Hendrikx Roy J P, Drewes Hanneke W, Spreeuwenberg Marieke, Ruwaard Dirk, Baan Caroline A
1 Tranzo Scientific Center for Care and Welfare, Tilburg School of Social and Behavioral Sciences, Tilburg University , Tilburg, the Netherlands .
2 Department for Quality of Care and Health Economics, Center for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment , Bilthoven, the Netherlands .
Popul Health Manag. 2018 Oct;21(5):422-427. doi: 10.1089/pop.2017.0101. Epub 2017 Nov 1.
Health care no longer focuses solely on patients and increasingly emphasizes regions and their populations. Strategies, such as population management (PM) initiatives, aim to improve population health and well-being by redesigning health care and community services. Hence, insight into population health is needed to tailor interventions and evaluate their effects. This study aims to assess whether population health differs between initiatives and to what extent demographic, personal, and lifestyle factors affect these differences. A population health survey that included the Short Form 12 version 2 (SF12, physical and mental health status), Patient Activation Measure 13 (PAM13), and demographic, personal, and lifestyle factors was administered in 9 Dutch PM initiatives. Potential confounders were determined by comparing these factors between PM initiatives using analyses of variance and chi-square tests. The influence of these potential confounders on the health outcomes was studied using multivariate linear regression. Age, education, origin, employment, body mass index, and smoking were identified as potential confounders for differences found between the 9 PM initiatives. Each had a noteworthy influence on all of the instruments' scores. Not all health differences between PM initiatives were explained, as the SF12 outcomes still differed between PM initiatives once corrected. For the PAM13, the differences were no longer significant. Demographic and lifestyle factors should be included in the evaluation of PM initiatives and population health differences found can be used to tailor initiatives. Other factors beyond health care (eg, air quality) should be considered to further refine the tailoring and evaluation of PM initiatives.
医疗保健不再仅仅关注患者，而是越来越强调地区及其人口。诸如人口管理（PM）举措等策略旨在通过重新设计医疗保健和社区服务来改善人口健康和福祉。因此，需要深入了解人口健康情况，以便调整干预措施并评估其效果。本研究旨在评估不同举措之间人口健康是否存在差异，以及人口统计学、个人和生活方式因素在多大程度上影响这些差异。在荷兰的9项PM举措中开展了一项人口健康调查，该调查涵盖简式健康调查问卷第2版（SF12，身心健康状况）、患者激活量表13（PAM13）以及人口统计学、个人和生活方式因素。通过使用方差分析和卡方检验比较这些因素在不同PM举措之间的差异，确定潜在混杂因素。使用多元线性回归研究这些潜在混杂因素对健康结果的影响。年龄、教育程度、出身、就业情况、体重指数和吸烟被确定为9项PM举措之间差异的潜在混杂因素。每一项对所有量表的得分都有显著影响。并非所有PM举措之间的健康差异都能得到解释，因为校正后SF12的结果在不同PM举措之间仍然存在差异。对于PAM13，差异不再显著。在评估PM举措时应纳入人口统计学和生活方式因素，发现的人口健康差异可用于调整举措。应考虑医疗保健之外的其他因素（如空气质量），以进一步完善PM举措的调整和评估。
