Dataset Information

Population indices measuring health outcomes: A scoping review.

ABSTRACT: Background:Population health indices such as disability adjusted life years (DALY) and quality adjusted life years (QALY) are often used in an effort to measure health of populations and identify areas of concern that require interventions. There has been an increase of number of population health indices since the last review published more than a decade ago. Therefore, this study aims to provide an overview of existing population health indices and examine the methods used to develop them. Methods:The search was conducted across three databases: PubMed, CINAHL and Emerald using four key concepts: 'health', 'index', 'context', 'develop', which was supplemented with Google searching and reference scanning. A researcher screened the titles, abstracts and subsequently full texts and confirmed the findings with the research team at each stage. Data charting was performed according to the included publications and identified indices. The collation was performed by describing the indices and made observation on its development method using a priori framework consist of four processes: underpinning theory, model or framework; data selection and processing; formation of index; testing of index. Results:Twenty-six publications describing population health indices were included, and 27 indices were identified. These indices covered the following health topics: overall health outcomes (n?=?15), outcomes for specific health topics (n?=?4), diseases outcome (n?=?6), assist health resource allocation for priority minority subgroup or geographic area (n?=?4), quality of health or health care (n?=?2). Twenty-one indices measure health for general populations while six measure defined subpopulations. Fourteen of the indices reported at least one of the development processes according to the a priori framework: underpinning theory, model or framework (n?=?7); data selection and processing (n?=?8); formation of index (n?=?12); testing of index (n?=?9). Conclusions:Few population health indices measure specific health topics or health of specific sub-population. There is also a lack of usage of theories, models or framework in developing these indices. Efforts to develop a guideline is proposed on how population health indices can be developed systematically and rigorously to ensure validity and comprehensive assessment of the indices.

SUBMITTER: Ashraf K

PROVIDER: S-EPMC6344069 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Population indices measuring health outcomes: A scoping review.

Ashraf Khalid K Ng Chirk Jenn CJ Teo Chin Hai CH Goh Kim Leng KL

Journal of global health 20190601 1

<h4>Background</h4>Population health indices such as disability adjusted life years (DALY) and quality adjusted life years (QALY) are often used in an effort to measure health of populations and identify areas of concern that require interventions. There has been an increase of number of population health indices since the last review published more than a decade ago. Therefore, this study aims to provide an overview of existing population health indices and examine the methods used to develop t ...[more]

PMID: 30701069

Similar Datasets

Project description:IntroductionMany regions in the world are using the population health approach and require a means to measure the health of their population of interest. Population health frameworks provide a theoretical grounding for conceptualization of population health and therefore a logical basis for selection of indicators. The aim of this scoping review was to provide an overview and summary of the characteristics of existing population health frameworks that have been used to conceptualize the measurement of population health.MethodsWe used the Population, Concept and Context (PCC) framework to define eligibility criteria of frameworks. We were interested in frameworks applicable for general populations, that contained components of measurement of health with or without its antecedents and applied at the population level or used a population health approach. Eligible reports of eligible frameworks should include at least domains and subdomains, purpose, or indicators. We searched 5 databases (Pubmed, EMBASE, Web of Science, NYAM Grey Literature Report, and OpenGrey), governmental and organizational sites on Google and websites of selected organizations using keywords from the PCC framework. Characteristics of the frameworks were summarized descriptively and narratively.ResultsFifty-seven frameworks were included. The majority originated from the US (46%), Europe (23%) and Canada (19%). Apart from 1 framework developed for rural populations and 2 for indigenous populations, the rest were for general urban populations. The numbers of domains, subdomains and indicators were highly variable. Health status and social determinants of health were the most common domains across all frameworks. Different frameworks had different priorities and therefore focus on different domains.ConclusionKey domains common across frameworks other than health status were social determinants of health, health behaviours and healthcare system performance. The results in this review serve as a useful resource for governments and healthcare organizations for informing their population health measurement efforts.

