Methods for structural sexism and population health research: Introducing a novel analytic framework to capture life-course and intersectional effects.

Accumulating evidence links structural sexism to gendered health inequities, yet methodological challenges have precluded comprehensive examinations into life-course and/or intersectional effects. To help address this gap, we introduce an analytic framework that uses sequential conditional mean models (SCMMs) to jointly account for longitudinal exposure trajectories and moderation by multiple dimensions of social identity/position, which we then apply to study how early life-course exposure to U.S. state-level structural sexism shapes mental health outcomes within and between gender groups. Data came from the Growing Up Today Study, a cohort of 16,875 children aged 9-14 years in 1996 who we followed through 2016. Using a composite index of relevant public policies and societal conditions (e.g., abortion bans, wage gaps), we assigned each U.S. state a year-specific structural sexism score and calculated participants' cumulative exposure by averaging the scores associated with states they had lived in during the study period, weighted according to duration of time spent in each. We then fit a series of SCMMs to estimate overall and group-specific associations between cumulative exposure from baseline through a given study wave and subsequent depressive symptomology; we also fit models using simplified (i.e., non-cumulative) exposure variables for comparison purposes. Analyses revealed that cumulative exposure to structural sexism: (1) was associated with significantly increased odds of experiencing depressive symptoms by the subsequent wave; (2) disproportionately impacted multiply marginalized groups (e.g., sexual minority girls/women); and (3) was more strongly associated with depressive symptomology compared to static or point-in-time exposure operationalizations (e.g., exposure in a single year). Substantively, these findings suggest that long-term exposure to structural sexism may contribute to the inequitable social patterning of mental distress among young people living in the U.S. More broadly, the proposed analytic framework represents a promising approach to examining the complex links between structural sexism and health across the life course and for diverse social groups.

Undercounting diagnoses in Australian general practice: a data quality study with implications for population health reporting.

BACKGROUND: Diagnosis can often be recorded in electronic medical records (EMRs) as free-text or using a term with a diagnosis code. Researchers, governments, and agencies, including organisations that deliver incentivised primary care quality improvement programs, frequently utilise coded data only and often ignore free-text entries. Diagnosis data are reported for population healthcare planning including resource allocation for patient care. This study sought to determine if diagnosis counts based on coded diagnosis data only, led to under-reporting of disease prevalence and if so, to what extent for six common or important chronic diseases. METHODS: This cross-sectional data quality study used de-identified EMR data from 84 general practices in Victoria, Australia. Data represented 456,125 patients who attended one of the general practices three or more times in two years between January 2021 and December 2022. We reviewed the percentage and proportional difference between patient counts of coded diagnosis entries alone and patient counts of clinically validated free-text entries for asthma, chronic kidney disease, chronic obstructive pulmonary disease, dementia, type 1 diabetes and type 2 diabetes. RESULTS: Undercounts were evident in all six diagnoses when using coded diagnoses alone (2.57-36.72% undercount), of these, five were statistically significant. Overall, 26.4% of all patient diagnoses had not been coded. There was high variation between practices in recording of coded diagnoses, but coding for type 2 diabetes was well captured by most practices. CONCLUSION: In Australia clinical decision support and the reporting of aggregated patient diagnosis data to government that relies on coded diagnoses can lead to significant underreporting of diagnoses compared to counts that also incorporate clinically validated free-text diagnoses. Diagnosis underreporting can impact on population health, healthcare planning, resource allocation, and patient care. We propose the use of phenotypes derived from clinically validated text entries to enhance the accuracy of diagnosis and disease reporting. There are existing technologies and collaborations from which to build trusted mechanisms to provide greater reliability of general practice EMR data used for secondary purposes.

The impact of the implementation of hierarchical medical system on population health: evidence from China.

