WayWay M. Hlaing

Background: Thirty-day readmission after stroke remains common, yet contemporary readmission rates, prediction model performance, and predictor domains have not been comprehensively synthesized. Methods: Following PRISMA guidelines, we searched PubMed, Embase, Web of Science, Scopus, and Google Scholar for studies published between 1 January 2021 and 9 October 2025. Readmission proportions and model discrimination, measured by area under the receiver operating characteristic curve (AUC), were pooled using random-effects meta-analysis. Heterogeneity was assessed using I². Predictors were summarized across studies by domain. Results: Twenty studies met inclusion criteria: 15 studies comprising 358,434 patients contributed quantitative data, and 5 were included in qualitative synthesis only. The pooled proportion was 12.9% (95% CI: 10.1–15.8%), with subgroup estimates of 14.2% (95% CI: 11.9–16.6%) for all-cause and 3.6% (95% CI: 0.5–6.7%) for stroke-specific readmissions. Study-level AUCs ranged from 0.59 to 0.88, with a pooled AUC of 0.69 (95% CI: 0.64–0.75), indicating moderate discrimination. Substantial heterogeneity was observed (I² > 98%, p < 0.001), and pooled estimates should be interpreted cautiously. Predictor selection was poorly standardized and largely driven by data availability, with inconsistent inclusion of key clinical and post-discharge domains such as stroke severity, functional status, discharge disposition, post-discharge care, and social determinants of health. Conclusions: Thirty-day readmission after stroke remains common, and currently available models demonstrate modest predictive discrimination; no consistently high-performing, broadly generalizable prediction model has yet emerged. Improving prediction will require broader predictors capturing stroke severity, care transitions, follow-up, and patient context, along with external validation and integration into clinical workflows.

Background/Objectives: Veterans differ in sociodemographic composition and experience higher frequencies of disability than non-Veterans of the same age. Yet the epidemiology of the long-term care needs of Veterans, specifically activities of daily living (ADLs) and instrumental activities of daily living (IADLs), remains an important gap in the literature. The objectives of this study were to (1) characterize Veterans across levels of hierarchy of ADL and IADL support needs; (2) compare Veterans across the degree of need for help, from those who can still “self-manage” to those with an “unmet need”; and (3) identify the types and prevalence of ADL and IADL need combination patterns. Methods: This study used cross-sectional data from the 2021 administration of the HERO CARE survey. We included Veterans ages 65+ in our analyses (N = 7424). We calculated the overall weighted descriptive statistics across a hierarchy of ADL and IADL problems and the degree of need for help. One-way ANOVA for continuous variables and Rao–Scott chi-square tests for categorical variables were conducted to examine associations between groups, followed by post hoc pairwise comparisons, as appropriate. Results: Veteran respondents mean age was 82.3 (SD: 8.2 years), and most were male, non-Hispanic White, and married. In weighted analyses, more Veterans with both ADL and IADL problems compared to only ADL problems reported food insecurity, missed appointments, low health literacy, and depression. Among Veterans with ADL or IADL problems, 32.3% reported an unmet need for help. Almost a quarter of Veterans with ADL problems reported difficulties performing all eight ADLs (23.9%), and over a quarter of Veterans with IADL problems reported difficulties performing all seven IADLs (31.3%). Conclusions: Our findings show that Veterans are demographically and clinically different based on their hierarchy of impairment and degree of need for help. Identifying the patterns and prevalence of ADL and IADL needs among Veterans provides valuable information to align the Veterans Affairs (VA) programs and services with Veterans’ needs.

Although epidemiology core competencies are established by the Association of Schools and Programs of Public Health for masters-level trainees, no equivalent currently exists for the doctoral level. Thus, the objective of the Doctoral Education in Epidemiology Survey (2019) was to collect information on doctoral-level competencies in general epidemiology (doctoral) degree programs and other pertinent information from accredited programs in the United States and Canada. Participants (doctoral program directors or knowledgeable representatives of the program) from 57 institutions were invited to respond to a 39-item survey (18 core competencies; 9 noncore or emerging topic–related competencies; and 12 program-related items). Participants from 55 institutions (96.5%) responded to the survey, of whom over 85% rated 11 out of 18 core competencies as “very important” or “extremely important.” More than 80% of the programs currently emphasize 2 of 9 noncore competencies (i.e., competency to ( 1) develop and write grant proposals, and ( 2) assess evidence for causality on the basis of different causal inference concepts). “Big data” is the most frequently cited topic currently lacking in doctoral curricula. Information gleaned from previous efforts and this survey should prompt a dialog among relevant stakeholders to establish a cohesive set of core competencies for doctoral training in epidemiology.