Suchergebnisse

Abstract

Background
The adverse health effects of incarceration are well-documented, affecting individuals throughout their life course. However, the influence of a history of incarceration on end-of-life (EOL) experiences remains unexplored. This study aims to examine how prior incarceration affects individuals' experiences and care needs as they approach the EOL.


Methods
Leveraging the Health and Retirement Study, we conducted secondary analyses on 1 710 individuals who participated between 2012 and 2018. Through retrospective cohort analysis, we explored the association between incarceration history and EOL care, focusing on pain and symptom burden.


Results
Analyses showed that individuals with a history of incarceration experienced significantly higher levels of pain (65% reported "moderate" or "severe" pain) compared to nonincarcerated individuals (50%; adjusted odds ratio = 1.45, 95% confidence interval [CI]: 1.22–1.71, p < .001). Additionally, the symptom burden index revealed that formerly incarcerated individuals had a higher average symptom score (2.8 vs 2.1; β = 0.7, 95% CI: 0.5–0.9, p < .001), indicating a greater range of symptoms in their final year of life. These disparities persisted after adjusting for demographic, health, and socioeconomic variables.


Conclusions
This study reveals that a history of incarceration significantly affects EOL experiences, with formerly incarcerated individuals facing higher levels of pain and a greater symptom burden compared to nonincarcerated individuals. This underscores the need for tailored palliative care to address the unique needs of this vulnerable population. This research highlights a critical area for intervention and calls for healthcare systems to adapt their practices to better serve those with incarceration histories.

Abstract
Introduced in 2010, the sub-discipline of gerontologic biostatistics was conceptualized to address the specific challenges of analyzing data from clinical research studies involving older adults. Since then, the evolving technological landscape has led to a proliferation of advancements in biostatistics and other data sciences that have significantly influenced the practice of gerontologic research, including studies beyond the clinic. Data science is the field at the intersection of statistics and computer science, and although the term "data science" was not widely used in 2010, the field has quickly made palpable impacts on gerontologic research. In this Review in Depth, we describe multiple advancements of biostatistics and data science that have been particularly impactful. Moreover, we propose the sub-discipline of "gerontologic biostatistics and data science", or GBDS, which subsumes gerontologic biostatistics into a more encompassing practice. Prominent GBDS advancements that we discuss herein include cutting-edge methods in experimental design and causal inference, adaptations of machine learning, the rigorous quantification of deep phenotypic measurement, and analysis of high-dimensional -omics data. We additionally describe the need for integration of information from multiple studies and propose strategies to foster reproducibility, replicability, and open science. Lastly, we provide information on software resources for GBDS practitioners to apply these approaches to their own work and propose areas where further advancement is needed. The methodological topics reviewed here aim to enhance data-rich research on aging and foster the next generation of gerontologic researchers.