Introduction: The implementation of a hierarchical medical system holds significant practical importance in advancing the Healthy China strategy and elevating the overall health status of the population of China. Methods: This article empirically examines the influence of the implementation of a hierarchical medical system on the health of the population using the latest 2020 China Family Panel Studies database. Furthermore, it investigates the variability of this impact across distinct health insurance participation statuses and literacy groups. Results: The findings of the study demonstrate that the implementation of the hierarchical medical system substantially enhances the health of the population, not only in terms of self-assessed health but also through a notable positive effect on alleviating chronic diseases. These results maintain their validity even after conducting robustness tests utilizing a replacement estimation model. Heterogeneity analysis reveals that the impact of the hierarchical medical system on the population's health status exhibits significant variation concerning health insurance participation and literacy. Specifically, regarding health insurance participation, the hierarchical medical system effectively improves both self-assessed health and chronic disease status among the insured population. However, for those not enrolled in health insurance, the hierarchical medical system only demonstrates improvement in chronic disease status, with insignificant results observed in enhancing self-assessed health status. Moreover, propensity score matching (PSM) was also used to address endogeneity problems resulting from sample selectivity bias. The findings demonstrate that endogeneity issues can be suitably addressed by the PSM model. Additionally, they point out that an overestimation of the impact of the hierarchical medical system on the population's self-assessed health state would result from failing to take sample selectivity bias into account. On the other hand, it will lead to the underestimation of the effect of the hierarchical medical system on the status of chronic diseases. Discussion: Moving forward, steadfast efforts should be directed toward further enhancing the implementation of the hierarchical medical system. This includes the comprehensive promotion and using the pivotal role of the hierarchical medical system in improving the health of the population.

Community, Public, and Population Health Content in Nursing Curricula: National Survey Results.

BACKGROUND: Policy, societal, and system changes are prompting nursing programs to expand population health content in curricula. This study examined the current state of community, public, and population health (CPPH) education in nursing curricula throughout the United States. METHOD: This descriptive study examined CPPH education in nursing programs nationally. A survey was developed and distributed to nursing programs from January to May 2021. RESULTS: CPPH content integration occurred across all program levels, and the majority of the participants were involved in the development of CPPH-specific curriculum. Programs experienced reductions in CPPH curriculum due to coronavirus disease 2019 (COVID-19), lack of experienced faculty, budget constraints, and an emphasis on acute care. CONCLUSION: The continuation of CPPH education in current nursing curricula is critical. National and academic nursing organizations must continue to monitor CPPH content in nursing curricula to assure a competent CPPH nursing workforce. [J Nurs Educ. 2024;63(5):282-291.].

A population health approach to workplace mental health: rationale, implementation and engagement.

Objectives: To describe a population health-based program to support employee and dependent mental health and learn from engagement trends. Methods: Retrospective analysis of a program utilizing an assessment of mental health risk. For scoring "at risk," a Care Concierge is offered to connect users with resources. Results: Participation was offered to 56,442 employees and dependents. Eight thousand seven hundred thirty-one completed the assessment (15%). Of those, 4,644 (53%) scored moderate or higher. A total of 418 (9%) engaged the Care Concierge. Factors that negatively influenced the decision to engage care included bodily pain, financial concerns. Positive influences were younger age, high stress, anxiety, PTSD and low social support. Conclusion: Proactive assessment plus access to a Care Concierge facilitates mental healthcare utilization. Several factors influence likelihood to engage in care. A better understanding of these factors may allow for more targeted outreach and improved engagement.

An Asset Framework to Guide Nonhealth Policy for Population Health.

This JAMA Forum discusses 3 types of assets (financial, physical, and social) that unlock access to resources and shape population health.

Longitudinal Trends in Insurer Participation in Multisector Population Health Activities.

Healthcare organizations increasingly engage in activities to identify and address social determinants of health (SDOH) among their patients to improve health outcomes and reduce costs. While several studies to date have focused on the evolving role of hospitals and physicians in these types of population health activities, much less is known about the role health insurers may play. We used data from the National Longitudinal Survey of Public Health Systems for the period 2006 to 2018 to examine trends in health insurer participation in population health activities and in the multi-sector collaborative networks that support these activities. We also used a difference-in-differences approach to examine the impact of Medicaid expansion on insurer participation in population health networks. Insurer participation increased in our study period both in the delivery of population health activities and in the integration into collaborative networks that support these activities. Insurers were most likely to participate in activities focusing on community health assessment and policy development. Results from our adjusted difference-in-differences models showed variation in association between insurer participation in population health networks and Medicaid expansion (Table 2). Population health networks in expansion states experienced significant increases insurer participation in assessment (4.48 percentage points, P < .05) and policy and planning (7.66 percentage points, P < .05) activities. Encouraging insurance coverage gains through policy mechanisms like Medicaid expansion may not only improve access to healthcare services but can also act as a driver of insurer integration into population health networks.

