Population Health Interventions Research Articles

A systematic scoping review evaluating sugar-sweetened beverage taxation from a systems perspective

Systems thinking can reveal surprising, counterintuitive or unintended reactions to population health interventions (PHIs), yet this lens has rarely been applied to sugar-sweetened beverage (SSB) taxation. Using a systematic scoping review approach, we identified 329 papers concerning SSB taxation, of which 45 considered influences and impacts of SSB taxation jointly, involving methodological approaches that may prove promising for operationalizing a systems informed approach to PHI evaluation. Influences and impacts concerning SSB taxation may be cyclically linked, and studies that consider both enable us to identify implications beyond a predicted linear effect. Only three studies explicitly used systems thinking informed methods. Finally, we developed an illustrative, feedback-oriented conceptual framework, emphasizing the processes that could result in an SSB tax being increased, maintained, eroded or repealed over time. Such a framework could be used to synthesize evidence from non-systems informed evaluations, leading to novel research questions and further policy development.

Development and validation of the Trust in Government measure (TGM)

BackgroundTrust in government is associated with health behaviours and is an important consideration in population health interventions. While there is a reported decline in public trust in government across OECD countries, the tools used to measure trust are limited in their use for informing action to (re)build trust, and have limitations related to reliability and validity. To address the limitations of existing measures available to track public trust, the aim of the present work was to develop a new measure of trust in government.MethodsFifty-six qualitative interviews (Aug-Oct 2021; oversampling for equity-deserving populations) were conducted to design a national survey, including factor analyses and validation testing (N = 878; June 1-14th 2022) in Canada.ResultsThe measure demonstrated strong internal consistency (α = 0.96) and test validity (CFI = 0.96, RMSEA = 0.09, SRMR = 0.03), suggesting that trust in government can be measured as a single underlying construct. It also demonstrated strong criterion validity, as measured by significant (p < 0.0001) associations of scores with vaccine hesitancy, vaccine conspiracy beliefs, COVID-19 conspiracy beliefs, trust in public health messaging about COVID-19, and trust in public health advice about COVID-19. We present the Trust in Government Measure (TGM); a 13-item unidimensional measure of trust in Federal government.ConclusionsThis measure can be used within high-income countries, particularly member countries within the OECD already in support of using tools to collect, publish and compare statistics. Our measure should be used by researchers and policy makers to measure trust in government as a key indicator of societal and public health.

The impact of smartphone use on childhood depression: A pediatric perspective

Introduction: the profound social and family changes experienced in recent decades have also played a decisive role in the rise of depression, especially changes in family models, competition as the axis of life (which is more important than other dimensions of existence) and the availability of mass media, which promote personal distancing. Objectives: to describe the scientific evidence on the impact of smartphone use on childhood depression from a pediatric perspective and what are the underlying factors that influence this relationship. Methods: a search was conducted in Pubmed, Scopus, Web of Science from January 2000 to October 2023. Results: this systematic review included several studies that evaluated different mental health interventions in various populations. Overall, we found that interventions based on smartphones, online cognitive behavioral therapy, and telemedicine had a positive impact on mental health. For example, postnatal mothers who received smartphone interventions experienced a significant reduction in depression. In addition, online therapy was shown to alleviate parental anxiety and depression and improve their quality of life. However, some studies had limitations, such as small samples or lack of effect on certain outcomes. More research would be needed to fully understand the scope and efficacy of these digital interventions. Conclusion: this review highlights the promising efficacy of smartphone-based interventions to improve mental health in different populations. Despite limitations and challenges, the use of mobile technology in mental health care is an evolving area that could offer significant benefits in the future, transforming the way we approach and treat childhood mental health disorders.

Traditional surveys versus ecological momentary assessments: Digital citizen science approaches to improve ethical physical activity surveillance among youth.

