Population Health Data Research Articles

EpiGraphDB: a database and data mining platform for health data science.

MotivationThe wealth of data resources on human phenotypes, risk factors, molecular traits and therapeutic interventions presents new opportunities for population health sciences. These opportunities are paralleled by a growing need for data integration, curation and mining to increase research efficiency, reduce mis-inference and ensure reproducible research.ResultsWe developed EpiGraphDB (https://epigraphdb.org/), a graph database containing an array of different biomedical and epidemiological relationships and an analytical platform to support their use in human population health data science. In addition, we present three case studies that illustrate the value of this platform. The first uses EpiGraphDB to evaluate potential pleiotropic relationships, addressing mis-inference in systematic causal analysis. In the second case study, we illustrate how protein–protein interaction data offer opportunities to identify new drug targets. The final case study integrates causal inference using Mendelian randomization with relationships mined from the biomedical literature to ‘triangulate’ evidence from different sources.Availability and implementationThe EpiGraphDB platform is openly available at https://epigraphdb.org. Code for replicating case study results is available at https://github.com/MRCIEU/epigraphdb as Jupyter notebooks using the API, and https://mrcieu.github.io/epigraphdb-r using the R package.Supplementary information Supplementary data are available at Bioinformatics online.

Incorporando las narrativas y las percepciones en los diagnósticos de salud locales: el caso de Bilbao

The subjectivity of a multitude of dimensions that affect health requires systemic approaches, study designs that integrate population health data and the narratives of the population, as well as specific methodological approaches that allow the capture of evidence from social and community processes. The use of participatory methodologies in urban health diagnoses is key to capturing the different perspectives and knowledge of the local context, contributing to a more complete analysis of reality. This methodological note presents the development of the participatory process carried out as part of the Bilbao health diagnosis; the identification of participating agents and selection criteria, the invitation to participate and the dynamics developed. Sharing experiences that have incorporated participatory processes is necessary to foster its methodological development, and thus, to promote it practice.

Using Mixture Modeling to Construct Subgroups of Cognitive Aging in the Wisconsin Longitudinal Study.

Longitudinal surveys of older adults increasingly incorporate assessments of cognitive performance. However, very few studies have used mixture modeling techniques to describe cognitive aging, identifying subgroups of people who display similar patterns of performance across discrete cognitive functions. We employ this approach to advance empirical evidence concerning interindividual variability and intraindividual change in patterns of cognitive aging. We drew upon data from 3,713 participants in the Wisconsin Longitudinal Study (WLS). We used latent class analysis to generate subgroups of cognitive aging based on assessments of verbal fluency and episodic memory at ages 65 and 72. We also employed latent transition analysis to identify how individual participants moved between subgroups over the 7-year period. There were 4 subgroups at each point in time. Approximately 3 quarters of the sample demonstrated continuity in the qualitative type of profile between ages 65 and 72, with 17.9% of the sample in a profile with sustained overall low performance at both ages 65 and 72. An additional 18.7% of participants made subgroup transitions indicating marked decline in episodic memory. Results demonstrate the utility of using mixture modeling to identify qualitatively and quantitatively distinct subgroups of cognitive aging among older adults. We discuss the implications of these results for the continued use of population health data to advance research on cognitive aging.

Deep learning and its role in COVID-19 medical imaging

COVID-19 is one of the greatest global public health challenges in history. COVID-19 is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is estimated to have an cumulative global case-fatality rate as high as 7.2% (Onder et al., 2020) [1]. As the SARS-CoV-2 spread across the globe it catalyzed new urgency in building systems to allow rapid sharing and dissemination of data between international healthcare infrastructures and governments in a worldwide effort focused on case tracking/tracing, identifying effective therapeutic protocols, securing healthcare resources, and in drug and vaccine research.In addition to the worldwide efforts to share clinical and routine population health data, there are many large-scale efforts to collect and disseminate medical imaging data, owing to the critical role that imaging has played in diagnosis and management around the world. Given reported false negative rates of the reverse transcriptase polymerase chain reaction (RT-PCR) of up to 61% (Centers for Disease Control and Prevention, Division of Viral Diseases, 2020; Kucirka et al., 2020) [2,3], imaging can be used as an important adjunct or alternative. Furthermore, there has been a shortage of test-kits worldwide and laboratories in many testing sites have struggled to process the available tests within a reasonable time frame.Given these issues surrounding COVID-19, many groups began to explore the benefits of ‘big data’ processing and algorithms to assist with the diagnosis and therapeutic development of COVID-19.

