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Background Geriatric syndromes, multimorbidity, and disability are prevalent among ageing population. However, no study empirically examined their additive or synergistic effect on healthcare use. The present study aims to estimate overlapping prevalence of geriatric syndromes, multimorbidity, and disability; and to examine associations of these three conditions with healthcare use. Methods A cross-sectional study was conducted in community-dwelling older adults aged 60 and above in 12 Hong Kong districts. Pearson’s chi-squared test for trend was performed to examine prevalence of geriatric syndromes, multimorbidity, and disability across three age groups (60–69, 70–79, and ≥ 80). Multiple logistic regression was conducted to explore associations of these three conditions with three types of healthcare use (hospital admission, general outpatient clinic and specialist outpatient clinic attendance) respectively. Results Among 2618 participants, 75.3, 41.8, and 22.5% had geriatric syndromes, multimorbidity, and disability respectively, and 10.4% had all the three conditions. Prevalence of the three conditions and their coexistence significantly increased with age ( p for trend < .001). Each condition was independently associated with at least two out of three types of healthcare use. Interestingly, the associations of multimorbidity and disability with specialist outpatient clinic attendance were weakened at older age, while the associations of geriatric syndromes with hospital admission and specialist outpatient clinic attendance were strengthened. Furthermore, the odds of all the three types of healthcare use increased with the number of conditions present ( p for trend < .001). Conclusions Our findings support that the three conditions overlap and increase healthcare use. Early identification, prevention and intervention targeting older adults living with multiple healthcare needs are necessary.

Adverse drug reactions (ADRs) represent a major burden on society, resulting in significant morbidity, mortality, and health care costs. Older patients living in the community are particularly susceptible to ADRs, and are at an increased risk of ADR-related hospitalization. This review summarizes the available evidence on ADR-related hospital admission in older patients living in the community, with a particular focus on risk factors for ADRs leading to hospital admission and the need for a prediction tool for risk of ADR-related hospitalization in these individuals. The reported proportion of hospital admissions due to ADRs has ranged from 6% to 12% of all admissions in older patients. The main risk factors or predictors for ADR-related admissions were advanced age, polypharmacy, comorbidity, and potentially inappropriate medications. There is a clear need to design intervention strategies to prevent ADR-related hospitalization in older patients. To ensure the cost-effectiveness of such strategies, it would be necessary to target them to those older individuals who are at highest risk of ADR-related hospitalization. Currently, there are no validated tools to assess the risk of ADRs in primary care. There is a clear need to investigate the utility of tools to identify high-risk patients to target appropriate interventions toward prevention of ADR-related hospital admissions.

Objective: In this paper, we review the epidemiological evidence on the relationship between ambient temperature and morbidity. We assessed the methodological issues in previous studies and proposed future research directions. Data sources and data extraction: We searched the PubMed database for epidemiological studies on ambient temperature and morbidity of noncommunicable diseases published in refereed English journals before 30 June 2010. Forty relevant studies were identified. Of these, 24 examined the relationship between ambient temperature and morbidity, 15 investigated the short-term effects of heat wave on morbidity, and 1 assessed both temperature and heat wave effects. Data synthesis: Descriptive and time-series studies were the two main research designs used to investigate the temperature-morbidity relationship. Measurements of temperature exposure and health outcomes used in these studies differed widely. The majority of studies reported a significant relationship between ambient temperature and total or cause-specific morbidities. However, there were some inconsistencies in the direction and magnitude of nonlinear lag effects. The lag effect of hot temperature on morbidity was shorter (several days) compared with that of cold temperature (up to a few weeks). The temperature-morbidity relationship may be confounded or modified by sociodemographic factors and air pollution. Conclusions: There is a significant short-term effect of ambient temperature on total and causespecific morbidities. However, further research is needed to determine an appropriate temperature measure, consider a diverse range of morbidities, and to use consistent methodology to make different studies more comparable.

Background: Risk prediction models have been developed to identify those at increased risk for emergency admissions, which could facilitate targeted interventions in primary care to prevent these events. Objective: Systematic review of validated risk prediction models for predicting emergency hospital admissions in communitydwelling adults. Methods: A systematic literature review and narrative analysis was conducted. Inclusion criteria were as follows; Population: communitydwelling adults (aged 18 years and above); Risk: risk prediction models, not contingent on an index hospital admission, with a derivation and ≥ 1 validation cohort; Primary outcome: emergency hospital admission (defined as unplanned overnight stay in hospital); Study design: retrospective or prospective cohort studies. Results: Of 18,983 records reviewed, 27 unique risk prediction models met the inclusion criteria. Eleven were developed in the United States, 11 in the United Kingdom, 3 in Italy, 1 in Spain, and 1 in Canada. Nine models were derived using self-report data, and the remainder (n=18) used routine administrative or clinical record data. Total study sample sizes ranged from 96 to 4.7 million participants. Predictor variables most frequently included in models were: (1) named medical diagnoses (n=23); (2) age (n=23); (3) prior emergency admission (n=22); and (4) sex (n=18). Eleven models included nonmedical factors, such as functional status and social supports. Regarding predictive accuracy, models developed using administrative or clinical record data tended to perform better than those developed using self-report data (c statistics 0.63–0.83 vs. 0.61–0.74, respectively). Six models reported c statistics of >0.8, indicating good performance. All 6 included variables for prior health care utilization, multimorbidity or polypharmacy, and named medical diagnoses or prescribed medications. Three predicted admissions regarded as being ambulatory care sensitive. Conclusions: This study suggests that risk models developed using administrative or clinical record data tend to perform better. In applying a risk prediction model to a new population, careful consideration needs to be given to the purpose of its use and local factors.

