Determinants of Overnight Stay in Health Centres and Length of Admission: A Study of Canadian Seniors

Socioeconomic differences in older adults' unplanned hospital admissions: the role of health status and social network

BACKGROUND

the socioeconomic distribution of unplanned hospital admissions in older adults is poorly understood. We compared associations of two life-course measures of socioeconomic status (SES) with unplanned hospital admissions while comprehensively accounting for health, and examined the role of social network in this association.

METHODS

in 2,862 community-dwelling adults aged 60+ in Sweden, we derived (i) an aggregate life-course SES measure grouping individuals into Low, Middle or High SES based on a summative score, and (ii) a latent class measure that additionally identified a Mixed SES group, characterised by financial difficulties in childhood and old age. The health assessment combined measures of morbidity and functioning. The social network measure included social connections and support components. Negative binomial models estimated the change in hospital admissions over 4 years in relation to SES. Stratification and statistical interaction assessed effect modification by social network.

RESULTS

adjusting for health and social network, unplanned hospitalisation rates were higher for the latent Low SES and Mixed SES group (incidence rate ratio [IRR] = 1.38, 95% confidence interval [CI]: 1.12-1.69, P = 0.002; IRR = 2.06, 95% CI: 1.44-2.94, P < 0.001; respectively; ref: High SES). Mixed SES was at a substantially greater risk of unplanned hospital admissions among those with poor (and not rich) social network (IRR: 2.43, 95% CI: 1.44-4.07; ref: High SES), but the statistical interaction test was non-significant (P = 0.493).

CONCLUSION

socioeconomic distributions of older adults' unplanned hospitalisations were largely driven by health, although considering SES dynamics across life can reveal at-risk sub-populations. Financially disadvantaged older adults might benefit from interventions aimed at improving their social network.

Direct healthcare cost of hospital admissions for chronic non-communicable diseases sensitive to primary care in the elderly

Aging has imposed changes in the epidemiological profile and an increase in the prevalence of chronic non-communicable diseases (CNCDs). The aim was to estimate the direct cost related to hospital admissions of elderly people affected by CNCDs (hypertension, heart failure and diabetes mellitus) sensitive to primary care, in a medium-sized hospital, in the period 2015-2019. Secondly, we investigated whether clinical and demographic factors explain the costs and length of stay. The medical records of 165 elderly people were analyzed. We found a predominance of women with a mean age of 76.9 years. The most frequent cause of hospitalization was heart failure (62%), and the average length of stay was 9.5 days, and 16% of hospitalizations corresponded to rehospitalizations. Of these, 81% were caused by complications from the previous hospitalization. The estimated total cost was R$ 3 million. Male patients had a longer hospital stay compared to female patients. Hypertension and the total number of procedures were significant predictors of cost and length of stay. We found that in 5 years, the costs of hospital admissions for conditions sensitive to primary care in the elderly are considerable, indicating the relevance of investments in primary care.

Direct healthcare cost of hospital admissions for chronic non-communicable diseases sensitive to primary care in the elderly

Abstract Aging has imposed changes in the epidemiological profile and an increase in the prevalence of chronic non-communicable diseases (CNCDs). The aim was to estimate the direct cost related to hospital admissions of elderly people affected by CNCDs (hypertension, heart failure and diabetes mellitus) sensitive to primary care, in a medium-sized hospital, in the period 2015-2019. Secondly, we investigated whether clinical and demographic factors explain the costs and length of stay. The medical records of 165 elderly people were analyzed. We found a predominance of women with a mean age of 76.9 years. The most frequent cause of hospitalization was heart failure (62%), and the average length of stay was 9.5 days, and 16% of hospitalizations corresponded to rehospitalizations. Of these, 81% were caused by complications from the previous hospitalization. The estimated total cost was R$ 3 million. Male patients had a longer hospital stay compared to female patients. Hypertension and the total number of procedures were significant predictors of cost and length of stay. We found that in 5 years, the costs of hospital admissions for conditions sensitive to primary care in the elderly are considerable, indicating the relevance of investments in primary care.

Pathways to reduced overnight hospitalizations in older adults: Evaluating 62 physical, behavioral, and psychosocial factors

As our society ages and healthcare costs escalate, researchers and policymakers urgently seek potentially modifiable predictors of reduced healthcare utilization. We aimed to determine whether changes in 62 candidate predictors were associated with reduced frequency, and duration, of overnight hospitalizations. We used data from 11,374 participants in the Health and Retirement Study—a national sample of adults aged >50 in the United States. Using generalized linear regression models with a lagged exposure-wide approach, we evaluated if changes in 62 predictors over four years (between t0;2006/2008 and t1;2010/2012) were associated with subsequent hospitalizations during the two years prior to t2 (2012–2014 (Cohort A) or 2014–2016 (Cohort B)). After robust covariate-adjustment, we observed that changes in some health behaviors (e.g., those engaging in frequent physical activity had 0.80 the rate of overnight hospital stays (95% CI [0.74, 0.87])), physical health conditions (e.g., those with cancer had 1.57 the rate of overnight hospital stays (95% CI [1.35, 1.82])), and psychosocial factors (e.g., those who helped friends/neighbors/relatives 100–199 hours/year had 0.73 the rate of overnight hospital stays (95% CI [0.63, 0.85])) were associated with subsequent hospitalizations. Findings for both the frequency, and duration, of hospitalizations were mostly similar. Changes in a number of diverse factors were associated with decreased frequency, and duration, of overnight hospitalizations. Notably, some psychosocial factors (e.g., informal helping) had effect sizes equivalent to or larger than some physical health conditions (e.g., diabetes) and health behaviors (e.g., smoking). These psychosocial factors are mostly modifiable and with further research could be novel intervention targets for reducing hospitalizations.