The role of physical activity (PA) in minimizing non-communicable diseases is well established. Measurement bias can be reduced via ecological momentary assessments (EMAs) deployed via citizen-owned smartphones. This study aims to engage citizen scientists to understand how PA reported digitally by retrospective and prospective measures varies within the same cohort. This study used the digital citizen science approach to collaborate with citizen scientists, aged 13-21 years over eight consecutive days via a custom-built app. Citizen scientists were recruited through schools in Regina, Saskatchewan, Canada in 2018 (August 31-December 31). Retrospective PA was assessed through a survey, which was adapted from three validated PA surveys to suit smartphone-based data collection, and prospective PA was assessed through time-triggered EMAs deployed consecutively every day, from day 1 to day 8, including weekdays and weekends. Data analyses included paired t-tests to understand the difference in PA reported retrospectively and prospectively, and linear regressions to assess contextual and demographic factors associated with PA reported retrospectively and prospectively. Findings showed a significant difference between PA reported retrospectively and prospectively (p = 0.001). Ethnicity (visible minorities: β = - 0.911, 95% C.I. = -1.677, -0.146), parental education (university: β = 0.978, 95% C.I. = 0.308, 1.649), and strength training (at least one day: β = 0.932, 95% C.I. = 0.108, 1.755) were associated with PA reported prospectively. In contrast, the number of active friends (at least one friend: β = 0.741, 95% C.I. = 0.026, 1.458) was associated with retrospective PA. Physical inactivity is the fourth leading cause of mortality globally, which requires accurate monitoring to inform population health interventions. In this digital age, where ubiquitous devices provide real-time engagement capabilities, digital citizen science can transform how we measure behaviours using citizen-owned ubiquitous digital tools to support prevention and treatment of non-communicable diseases.

Caught Between a Well-Intentioned State and a Hostile Federal System: Local Implementation of Inclusive Immigrant Policies.

In the United States, inclusive state-level policies can advance immigrant health and health care access by extending noncitizens' access to public benefits, workplace rights, and protections from immigration enforcement. Although state policies carry promise as structural population health interventions, there has been little examination of their implementation at the local level. Local jurisdictions play multiple roles in state policy implementation and possess distinct immigration climates. Examining the local implementation of state immigrant policy can address challenges and opportunities to ensure the health benefits of inclusive policies are realized equitably across states' regions. To examine the local implementation of state immigrant policies, we selected a purposive sample of California counties with large immigrant populations and distinct social and political dynamics and conducted and analyzed in-depth interviews with 20 community-based organizations that provided health, safety net, and other services. We found that there were tensions between the inclusionary goals of state immigrant policies and local anti-immigrant climates and federal policy changes. First, there were tensions between state policy goals and resistance from local law enforcement agencies and policymakers (e.g., Board of Supervisors). Second, because of the ongoing threats from federal immigration policies, there was a mismatch between the services and resources provided by state policies and local community needs. Finally, organizations that served immigrants were responsible for contributing to policy implementation but lacked resources to meet community needs while countering local resistance and federal policy threats. This study contributes knowledge regarding the challenges that emerge after state immigrant policies are enacted. The tensions among state immigrant policies, local immigration climates, and federal policy changes indicate that state immigrant policies are not implemented equally across state communities, resulting in challenges and limited benefits from policies for many immigrant communities.

Systematic comparison of Mendelian randomisation studies and randomised controlled trials using electronic databases

ObjectiveTo scope the potential for (semi)-automated triangulation of Mendelian randomisation (MR) and randomised controlled trials (RCTs) evidence since the two methods have distinct assumptions that make comparisons between their results...

An omni-channel, outcomes-focused approach to scale digital health interventions in resource-limited populations: a case study.

Populations in resource-limited communities have low health awareness, low financial literacy levels, and inadequate access to primary healthcare, leading to low adoption of preventive health behaviours, low healthcare-seeking behaviours, and poor health outcomes. Healthcare providers have limited reach and insights, limiting their ability to design relevant products for resource limited settings. Our primary preventive health intervention, called the Saathealth family health interventions, is a scaled digital offering that aims to improve knowledge levels on various health topics, nudge positive behaviour changes, and drive improved health outcomes. This case study presents our learnings and best practices in scaling these digital health interventions in resource-limited settings and maximising their impact. We scaled the Saathealth interventions to cumulatively reach >10 million users across India using a multi-pronged approach: (1) ensuring localization and cultural relevance of the health content delivered through user research; (2) disseminating content using omni-channel approaches, which involved using diverse content types and multiple digital platforms; (3) using iterative product features such as gamification and artificial intelligence-based (AI-based) predictive models; (4) using real-time analytics to adapt the user's digital experience by using interactive content to drive them towards products and services and (5) experiments with sustainability models to yield some early successes. The Saathealth family health mobile app had >25,000 downloads and the intervention reached >873,000 users in India every month through the mobile app, Facebook, and Instagram combined, from the time period of February 2022 to January 2023. We repeatedly observed videos and quizzes to be the most popular content types across all digital channels being used. Our AI-based predictive models helped improve user retention and content consumption, contributing to the sustainability of the mobile apps. In addition to reaching a high number of users across India, our scaling strategies contributed to deepened engagement and improved health-seeking behaviour. We hope these strategies help guide the sustainable and impactful scaling of mobile health interventions in other resource-limited settings.