Background The Institute for Healthcare Improvement identifies medication reconciliation as the shared responsibility of nurses, pharmacists, and physicians, where each has a defined role. The study aims to assess the clinical impact of pharmacy-led medication reconciliation performed on day one of hospital admission to the internal medicine service. Methods This is a pilot prospective study conducted at two tertiary care teaching hospitals in Lebanon. Student pharmacists who were properly trained and closely supervised, collected the medication history, and pharmacists at the corresponding sites performed the reconciliation process. Interventions related to the unintended discrepancies were relayed to the medical team. The main outcome was the number of unintended discrepancies identified. The time needed for medication history, and the information sources used to complete the Best Possible Medication History were also assessed. The unintended discrepancies were classified by medication class and route of medication administration, by potential severity, and by proximal cause leading to the discrepancy. For the bivariate and multivariable analysis, the dependent variable was the incidence of unintended discrepancies. The “total number of unintended discrepancies” was dichotomized into yes (≥ 1 unintended discrepancy) or no (0 unintended discrepancies). Independent variables tested for their association with the dependent variable consisted of the following: gender, age, creatinine clearance, number of home medications, allergies, previous adverse drug reactions, and number of information sources used to obtain the BPMH. Results were assumed to be significant when p was < 0.05. Results During the study period, 204 patients were included, and 195 unintended discrepancies were identified. The most common discrepancies consisted of medication omission (71.8%), and the most common agents involved were dietary supplements (27.7%). Around 36% of the unintended discrepancies were judged as clinically significant, and only 1% were judged as serious. The most common interventions included the addition of a medication (71.8%) and the adjustment of a dose (12.8%). The number of home medications was significantly associated with the occurrence of unintended discrepancies (ORa = 1.11 (1.03–1.19) p  = 0.007). Conclusions Pharmacy-led medication reconciliation upon admission, along with student pharmacist involvement and physician communication can reduce unintended discrepancies and improve medication safety and patient outcomes.

Background In developing countries, ambient sulfur dioxide (SO 2 ) is a serious air pollutant concern, but there is no enough and consistent epidemiological evidence about its health effects on stroke hospitalization. Methods We collected the daily air pollution data, meteorological data and number of daily hospital admissions for ischemic and hemorrhagic stroke, in Guangzhou from January 1st 2009 to December 31st 2014. Then we applied generalized additive model with a quasi-Poisson link to assess the relationship between short-term SO 2 exposure and the total number of hospital admissions for ischemic and hemorrhagic stroke. In addition, we evaluated the effect of ambient SO 2 by age (< 65 years and ≥ 65 years). Results During the study period, a 24-h mean concentration of ambient SO 2 of 27.82 μg/m 3 , a total of 58,473 ischemic stroke and 9167 hemorrhagic stroke hospital admissions hospital were recorded. Ambient SO 2 was found to increase the risk for both ischemic and hemorrhagic stroke hospital admission in single pollutant model. The maximum value of percentage changes for ischemic and hemorrhagic stroke occurred in lag 0 day and lag 1 day, per 10 μg/m 3 increase in SO 2 concentrations was corresponded to a 1.27% (95% confidence interval (CI), 0.42–2.12%) and 1.55% (95%CI, 0.02–3.11%) increased risk, respectively. The association between SO 2 and ischemic stroke hospitalization was robust to two pollutant model, but for hemorrhagic stroke it’s partially weakened after adjusting for co-pollutants. The effect of ambient SO 2 on ischemic stroke appeared to be greater for people < 65 years old, but null effect on hemorrhagic stroke was identified for both age groups. Conclusions We found short-term exposure to ambient SO 2 may significantly increase the risks of hospitalization for ischemic stroke. The findings may contribute to a better understanding of the health effects of low-levels of SO 2 .

Robust and accurate prediction of severity for patients with COVID-19 is crucial for patient triaging decisions. Many proposed models were prone to either high bias risk or low-to-moderate discrimination. Some also suffered from a lack of clinical interpretability and were developed based on early pandemic period data. Hence, there has been a compelling need for advancements in prediction models for better clinical applicability. The primary objective of this study was to develop and validate a machine learning-based Robust and Interpretable Early Triaging Support (RIETS) system that predicts severity progression (involving any of the following events: intensive care unit admission, in-hospital death, mechanical ventilation required, or extracorporeal membrane oxygenation required) within 15 days upon hospitalization based on routinely available clinical and laboratory biomarkers. We included data from 5945 hospitalized patients with COVID-19 from 19 hospitals in South Korea collected between January 2020 and August 2022. For model development and external validation, the whole data set was partitioned into 2 independent cohorts by stratified random cluster sampling according to hospital type (general and tertiary care) and geographical location (metropolitan and nonmetropolitan). Machine learning models were trained and internally validated through a cross-validation technique on the development cohort. They were externally validated using a bootstrapped sampling technique on the external validation cohort. The best-performing model was selected primarily based on the area under the receiver operating characteristic curve (AUROC), and its robustness was evaluated using bias risk assessment. For model interpretability, we used Shapley and patient clustering methods. Our final model, RIETS, was developed based on a deep neural network of 11 clinical and laboratory biomarkers that are readily available within the first day of hospitalization. The features predictive of severity included lactate dehydrogenase, age, absolute lymphocyte count, dyspnea, respiratory rate, diabetes mellitus, c-reactive protein, absolute neutrophil count, platelet count, white blood cell count, and saturation of peripheral oxygen. RIETS demonstrated excellent discrimination (AUROC=0.937; 95% CI 0.935-0.938) with high calibration (integrated calibration index=0.041), satisfied all the criteria of low bias risk in a risk assessment tool, and provided detailed interpretations of model parameters and patient clusters. In addition, RIETS showed potential for transportability across variant periods with its sustainable prediction on Omicron cases (AUROC=0.903, 95% CI 0.897-0.910). RIETS was developed and validated to assist early triaging by promptly predicting the severity of hospitalized patients with COVID-19. Its high performance with low bias risk ensures considerably reliable prediction. The use of a nationwide multicenter cohort in the model development and validation implicates generalizability. The use of routinely collected features may enable wide adaptability. Interpretations of model parameters and patients can promote clinical applicability. Together, we anticipate that RIETS will facilitate the patient triaging workflow and efficient resource allocation when incorporated into a routine clinical practice.

Background Evidence on the most effective and cost-effective management of ankle fractures is sparse but evolving. A recent large RCT in older patients with unstable fractures found that management with close-contact-casting was functionally equivalent and more cost-effective than internal fixation. We describe temporal and geographic variation in ankle fracture management and estimate the potential savings if close-contact-casting was used more often in older patients. Methods Patients admitted to hospital in England between 2007/08 and 2016/17 with an ankle fracture were identified using routine hospital episode statistics. We tested whether the use of internal fixation, and the proportion of internal fixations using intramedullary implants, changed over time. We estimated the potential annual cost savings if patients aged 60+ years were treated with close-contact-casting rather than internal fixation, in line with emerging evidence. Results Over the 10-year period, there were 223,465 hospital admissions with a primary ankle fracture diagnosis. The incidence (per 100,000) of internal fixation was fairly consistent over time in younger (33.2 in 2007/08, 30.9 in 2016/17) and older (36.5 in 2007/08, 37.4 in 2016/17) patients. The proportion of internal fixations which used intramedullary implants increased in both age groups (17.0–19.5% < 60 years; 15.2–17.4% 60+ years). In 2016/17, the cost of inpatient hospital care for ankle fractures in England was over £63.1million. If 50% of older patients who had an internal fixation instead had close-contact-casting, we estimate that approximately £1.56million could have been saved. Conclusions Despite emerging evidence that non-surgical and surgical management achieve equivalent functional outcomes in older patients, the rate of surgical fixation has remained relatively stable over the decade. The health service could achieve substantial savings if a higher proportion of older patients were treated with close-contact-casting, in line with recent evidence.

Background Given system-level focus on avoidance of unnecessary hospitalizations, better understanding admission decision-making is of utility. Our study sought to identify factors associated with hospital admission versus discharge from the Emergency Department (ED) for a population of patients who were assessed as having low medical acuity at time of decision. Methods Using an institutional database, we identified ED admission requests received from March 1, 2018 to Feb 28, 2019 that were assessed by a physician at the time of request as potentially inappropriate based on lack of medical acuity. Focused chart review was performed to extract data related to patient demographics, socioeconomic information, measures of illness, and system-level factors such as previous healthcare utilization and day/time of presentation. A binary logistic regression model was constructed to correlate patient and system factors with disposition outcome of admission to the hospital versus discharge from the ED. Physician-reported contributors to admission decision-making and chief complaint/reason for admission were summarized. Results A total of 349 (77.2%) of 452 calls resulted in admission to the hospital and 103 (22.8%) resulted in discharge from the ED. Predictors of admission included age over 65 (OR 3.5 [95%CI 1.1–11.6], p  = 0.039), homelessness (OR 3.3 [95% CI 1.7–6.4], p =0.001), and night/weekend presentation (OR 2.0 [95%CI 1.1–3.5], p  = 0.020). The most common contributing factors to the decision to admit reported by the responding physician included: lack of outpatient social support (35.8% of admissions), homelessness (33.0% of admissions), and substance use disorder (23.5% of admissions). Conclusions Physician medical decision-making regarding the need for hospitalization incorporates consideration of individual patient characteristics, social setting, and system-level barriers. Interventions aimed at reducing unnecessary hospitalizations, especially those involving patients with low medical acuity, should focus on underlying unmet needs and involve a broad set of perspectives.