Research on the spatial and temporal patterns of ozone concentration and population health effects in the Central Plains Urban Agglomeration from 2017 to 2020.

Fine particulate matter (PM2.5) and near-surface ozone (O3) are the main atmospheric pollutants in China. Long-term exposure to high ozone concentrations adversely affects human health. It is of great significance to systematically analyze the spatiotemporal evolution mechanism and health effects of ozone pollution. Based on the ozone data of 91 monitoring stations in the Central Plains Urban Agglomeration from 2017 to 2020, the research used Kriging method and spatial autocorrelation analysis to investigate the spatiotemporal variations of ozone concentration. Additionally, the study assessed the health effects of ozone on the population using the population exposure risk model and exposure-response relationship model. The results indicated that: (1) The number of premature deaths caused by ozone pollution in the warm season were 37,053 at 95% confidence interval (95% CI: 28,190-45,930) in 2017, 37,685 (95% CI: 28,669-46,713) in 2018, and 37,655 (95% CI: 28,647-46,676) in 2019. (2) The ozone concentration of the Central Plains urban agglomeration showed a decreasing trend throughout the year and during the warm season from 2017 to 2020, there are two peaks monthly, one is June, and the other is September. (3) In the warm season, the high-risk areas of population exposure to ozone in the Central Plains Urban Agglomeration were mainly concentrated in urban areas. In general, the population exposure risk of the south is lower than that of the north. The number of premature deaths attributed to ozone concentration during the warm season has decreased, but some southern cities such as Xinyang and Zhumadian have also seen an increase in premature deaths. China has achieved significant results in air pollution control, but in areas with high ozone concentrations and high population density, the health burden caused by air pollution remains heavy, and stricter air pollution control policies need to be implemented.

Challenges of determining the relative contribution of determinants of health on population health: a Canadian perspective.

This article investigates the source of frequently cited data regarding the relative contribution of determinants of health to population health in Canada. It critically discusses the imperative for such national or regional data in policymaking, and the challenges and limitations of this approach.

The study of ableism in population health: a critical review.

Over the past three decades, health equity has become a guiding framework for documenting, explaining, and informing the promotion of population health. With these developments, scholars have widened public health's aperture, bringing systems of oppression sharply into focus. Additionally, some researchers in disability and health have advocated for utilizing socially grounded frameworks to investigate the health of disabled people. Yet, naming ableism, much less operationalizing it for the empirical study of health, remains scant. This paper critically reviews the study of ableism as a social determinant of disabled people's health within population health research. First, we provide an orientation to the present state of this literature by looking to the past. We briefly trace a history of traditional approaches to studying disability and health and alternatives that have emerged from critiques of the individualized lens that has dominated this work. Next, we delineate the operation of ableism across social levels. We characterize how ableism has been studied in population health in terms of levels of analysis (intrapersonal, interpersonal, institutional, and structural) and measures of interest. To conclude, we discuss hinderances to and promising avenues toward population health research that advances health equity for disabled people.

Measuring population health using health expectancy estimates from morbidity and mortality databases.

The progressive incorporation of quality of life indicators in health planning meets a critical need: The evaluation of the performance of health services, which are under stress by multiple causes, but in particular by an ageing population. In general, national health plans rely on health expectancies obtained using the Sullivan method. The Sullivan health expectancy index combines age-specific mortality rates and age-specific prevalence of healthy life, obtained from health surveys. The objective of this work is to investigate an equivalent estimation, using available information from morbidity and mortality datasets. Mortality and morbidity information, corresponding to years 2016 and 2017, was obtained for the population of the county of Baix Empordà (Catalonia), N = 91,130. Anonymized individual information on diagnoses, procedures and pharmacy consumption contained in the individual clinical record (ICD and ATC codes), were classified into health states. Based on the observed health transitions and mortality, life expectancies by health state were obtained from a multistate microsimulation model. Healthy life expectancies at birth and 65 years for females and males were respectively HLE0female = 39.94, HLE0male = 42.87, HLE65female = 2.43, HLE65male = 2.17. These results differed considerably from the Sullivan equivalents, e.g., 8.25 years less for HLE65female, 9.26 less for HLE65male. Point estimates for global life expectancies at birth and 65 years of age: LE0female = 85.82, LE0male = 80.58, LE65female = 22.31, LE65male = 18.86. Health indicators can be efficiently obtained from multistate models based on mortality and morbidity information, without the use of health surveys. This alternative method could be used for monitoring populations in the context of health planning. Life Expectancy results were consistent with the standard government reports. Due to the different approximation to the concept of health (data-based versus self-perception), healthy life expectancies obtained from multistate micro simulation are consistently lower than those calculated with the standard Sullivan method.