Project description:PurposeThis study is a scoping review of the different methods used to measure rurality in the health services research (HSR) literature.MethodsWe identified peer-reviewed empirical studies from 2010-2020 from seven leading HSR journals, including the Journal of Rural Health, that used any definition to measure rurality as a part of their analysis. From each study, we identified the geographic unit (e.g., county, zip code) and definition (e.g., Rural Urban Continuum Codes, Rural Urban Commuting Areas) used to classify categories of rurality. We analyzed whether geographic units and definitions used to classify rurality differed by focus area of studies, including costs, quality, and access to care. Lastly, we examined the number of rural categories used by authors to assess rural areas.FindingsIn 103 included studies, five different geographic units and 11 definitions were used to measure rurality. The most common geographic units used to measure rurality were county (n = 59, 57%), which was used most frequently in studies examining cost (n = 12, 75%) and access (n = 33, 57.9%). Rural Urban Commuting Area codes were the most common definition used to measure rurality for studies examining access (n = 13, 22.8%) and quality (n = 10, 44%). The majority of included studies made rural versus urban comparisons (n = 82, 80%) as opposed to focusing on rural populations only (n = 21, 20%). Among studies that compared rural and urban populations, most studies used only one category to identify rural locations (n = 49 of 82 studies, 60%).ConclusionGeographic units and definitions to determine rurality were used inconsistently within and across studies with an HSR focus. This finding may affect how health disparities by rural location are determined and thus how resources and federal funds are allocated. Future research should focus on developing a standardized system to determine under what circumstances researchers should use different geographic units and methods to determine rurality by HSR focus area.

Project description:BackgroundA scoping review was conducted in order to map and determine the gaps in literature on the impact of simulation as an educational approach to improve mental health care outcomes. As it became apparent that no literature existed on this topic, the study aimed to examine the educational impact of simulation on mental health education.MethodsAn established five-stage scoping methodology was used: (1) identification of the research question, (2) identification of relevant studies, (3) study selection, (4) charting the data and (5) collation, summarising and reporting of results. CINAHL, ProQuest, PubMed, MEDLINE, EMBASE and PsychINFO databases were searched. These databases were deemed to represent a majority of the literature while accommodating for the particular search strategy used for this review. Websites that provide grey literature were also searched for articles of relevance.ResultsA total of 48 articles were included in this review, with a considerable portion of studies conducted in the USA and UK. Others were conducted in an array of locations including Australia, Canada, Iran and Taiwan. Of the included articles, seven groups of simulation methods (including standardised patients, virtual reality and manikins as patients) were evident, with standardised patients being most prominent.ConclusionsLiterature is lacking to evidence the benefit of simulation on mental health patient outcomes. However, the available literature suggests a variety of simulation-based education, and training methods are currently being used within mental healthcare education. The findings do suggest some methods of simulation, such as the use of standardised patients, are more commonly used in education and have been deemed as effective to assist in mental health education. As no article specifically examining the mental health outcomes of patients treated by health professionals taught by simulation was identified, the educational outcomes outlined in this paper may be used to inform further research, incorporating mental health patient outcomes.

Project description:BackgroundSince the 2014-2016 West Africa Ebola epidemic, the concept of measuring health security capacity has become increasingly important within the broader context of health systems-strengthening, enhancing responses to public health emergencies, and reducing global catastrophic biological risks. Efforts to regularly and sustainably track the evolution of health security capabilities and capacities over time - while also accounting for political, social, and environmental risks - could help countries progress toward eliminating sources of health insecurity. We sought to aggregate evidence-based principles that capture a country's baseline public health and healthcare capabilities, its health security system performance before and during infectious disease crises, and its broader social, political, security, and ecological risk environments.MethodsWe conducted a scoping review of English-language scholarly and gray literature to identify evidence- and practice-based indicators and proxies for measuring health security at the country level over time. We then used a qualitative coding framework to identify recurrent themes in the literature and synthesize foundational principles for measuring global health security. Documents reviewed included English-language literature published after 2001 until the end of the research period-September 2017-to ensure relevance to the current global health security landscape; literature examining acute infectious disease threats with potential for transnational spread; and literature addressing global health security efforts at the country level.ResultsWe synthesized four foundational principles for measuring global health security: measurement requires assessment of existing capacities, as well as efforts to build core public health, healthcare, and biosecurity capabilities; assessments of national programs and efforts to mitigate a critical subset of priority threats could inform efforts to generate useful metrics for global health security; there are measurable enabling factors facilitating health security-strengthening efforts; and finally, measurement requires consideration of social, political, and ecological risk environments.ConclusionThe themes identified in this review could inform efforts to systematically assess the impacts and effectiveness of activities undertaken to strengthen global health security.