The VicHealth Indicators population survey: methodology, prevalence of behavioural risk factors, and use in local policy

BackgroundThe Victorian Health Promotion Foundation (VicHealth) is an Australian state-based government agency with a remit to promote health by targeting physical activity, diet, mental wellbeing, tobacco use and alcohol consumption. Population health data is crucial to this work. This paper reports on the measures and methods used in surveillance, examines the prevalence of risk factors in sub-populations and use of risk factor data in local policy and planning.MethodsThe VicHealth Indicators (VHI) cross-sectional population telephone survey of behavioural and attitudinal health risk factors involved interviews with 22,819 respondents aged 18 years+ from the state of Victoria in 2015. Means or percent prevalences (with 95% CIs) of indicators are presented. Statistically significant differences between the state level and sub-population estimates were deemed to exist when confidence intervals of estimates did not overlap. Use of the data in local policy was assessed through an audit of 77 Municipal Public Health and Wellbeing Plans for 2017–2021. Use in municipal plans according to the municipality’s geographical region type and SES was analysed using Welch’s ANOVA.ResultsThe average vegetable intake was 2.2 serves per day, far below the national guidelines of 5 serves per day, and only 4 in 10 Victorians were sufficiently active. Young males were twice as likely to be at high risk of alcohol harm compared to the state estimate. Women were twice as likely to feel unsafe walking after dark compared to males. There was a consistent pattern of significantly less favourable results for people living in outer metropolitan areas and a socio-economic gradient was evident for most risk factors. Almost 50% of municipalities used VHI data in their local policy plans. Use of VHI data was significantly higher in high SES municipalities and significantly lower in low SES municipalities relative to the mean.ConclusionsThe findings indicate the need for continued targeted action on behavioural risk factors, particularly diet and physical activity, and that more intensive policy and practice action is required to address health inequities to ensure that all Victorians can experience good health. Increased support for low SES municipality policy planning may be warranted.

283 Using Google Trends to Determine Perceived Viral Exposure during the Early Phase of the COVID-19 Pandemic in the United States

