谷歌浏览器插件
订阅小程序
在清言上使用

The Population Impact of Common Mental Disorders and Long-Term Physical Conditions on Disability and Hospital Admission

Psychological medicine(2012)

引用 6|浏览3
暂无评分
摘要
BackgroundLong-term physical conditions (LTCs) consume the largest share of healthcare budgets. Although common mental disorders (CMDs) and LTCs often co-occur, the potential impact of improved mental health treatment on severe disability and hospital admissions for physical health problems remains unknown.MethodA cross-sectional study of 7403 adults aged 16–95 years living in private households in England was performed. LTCs were ascertained by prompted self-report. CMDs were ascertained by structured clinical interview. Disability was assessed using questions about problems with activities of daily living. Population impact and potential preventive gain were estimated using population-attributable fraction (PAF), and conservative estimates were obtained using ‘treated non-cases’ as the reference group.ResultsOf the respondents, 20.7% reported at least one LTC. The prevalence of CMDs increased with the number of LTCs, but over two-thirds (71.2%) of CMD cases in people with LTCs were untreated. Statistically significant PAFs were found for CMDs and recent hospital admission [13.5%, 95% confidence intervals (CI) 6.6–20.0] and severe disability (31.3%, 95% CI 27.1–35.2) after adjusting for LTCs and other confounders. Only the latter remained significant when using the most conservative estimate of PAF (21.8%, 95% CI 14.0–28.9), and this was reduced only slightly when considering only participants with LTCs (18.5%, 95% CI 7.9–27.9).ConclusionsBetter treatments for CMDs in people with LTCs could achieve almost the same population health gain in terms of reducing severe disability as those targeted at the entire population. Interventions to reduce the prevalence of CMDs among people with LTCs should be part of routine medical care.
更多
查看译文
关键词
Common mental disorders,disability,long-term conditions,population attributable fraction
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要