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dc.contributor.authorHendrikx, Roy J P
dc.contributor.authorDrewes, Hanneke W
dc.contributor.authorSpreeuwenberg, Marieke
dc.contributor.authorRuwaard, Dirk
dc.contributor.authorBaan, Caroline A
dc.date.accessioned2018-04-04T09:12:12Z
dc.date.available2018-04-04T09:12:12Z
dc.date.issued2017-11-01
dc.identifier.citationComparing the Health of Populations: Methods to Evaluate and Tailor Population Management Initiatives in the Netherlands. 2017 Popul Health Managen
dc.identifier.issn1942-7905
dc.identifier.pmid29091019
dc.identifier.doi10.1089/pop.2017.0101
dc.identifier.urihttp://hdl.handle.net/10029/621742
dc.description.abstractHealth 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.
dc.language.isoenen
dc.rightsinfo:eu-repo/semantics/closedAccessen
dc.titleComparing the Health of Populations: Methods to Evaluate and Tailor Population Management Initiatives in the Netherlands.en
dc.typeArticleen
dc.identifier.journalPopul Health Manag 2018; 21(5):422-7en
html.description.abstractHealth 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.


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