Public interest in diseases during outbreaks has been previously studied by examining internet activity via Google Trends (Google, Mountain View, California), a tool that measures the popularity of internet searches longitudinally and geographically. Recently, Google Trends has been used as a surveillance and retrospective epidemiological tool to study the impact of COVID-19 in China, Taiwan, France, and Iran. However, studies that focus on the United States are significantly lacking. Using Google Trends, our aim in this study was to assess the extent of the public’s perceived exposure to COVID-19 as it relates to disease prevalence during the early phase of the pandemic in the United States. We utilized Google Trends to determine the search activity for: “Do I have coronavirus,” “How to get tested for coronavirus,” “What is coronavirus,” “Signs and symptoms of coronavirus” and “How is coronavirus spread.” We collected four weeks of Search Volume Index (SVI) data between February 17th and March 16th, 2020. The mean SVIs for the 5 states with the highest and lowest number of COVID-19 confirmed cases (as of March 16, 2020) were calculated for each query. To obtain the number of confirmed COVID-19 cases in the United States, we referred to a tracker provided by Johns Hopkins University. Scatterplots were then created to compare SVI and number of COVID-19 cases on a state level. Pearson correlations were determined to examine the association between SVI and the number of COVID-19 confirmed cases as of March 16, 2020. Peak SVI for all queries took place on March 12, just a day prior to the U.S. declaration of national emergency. “Do I have coronavirus” (p=0.005), “How to get tested for coronavirus” (p=0.01), and “Signs and symptoms of coronavirus” (p=0.05) were identified as having statistically significant differences in mean SVI between states with the highest and lowest number of COVID-19 cases (Table 1). Mean SVI for “Do I have coronavirus” and “How to get tested for coronavirus” was higher in the states with the most COVID-19 cases compared to the bottom 5 states with the least cases. However, mean SVI for “Signs and symptoms of coronavirus” was higher in the bottom 5 states compared to the top 5 states. There were no statistically significant differences in mean SVI for the remaining queries: “What is coronavirus” (p=0.48) and “How is coronavirus spread” (p=0.21).When looking at all 50 states and the District of Columbia, we found that SVI also positively correlated with the number of confirmed COVID-19 cases for “Do I have coronavirus” and “How to get tested for coronavirus” (R=.387, p=0.005; R=0.367, p=0.008). No statistically significant correlations were found for “How is coronavirus spread” (p=0.45), “What is coronavirus” (p=0.39), and “Signs and symptoms of coronavirus” (p=0.22). Non-generic queries in Google Trends may yield better insights into health information-seeking behavior. Specifically, queries formatted as “How to get tested for ____” and “Do I have _____” could reflect perceived exposure to a communicable disease on a population level. To our knowledge, our study is the first to use Google Trends to distinguish queries that reflect perceived exposure to COVID-19 from those that are borne out by general interest in the United States. Early access to population health data is crucial and potentially life-saving during outbreaks. Digital tools such as Google Trends may help bridge the gap in knowledge and transparency.Table 1This table shows the Google queries and their corresponding SVIs for states that have the highest and lowest number of confirmed COVID-19 casesGoogle QueriesStates most affected by COVID-19 (# of confirmed cases)States least affected by COVID-19 (# of confirmed cases)Mean SVI for top 5 states (SD)Mean SVI for bottom 5 states (SD)P-value“Do I have coronavirus”New York (732) Washington (643) California (426) Massachusetts 164) Colorado (131)Oklahoma (7) Hawaii (6) Idaho (5) Missouri (5) Wyoming (3)* Alaska (1)* North Dakota (1)*76 (7)54 (11)0.005“How to get tested for coronavirus”59 (5)38 (14)0.01“Signs and symptoms of coronavirus”28 (4)45 (16)0.05“What is coronavirus”81 (7)85 (10)0.48“How is coronavirus spread”56 (7)65 (13)0.21* For some queries, these states did not yield an SVI due to insufficient data in Google Trends and were therefore not included in the analysis. Open table in a new tab

Individual and neighbourhood socioeconomic status increase risk of avoidable hospitalizations among Canadian adults: A retrospective cohort study of linked population health data.

IntroductionAvoidable hospitalizations refer to acute care use for conditions that should normally be managed in primary care settings. Lower socioeconomic status that is often measured using area-based indicators (e.g. median household income) has been shown to increase risk of avoidable hospitalizations. However, both area- and individual-level socioeconomic status can contribute to hospitalization risk, but previous data limitations have prevented separate analyses. Further, the joint effect of individual and neighbourhood socioeconomic status has not been established in the Canadian population. To address this, this study links individual-level household income and neighbourhood-level material deprivation data within a population-based Canadian cohort.ObjectivesTo determine the individual and joint effect of individual-level household income and neighbourhood-level material deprivation on risk of hospitalization for a set of chronic ambulatory care sensitive conditions using linked health survey, hospital discharge, and census-derived data.MethodsA pooled cohort was created by linking sociodemographic and health information from eight cycles of the Canadian Community Health Survey (2000/2001 - 2010) to hospital discharge records and Canadian Marginalization Indices (2001, 2006) (N = 354,595). The primary outcome variable was risk of index hospitalization with a primary diagnosis of angina, asthma, congestive heart failure, chronic obstructive pulmonary disease, diabetes, epilepsy, or hypertension. The primary exposure variable was joint individual-level national household income quintile and neighbourhood-level material deprivation quintile. Relative risk (RR) was estimated by constructing modified Poisson regression models with robust error variance.ResultsIn fully adjusted models with income and deprivation considered separately, individuals in the lowest household income quintile and highest material deprivation quintile were at increased risk of hospitalization (Income RR: 1.82 (95% CI 1.56-2.13) Deprivation RR: 1.67 (1.44-1.95)). When income and deprivation were jointly considered, those with low individual income living in high deprivation neighbourhoods were at greatest risk of hospitalization (RR 1.83 (95% CI 1.63 - 2.05)). ConclusionBoth individual income and neighbourhood deprivation separately and jointly increase risk of avoidable hospitalizations. Additional research is needed to understand their mechanisms of action. However, both levels should be considered when designing effective policies and interventions to reduce avoidable hospitalizations.

