The effects of a variant of the program for all-inclusive care of the elderly on hospital utilization and outcomes

OBJECTIVES: To compare the effects of the Wisconsin Partnership Program (WPP) on hospital, emergency department (ED), and nursing home utilization with those of traditional care. DESIGN: Quasi-experimental longitudinal cohort design. SETTING: Selected counties in Wisconsin. PARTICIPANTS: WPP elderly enrollees and two matched control groups consisting of frail older people enrolled in fee-for-service insurance plans, Medicare, and Medicaid and receiving home- and community-based waiver services, one from the same geographic area as the WPP and another from a location in the state where the WPP was not offered. MEASUREMENTS: Data came from administrative records. Regression and survival analyses were adjusted for case-mix variables. RESULTS: No significant differences in hospital utilization, ED visits, preventable hospitalizations, risk of entry into nursing homes, or mortality were found. WPP enrollees had more contact with care providers than did controls. CONCLUSION: WPP did not dramatically alter the pattern of care. Part of the weak effect may be attributable to the small numbers of WPP cases per participating physician.     

The Evolving Health Policy Landscape and Suggested Geriatric Tenets to Guide Future Responses

We cannot view the future of healthcare but we can sense that big changes are afoot. Many revolve around the plans to “repeal and replace” the Affordable Care Act. We speculate on some potential areas of change in the context of a set of tenets about what care for older persons should address.     

Hospital readmissions for asthma in children and young adults in Canada

To examine the incidence rate of hospital readmission for asthma in relation to sex and age among Canadian children and young adults, we used data from 86,863 subjects under age 20 years when they had a first admission for asthma as 1 of first 5 diagnoses in Canada between April 1, 1994 and March 31, 1997. We calculated age- and sex-specific incidence rates, and used the Cox proportional hazards model for multivariate analysis. Of these subjects, 20,277 (23.3%) were readmitted to hospital for asthma during the study period. After adjusting for length of stay for first admission and province, the rate ratio for females vs. males was 0.86 for those under age 1 year, and close to unity for the 1-4-year and 5-9-year age groups, whereas it was 1.47 and 1.35 for the 10-14-year and 15-19-year age groups, respectively. The data showed similar trends for rehospitalization asthma as a primary diagnosis. The incidence rate of rehospitalization showed little sex difference between ages 1-9 years, but was markedly higher in females than in males 10-19 years of age. Airway size, female hormonal changes, increased use of cosmetic products, and cigarette smoking among adolescent girls may contribute to the age- and sex-differences in adolescence.     

Health care expenditures for Medicaid‐covered males with haemophilia in the United States, 2008

Summary. Although haemophilia is an expensive disorder, no studies have estimated health care costs for Americans with haemophilia enrolled in Medicaid as distinct from those with employer‐sponsored insurance (ESI). The objective of this study is to provide information on health care utilization and expenditures for publicly insured people with haemophilia in the United States in comparison with people with haemophilia who have ESI. Data from the MarketScan^® Medicaid Multi‐State, Commercial and Medicare Supplemental databases were used for the period 2004?2008 to identify cases of haemophilia and to estimate medical expenditures during 2008. A total of 511 Medicaid‐enrolled males with haemophilia were identified, 435 of whom were enrolled in Medicaid for at least 11 months during 2008. Most people with haemophilia qualified for Medicaid based on ‘disability’. Average Medicaid expenditures in 2008 were $142,987 [median, $46,737], similar to findings for people with ESI. Average costs for males with haemophilia A and an inhibitor were 3.6 times higher than those for individuals without an inhibitor. Average costs for 56 adult Medicaid enrollees with HCV or HIV infection were not statistically different from those for adults without the infection, but median costs were 1.6 times higher for those treated for blood‐borne infections. Haemophilia treatment can lead to high costs for payers. Further research is needed to understand the effects of public health insurance on haemophilia care and expenditures, to evaluate treatment strategies and to implement strategies that may improve outcomes and reduce costs of care.     

Risk factors for hospitalizations and readmissions among individuals with sickle cell disease: results of a U.S. survey study

Objective: Hospital admissions are significant events in the care of individuals with sickle cell disease (SCD) due to associated costs and potential for quality of life compromise.

