Health Policy Decisions Research Articles

The Ministerial Advisory Committees and 3 years of COVID-19 expertise - is the Department of Health's model for information-sharing pandemic-ready?

Effective pandemic decision-making depends on scientific expert evidence, transparency about public health policy decision-making, its rationales and the evidence on which it is based. The South African government laudably committed its COVID-19 response to be guided by science and evidence. Yet, the expert advice that it received was not always readily made available to the public. This submission analyses the time elapsed between the submission of COVID-19 Ministerial Advisory Committees memoranda to the Minister of Health, and the Department of Health's subsequent publication of these for the period August 2020 - January 2024. It also summarises the outcomes of the Health Justice Initiative's access to information legal action against the department on expert advice and government decision-making during the pandemic.

Impact of Structural Differences on the Modeled Cost-Effectiveness of Noninvasive Prenatal Testing.

Noninvasive prenatal testing (NIPT) was developed to improve the accuracy of prenatal screening to detect chromosomal abnormalities. Published economic analyses have yielded different incremental cost-effective ratios (ICERs), leading to conclusions of NIPT being dominant, cost-effective, and cost-ineffective. These analyses have used different model structures, and the extent to which these structural variations have contributed to differences in ICERs is unclear. To assess the impact of different model structures on the cost-effectiveness of NIPT for the detection of trisomy 21 (T21; Down syndrome). A systematic review identified economic models comparing NIPT to conventional screening. The key variations in identified model structures were the number of health states and modeling approach. New models with different structures were developed in TreeAge and populated with consistent parameters to enable a comparison of the impact of selected structural variations on results. The review identified 34 economic models. Based on these findings, demonstration models were developed: 1) a decision tree with 3 health states, 2) a decision tree with 5 health states, 3) a microsimulation with 3 health states, and 4) a microsimulation with 5 health states. The base-case ICER from each model was 1) USD$34,474 (2023)/quality-adjusted life-year (QALY), 2) USD$14,990 (2023)/QALY, (3) USD$54,983 (2023)/QALY, and (4) NIPT was dominated. Model-structuring choices can have a large impact on the ICER and conclusions regarding cost-effectiveness, which may inadvertently affect policy decisions to support or not support funding for NIPT. The use of reference models could improve international consistency in health policy decision making for prenatal screening. NIPT is a clinical area in which a variety of modeling approaches have been published, with wide variation in reported cost-effectiveness.This study shows that when broader contextual factors are held constant, varying the model structure yields results that range from NIPT being less effective and more expensive than conventional screening (i.e., NIPT was dominated) through to NIPT being more effective and more expensive than conventional screening with an ICER of USD$54,983 (2023)/QALY.Model-structuring choices may inadvertently affect policy decisions to support or not support funding of NIPT. Reference models could improve international consistency in health policy decision making for prenatal screening.

Bridging the divide: addressing discrepancies between clinical guidelines, policy guidelines, and biomarker utilization.

This paper aims to identify and address gaps in cancer treatment and diagnosis within European health services, focusing specifically on discrepancies between clinical guidelines and policy guidelines. It seeks to highlight how the underutilization of advanced diagnostic techniques recommended by medical societies contributes to missed opportunities for improving patient outcomes. A comprehensive analysis was conducted across multiple European countries to assess the compliance and integration of clinical guidelines with the availability of advanced diagnostic technologies. Secondary data related to clinical and policy guidelines in cancer care were collected and analyzed. Key indicators of adoption and utilization of next-generation sequencing and liquid biopsy were examined to evaluate their impact on health service efficiency and patient care. The analysis revealed significant discrepancies between the recommendations of medical societies regarding advanced diagnostic techniques and their adoption in health policy decisions across Europe. Country-specific assessments indicated varying levels of alignment between clinical guidelines and the availability of advanced diagnostics. These findings underscored missed opportunities for optimizing patient care and health service efficiency through better alignment and integration of clinical guidelines with policy decisions. This study concludes that there is a critical need for health policy decision-makers to prioritize the adoption of clinical guidelines in resource allocation and health service organization. Greater attention to the recommendations of medical societies regarding advanced diagnostic techniques could significantly enhance diagnostic accuracy, treatment efficacy, and overall patient outcomes in cancer care. The paper advocates for policy reforms that acknowledge and leverage the potential benefits of advanced diagnostics in improving health service performance and patient-centered care across Europe.

