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      Identifying optimal indicators and purposes of population segmentation through engagement of key stakeholders: a qualitative study

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          Abstract

          Background

          Various population segmentation tools have been developed to inform the design of interventions that improve population health. However, there has been little consensus on the core indicators and purposes of population segmentation. The existing frameworks were further limited by their applicability in different practice settings involving stakeholders at all levels. The aim of this study was to generate a comprehensive set of indicators and purposes of population segmentation based on the experience and perspectives of key stakeholders involved in population health.

          Methods

          We conducted in-depth semi-structured interviews using purposive sampling with key stakeholders (e.g. government officials, healthcare professionals, social service providers, researchers) involved in population health at three distinct levels (micro, meso, macro) in Singapore. The interviews were audio-recorded and transcribed verbatim. Thematic content analysis was undertaken using NVivo 12.

          Results

          A total of 25 interviews were conducted. Eight core indicators (demographic characteristics, economic characteristics, behavioural characteristics, disease state, functional status, organisation of care, psychosocial factors and service needs of patients) and 21 sub-indicators were identified. Age and financial status were commonly stated as important indicators that could potentially be used for population segmentation across three levels of participants. Six intended purposes for population segmentation included improving health outcomes, planning for resource allocation, optimising healthcare utilisation, enhancing psychosocial and behavioural outcomes, strengthening preventive efforts and driving policy changes. There was consensus that planning for resource allocation and improving health outcomes were considered two of the most important purposes for population segmentation.

          Conclusions

          Our findings shed light on the need for a more person-centric population segmentation framework that incorporates upstream and holistic indicators to be able to measure population health outcomes and to plan for appropriate resource allocation. Core elements of the framework may apply to other healthcare settings and systems responsible for improving population health.

          Trial registration

          The study was approved by the SingHealth Institutional Review Board (CIRB Reference number: 2017/2597).

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          Most cited references50

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          Mental health promotion in public health: perspectives and strategies from positive psychology.

          Positive psychology is the study of what is "right" about people-their positive attributes, psychological assets, and strengths. Its aim is to understand and foster the factors that allow individuals, communities, and societies to thrive. Cross-sectional, experimental, and longitudinal research demonstrates that positive emotions are associated with numerous benefits related to health, work, family, and economic status. Growing biomedical research supports the view that positive emotions are not merely the opposite of negative emotions but may be independent dimensions of mental affect. The asset-based paradigms of positive psychology offer new approaches for bolstering psychological resilience and promoting mental health. Ultimately, greater synergy between positive psychology and public health might help promote mental health in innovative ways.
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            Effectiveness of eHealth interventions for reducing mental health conditions in employees: A systematic review and meta-analysis

            Background Many organisations promote eHealth applications as a feasible, low-cost method of addressing mental ill-health and stress amongst their employees. However, there are good reasons why the efficacy identified in clinical or other samples may not generalize to employees, and many Apps are being developed specifically for this group. The aim of this paper is to conduct the first comprehensive systematic review and meta-analysis evaluating the evidence for the effectiveness and examine the relative efficacy of different types of eHealth interventions for employees. Methods Systematic searches were conducted for relevant articles published from 1975 until November 17, 2016, of trials of eHealth mental health interventions (App or web-based) focused on the mental health of employees. The quality and bias of all identified studies was assessed. We extracted means and standard deviations from published reports, comparing the difference in effect sizes (Hedge’s g) in standardized mental health outcomes. We meta-analysed these using a random effects model, stratified by length of follow up, intervention type, and whether the intervention was universal (unselected) or targeted to selected groups e.g. “stressed”. Results 23 controlled trials of eHealth interventions were identified which overall suggested a small positive effect at both post intervention (g = 0.24, 95% CI 0.13 to 0.35) and follow up (g = 0.23, 95% CI 0.03 to 0.42). There were differential short term effects seen between the intervention types whereby Mindfulness based interventions (g = 0.60, 95% CI 0.34 to 0.85, n = 6) showed larger effects than the Cognitive Behaviour Therapy (CBT) based (g = 0.15, 95% CI 0.02 to 0.29, n = 11) and Stress Management based (g = 0.17, 95%CI -0.01 to 0.34, n = 6) interventions. The Stress Management interventions however differed by whether delivered to universal or targeted groups with a moderately large effect size at both post-intervention (g = 0.64, 95% CI 0.54 to 0.85) and follow-up (g = 0.69, 95% CI 0.06 to 1.33) in targeted groups, but no effect in unselected groups. Interpretation There is reasonable evidence that eHealth interventions delivered to employees may reduce mental health and stress symptoms post intervention and still have a benefit, although reduced at follow-up. Despite the enthusiasm in the corporate world for such approaches, employers and other organisations should be aware not all such interventions are equal, many lack evidence, and achieving the best outcomes depends upon providing the right type of intervention to the correct population.
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              A survey of primary care physicians in eleven countries, 2009: perspectives on care, costs, and experiences.

              This 2009 survey of primary care doctors in Australia, Canada, France, Germany, Italy, the Netherlands, New Zealand, Norway, Sweden, the United Kingdom, and the United States finds wide differences in practice systems, incentives, perceptions of access to care, use of health information technology (IT), and programs to improve quality. Response rates exceeded 40 percent except in four countries: Canada, France, the United Kingdom, and the United States. U.S. and Canadian physicians lag in the adoption of IT. U.S. doctors were the most likely to report that there are insurance restrictions on obtaining medication and treatment for their patients and that their patients often have difficulty with costs. We believe that opportunities exist for cross-national learning in disease management, use of teams, and performance feedback to improve primary care globally.
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                Author and article information

                Contributors
                low.lian.leng@singhealth.com.sg
                Journal
                Health Res Policy Syst
                Health Res Policy Syst
                Health Research Policy and Systems
                BioMed Central (London )
                1478-4505
                21 February 2020
                21 February 2020
                2020
                : 18
                : 26
                Affiliations
                [1 ]ISNI 0000 0004 0385 0924, GRID grid.428397.3, Program in Health Services and Systems Research, , Duke-NUS Medical School, ; Singapore, Singapore
                [2 ]ISNI 0000 0004 0469 9402, GRID grid.453420.4, Regional Health System, , Singapore Health Services, ; Singapore, Singapore
                [3 ]ISNI 0000 0001 2180 6431, GRID grid.4280.e, Faculty of Science, , National University of Singapore, ; Singapore, Singapore
                [4 ]ISNI 0000 0000 9486 5048, GRID grid.163555.1, Department of Rheumatology and Immunology, , Singapore General Hospital, ; Singapore, Singapore
                [5 ]ISNI 0000 0001 2180 6431, GRID grid.4280.e, Department of Medicine, Yong Loo Lin School of Medicine, , National University of Singapore, ; Singapore, Singapore
                [6 ]ISNI 0000 0000 9486 5048, GRID grid.163555.1, Department of Family Medicine and Continuing Care, , Singapore General Hospital, ; Singapore, Singapore
                Author information
                http://orcid.org/0000-0003-4228-2862
                Article
                519
                10.1186/s12961-019-0519-x
                7035731
                32085714
                dde6e1ef-63b5-4885-9350-cda5fed52d29
                © The Author(s). 2020

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 8 July 2019
                : 16 December 2019
                Funding
                Funded by: SingHealth PULSES Centre
                Award ID: NMRC/CG/027/2017
                Categories
                Research
                Custom metadata
                © The Author(s) 2020

                Health & Social care
                population segmentation,expert driven,data driven,indicator,purpose
                Health & Social care
                population segmentation, expert driven, data driven, indicator, purpose

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