Project description:BackgroundAlthough climate change is one of the biggest global health threats, individual-level and short-term data on direct exposure and health impacts are still scarce. Wearable electronic devices (wearables) present a potential solution to this research gap. Wearables have become widely accepted in various areas of health research for ecological momentary assessment, and some studies have used wearables in the field of climate change and health. However, these studies vary in study design, demographics, and outcome variables, and existing research has not been mapped.ObjectiveIn this review, we aimed to map existing research on wearables used to detect direct health impacts and individual exposure during climate change-induced weather extremes, such as heat waves or wildfires.MethodsWe conducted a scoping review according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework and systematically searched 6 databases (PubMed [MEDLINE], IEEE Xplore, CINAHL [EBSCOhost], WoS, Scopus, Ovid [MEDLINE], and Google Scholar). The search yielded 1871 results. Abstracts and full texts were screened by 2 reviewers (MK and IM) independently using the inclusion and exclusion criteria. The inclusion criteria comprised studies published since 2010 that used off-the-shelf wearables that were neither invasive nor obtrusive to the user in the setting of climate change-related weather extremes. Data were charted using a structured form, and the study outcomes were narratively synthesized.ResultsThe review included 55,284 study participants using wearables in 53 studies. Most studies were conducted in upper-middle-income and high-income countries (50/53, 94%) in urban environments (25/53, 47%) or in a climatic chamber (19/53, 36%) and assessed the health effects of heat exposure (52/53, 98%). The majority reported adverse health effects of heat exposure on sleep, physical activity, and heart rate. The remaining studies assessed occupational heat stress or compared individual- and area-level heat exposure. In total, 26% (14/53) of studies determined that all examined wearables were valid and reliable for measuring health parameters during heat exposure when compared with standard methods.ConclusionsWearables have been used successfully in large-scale research to measure the health implications of climate change-related weather extremes. More research is needed in low-income countries and vulnerable populations with pre-existing conditions. In addition, further research could focus on the health impacts of other climate change-related conditions and the effectiveness of adaptation measures at the individual level to such weather extremes.

Project description:BackgroundThere are many described benefits of community-based participatory research (CBPR), such as increased relevance of research for those who must act on its findings. This has prompted researchers to better understand how CBPR functions to achieve these benefits through building sustainable research partnerships. Several studies have identified "trust" as a key mechanism to achieve sustainable partnerships, which themselves constitute social networks. Although existing literature discusses trust and CBPR, or trust and social networks, preliminary searches reveal that none link all three concepts of trust, CBPR, and social networks. Thus, we present our scoping review to systematically review and synthesize the literature exploring how trust is conceptualised, operationalised, and measured in CBPR and social networks.MethodsThis review follows the guidance and framework of Peters et al. which is underpinned by the widely used framework of Levac and colleagues. Levac and colleagues provided enhancements to the methodological framework of Arksey and O'Malley. We explored several electronic databases including Scopus, Medline, PubMed, Web of Science, CINAHL, Cochrane Library, Google Scholar, and PsychINFO. A search strategy was identified and agreed upon by the team in conjunction with a research librarian. Two independent reviewers screened articles by title and abstract, then by full-text based on pre-determined exclusion/inclusion criteria. A third reviewer arbitrated discrepancies regarding inclusions/exclusions. A thematic analysis was then conducted to identify relevant themes and sub-themes.ResultsBased on the 26 extracted references, several key themes and sub-themes were identified which highlighted the complexity and multidimensionality of trust as a concept. Our analysis revealed an additional emergent category that highlighted another important dimension of trust-outcomes pertaining to trust. Further, variation within how the studies conceptualised, operationalised, and measured trust was illuminated. Finally, the multidimensionality of trust provided important insight into how trust operates as a context, mechanism, and outcome.ConclusionsFindings provide support for future research to incorporate trust as a lens to explore the social-relational aspects of partnerships and the scope to develop interventions to support trust in partnerships.