Choices of morbidity outcomes and concentration-response functions for health risk assessment of long-term exposure to air pollution.

Air pollution health risk assessment (HRA) has been typically conducted for all causes and cause-specific mortality based on concentration-response functions (CRFs) from meta-analyses that synthesize the evidence on air pollution health effects. There is a need for a similar systematic approach for HRA for morbidity outcomes, which have often been omitted from HRA of air pollution, thus underestimating the full air pollution burden. We aimed to compile from the existing systematic reviews and meta-analyses CRFs for the incidence of several diseases that could be applied in HRA. To achieve this goal, we have developed a comprehensive strategy for the appraisal of the systematic reviews and meta-analyses that examine the relationship between long-term exposure to particulate matter with an aerodynamic diameter smaller than 2.5 µm (PM2.5), nitrogen dioxide (NO2), or ozone (O3) and incidence of various diseases. To establish the basis for our evaluation, we considered the causality determinations provided by the US Environmental Protection Agency Integrated Science Assessment for PM2.5, NO2, and O3. We developed a list of pollutant/outcome pairs based on these assessments and the evidence of a causal relationship between air pollutants and specific health outcomes. We conducted a comprehensive literature search using two databases and identified 75 relevant systematic reviews and meta-analyses for PM2.5 and NO2. We found no relevant reviews for long-term exposure to ozone. We evaluated the reliability of these studies using an adaptation of the AMSTAR 2 tool, which assesses various characteristics of the reviews, such as literature search, data extraction, statistical analysis, and bias evaluation. The tool's adaptation focused on issues relevant to studies on the health effects of air pollution. Based on our assessment, we selected reviews that could be credible sources of CRF for HRA. We also assessed the confidence in the findings of the selected systematic reviews and meta-analyses as the sources of CRF for HRA. We developed specific criteria for the evaluation, considering factors such as the number of included studies, their geographical distribution, heterogeneity of study results, the statistical significance and precision of the pooled risk estimate in the meta-analysis, and consistency with more recent studies. Based on our assessment, we classified the outcomes into three lists: list A (a reliable quantification of health effects is possible in an HRA), list B+ (HRA is possible, but there is greater uncertainty around the reliability of the CRF compared to those included on list A), and list B- (HRA is not recommended because of the substantial uncertainty of the CRF). In our final evaluation, list A includes six CRFs for PM2.5 (asthma in children, chronic obstructive pulmonary disease, ischemic heart disease events, stroke, hypertension, and lung cancer) and three outcomes for NO2 (asthma in children and in adults, and acute lower respiratory infections in children). Three additional outcomes (diabetes, dementia, and autism spectrum disorders) for PM2.5 were included in list B+. Recommended CRFs are related to the incidence (onset) of the diseases. The International Classification of Diseases, 10th revision codes, age ranges, and suggested concentration ranges are also specified to ensure consistency and applicability in an HRA. No specific suggestions were given for ozone because of the lack of relevant systematic reviews. The suggestions formulated in this study, including CRFs selected from the available systematic reviews, can assist in conducting reliable HRAs and contribute to evidence-based decision-making in public health and environmental policy. Future research should continue to update and refine these suggestions as new evidence becomes available and methodologies evolve.