Abstract IA08: Improving researcher accessibility to publicly available data through creative integration, geospatial visualization, and open data portals

Abstract The increased accessibility of population-level data made available by the government and public health and consumer agencies provides a unique opportunity for integrative data analysis, spatial visualization with much higher resolution to identify clusters of disease, and their correlation with geospatial, socioeconomic, and demographic predictors. However, interactive mapping and spatial analysis tools are underutilized by health researchers and decision-makers as a result of scarce training materials, few examples demonstrating their successful use, and poor mechanisms for sharing results generated by geovisualization. Further, in the wake of massive amounts of new data and analytical tool availability, consumers of cancer population health data, such as academic researchers and public health practitioners, are facing an ongoing transformation of practice resulting in the need for effective collaboration and sharing of resources within and across disciplinary and geographic boundaries. In this talk we will summarize three of our ongoing projects that leverage web-based technologies with the aim to reduce barriers to data sharing, promote simultaneous analysis of multiple datasets, and enable geovisualization of cancer outcomes and their interrelationships with social and spatial factors. The Disentangling Disparities Data Warehouse, or D3W, is a population-based data resource that includes geotagged California Cancer Registry data linked to census, American Community Survey, and other curated sources of neighborhood-level contextual and environmental data. The D3W allows ecologic and/or multilevel statistical analysis and supports sophisticated analysis of the spatial dynamics of cancer in California. The HealthWebMapper is a highly interactive data visualization tool with a simple two-tier web geographic information systems (GIS) framework. This dynamic web GIS/mapping tool was created with open-source JavaScript library, Leaflet, and free web authoring tools (bootstrap, jquery, and Google Chart) to provide user-friendly maps and data-mining functions, including multiple classification methods, correlation analysis, data export, and side-by-side displays. HealthWebMapper is an open source application and available via a public Github repository, and it can be easily installed on any website without specialized GIS servers or databases. Finally, to promote ease of access to the D3W and HealthWebMapper as well as other research data resources, we are developing the Health Data Open Analytic Portal, with support from the newly established NIH-funded HealthLINK Center for Population Health and Health Disparities Research at San Diego State University. The key functions of the open data portal are to archive, manage, download, and integrate disease, environmental, socioeconomic, and health behavior data. The data portal will enable the sharing, archiving, and learning of research procedures and health data resources. The searchable and downloadable data portal will also provide comprehensive research investigator profiles and online training materials to facilitate transdisciplinary research collaborations in cancer population health and beyond. Citation Format: Caroline A. Thompson, Ming-Hsiang Tsou. Improving researcher accessibility to publicly available data through creative integration, geospatial visualization, and open data portals [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr IA08.