EE19 Cost-Effectiveness Analysis of Nonpharmaceutical Interventions Combined with Inactivated Vaccination and Oral Medicine in China

Various interventions were used to control the COVID-19 pandemic and protect population health, including vaccination, medication and nonpharmaceutical interventions (NPIs). This study aims to examine the cost-effectiveness of different combinations of NPIs (including social distancing, mask wearing, tracing-testing-isolation, mass testing, and lockdown), oral medicine (Paxlovid), and vaccination (including two-dose and three-dose vaccination) under the Delta and Omicron pandemic in China. We constructed a Markov model using a SIRI structure with a one-week cycle length over one-year time horizon to estimate the cost-effectiveness of different combinations in China from societal perspective. Effectiveness of interventions, disease transition probabilities and costs were from published data, quality-adjusted life years (QALYs) gained and incremental cost-effectiveness ratios (ICER) and net monetary benefits were calculated for one-year time horizon. One-way and probabilistic sensitivity analyses were performed to test the robustness of the model. Scenario analysis was developed to examine different situations under the Omicron pandemic. Under the Delta pandemic, implementing the combination of social distancing, mask wearing, mass testing and three-dose vaccination was the optimal strategy, with cost at $11165635.33 and utility of 94309.94 QALYs, and had 60% probability of being cost-effective compared with other strategies. Three-dose vaccination combinations were better than two-dose combinations. Under the Omicron pandemic, antigen testing was better than nucleic testing by avoiding cross infections; second, adding Paxlovid or lockdown to the combined intervention strategies could increase limited health outcomes at huge cost and thus were not cost-effective; last, encouraging patients to stay at home can save societal costs compared with concentrated quarantine at hospitals. Three-dose vaccination and self-quarantine of asymptomatic and mild cases can save total costs. Under the Omicron pandemic outbreak, antigen testing is a better way to control the pandemic, and adding Paxlovid or lockdown to intervention combinations is not cost-effective.

Unwanted or Mistimed Pregnancy and Developmental Issues in Ecuadorian Children Aged 3 to 5: A Doubly Robust Estimate Using Data from the National Health and Nutrition Survey 2018.

Past studies in high-income countries have shown an association between unintended (unwanted or mistimed) pregnancy and child development; no national-level studies in low-and-middle-income countries have been conducted. Moreover, extant studies often adjust for potential mediators, underestimating the average population effect. We aimed to estimate the effect of unintended pregnancy on early childhood development in Ecuadorian children aged 3 to 5, participating in the National Health and Nutrition Survey 2018. We used a design-based doubly robust estimate. First, we used propensity score matching (1:1) to identify a subsample equally likely to come from a desired vs. unintended pregnancy based on geographic area, household income, paternal intendedness, the mother's current marital status, age, ethnicity, and educational level, depressive symptoms, and the child's gender and age. Then, we used a logistic regression model to explore the relation of maternal pregnancy intentions with adequate development, as defined by the Early Childhood Development Index (ECDI). While exempt from review by an Institutional Review Board, this secondary research was conducted in accord with prevailing ethical principles. Among 1694 observations representing 162,285 Ecuadorian children, unintended pregnancy associated with inadequate development (odds ratio: 1.56; 95% confidence interval: 1.06; 2.29), after adjusting for all relevant confounders. Unintended pregnancy was also negatively associated with all four ECDI domains, socio-emotional development being the most affected. Our doubly robust design found evidence of the relation between the maternal perception of pregnancy and early child development. Addressing this relation to achieve reproductive justice entails considering a wide spectrum of population health and legal interventions to allow adequate access to education, contraception, and safe abortion. Moreover, pre- and post-natal check-ups could screen for unintended pregnancy and provide support accordingly.

HSD68 Improving Care Via Timely Health Information Exchange: A Blue Cross and Blue Shield of Louisiana ADT System Referral Program Evaluation

The admission-discharge-transfer (ADT) system is a key source for member referrals to the Population Health intervention program at Blue Cross and Blue Shield of Louisiana (BCBSLA). It is triggered by an episodic event such as an emergency department visit or hospitalization. The system notifies BCBSLA’s Care Management team in near real time so they may act promptly. Their actions (such as health coaching, personalized disease self-management skills for members to adhere to recommended treatment plans) aim to improve quality of care and reduce health care costs. The purpose of this study is to evaluate the effect of timely referrals for Care Management interventions.