Exploration of understanding of impactibility analysis and application through workshops to inform model design for population health management policy: a qualitative assessment.

OBJECTIVES: Assess understanding of impactibility modelling definitions, benefits, challenges and approaches. DESIGN: Qualitative assessment. SETTING: Two workshops were developed. Workshop 1 was to consider impactibility definitions and terminology through moderated open discussion, what the potential pros and cons might be, and what factors would be best to assess. In workshop 2, participants appraised five approaches to impactibility modelling identified in the literature. PARTICIPANTS: National Health Service (NHS) analysts, policy-makers, academics and members of non-governmental think tank organisations identified through existing networks and via a general announcement on social media. Interested participants could enrol after signing informed consent. OUTCOME MEASURES: Descriptive assessment of responses to gain understanding of the concept of impactibility (defining impactibility analysis), the benefits and challenges of using this type of modelling and most relevant approach to building an impactibility model for the NHS. RESULTS: 37 people attended 1 or 2 workshops in small groups (maximum 10 participants): 21 attended both workshops, 6 only workshop 1 and 10 only workshop 2. Discussions in workshop 1 illustrated that impactibility modelling is not clearly understood, with it generally being viewed as a cross-sectional way to identify patients rather than considering patients by iterative follow-up. Recurrent factors arising from workshop 2 were the shortage of benchmarks; incomplete access to/recording of primary care data and social factors (which were seen as important to understanding amenability to treatment); the need for outcome/action suggestions as well as providing the data and the risk of increasing healthcare inequality. CONCLUSIONS: Understanding of impactibility modelling was poor among our workshop attendees, but it is an emerging concept for which few studies have been published. Implementation would require formal planning and training and should be performed by groups with expertise in the procurement and handling of the most relevant health-related real-world data.

Measuring social determinants of health in the All of Us Research Program.

To accelerate medical breakthroughs, the All of Us Research Program aims to collect data from over one million participants. This report outlines processes used to construct the All of Us Social Determinants of Health (SDOH) survey and presents the psychometric characteristics of SDOH survey measures in All of Us. A consensus process was used to select SDOH measures, prioritizing concepts validated in diverse populations and other national cohort surveys. Survey item non-response was calculated, and Cronbach's alpha was used to analyze psychometric properties of scales. Multivariable logistic regression models were used to examine associations between demographic categories and item non-response. Twenty-nine percent (N = 117,783) of eligible All of Us participants submitted SDOH survey data for these analyses. Most scales had less than 5% incalculable scores due to item non-response. Patterns of item non-response were seen by racial identity, educational attainment, income level, survey language, and age. Internal consistency reliability was greater than 0.80 for almost all scales and most demographic groups. The SDOH survey demonstrated good to excellent reliability across several measures and within multiple populations underrepresented in biomedical research. Bias due to survey non-response and item non-response will be monitored and addressed as the survey is fielded more completely.

[Digital environment and population health]. / Environnement digital et santé des populations.

Health and risk of disease are determined by exposure to the physical, socio-economic, and political environment and to this has been added exposure to the digital environment. Our increasingly digital lives have major implications for people's health and its monitoring, as well as for prevention and care. Digital health, which encompasses the use of health applications, connected devices and artificial intelligence medical tools, is transforming medical and healthcare practices. Used properly, it could facilitate patient-centered, inter-professional and data-driven care. However, its implementation raises major concerns and ethical issues, particularly in relation to privacy, equity, and the therapeutic relationship.