Methods: This cross-sectional cohort study evaluated risk factors for admissions and readmissions between October 2014 and March 2016 in adults with SCD (n?=?201) and caregivers of children with SCD (n?=?330) at six centres across the U.S. Survey items assessed social determinants of health (e.g. educational attainment, difficulty paying bills), depressive symptoms, social support, health literacy, spirituality, missed clinic appointments, and outcomes hospital admissions and 30-day readmissions in the previous year.

Results: A majority of adults (64%) and almost half of children (reported by caregivers: 43%) were admitted, and fewer readmitted (adults: 28%; children: 9%). The most common reason for hospitalization was uncontrolled pain (admission: adults: 84%, children: 69%; readmissions: adults: 83%, children: 69%). Children were less likely to have admissions/readmissions than adults (Admissions: OR: 0.35, 95% CI: [0.23,0.52]); Readmissions: 0.23 [0.13,0.41]). For all participants, missing appointments were associated with admissions (1.66 [1.07, 2.58]) and readmissions (2.68 [1.28, 6.29]), as were depressive symptoms (admissions: 1.36 [1.16,1.59]; readmissions: 1.24 [1.04, 1.49]). In adults, difficulty paying bills was associated with more admissions, (3.11 [1.47,6.62]) readmissions (3.7 [1.76,7.79]), and higher spirituality was associated with fewer readmissions (0.39 [0.18,0.81]).

Discussion: Missing appointments was significantly associated with admissions and readmissions. Findings confirm that age, mental health, financial insecurity, spirituality, and clinic attendance are all modifiable factors that are associated with admissions and readmissions; addressing them could reduce hospitalizations.     

Risk factors for early hospital readmission in patients with AIDS and pneumonia

OBJECTIVE: To determine risk factors for early readmission to the hospital in patients with AIDS and pneumonia. DESIGN: Case-control analysis. SETTING: A municipal teaching hospital serving an indigent population. PATIENTS: Case patients were all AIDS patients hospitalized with Pneumocystis carinii pneumonia or bacterial pneumonia between January 1992 and March 1995 who were readmitted for any nonelective reason within 2 weeks of discharge (n = 90). Control patients were randomly selected AIDS patients admitted during the study period who were not early readmissions (n = 87), matched by proportion of Pneumocystis carinii to bacterial pneumonia. MEASUREMENTS AND MAIN RESULTS: Demographics, social support, health-related behaviors, clinical aspects of the acute hospitalization, and general medical status were the main predictors measured. RESULTS: Patients were at significantly increased risk of early readmission if they left the hospital unaccompanied by family or friend (odds ratio [OR] 4.76; 95% confidence interval [CI] 2.06, 11.0; p =.0003), used crack cocaine (OR 3.40; 95% CI 1.02, 11.3; p =. 046), had one or more coincident AIDS diagnoses (OR 3.65; 95% CI 1. 44, 9.26; p =.0065), or had been admitted in the preceding 6 months (OR 2.82; 95% CI 1.21, 6.57; p =.016). Demographic characteristics, alcoholism, intravenous drug use, illness severity on admission, and length of hospitalization did not predict early readmission. CONCLUSIONS: Absence of companion at discharge and crack use were important risk factors for early readmission in patients with AIDS and pneumonia. Additional AIDS comorbidity and recent antecedent hospitalization were also risk factors; however, demographics and measures of acute illness during index hospitalization did not predict early readmission.     

Cohort design to assess the association between post-hospital primary care physician follow-up visits and hospital readmissions

While multifaceted post-hospitalization interventions can succeed in preventing hospital readmissions, many of these interventions are labor-intensive and costly. We hypothesized that a timely post-discharge primary care physician (PCP) visit alone might prevent hospital readmission. We conducted a retrospective cohort study to assess whether post-hospitalization PCP visits within 14 days of discharge were associated with lower rates of 30-day hospital readmission. In a secondary analysis we also assessed: whether visits with a PCP at 7-days post-discharge changed rates of hospital readmissions and whether post-hospitalization PCP visits were associated with decreased 90-day hospital readmissions. We included all adults with a PCP who were discharged from an inpatient medical service in a large, urban integrated academic health system from January 1, 2019 to September 9, 2019 in our analysis. We performed unadjusted bivariate analyses to measure the associations between having a PCP visit within 14 and 7 days of discharge and hospital readmission within 30 and 90 days. Then we constructed multivariate logistic regression models including patient medical and utilization characteristics to estimate the adjusted odds of a patient with a post-hospitalization PCP visit experiencing a 30-day hospital readmission (primary outcome) and 90-day readmission (secondary outcome). A total of 9236 patients were discharged; mean age was 57.9 years and 59.7% were female. Of the study population, 35.6% (n = 3284) and 24.1% (n = 2224) of patients had a post-hospitalization PCP visit within 14 days and or 7 days, respectively. Overall, 1259 (13.6%) and 2153 (23.3%) of discharged patients were readmitted at 30 and 90 days, respectively. In unadjusted analyses, having a post discharge PCP visit was not associated with decreased hospital readmission rates, but after adjusting for sociodemographic, medical and utilization characteristics, having a post-hospitalization PCP visit at 14 and 7 days was associated with lower hospital readmission rates at 30 days: 0.68 (95% CI 0.59–0.79) and 0.76 (95% CI 0.66–0.89), respectively; and 90 days: 0.76 (95% CI 0.68–0.85) and 0.80 (95% CI 0.70–0.91), respectively. In this large integrated urban academic health system, having a post-hospitalization PCP visit within 14- and 7-days of hospital discharge was associated with lower rates of readmission at 30 and 90 days. Further studies should examine whether improving access to PCP visits post hospitalization reduces readmissions rates.     

Continuity of care and patient outcomes after hospital discharge

BACKGROUND: Patients are often treated in hospital by physicians other than their regular community doctor. After they are discharged, their care is often returned to their regular community doctor and patients may not see the hospital physician. Transfer of information between physicians can be poor. We determined whether early postdischarge outcomes changed when patients were seen after discharge by physicians who treated them in the hospital. METHODS: This cohort study used population-based administrative databases to follow 938833 adults from Ontario, Canada, after they were discharged alive from a nonelective medical or surgical hospitalization between April 1, 1995, and March 1, 2000. We determined when patients were seen after discharge by physicians who treated them in the hospital, physicians who treated them 3 months prior to admission (community physicians), and specialists. The outcome of interest was 30-day death or nonelective readmission to hospital. RESULTS: Of patients studied, 7.7% died or were readmitted. The adjusted relative risk of death or readmission decreased by 5% (95% confidence interval [CI], 4% to 5%) and 3% (95% CI, 2% to 3%) with each additional visit to a hospital physician rather than a community physician or specialist, respectively. The effect of hospital physician visits was cumulative, with the adjusted risk of 30-day death or nonelective readmission reduced to 7.3%, 7.0%, and 6.7% if patients had 1, 2, or 3 visits, respectively, with a hospital rather than a community physician. The effect was consistent across important subgroups. CONCLUSIONS: Patient outcomes could be improved if their early postdischarge visits were with physicians who treated them in hospital rather than with other physicians. Follow-up visits with a hospital physician, rather than another physician, could be a modifiable factor to improve patient outcomes following discharge from hospital.     

Machine‐learning algorithms for predicting hospital re‐admissions in sickle cell disease

Reducing preventable hospital re‐admissions in Sickle Cell Disease (SCD) could potentially improve outcomes and decrease healthcare costs. In a retrospective study of electronic health records, we hypothesized Machine‐Learning (ML) algorithms may outperform standard re‐admission scoring systems (LACE and HOSPITAL indices). Participants (n = 446) included patients with SCD with at least one unplanned inpatient encounter between January 1, 2013, and November 1, 2018. Patients were randomly partitioned into training and testing groups. Unplanned hospital admissions (n = 3299) were stratified to training and testing samples. Potential predictors (n = 486), measured from the last unplanned inpatient discharge to the current unplanned inpatient visit, were obtained via both data‐driven methods and clinical knowledge. Three standard ML algorithms, Logistic Regression (LR), Support‐Vector Machine (SVM), and Random Forest (RF) were applied. Prediction performance was assessed using the C‐statistic, sensitivity, and specificity. In addition, we reported the most important predictors in our best models. In this dataset, ML algorithms outperformed LACE [C‐statistic 0·6, 95% Confidence Interval (CI) 0·57–0·64] and HOSPITAL (C‐statistic 0·69, 95% CI 0·66–0·72), with the RF (C‐statistic 0·77, 95% CI 0·73–0·79) and LR (C‐statistic 0·77, 95% CI 0·73–0·8) performing the best. ML algorithms can be powerful tools in predicting re‐admission in high‐risk patient groups.