E-Cigarette Characteristics and Cigarette Cessation Among Adults Who Use E-Cigarettes

Population-level health outcomes associated with e-cigarettes depend in part on the association between e-cigarettes and combustible cigarette cessation. The US Food and Drug Administration has authority to regulate e-cigarette characteristics, including flavor and device type. To investigate whether e-cigarette characteristics are associated with cigarette cessation behaviors among adults in the US population who use e-cigarettes. This cohort study was conducted using longitudinal data collected in 2014 to 2021 by the Population Assessment of Tobacco and Health Study, a population-based, US nationally representative study. Participants were sampled from the civilian noninstitutionalized population using a 4-staged, stratified sampling design. Data were weighted and analyzed from 1985 adults ages 21 or older who smoked cigarettes daily and had used e-cigarettes in the past 30 days. Data were analyzed in May 2021 to May 2024. The following e-cigarette characteristics were assessed: use frequency (daily and nondaily), flavor type (tobacco, menthol or mint, sweet, and combination), device type (disposable, cartridge, and tank), and year of data collection as a proxy for the evolving e-cigarette marketplace. The following cigarette cessation behaviors were assessed: making a cigarette quit attempt, cigarette cessation among individuals who made a quit attempt, and overall cigarette discontinuation regardless of quit attempts. Associations were evaluated between e-cigarette characteristics (assessed at baseline in 1 approach and assessed at follow-up in another approach) and cigarette cessation outcomes, controlling for demographic, cigarette smoking, and other e-cigarette use characteristics. The study sample consisted of 1985 participants representing adults in the population (mean age, 40.0 years [95% CI, 39.2-40.9 years]; 49.4% [95% CI, 46.3%-52.6%] male; 11.4% [95% CI, 9.6%-13.4%] Black, 80.7% [95% CI, 77.8%-83.3%] White, and 8.0% [95% CI, 6.3%-10.0%] other race; 9.2% [95% CI, 7.5%-11.2%] Hispanic). Daily vs nondaily e-cigarette use was associated with greater overall cigarette discontinuation rates (12.8% [95% CI, 9.1%-17.7%] vs 6.1% [95% CI, 4.8%-7.7%]; adjusted odds ratio [aOR], 2.26 [95% CI, 1.34-3.81]), and use of e-cigarettes in 2019 to 2021 vs 2014-2015 to 2015-2016 was also associated with greater overall cigarette discontinuation rates (12.0% [95% CI, 8.8%-16.0%] vs 5.3% [95% CI, 2.9%-9.3%]; aOR, 2.75 [95% CI, 1.13-6.67]). Use of menthol or mint vs tobacco flavor e-cigarettes was associated with greater overall cigarette discontinuation rates (9.2% [95% CI, 6.6%-12.8%] vs 4.7% [95% CI, 3.0%-7.1%]; aOR, 2.63 [95% CI, 1.32-5.27]) only when assessing e-cigarette use at baseline. E-cigarette device type was not associated with cigarette discontinuation rates in adjusted analyses. In this study, daily e-cigarette use and use of e-cigarettes in 2019 to 2021 were consistently associated with greater cigarette discontinuation rates. These findings suggest that research focused on e-cigarettes marketed in recent years is needed to inform product regulation and public health policy decisions.

The Risk Of Perpetuating Health Disparities Through Cost-Effectiveness Analyses.

Cost-effectiveness analyses are commonly used to inform health care and public health policy decisions. However, standard approaches may systematically disadvantage marginalized groups by incorporating assumptions of persisting health inequities. We examined how competing risks, baseline health care costs, and indirect costs can differentially affect cost-effectiveness analyses for racial and ethnic minority populations. We illustrate that these structural factors can reduce estimated quality-adjusted life-years and cost savings for disadvantaged groups, making interventions focused on disadvantaged populations appear less cost-effective. For example, analyses of a sugar-sweetened beverage tax may estimate higher costs per quality-adjusted life-year gained for Black versus White populations because of differences in competing risks and insurance status that manifest in higher health care cost savings from averted disease among White people. To ensure that cost-effectiveness assessments do not perpetuate inequities, alternative approaches are needed that account for the impact of structural factors on different groups and that consider scenarios in which health inequities are reduced. Sensitivity analyses focusing on health equity could help advance interventions that disproportionately benefit disadvantaged communities.