Monitoring knowledge translation and the impact of HI in European countries

Abstract Background National health information systems (NHIS) report on the health of populations, the determinants of health and service use within countries. Thus far, the evaluation of HIS has largely focused on data production, and less on their impact in policy and practice. This entails that there is no standardized framework for monitoring the uptake of Health Information (HI) in the healthcare system. Methods An international Delphi with public health professionals and policy makers allowed reaching agreement on criteria for monitoring the impact of NHIS, based on four domains: (a) Health information Evidence Quality, (b) HIS Responsiveness, (c) Stakeholder Engagement, and (d) Knowledge Integration. Next, we pretested the Index among participating countries in the European Joint Action on Health Information. Results Over 130 public health professionals and policy-makers from 38 countries participated in the Delphi. The panel reached agreement on 30 criteria to monitor the impact of HIS in policy and practice; these constitute the HI-Impact Index. Eight countries participated in pretesting the HI-Impact Index. InfAct country representative found the HIS evaluation tool user friendly and the time needed to fill the evaluation acceptable. They noted however the need to refine the auditing guidelines further as to who shall be responsible for conducting the evaluation (i.e. multistakeholder evaluation vs. single governing body), and which type of population health data sources should be included as part of the evaluation. Conclusions Knowledge translation facilitates the implementation of practices that will benefit population health and well-being. The HI-Impact Index addresses a growing demand for more transparency and accountability in the use of HI and scientific evidence within countries. Next steps consist in further piloting the HI-Impact Index for use in specific health areas (i.e. maternal and child health, cancer, antimicrobial resistance).

A spatial analysis of county-level education context and population health and wellbeing

BackgroundStark inequalities in health and wellbeing exist across US counties. Schools are a key feature of counties and residents often make decisions about where to live based on the quality of the local schools. How counties invest in their local schools may reflect one way that health and wellbeing become embedded in geographic places. MethodsWe constructed four indicators of county-level education context using administrative data from all US public schools and linked these to population health data from the RWJF County Health Rankings and Roadmaps Program. Using state-fixed effects spatial error models, we examined if county-level education context relates to county rankings on two indices of population health and wellbeing – quality of life and health behaviors – after adjusting for county-level demographic, economic, and social contexts. ResultsTotal county revenue from local sources and the percentage of a county's high school students enrolled in advanced placement coursework were associated with better county rankings on quality of life and health behavior. We also found a curvilinear relationship between county-level per-pupil spending and county rankings on our population health and wellbeing outcomes. In fully adjusted models, the education context indicators were attenuated; all three remained significantly associated with county rankings on health behavior, but only revenue from local sources was significantly associated with county rankings on quality of life. ConclusionProviding a quality education context may yield health and wellbeing dividends at the county-level given the public education system's far-reaching influence on social and economic wellbeing.

International population-based health surveys linked to outcome data: A new resource for public health and epidemiology.

National health surveys linked to vital statistics and health care information provide a growing source of individual-level population health data. Pooling linked surveys across jurisdictions would create comprehensive datasets that are larger than most existing cohort studies, and that have a unique international and population perspective. This paper's objectives are to examine the feasibility of pooling linked population health surveys from three countries, facilitate the examination of health behaviours, and present useful information to assist in the planning of international population health surveillance and research studies. The design, methodologies and content of the Canadian Community Health Survey (2003 to 2008), the United States National Health Interview Survey (2000, 2005) and the Scottish Health Survey (SHeS) (2003, 2008 to 2010) were examined for comparability and consistency. The feasibility of creating common variables for measuring smoking, alcohol consumption, physical activity and diet was assessed. Sample size and estimated mortality events were collected. The surveys have comparable purposes, designs, sampling and administration methodologies, target populations, exclusions, and content. Similar health behaviour questions allow for comparable variables to be created across the surveys. However, the SHeS uses a more detailed risk factor evaluation for alcohol consumption and diet data. Therefore, comparisons of alcohol consumption and diet data between the SHeS and the other two surveys should be performed with caution. Pooling these linked surveys would create a dataset with over 350,000 participants, 28,424 deaths and over 2.4 million person-years of follow-up. Pooling linked national population health surveys could improve population health research and surveillance. Innovative methodologies must be used to account for survey dissimilarities, and further discussion is needed on how to best access and analyze data across jurisdictions.

Clinical Decision Support for Worker Health: A Five-Site Qualitative Needs Assessment in Primary Care Settings.