Socioeconomic Inequalities in Elective and Nonelective Hospitalizations in Older Men

IMPORTANCE

Among older adults, there is limited and inconsistent evidence on the association between socioeconomic position (SEP) and elective and nonelective hospitalization.

OBJECTIVE

To evaluate the association between SEP and all-cause and cause-specific elective and nonelective hospitalization and hospital length of stay among older men.

DESIGN, SETTING, AND PARTICIPANTS

This population-based, prospective cohort study used data from the Concord Health and Aging in Men Project (CHAMP). CHAMP recruited 1705 men aged 70 years or older between January 28, 2005, and June 4, 2007, in Sydney, Australia. Data were analyzed from February 1 to September 30, 2021.

EXPOSURES

Indicators of SEP, including education (university degree certificate, diploma or no postschool qualifications), occupation (professionals and managers; small employers and self-employed; or lower clerical, service, sales workers, skilled, and unskilled workers), and source of income (other sources of income than government pension, reliance on government pensions and other sources of income, or reliant solely on a government pension), and a cumulative SEP score (tertiles) as SEP indicators; 3-level variables present high, intermediate, and low SEP.

MAIN OUTCOMES AND MEASURES

All-cause and cause-specific elective and nonelective hospitalizations, number of hospitalizations, and length of stay were the study outcomes, ascertained through data linkage. Associations were quantified using competing-risks survival regression and negative binomial regression.

RESULTS

A total of 1566 men (mean [SD] age, 76.8 [5.4] years) were included. During a mean (SD) 9.07 (3.53) years of follow-up, 1067 men had at least 1 elective hospitalization, and 1255 men had at least 1 nonelective hospitalization. No associations were found between SEP and elective hospitalizations. Being in the lowest tertile for educational level (subhazard ratio [SHR], 1.32; 95% CI, 1.11-1.58), occupational position (SHR, 1.30; 95% CI, 1.12-1.50), sources of income (SHR, 1.33; 95% CI, 1.17-1.52), and cumulative SEP tertile groups (SHR, 1.45; 95% CI, 1.24-1.68) were all associated with having at least 1 nonelective hospitalization compared with those in the highest tertiles. Significant associations were found between being in the lowest SEP groups and increased numbers and longer length of stay of nonelective hospitalizations.

CONCLUSIONS AND RELEVANCE

In this prospective cohort study, low SEP was inversely associated with nonelective hospitalizations but not elective hospitalization in older men in Australia. These findings point to the existence of socioeconomic inequalities in health care use, indicative of a need to take action to reduce these inequalities.

Institution-Specific Machine Learning Models for Prehospital Assessment to Predict Hospital Admission: Prediction Model Development Study

Background

Although multiple prediction models have been developed to predict hospital admission to emergency departments (EDs) to address overcrowding and patient safety, only a few studies have examined prediction models for prehospital use. Development of institution-specific prediction models is feasible in this age of data science, provided that predictor-related information is readily collectable.

Objective

We aimed to develop a hospital admission prediction model based on patient information that is commonly available during ambulance transport before hospitalization.

Methods

Patients transported by ambulance to our ED from April 2018 through March 2019 were enrolled. Candidate predictors were age, sex, chief complaint, vital signs, and patient medical history, all of which were recorded by emergency medical teams during ambulance transport. Patients were divided into two cohorts for derivation (3601/5145, 70.0%) and validation (1544/5145, 30.0%). For statistical models, logistic regression, logistic lasso, random forest, and gradient boosting machine were used. Prediction models were developed in the derivation cohort. Model performance was assessed by area under the receiver operating characteristic curve (AUROC) and association measures in the validation cohort.

Results

Of 5145 patients transported by ambulance, including deaths in the ED and hospital transfers, 2699 (52.5%) required hospital admission. Prediction performance was higher with the addition of predictive factors, attaining the best performance with an AUROC of 0.818 (95% CI 0.792-0.839) with a machine learning model and predictive factors of age, sex, chief complaint, and vital signs. Sensitivity and specificity of this model were 0.744 (95% CI 0.716-0.773) and 0.745 (95% CI 0.709-0.776), respectively.

Conclusions

For patients transferred to EDs, we developed a well-performing hospital admission prediction model based on routinely collected prehospital information including chief complaints.