Machine Learning Methods to Estimate Individualized Treatment Effects for Use in Health Technology Assessment.

Recent developments in causal inference and machine learning (ML) allow for the estimation of individualized treatment effects (ITEs), which reveal whether treatment effectiveness varies according to patients' observed covariates. ITEs can be used to stratify health policy decisions according to individual characteristics and potentially achieve greater population health. Little is known about the appropriateness of available ML methods for use in health technology assessment. In this scoping review, we evaluate ML methods available for estimating ITEs, aiming to help practitioners assess their suitability in health technology assessment. We present a taxonomy of ML approaches, categorized by key challenges in health technology assessment using observational data, including handling time-varying confounding and time-to event data and quantifying uncertainty. We found a wide range of algorithms for simpler settings with baseline confounding and continuous or binary outcomes. Not many ML algorithms can handle time-varying or unobserved confounding, and at the time of writing, no ML algorithm was capable of estimating ITEs for time-to-event outcomes while accounting for time-varying confounding. Many of the ML algorithms that estimate ITEs in longitudinal settings do not formally quantify uncertainty around the point estimates. This scoping review may not cover all relevant ML methods and algorithms as they are continuously evolving. Existing ML methods available for ITE estimation are limited in handling important challenges posed by observational data when used for cost-effectiveness analysis, such as time-to-event outcomes, time-varying and hidden confounding, or the need to estimate sampling uncertainty around the estimates. ML methods are promising but need further development before they can be used to estimate ITEs for health technology assessments. Estimating individualized treatment effects (ITEs) using observational data and machine learning (ML) can support personalized treatment advice and help deliver more customized information on the effectiveness and cost-effectiveness of health technologies.ML methods for ITE estimation are mostly designed for handling confounding at baseline but not time-varying or unobserved confounding. The few models that account for time-varying confounding are designed for continuous or binary outcomes, not time-to-event outcomes.Not all ML methods for estimating ITEs can quantify the uncertainty of their predictions.Future work on developing ML that addresses the concerns summarized in this review is needed before these methods can be widely used in clinical and health technology assessment-like decision making.

Crisis management through citizen engagement on Twitter: The case of the covid-19 pandemic in Latin American municipalities

COVID-19 created significant challenges in crisis management worldwide, especially for local governments that needed to communicate with citizens about health risks, policy decisions, and real-time information related to the pandemic. We analyze the evolution of the engagement between citizens and local governments on Twitter during the different phases of the first wave of the pandemic in Latin America, employing social media intelligence techniques to analyze 106,925 tweets from 103 local governments. Our results show enhanced engagement, especially in participation and collaboration, during the phases studied. Moreover, the increased use of social media as a communication channel was associated with mobility restrictions during the pandemic. Our research contributes to a deeper understanding of the role of social media as a communication channel between governments and citizens in the different phases of crisis management.

Use of Cost-Effectiveness Thresholds in Healthcare Public Policy: Progress and Challenges.

The article offers a comparative analysis of the influence of cost-effectiveness thresholds in the decision-making processes in financing policies, coverage, and price regulation of health technologies in nine countries. We investigated whether countries used cost-effectiveness thresholds for public health policy decision making and found that few countries have adopted the cost-effectiveness threshold as an official criterion for financing, reimbursement, or pricing. However, in countries where it is applied, such as Thailand, the results have been very favorable in terms of minimizing health technology prices and ensuring the financial sustainability of the health system. Although the cost-effectiveness threshold has opportunities for improvement, particularly in certain institutional contexts and with adequate participation of the different strategic actors in the formulation of public policy, its potential use and added value are significant in various aspects.