La santé et le risque de maladies sont déterminés par l'exposition aux environnements physiques, socio-économiques et politiques, et à cela s'est ajouté l'exposition à l'environnement digital. Notre vie digitale a des implications majeures, d'une part, sur la santé des populations et son monitoring et, d'autre part, sur la prévention et les soins. Ainsi, la santé digitale (digital health), qui englobe l'utilisation d'applications de santé, d'appareils connectés, ou d'outils médicaux d'intelligence artificielle, modifie les pratiques médico-soignantes. Bien utilisée, elle pourrait faciliter les soins centrés sur le patient, interprofessionnels et guidés par les données. Cependant, sa mise en œuvre soulève d'importants craintes et enjeux éthiques en lien notamment avec la protection des données, l'équité et la relation thérapeutique.

[The analysis of dynamics of indicator of mortality of population of the Siberian Federal Okrug resulted from toxic impact of alcohol in 2011-2020].

The purpose of the study is to investigate dynamics of mortality of population of the Siberian Federal Okrug resulted from toxic impact of alcohol in 2011-2020 and to comparatively analyze mortality of population of the regions of the Siberian Federal Okrug with general Okrug values. The study uses statistical data of the Federal State Statistics Service of the Russian Federation. The analysis was applied to mortality of population of the Russian Federation, the Siberian Federal Okrug and the regions of the Siberian Federal Okrug (the Republic of Altai, the Republic of Tuva, the Republic of Khakassia, the Altai Kray, the Krasnoyarsk Kray, Irkutsk, Kemerovo, Novosibirsk, Omsk and Tomsk Oblast) resulted from toxic impact of alcohol. The comparative analysis of regional and general Okrug indicators of mortality was implemented. The statistical processing of data included common methods of calculating extensive and intensive indicators, indicator average error and evaluation of reliability of differences. It was established that in 2011-2020, mortality of population of the Siberian Federal Okrug resulted from toxic impact of alcohol decreased by 3.3 times. The decrease of mortality of the population was registered in all regions of the Siberian Federal Okrug. The fastest decreasing of mortality of population occurred in the Altai Kray (7.7 times), the Republic of Tuva (7.0 times), the Krasnoyarsk Kray (6.4 times). The investigation of dynamics of mortality of population in the regions of the Siberian Federal Okrug resulted from toxic impact of alcohol permits to identify characteristics of toxicological situation at the regional level and to determine strategic directions of the regional policy of chemical safety and population health protection.

[The personnel maintenance of primary health care physicians in condition of challenges from menaces to population health].

In condition of challenges from menaces to health due to COVID-19 the role of primary health care physicians increases that requires development of management decisions at the regional level. The purpose of the study. On the basis of analysis of medical demographic indicators and data concerning provision of primary health care personnel in the Russian Federation, proposals were made related to reforming of primary health care system in conditions of implementation of general medical practice with purpose to counteract menaces to population health. The analytical and statistical methods were applied. The official statistics data from the Minzdrav of the Russian Federation and Rosstat, results of original researches with content analysis and interpretation were used. The COVID-19 pandemic negatively impacted population health. The indicator of newly detected morbidity in the Russian Federation in 2020 decreased as compared to 2019 from 78024.3 to 75989.7‰oo (by 2.6%). This is the result of decreasing of dispensary and preventive activities among population. In next COVID-19 year (2021), as compared to 2020, indicator of primary morbidity increased to 85531.6‰oo that 12.6% higher than in 2020. The increase occurred in all classes of diseases that caused necessity of rehabilitation of these patients. The COVID-19 morbidity increased up to 2.4 times i.e. from 3391.1 in 2020 to 8085.7‰oo in 2021. The provision of physicians in the Russian Federation was 37.7‰o in 2021. Across the Federal Okrugs differences in indicators made up to 1.3 times and in subjects of the Russian Federation - up to 2.7 times. In conditions of COVID-19 significance of menaces to population health worsened. The situation requires both enhancement of primary health care and implementation of physicians of new formation - general practitioners responsible for patient health. The general practice (GP) widely developed in Russia in the 1990s during last ten years loses its significance in most subjects of the Russian Federation. In 2021, provision of general practitioners in the Russian Federation made up to 0.67‰o. In the Federal Okrugs, difference between indicators made up to 5.6 times. In the subjects of the Russian Federation the difference is enormous - 141.5 times. The article presents and scientifically substantiates prospective functional organizational models of general practice. The established situation with COVID-19 infection requires development of management decisions and measures at the regional level concerning improvement of organization of primary health care and implementation of general practice in conditions of counteracting risks of menaces to population health and health preservation.