Although patients who work and have related health issues are usually first seen in primary care, providers in these settings do not routinely ask questions about work. Guidelines to help manage such patients are rarely used in primary care. Electronic health record (EHR) systems with worker health clinical decision support (CDS) tools have potential for assisting these practices. This study aimed to identify the need for, and barriers and facilitators related to, implementation of CDS tools for the clinical management of working patients in a variety of primary care settings. We used a qualitative design that included analysis of interview transcripts and observational field notes from 10 clinics in five organizations. We interviewed 83 providers, staff members, managers, informatics and information technology experts, and leaders and spent 35 hours observing. We identified eight themes in four categories related to CDS for worker health (operational issues, usefulness of proposed CDS, effort and time-related issues, and topic-specific issues). These categories were classified as facilitators or barriers to the use of the CDS tools. Facilitators related to operational issues include current technical feasibility and new work patterns associated with the coordinated care model. Facilitators concerning usefulness include users' need for awareness and evidence-based tools, appropriateness of the proposed CDS for their patients, and the benefits of population health data. Barriers that are effort-related include additional time this proposed CDS might take, and other pressing organizational priorities. Barriers that are topic-specific include sensitive issues related to health and work and the complexities of information about work. We discovered several themes not previously described that can guide future CDS development: technical feasibility of the proposed CDS within commercial EHRs, the sensitive nature of some CDS content, and the need to assist the entire health care team in managing worker health.

Practitioner perceptions of the health of Australian First Nations' Peoples: Preliminary findings.

To probe health practitioner perceptions of Australian First Nations' Peoples' health and to compare these with reported burden of disease, social determinants and culturally safe health systems data. An online survey tool was used to collect self-reported responses from health practitioners to answer literature-derived questions regarding underlying issues in Australian First Nations' Peoples' health. Responses to the survey were sought from rural and urban health service locations across all Australian states and territories. Allied health, nursing, pharmacy, psychology, social work and related discipline students and practitioners currently working or studying in Australia. Degrees of correspondence between health discipline student and practitioner perceptions on 'major health issues' and 'health systems issues' and published population health and health systems data. Metrics for 'connectedness to' and 'preparedness to engage with' Australian First Nations' Peoples were also reported. Significant differences between practitioner perceptions of 'major health issues' and the disease burden/social determinants published evidence, and with the 'culturally safe health systems' published evidence, were noted. Positive impacts of social and professional relationships (connectedness) between practitioners and First Nations' Peoples were demonstrated. The inclusion of basic population health and culturally safe health systems training in curricula for all genres of health practitioners appears to be indicated by these findings. Further, a meaningful role for the intentional nurture of social and professional relationships with Australian First Nations' Peoples across all health disciplines is suggested as part of efforts to address health systems and equity issues.

A mid-South chronic disease registry and practice-based research network to address disparities.

To describe an innovative health information technology (HIT) model for supporting community-wide health improvement through multiprovider collaboration in a regional population health registry and practice-based research network (PBRN). Case study. We describe the HIT data structure and governance of the Diabetes Wellness and Prevention Coalition (DWPC) Registry and PBRN based in Memphis, Tennessee. The population served and their characteristics were assessed for all adult patients with at least 1 encounter in a participating health care delivery system from January 1, 2013, to March 31, 2019. Disparities in access and health care utilization were assessed by residential zip code. The DWPC Registry is a chronic disease and population health data warehouse designed to facilitate chronic disease surveillance and tracking of processes and outcomes of care in medically underserved areas of the mid-South. The Registry primarily focuses on obesity-associated chronic conditions such as diabetes, hypertension, hyperlipidemia, and chronic kidney disease. It combines patient data from 7 regional health systems, which include 6 adult hospitals and more than 50 outpatient practices, covering 462,223 adults with 2,032,425 clinic visits and 602,679 hospitalizations and/or emergency department visits from January 1, 2013, to March 31, 2019. The most prevalent chronic conditions include obesity (37.2%), hypertension (34.4%), overweight (26.4%), hyperlipidemia (18.0%), and type 2 diabetes (14.0%). The Registry provides quarterly practice improvement reports to participating clinics, facilitates surveillance of and outreach to patients with unmet health needs, and supports a pragmatic clinical trial and multiple cohort studies. Regional registries and PBRNs are powerful tools that can support real-world quality improvement and population health efforts to reduce disparities and improve equity in chronic disease care in medically underserved communities across the United States.