Collecting routine and timely cancer stage at diagnosis by implementing a cancer staging tiered framework: the Western Australian Cancer Registry experience

BackgroundCurrent processes collecting cancer stage data in population-based cancer registries (PBCRs) lack standardisation, resulting in difficulty utilising diverse data sources and incomplete, low-quality data. Implementing a cancer staging tiered framework aims to improve stage collection and facilitate inter-PBCR benchmarking.ObjectiveDemonstrate the application of a cancer staging tiered framework in the Western Australian Cancer Staging Project to establish a standardised method for collecting cancer stage at diagnosis data in PBCRs.MethodsThe tiered framework, developed in collaboration with a Project Advisory Group and applied to breast, colorectal, and melanoma cancers, provides business rules – procedures for stage collection. Tier 1 represents the highest staging level, involving complete American Joint Committee on Cancer (AJCC) tumour–node–metastasis (TNM) data collection and other critical staging information. Tier 2 (registry-derived stage) relies on supplementary data, including hospital admission data, to make assumptions based on data availability. Tier 3 (pathology stage) solely uses pathology reports.FindingsThe tiered framework promotes flexible utilisation of staging data, recognising various levels of data completeness. Tier 1 is suitable for all purposes, including clinical and epidemiological applications. Tiers 2 and 3 are recommended for epidemiological analysis alone. Lower tiers provide valuable insights into disease patterns, risk factors, and overall disease burden for public health planning and policy decisions. Capture of staging at each tier depends on data availability, with potential shifts to higher tiers as new data sources are acquired.ConclusionsThe tiered framework offers a dynamic approach for PBCRs to record stage at diagnosis, promoting consistency in population-level staging data and enabling practical use for benchmarking across jurisdictions, public health planning, policy development, epidemiological analyses, and assessing cancer outcomes. Evolution with staging classifications and data variable changes will futureproof the tiered framework. Its adaptability fosters continuous refinement of data collection processes and encourages improvements in data quality.

Advancing Healthcare Infrastructure: The Features of Iran’s Sina Electronic Health Record System

Background and aims: The Sina Electronic Health Record (SinaEHR) system was developed to support primary care delivery across Iran by enabling chronic disease monitoring, infectious disease surveillance, health policy decision-making, and quality improvement initiatives. Methods: The SinaEHR system was implemented by Mashhad University of Medical Sciences which contains health records of over 5 million people across various regions of Iran. It utilizes the International Classification of Diseases (ICD) version 10, integrating laboratory results through a laboratory information system to facilitate real-time clinical decision-making. Results: Widespread implementation of the SinaEHR system has established an invaluable healthcare infrastructure resource for the improvement of primary care services across Iran. Strict security and confidentiality measures are enforced to protect sensitive patient information. Conclusion: The SinaEHR system significantly advances Iran’s healthcare infrastructure by providing a comprehensive electronic health record platform to improve healthcare delivery and promote evidence-based decision-making.

Epidemiology of back pain visits and medication usage among United States emergency departments from 2016 to 2023

IntroductionLow back pain is a common reason for presentation to the Emergency Department (ED). However, there are limited large-scale, recent data on the epidemiology, disposition, and medication administration for this condition. The objective of this was to assess the incidence, admission rates, medication administrations, and discharge prescriptions among ED visits for low back pain in the United States. MethodsThis was a cross-sectional study of ED presentations for low back pain from 1/1/2016 to 12/31/2023 using the Epic Cosmos database. All ED visits for adults with low back pain identified by ICD-10 codes were included. Outcomes included admission rates, distribution of opioid, benzodiazepine, (non-benzodiazepine) muscle relaxant, acetaminophen, NSAID, and corticosteroid medications administered in the ED, and distribution of opioid, benzodiazepine, muscle relaxant, and corticosteroid medications given upon discharge. Subgroup analyses were performed by specific medication. ResultsOf 207,154,419 ED encounters, 12,241,240 (5.9%) were due to back pain with 1,957,299 of these (16.0%) admitted. The admission rate increased over time from 12.8% to 17.1%. The most common medication given in the ED was opioids (40.7%), followed by acetaminophen (37.8%), NSAIDs (22.6%), muscle relaxants (18.4%) benzodiazepines (12.8%), and corticosteroids (5.5%). The most common medications prescribed upon discharge were muscle relaxants (32.1%), followed by opioids (23.2%), corticosteroids (12.2%), and benzodiazepines (3.0%). ConclusionLow back pain represents a common reason for presentation to the ED, and admissions have been increasing over time. Opioids remain the most common ED medication, whereas muscle relaxants have arisen as the most common discharge prescription. These findings can help inform health policy decisions, resource allocation, and evidence-based interventions for medication administration.