DECLIC patient education program: assessment through a population health intervention research approach (LE DECLIC EPRI)

DECLIC patient education program: assessment through a population health intervention research approach (LE DECLIC EPRI)

Key considerations in digital population health interventions

Key considerations in digital population health interventions

Population Health Intervention Research, Health Technology Assessment, Health Services Research and Intervention Implementation Research:Convergences and Singularities

In public health, intervention is an object of research and evaluation which, over time, has given rise to numerous approaches. The first part of the article proposes to reposition intervention research in population health and intervention evaluation on a continuum. Although the former has a more cognitive objective and the latter a more pragmatic objective, they are not mutually exclusive. The distinction between these two practices is based on the predominance of the following characteristics: the objectives pursued, the scope of the investigations, the regulatory constraints, the financing obtained, the ethical approaches taken, and the deliverables established. The second part of the article offers a glance different fields and approaches within the continuum between these two poles: Health Technology Assessment, Health Services Research and Implementation Research. While all of them have the study of health interventions at their core, but each has developed through specialisation in one or the other type of intervention, in a particular scope or context, in certain evaluation questions, or in specific approaches. all as gateways to the study of public health intervention, these different approaches are by no means mutually exclusive.

Air pollution and cardiovascular heath in a changing climate

Air pollution and cardiovascular heath in a changing climate

Evaluating population health interventions:The contributions of theory-driven evaluations

Population health intervention research has been characterized by the deployment of scientific methods designed to produce knowledge on policy and program interventions directly or indirectly involving the health sector, and potentially affecting population health. The proposed solutions encompass a multitude of interventions of variable types, scale, focus and implementation, rendering them particularly complex and difficult to understand. This complexity raises major conceptual and methodological issues because in reality, we assess not an intervention, but rather a set of interventional elements interacting with those specific to the context in which they appear. It is the interactions that produce effects, to the extent that it makes little sense to speak of an intervention, and more sense to consider an interventional system defined by the interactions. To grasp the numerous elements brought into play, it behooves us to amalgamate evaluation paradigms and approaches. In a precise context, theory-driven evaluations are of pronounced interest. This article presents the main principles of this type of evaluation by focusing on its capacity to shed light on the stakes involved in intervention/context interplay, and by putting forward conclusions transferable to population health research.

Identifying and estimating effects of sustained interventions under parallel trends assumptions.

Many research questions in public health and medicine concern sustained interventions in populations defined by substantive priorities. Existing methods to answer such questions typically require a measured covariate set sufficient to control confounding, which can be questionable in observational studies. Differences-in-differences rely instead on the parallel trends assumption, allowing for some types of time-invariant unmeasured confounding. However, most existing difference-in-differences implementations are limited to point treatments in restricted subpopulations. We derive identification results for population effects of sustained treatments under parallel trends assumptions. In particular, in settings where all individuals begin follow-up with exposure status consistent with the treatment plan of interest but may deviate at later times, a version of Robins' g-formula identifies the intervention-specific mean under stable unit treatment value assumption, positivity, and parallel trends. We develop consistent asymptotically normal estimators based on inverse-probability weighting, outcome regression, and a double robust estimator based on targeted maximum likelihood. Simulation studies confirm theoretical results and support the use of the proposed estimators at realistic sample sizes. As an example, the methods are used to estimate the effect of a hypothetical federal stay-at-home order on all-cause mortality during the COVID-19 pandemic in spring 2020 in the UnitedStates.

Individuals with a Family History of Colorectal Cancer Warrant Tailored Interventions to Address Patient-Reported Barriers to Screening.

Population health interventions to increase colorectal cancer (CRC) screening rates often exclude individuals with a family history of CRC, and interventions to increase screening in this high-risk group are rare. We aimed to determine the screening rate and barriers and facilitators to screening in this population to inform interventions to increase screening participation. We performed a retrospective chart review and cross-sectional survey of patients excluded from mailed fecal immunochemical test (FIT) outreach due to a family history of CRC in a large health system. We used Chi-square, Fisher's exact, and Student's t-tests to compare demographic and clinical characteristics of individuals overdue and not overdue for screening. We then administered a survey (mailed and telephone) to overdue patients to assess barriers and facilitators to screening. There were 296 patients excluded from mailed FIT outreach and 233 had a confirmed family history of CRC. Screening participation was low (21.9%), and there were no significant demographic or clinical differences between those overdue and not overdue for screening. There were 79 survey participants. Major patient-reported barriers to screening colonoscopy were patient forgetfulness (35.9%), fear of pain during colonoscopy (17.7%), and hesitancy about bowel preparation (29.4%). To facilitate screening colonoscopy, patients recommended reminders (56.3%), education about familial risk (50%), and colonoscopy education (35.9%). Patients with a family history of CRC that are excluded from mailed FIT outreach have low screening rates and report multiple mutable barriers to screening. They warrant targeted efforts to increase screening participation.