Use of public water supply fluoride concentration as an indicator of population exposure to fluoride in England 1995\u20132015

Public health monitoring of Community Water Fluoridation (CWF) schemes requires estimates of exposure to fluoride in public water supplies (PWS). We aimed to use routine data to estimate population exposure to PWS-fluoride in England and to determine whether PWS-fluoride exposure from 2005 to 2015 could be used as a proxy for exposure for 1995–2004, when fluoride concentration data that could be linked to population health data were unavailable. We calculated annual mean water supply zone PWS-fluoride concentrations from monitoring data for 1995–2015, stratified by fluoridation scheme-flagging. We allocated annual 2005–2015 mean PWS-fluoride concentrations to small area boundaries to describe population exposure within five concentration categories (< 0.1 to ≥ 0.7 mg/L). We compared zone-level 1995–2004 and 2005–2015 mean PWS-fluoride concentrations using Spearman correlation. Most (72%) of the population received PWS with < 0.2 mg/L fluoride and 10% with ≥ 0.7 mg/L. Fluoride concentrations in 1995–2004 and 2005–2015 were similar (median 0.11 mg/L (lower quartile–upper quartile (LQ–UQ) 0.06–0.17) and 0.11 mg/L (LQ–UQ 0.07–0.17), respectively) and highly correlated (coefficient 0.93) if un-fluoridated but differed (1995–2004 median 0.78 mg/L (LQ–UQ 0.59–0.92); 2005–2015 0.84 mg/L (LQ–UQ 0.72–0.95)) and correlated weakly (coefficient 0.31) if fluoridated. Fluoride concentrations in 2005–2015 approximate those in 1995–2004 but with a greater risk of misclassification in fluoridation schemes.

Subnational population health monitoring and reporting: public health at the grassroots level

Population health monitoring and reporting provides regular and up-to-date information on health outcomes and its determinants. The outputs of population health monitoring aim to inform policymakers and other stakeholders to develop fact-based decisions. Population health monitoring is located at the national, but also federal state and county level. This paper describes the legal basis for population health monitoring in the federal states' public health acts as well as current challenges and possibilities for further development from afederal state and county perspective on population health monitoring.Alegal basis for population health monitoring on the federal state and county level exists for almost all federal states in Germany. The level of detail of these laws varies in terms of responsibility, periodicity of population health monitoring and reporting, content, and designated use. Population health monitoring needs to respond to challenges in the areas of population health monitoring resources, data sources, (intersectoral) reporting, and impact. Practical examples illustrate how the challenges can be handled and further development can take place.Based on its solid legal basis, being aroutine task, and its close link to the living conditions of the population, population health monitoring on the federal state and county level has thepossibility for cocreating public health at the grassroots level and to be apioneer for health equity.

Socioeconomic determinants of health and county-level variation in cardiovascular disease mortality: An exploratory analysis of Georgia during 2014-2016.

Since cardiovascular disease (CVD) represents the leading cause of death in the state of Georgia, we sought to describe the relationship between socioeconomic determinants of health (SDH) and CVD-related mortality in Georgia using publicly available population health and economic data. A multivariate regression model was estimated to examine physical inactivity, median household income, health insurance status, urban–rural status, and air quality on CVD mortality in Georgia between 2014 and 2016. We find that the median household income and annual average ambient concentrations of PM2.5 were the most significant factors in explaining CVD mortality. Lower levels of median household income and higher concentrations of PM2.5 were associated with higher CVD mortality rates. Leisure-time physical inactivity, health insurance status, and urban–rural status were not associated with worsened CVD-related mortality. As such, policies and interventions aimed at improving socioeconomic status in Georgia should be explored in an effort to positively impact CVD outcomes. Furthermore, this exploratory study could be extended for all counties in the U.S.

Supervised Bayesian Statistical Learning to Identify Prognostic Risk Factor Patterns from Population Data.