Indigenous Identity and Household Food Insecurity are Associated with Poor Health Outcomes in Canada.

Purpose: To examine whether Indigenous identity and food insecurity combined were associated with self-reported poor health.Methods: Data from the 2015-2016 Canadian Community Health Survey and multiple logistic regression were employed to evaluate the association between Indigenous identity, household food insecurity, and health outcomes, adjusted for individual and household covariates. The Alexander Research Committee in Alexander First Nation (Treaty 6) reviewed the manuscript and commented on the interpretation of study findings.Results: Data were from 59082 adults (3756 Indigenous). The prevalence of household food insecurity was 26.3% for Indigenous adults and 9.8% for non-Indigenous adults (weighted to the Canadian population). Food-secure Indigenous adults, food-insecure non-Indigenous adults, and food-insecure Indigenous adults had significantly (p < 0.001) greater odds of poor health outcomes than food-secure non-Indigenous adults (referent group). Food-insecure Indigenous adults had 1.96 [95% CI:1.53,2.52], 3.73 [95% CI: 2.95,4.72], 3.00 [95% CI:2.37,3.79], and 3.94 [95% CI:3.02,5.14] greater odds of a chronic health condition, a chronic mental health disorder, poor general health, and poor mental health, respectively, compared to food-secure non-Indigenous adults.Conclusions: Health policy decisions and programs should focus on food security initiatives for all Canadians, including addressing the unique challenges of Indigenous communities, irrespective of their food security status.

"Recognize nurses for what they are: invaluable"

Appointed Director General of the International Council of Nurses (ICN) in February 2019, Howard Catton is committed to ensuring that the ICN can effectively represent nursing around the world. He firmly believes that nurses must be at the heart of health policy decisions. With a nursing diploma and a master's degree in labor relations, he held a number of nursing positions in England and the United States before joining the New Zealand Nurses Organisation. For ten years, he directed the political and international affairs of the UK's Royal College of Nursing, then led ICN's action during the Covid-19 pandemic, while positioning himself in favor of protecting and investing in the nursing profession.

Active recess: School break time as a setting for physical activity promotion in European primary schools

Objective: This study investigated active recess as an innovative approach to improving physical activity levels in European primary schools, addressing the need to counteract sedentary behaviour and enhance health in students. Method and Design: Using rapid reviewing, the article summarises empirical evidence from a variety of sources, including cross-sectional and intervention studies. Different approaches are investigated to determine their impact on physical activity during recess. Results: Different playground settings have distinct effects on physical activity, with grassland and designated zones in particular promoting moderate-to-vigorous physical activity. Gender inequalities in physical activity patterns suggest the need for gender-sensitive interventions. Playground marking and scheduled activities temporarily increase physical activity, whereas recess of more than 60 minutes dramatically enhances physically active participation. Conclusions: This study demonstrates the impact of active recess in facilitating children to engage in higher levels of physical activity. The findings have implications for school health policy decisions.

EADV Task Force Pruritus White Paper on chronic pruritus and chronic prurigo: Current challenges and future solutions.