Integrating Public Health Into Undergraduate Medicine in North America: A Systematic Review.

The coronavirus disease 2019 (COVID-19)pandemic has served as a stark reminder of the importance of foundational public health training for all physicians. However, the most effective way to incorporate these concepts into undergraduate medical education remains unclear. Here, we characterize the literature regarding the effectiveness of public health integration into undergraduate medical education in North America. We systematically searched MEDLINE, Embase, Cochrane Central, and Education Resources Information Center (ERIC) in accordance with preferred reporting items for systematic review and meta-analysis (PRISMA) guidelines for North American peer-reviewed literature, published from 01/01/2000 to 30/08/2021, that described outcomes of integrating public health training within an undergraduate medical curriculum. Results were qualitatively synthesized into key themes. A total of 38 studies, involving interventions across 43 medical schools, were included. Studies reported on a combination of public (n=13), global (n=9), population (n=9), community (n=6), and epidemiological (n=1) health interventions, and either implemented one-off workshops, electives, or international experiences (n=19); a longitudinal theme or long-term enrichment pathway (n=14); or a case-based learning curriculum (n=8). The majority (81.5%, 31/38) of integrations were self-described as successful and, of studies reporting on feasibility, most (94.1%, 16/17) were indicated as feasible. The definition of what constituted such success, however, was unclear. Innovative examples included the use of simulation workshops and mobile-optimized media content. Key challenges were noted, however, in securing adequate funding and buy-in from administrative leadership. Robust community partnerships and iterative cycles of implementation of the intervention were critical factors to success. In summary, foundational public health components can be effectively integrated into medical school curricula and would benefit from adequate resourcing, innovation, community partnerships, and continuous improvement.

Big Data Analytics to Reduce Preventable Hospitalizations—Using Real-World Data to Predict Ambulatory Care-Sensitive Conditions

The purpose of this study was to develop a prediction model to identify individuals and populations with a high risk of being hospitalized due to an ambulatory care-sensitive condition who might benefit from preventative actions or tailored treatment options to avoid subsequent hospital admission. A rate of 4.8% of all individuals observed had an ambulatory care-sensitive hospitalization in 2019 and 6389.3 hospital cases per 100,000 individuals could be observed. Based on real-world claims data, the predictive performance was compared between a machine learning model (Random Forest) and a statistical logistic regression model. One result was that both models achieve a generally comparable performance with c-values above 0.75, whereas the Random Forest model reached slightly higher c-values. The prediction models developed in this study reached c-values comparable to existing study results of prediction models for (avoidable) hospitalization from the literature. The prediction models were designed in such a way that they can support integrated care or public and population health interventions with little effort with an additional risk assessment tool in the case of availability of claims data. For the regions analyzed, the logistic regression revealed that switching to a higher age class or to a higher level of long-term care and unit from prior hospitalizations (all-cause and due to an ambulatory care-sensitive condition) increases the odds of having an ambulatory care-sensitive hospitalization in the upcoming year. This is also true for patients with prior diagnoses from the diagnosis groups of maternal disorders related to pregnancy, mental disorders due to alcohol/opioids, alcoholic liver disease and certain diseases of the circulatory system. Further model refinement activities and the integration of additional data, such as behavioral, social or environmental data would improve both model performance and the individual risk scores. The implementation of risk scores identifying populations potentially benefitting from public health and population health activities would be the next step to enable an evaluation of whether ambulatory care-sensitive hospitalizations can be prevented.

Automated Clinical Pathway Utilizing Custom Risk Stratification Identifies Substantial Rates of Overdue Follow-up Among Patients With Inflammatory Bowel Disease and Facilitates Population Health Interventions.

Lay Summary We describe the development and implementation of a dynamic clinical pathway, the IBD CarePath, integrated into the electronic health record that applies custom risk stratification to identify patients with IBD who are overdue for clinical follow-up.