as well as characteristics of the fall. The logistic regression analysis revealed that older people who reported multimorbidity are 3.16 times more likely to be afraid of falling than older people who did not report multimorbidity, regardless of sex, age, frailty, and the number of falls in the last year. These results could aid in improving hospital approaches in relation to multimorbidity and fear of falling, decreasing preventable hospitalizations due to falls.

The Burden of Diabetes-Related Preventable Hospitalization: 11-Year Trend and Associated Factors in a Region of Southern Italy

(1) Introduction: Diabetes care is complex and delivered by different care providers in different settings across the healthcare system. Better coordination through all levels of care can lead to better outcomes and fewer hospitalizations. Prevention quality indicators (PQIs) for diabetes allow us to monitor diabetes-related avoidable admissions. The aim of this research is to assess the trend of diabetes-related preventable hospitalizations and associated risk factors in a southern Italian region. (2) Methods: The study considered all hospital admissions performed from 2008 to 2018 in the Abruzzo region, Southern Italy. Data were collected from hospital discharge records. Four different indicators were evaluated as follows: short-term complications (PQI-01), long-term complications (PQI-03), uncontrolled diabetes (PQI-14) and lower-extremity amputations (PQI-16). Joinpoint models were used to evaluate the time trends of standardized rates and the average annual percent change (AAPC). (3) Results: During study period, 8660 DRPH were performed: 1298 among PQI-01, 3217 among PQI-03, 1975 among PQI-14 and 2170 among PQI-16. During the study period, PQI-01and PQI-04 showed decreasing trends. An increasing trend was showed by PQI-16. (4) Conclusions: During an 11-year period, admissions for short-term diabetes complications and for uncontrolled diabetes significantly decreased. The use of standardized tools as PQIs can help the evaluation of healthcare providers in developing preventive strategy.