Chronic pruritus (CP) is frequent in general medicine and the most common complaint in general dermatology. The prevalence of CP is expected to rise in the future due to the ageing population. The clinical presentation, underlying aetiology and treatment strategy of CP are heterogeneous. Also, individual treatment aims and physical, psychic and economic burdens of patients might vary. Chronic prurigo (CPG) is the most severe disease in the chronic pruritus spectrum, being associated with long-standing scratch-induced skin lesions and a therapy refractory itch-scratch-cycle. It is thus important to raise disease awareness for CP and CPG in the general public and among decision-makers in the health system. Further, there is a need to support a rational clinical framework to optimize both diagnostics and therapeutics. Currently, there is still a shortcoming regarding approved therapies and understanding CP/CPG as severe medical conditions. Therefore, the EADV Task Force Pruritus decided to publish this white paper based on several consensus meetings. The group consented on the following goals: (a) ensure that CP is recognized as a serious condition, (b) increase public awareness and understanding of CP and CPG as chronic and burdensome diseases that can greatly affect a person's quality of life, (c) clarify that in most cases CP and CPG are non-communicable and not caused by a psychiatric disease, (d) improve the support and treatment given to patients with CP to help them manage their disease and (e) publicize existing therapies including current guidelines. We aim to point to necessary improvements in access and quality of care directed to decision-makers in health policy, among payers and administrations as well as in practical care.

Drivers of success in global health outcomes: A content analysis of Exemplar studies.

Applying a positive outlier lens is one effective approach for generating evidence to inform global health policy, program, and funding decisions. Exemplars in Global Health (EGH) is a program that studies positive outlier countries that have made extraordinary progress in health outcomes (despite limited resources) and disseminates their successes through multiple types of outputs. To date, EGH has studied, or is studying, 14 global health topics in 28 countries. This paper aims to identify findings, summarized as themes and sub-themes, that appear among all completed EGH studies. We developed a conceptual framework and used a content analysis approach to identify the top thematic areas that appear as drivers for programmatic success across EGH studies that were completed between June 2020-May 2023. The EGH studies (N = 31) spanned six topics including under-five child mortality (n = 6), childhood stunting (n = 5), community health workers (CHW) (n = 4), vaccine delivery (n = 3), COVID-19 response (n = 6), and newborn and maternal mortality reduction (n = 7) across 19 countries in sub-Saharan Africa, Latin America, South and Central Asia, and the Caribbean regions. Top drivers of success were defined as those critical or catalytic in achieving the intended outcome. Eight key drivers were identified: (1) efficient data collection and use for decision-making, (2) strong political commitment and health leadership, (3) effective stakeholder coordination, (4) a local, connected, and capacitated workforce, (5) intentional women's empowerment and engagement, (6) effective adoption and implementation of national policies, (7) effective and sustainable financing, and (8) equitable, efficient outreach and targeting. These cross-cutting drivers span a broad range of development outcomes, sectors, and populations, and indicate a need to effectively integrate people, systems, and sectors to improve global health outcomes. Findings from this study aim to support peer learning among countries and support evidence-based decision-making for funders, policymakers, and other key stakeholders.

The Health Status of the Population of Sellye District, a Disadvantaged Micro-Region, in the Light of Sociodemographic Factors- Literature Review

Tackling health inequalities is a global goal also set by the WHO and is in the common interest of all societies. Keeping in mind the extremely close correlation between socio-economic status and health outcomes is of particular importance in planning health promotion and making health policy decisions. The research aimed at assessing the health status of the population of Sellye district, one of the most disadvantaged micro-regions according to available statistics, to examine the relationship between socio-economic status and health conditions, and to compare health status internationally with populations living in other disadvantaged regions. Research questions: How does the impact of sociodemographic factors on health prevail? Through what mechanisms does race/ethnicity affect health? Our present study is a literature review, the first stage of a comprehensive investigation, in which we examine the Hungarian and international literature. Health status is largely determined by sociodemographic characteristics, it is also influenced by the wider social, economic, political, and cultural environment. Health must always be considered in the context of the environment. Ethnicity is strongly connected to social class, but it can still be important in terms of social cohesion and the resources available to the Roma

Positive relationship between bird diversity and human mental health: an analysis of repeated cross-sectional data