Project description:OBJECTIVE:To determine how machine learning has been applied to prediction applications in population health contexts. Specifically, to describe which outcomes have been studied, the data sources most widely used and whether reporting of machine learning predictive models aligns with established reporting guidelines. DESIGN:A scoping review. DATA SOURCES:MEDLINE, EMBASE, CINAHL, ProQuest, Scopus, Web of Science, Cochrane Library, INSPEC and ACM Digital Library were searched on 18 July 2018. ELIGIBILITY CRITERIA:We included English articles published between 1980 and 2018 that used machine learning to predict population-health-related outcomes. We excluded studies that only used logistic regression or were restricted to a clinical context. DATA EXTRACTION AND SYNTHESIS:We summarised findings extracted from published reports, which included general study characteristics, aspects of model development, reporting of results and model discussion items. RESULTS:Of 22 618 articles found by our search, 231 were included in the review. The USA (n=71, 30.74%) and China (n=40, 17.32%) produced the most studies. Cardiovascular disease (n=22, 9.52%) was the most studied outcome. The median number of observations was 5414 (IQR=16?543.5) and the median number of features was 17 (IQR=31). Health records (n=126, 54.5%) and investigator-generated data (n=86, 37.2%) were the most common data sources. Many studies did not incorporate recommended guidelines on machine learning and predictive modelling. Predictive discrimination was commonly assessed using area under the receiver operator curve (n=98, 42.42%) and calibration was rarely assessed (n=22, 9.52%). CONCLUSIONS:Machine learning applications in population health have concentrated on regions and diseases well represented in traditional data sources, infrequently using big data. Important aspects of model development were under-reported. Greater use of big data and reporting guidelines for predictive modelling could improve machine learning applications in population health. REGISTRATION NUMBER:Registered on the Open Science Framework on 17 July 2018 (available at https://osf.io/rnqe6/).

Project description:IntroductionA participatory approach to co-creating new knowledge in health research has gained significant momentum in recent decades. This is founded on the described benefits of community-based participatory research (CBPR), such as increased relevance of research for those who must act on its findings. This has prompted researchers to better understand how CBPR functions to achieve these benefits through building sustainable research partnerships. Several studies have identified 'trust' as a key mechanism to achieve sustainable partnerships, which themselves constitute social networks. Although existing literature discuss trust and CBPR, or trust and social networks, preliminary searches reveal that none link all three concepts of trust, CBPR and social networks. Thus, we present our scoping review protocol to systematically review and synthesise the literature exploring how trust is conceptualised, operationalised and measured in CBPR and social networks.Methods and analysisThis protocol follows guidelines from Levac et al (Scoping studies: advancing themethodology. Implement Sci 2010;5:69), which follow the methodological framework of Arksey and O'Malley. This scoping review explores several electronic databases including Scopus, Medline, PubMed, Web of Science, CINAHL, Cochrane Library, Google Scholar and PsychINFO. Grey literature such as theses/dissertations and reports will be included. A search strategy was identified and agreed on by the team in conjunction with a research librarian. Two independent reviewers will screen articles by title and abstract, then by full text based on pre-determined exclusion/inclusion criteria. A third reviewer will arbitrate discrepancies regarding inclusions/exclusions. We plan to incorporate a thematic analysis.Ethics and disseminationEthics is not required for this review specifically. It is a component of a larger study that received ethical approval from the University of Limerick research ethics committee (#2018_05_12_EHS). Translation of results to key domains is integrated through active collaboration of stakeholders from community, health services and academic sectors. Findings will be disseminated through academic conferences, and peer review publications targeting public and patient involvement in health research.

Dataset Information

Population indices measuring health outcomes: A scoping review.

Publications

Population indices measuring health outcomes: A scoping review.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets