BMC Geriatrics最新文献

Background: Older adults usually experience polypharmacy which increases their risk of adverse drug events, drug-drug interactions, and medication non-adherence. Clinical pharmacists, with specialized expertise in pharmacotherapy, are capable of engaging in deprescribing interventions aimed at reducing potentially inappropriate medications and overall medication burden. However, the overall impact of these pharmacist-led strategies remains unclear due to heterogeneity in study designs, settings, and outcomes.

Objectives: The aim of this study was to assess the impact of pharmacist-led deprescribing interventions among older adults across different settings.

Methods: We searched PubMed/MEDLINE, ScienceDirect, the Cochrane Library, and Google Scholar for English language randomized controlled trials and high-quality nonrandomized studies published from January 2015 onward, comparing pharmacist-led deprescribing interventions to usual care in any setting (community, outpatient, hospital, or long-term care). Primary outcomes were mean change in total number of medications per patient and the proportion of patients achieving effective deprescribing (discontinuation of ≥ 1 PIM or ≥ 0.5 reduction in a drug burden index).

Results: Seven studies (five RCTs, two non-randomized) encompassing 3,607 older adults met inclusion criteria. The pooled mean difference (MD) in total medications at last follow-up favored intervention by - 0.55 medications (95% CI - 2.17 to 1.07; I² = 83.1%), and the pooled risk ratio (RR) for effective deprescribing was 1.85 (95% CI 0.63-5.45; I² = 73.5%), though neither reached statistical significance. Secondary outcomes indicated improvements in medication burden indices without increased adverse events.

Conclusion: Pharmacist-led deprescribing reduces inappropriate medication use in targeted settings, while pooled effects on total medication count and hard clinical outcomes remain uncertain. Variability in study designs and outcomes underscores the need for larger, thoroughly designed trials with standardized protocols, longer follow-up, and comprehensive evaluations of clinical, economic, and patient-reported outcomes to establish scalable, sustainable deprescribing practices across diverse healthcare settings.

Protocol registration: The protocol for this systematic review was registered in International Prospective Register of Systematic Reviews (PROSPERO identifier: CRD420251072072).

Background: Solid fuel use remains a major source of indoor air pollution worldwide, and limited social engagement is increasingly recognized as a behavioral determinant of cognitive aging. However, evidence regarding their independent and combined effects on cognitive decline among older adults is scarce. This study aimed to evaluate the associations of household fuel use and social engagement with cognitive decline and to assess whether social engagement modifies the fuel-cognition relationship.

Methods: Data were drawn from 4,298 adults aged ≥ 60 years participating in the 2011-2020 waves of the China Health and Retirement Longitudinal Study (CHARLS). Household fuel use was measured at baseline and categorized into three types: clean fuels, coal, and biomass fuels. Social engagement was assessed at baseline based on participation in 11 types of social activities. Cognitive decline was defined as a ≥ 3-point reduction in global cognitive score from baseline at any follow-up wave. Cox proportional hazards models were fitted to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Multiplicative interaction between fuel type and social engagement was tested by including cross-product terms.

Results: During follow-up, 1,821 participants experienced cognitive decline. Compared with clean fuel use, coal use was associated with a significantly higher risk of cognitive decline (HR = 1.249, 95% CI: 1.061-1.470), whereas biomass fuel use was not (HR = 1.062, 95% CI: 0.944-1.194). Higher social engagement was consistently protective, with each additional activity associated with an 8.4% reduction in risk (HR = 0.916, 95% CI: 0.867-0.968). None of the interaction terms reached significance (coal × social engagement: HR = 0.977, 95% CI: 0.815-1.171), indicating no evidence that social engagement modifies the fuel-cognition association. Additional analyses showed that fuel use was not associated with follow-up cognitive scores (all p > 0.05), whereas social engagement remained positively associated with global cognition (β = 0.114, p = 0.006).

Conclusion: Coal use increases the risk of cognitive decline, whereas higher social engagement is protective; these influences operate independently without evidence of interaction. Reducing household reliance on coal while promoting meaningful social participation may represent complementary strategies to preserve cognitive health in aging populations.

Background: China's aging population and declining birth rates create urgent demand for elderly care infrastructure. Rural areas face dual challenges: over 100,000 vacant schools from declining enrollment and elderly care facilities meeting less than 30% of demand. This study develops and validates a comprehensive framework for assessing rural school-to-elderly care facility conversion feasibility.

Methods: An integrated computational-expert consensus framework was developed combining natural language processing with expert validation. SciBERT extracted candidate indicators from 89 academic articles, 25 policy documents, and 31 stakeholder interviews. Fifteen multidisciplinary experts validated indicators through two-round Fuzzy Delphi process using triangular fuzzy numbers. Consensus was assessed using Kendall's coefficient of concordance with 80% acceptance threshold.

Results: The model achieved F1-score of 0.85, precision of 0.87, and recall of 0.83. Expert validation yielded high consensus (Kendall's W=0.781, p<0.001), producing 17 validated indicators across four dimensions: building conditions, functional conversion, environmental renovation, and socioeconomic factors. Policy support emerged as most critical (weight=0.0792), followed by resource utilization and catering function (both 0.0669). All indicators achieved consensus rates of 80-100%.

Conclusions: This validated framework integrates computational objectivity with expert knowledge for evaluating rural school conversion feasibility. It provides practical guidance for policymakers and developers to address rural elderly care shortages through sustainable infrastructure adaptation, applicable to aging societies globally.