An increasing body of research has examined the link between biodiversity of birds and human mental health, but most studies only use cross-sectional data. Few studies have used longitudinal or repeated cross-sectional data to investigate the mental health benefits of bird diversity. The aim of this study is to analyse the relationship between bird diversity and mental health at the national level using a unique repeated cross-sectional dataset. I used repeated cross-sectional health data from the German National Cohort health study, collected between March, 2014, and September, 2019, and annual bird citizen science data to investigate the effects of bird-diversity exposure on mental health. Mental health was measured using the summary score of the Patient Health Questionnaire depression module 9 (SumPHQ) and the Short Form Health Survey-12 Mental Health Component Scale. As a proxy for bird diversity, I created a unique indicator called reporting-rate richness and combined it with the health data. Reporting-rate richness measures the number of bird species within postcode areas across Germany in probabilities while accounting for variation in survey efforts. Alternative indicators of bird diversity, such as bird-species richness or abundance, were also calculated. Associations between bird diversity and mental health were estimated using linear regression with region and time fixed effects, adjusted for a range of sociodemographic and environmental confounders and spatial autocorrelation. Interaction terms between income levels and reporting-rate richness were also analysed to examine the moderating effect of socioeconomic status. I did the analyses for an unbalanced (n=176 362) and balanced (n=125 423) dataset, with the balanced dataset comprising only regions (postcode areas) in which health data were available for each year. The linear fixed-effects regression analysis indicated a significant negative association between reporting-rate richness and SumPHQ, as observed in both the unbalanced dataset (β -0·02, p=0·017) and the balanced dataset (β -0·03, p=0·0037). Similarly, regression results with both datasets showed a positive relationship between reporting-rate richness and Mental Health Component Scale (MCS; unbalanced β 0·02, p=0·0086; balanced β 0·03, p=0·0018). The moderator analyses revealed a significant influence of socioeconomic status on the relationship between reporting-rate richness and mental health. The robustness of these findings was confirmed through sensitivity analyses. The results suggest that a greater likelihood of having many different bird species in a person's area of residence might positively contribute to mental health, especially for people with lower socioeconomic status. These findings could have implications for biodiversity conservation and health policy decisions, as governments are facing challenges such as global biodiversity loss and growing public mental health problems. None.

Elucidating vaccine efficacy using a correlate of protection, demographics, and logistic regression

BackgroundVaccine efficacy (VE) assessed in a randomized controlled clinical trial can be affected by demographic, clinical, and other subject-specific characteristics evaluated as baseline covariates. Understanding the effect of covariates on efficacy is key to decisions by vaccine developers and public health authorities.MethodsThis work evaluates the impact of including correlate of protection (CoP) data in logistic regression on its performance in identifying statistically and clinically significant covariates in settings typical for a vaccine phase 3 trial. The proposed approach uses CoP data and covariate data as predictors of clinical outcome (diseased versus non-diseased) and is compared to logistic regression (without CoP data) to relate vaccination status and covariate data to clinical outcome.ResultsClinical trial simulations, in which the true relationship between CoP data and clinical outcome probability is a sigmoid function, show that use of CoP data increases the positive predictive value for detection of a covariate effect. If the true relationship is characterized by a decreasing convex function, use of CoP data does not substantially change positive or negative predictive value. In either scenario, vaccine efficacy is estimated more precisely (i.e., confidence intervals are narrower) in covariate-defined subgroups if CoP data are used, implying that using CoP data increases the ability to determine clinical significance of baseline covariate effects on efficacy.ConclusionsThis study proposes and evaluates a novel approach for assessing baseline demographic covariates potentially affecting VE. Results show that the proposed approach can sensitively and specifically identify potentially important covariates and provides a method for evaluating their likely clinical significance in terms of predicted impact on vaccine efficacy. It shows further that inclusion of CoP data can enable more precise VE estimation, thus enhancing study power and/or efficiency and providing even better information to support health policy and development decisions.