Current methods for building risk models assume averaged uniform effects across populations. They use weighted sums of individual risk factors from regression models with only a few interactions, such as age. This does not allow risk factor effects to vary in different morbidity contexts. This study modified a supervised Bayesian statistical learning method of topic modelling, allowing individual factors to have different effects depending on a patient's other comorbidity. This study used topic modelling to assess more than 71,000 unique risk factors in a population cohort of 1.4 million adults within routine data. The model learnt prognostically important risk factor patterns that predicted 5 year survival, and the resulting model achieved excellent calibration and discrimination with a C statistic of 0.9 in a held out validation cohort. The model explained 92% of the observed variation in 5 year survival in the population. This paper validates using survival supervised Bayesian topic modelling within large routine electronic population health data to identify prognostically important risk factor patterns.

Strengthening literature search strategies for systematic reviews reporting population health in the Middle East and North Africa: A meta-research study.

ObjectiveIn the Middle East and North Africa (MENA), data are produced in languages other than English and available through gray literature sources. We assessed the comprehensiveness of literature search strategies of systematic reviews (SRs) reporting population health primary data on MENA.MethodsUtilizing the registered protocol (PROSPERO CRD42017076736), we conducted a meta‐research analysis on a cohort of SRs (systematic PubMed search: from 2008 to 2016) and evaluated their search strategies following AMSTAR recommendations.ResultsA total of 379 SRs were included. Few SRs (10.3%, n = 39) conducted a comprehensive literature search including at least two databases, reference lists of included primary studies, gray literature sources, and no language restriction. Nevertheless, 90.5% (n = 343) searched at least two databases and 67.0% (n = 254) searched gray literature sources. Authors from MENA searched statistically more for gray literature than authors from Western countries (P = 0.022). Reference lists of the included studies were searched in 40.4% (n = 153) of the SRs. Searching the reference lists was positively associated with searching for gray literature (P < 0.001). Only 38.8% (n = 147) of the SRs had no language restriction or searched in English and in at least one language relevant to MENA, whereas 27.2% (n = 103) did not report this information.ConclusionsLiterature searches for SRs reporting population health data on MENA were limited in reporting quality, language restrictions, and lack of reference list searches. This was probably due to lack of adherence to the reporting guidelines. To ensure compilation of optimum evidence, expanding literature searches to reference list search and for additional languages relevant to MENA are required.

Cardiac procedures in ST-segment-elevation myocardial infarction - the influence of age, geography and Aboriginality

BackgroundTimely restoration of bloodflow acute ST-segment elevation myocardial infarction (STEMI) reduces myocardial damage and improves prognosis. The objective of this study was describe the association of demographic factors with hospitalisation rates for STEMI and time to angiography, Percutaneous Coronary Intervention (PCI) and Coronary Artery Bypass Graft (CABG) in New South Wales (NSW) and the Australian Capital Territory (ACT), Australia.MethodsThis was an observational cohort study using linked population health data. We used linked records of NSW and the ACT hospitalisations and the Australian Government Medicare Benefits Schedule (MBS) for persons aged 35 and over hospitalised with STEMI in the period 1 July 2010 to 30 June 2014. Survival analysis was used to determine the time between STEMI admission and angiography, PCI and CABG, with a competing risk of death without cardiac procedure.ResultsOf 13,117 STEMI hospitalisations, 71% were among males; 55% were 65-plus years; 64% lived in major cities, and 2.6% were Aboriginal people. STEMI hospitalisation occurred at a younger age in males than females. Angiography and PCI rates decreased with age: angiography 69% vs 42% and PCI 60% vs 34% on day 0 for ages 35-44 and 75-plus respectively. Lower angiography and PCI rates and higher CABG rates were observed outside major cities. Aboriginal people with STEMI were younger and more likely to live outside a major city. Angiography, PCI and CABG rates were similar for Aboriginal and non-Aboriginal people of the same age and remoteness area.ConclusionsThere is a need to improve access to definitive revascularisation for STEMI among appropriately selected older patients and in regional areas. Aboriginal people with STEMI, as a population, are disproportionately affected by access to definitive revascularisation outside major cities. Improving access to timely definitive revascularisation in regional areas may assist in closing the gap in cardiovascular outcomes between Aboriginal and non-Aboriginal people.