Policy-relevant Questions Research Articles

From Data to Decisions: Analyzing PaRIS for Health Policy Insights

Abstract To inform countries on how well they perform from the perspective of people living with chronic conditions in comparison with other countries, we propose an analytical framework for the data collected in the PaRIS survey, detailing how the data will be analysed in a multilevel approach for cross-country comparison. The data structure of the PaRIS survey represents three levels: countries/health systems, primary care practices and patients. Multilevel analysis is used because of its accuracy in estimating country-level outcomes, its flexibility in modelling relationships, and its opportunities in connecting to relevant policy questions. Country-level outcomes will be estimated to facilitate cross-country comparison and (future) within-country comparison over time. A first indication for areas for cross-country learning and policy making is the distribution of variation over patients, care providers and countries. Characteristics of patients that potentially explain variation in patient-reported outcomes and experiences can be linked to primary care practice and country/health system characteristics. The second indication for areas for cross-country learning and policy making is the variation in the slope of the association between patient and practice characteristics and patient-reported outcomes and experiences. This makes it possible to address policy-relevant questions relating, e.g., to whether some health systems or providers are better able to produce positive outcomes and experiences for (e.g.) older patients, and to the impact of chronic care management on patients with a specific chronic condition. To ensure fair cross-country comparisons, all outcomes will be estimated for an OECD (age and sex standardised) reference population, also including case-mix adjustment where necessary. Analyses will be repeated using a country-specific standard population.

7.L. Practice session: Is there an investment case for health at older ages?

Abstract The global demographic landscape is undergoing significant transformation due to increases in longevity and declining birth rates. According to the United Nations, the world’s population aged 65 and over is projected to more than double, reaching over 1.5 billion by 2050, while global fertility rates are expected to decline to 2.4 births per woman by 2030 and continue decreasing to 2.2 births per woman by 2050. Ageing populations have frequently concerned decision-makers due to the potential implications of changes in population age-mix for labour markets and the economy, as well as for the sustainability of public programs and spending, particularly the health sector. For example, as the share of the population at older age increases, there are concerns over how to cope with the possible economic implications of having a smaller share of people at traditional working age (with potentially more people obtaining income security through both public and private pensions) and how to respond to greater health and long-term care needs. Without appropriate policy responses, there are worries that demographic changes will have inevitable consequences for households, public finances and possibly for economic growth and development. To support policy decisions related to population ageing it is important to have informative evidence and modelling tools that allow policymakers to better understand both the implications of population ageing as well as the consequences of policy action or inaction. To this end, this session presents three recently developed modelling tools that aim to address a number of policy relevant questions related to ageing: 1) How does ageing affect health financing and are health systems unsustainable? 2) How does ageing affect economic objectives and does health at older ages matter? 3) Can we develop economically relevant measures of health at older ages if older people do not have formal ties to the labour force? Using data and analyses from a range of European countries, we will demonstrate empirically that the way ageing affects health financing systems, economies, and societies more broadly reflects policy choices and is not an inevitable consequence of demographic changes. Key messages • Policymakers must prioritize informed policies to comprehensively understand the impact of aging on health financing and economies, facilitating the development of sustainable solutions. • Immediate action is imperative to address the implications of aging on labor markets and public spending, ensuring future economic stability and safeguarding societal well-being.

Introducing the Policy Lab Method

Abstract The workshop will begin with a brief presentation providing an overview of the Policy Lab method. The presentation will address how to develop a policy-relevant question from research, introduce stakeholder mapping, and introduce techniques for designing a lab agenda and engagement material. This introductory presentation will lay the groundwork for the practical sections of the workshop in which participants will explore these techniques.

2.A. Practice session: Policy labs: getting public health research into policy and practice

Abstract The process of translating health research into policy and practice benefits is often too slow, taking an average of 17 years globally. Additionally, health policy changes fail when they have an inadequate understanding of the communities and challenges they seek to address. The Policy Institute at King’s College London has developed an innovative ‘Policy Lab’ method, which convenes diverse policy and practice stakeholders such as health workers, patients and policymakers, to discuss a policy challenge, assess the evidence and co-produce viable policy options. The proposed workshop introduces this method in a practical, impactful session aimed at helping participants from any country and research background mobilise their research to improve health outcomes. Policy Labs have been shown to be highly successful method. Previous Policy Lab outcomes include influencing changes to the Mental Health Act in the UK, new international guidance on treatment of type 2 diabetes, and a global coalition on paying for prevention in dentistry. The method can be adapted to different health challenges and considers the potential for change in local, national and international arenas. The workshop teaches this method in a way that enables researchers to apply it to their own work, harnessing the potential of research to improve health outcomes. Objectives of the workshop The workshop aims to teach people the skills to design and run a Policy Lab so that they can better mobilise their research into policy and practice. Participants will learn To develop policy-relevant questions from their research; Stakeholder mapping; To design a policy lab agenda; To develop a research engagement plan. What is the added value of the workshop? The workshop teaches an innovative method that helps researchers greatly increase the policy impact from their work. It provides attendees with practical tools to employ in their own work and bridge gaps between their research and policy and practice changes. The format of the workshop will involve a short presentation on Policy Labs by facilitators, followed by four brief interactive activities for participants, as follows: Facilitator presentation: (10 mins) Introducing the Policy Lab method; Group work 1: (10 mins) Developing a policy-relevant question; Group work 2: (10 mins) Stakeholder mapping; Group work 3: (10 mins) Designing a lab agenda; Group work 4: (10 mins) Developing a research engagement plan. We have allowed 10 minutes to adapt to any arising demands, as well as to bring the session to a close with some key messages. Participants will be guided by the facilitators, and we will use template handouts for participants to complete during the session to reinforce learning. The handouts also ensure that those who wish to work independently rather than in groups may do so if preferred, and will provide participants with the practical tools necessary should be they arrive late or need to leave early. Key messages • Policy Labs are an effective way for researchers to have policy and practice impact with their work. • Participants will learn how to run a successful Policy Lab, and improve their research impact.

Long-run trends in partisan polarization of climate policy-relevant attitudes across countries

ABSTRACT We summarize long-run trends in partisan polarization of voters’ climate policy-relevant attitudes across 36 countries and multiple decades (1993–2020). We find substantial growth in partisan polarization of these attitudes in the US, other Anglophone countries and much of Western Europe, but not elsewhere. Comparing Western European to Anglophone countries, partisan polarization is more prominent on different climate policy-relevant attitudes, and primarily involves supporters of different party types. Observed partisan polarization patterns are not well explained by changes in either linkage to economic ideology or levels of general societal disagreement on climate policy-relevant questions. Growing partisan polarization does not generally reflect all partisan groups becoming more accepting of climate reform yet diverging because of differing rates of change. Instead, what disagreements there are on these matters have become increasingly tied to party support. Our findings highlight the increasing difficulty of achieving sustained political consensus for effective climate reform across many countries.

A reassessment of graduation modeling for policy design

A vast and diverse literature estimates graduation chances using logistic models set in an arbitrary timeframe, where a graduation indicator is checked at a conventional point in time and associated with covariates measured at some date. Survival models emerged over time as a robust alternative, for being able to estimate time-to-degree and time-varying effects of predictors. This paper reconsiders the effectiveness of both modeling approaches in addressing policy-relevant questions, particularly in light of the increasingly automated and algorithm-based educational policies. We find that both methods exhibit blind spots and limitations, but that adopting a simple pragmatic approach logistic models can achieve a comparable level of effectiveness at depicting graduation dynamics while also being capable of answering questions that are problematic for survival models. We exploit a unique dataset and the nature of discrete-time survival models as combinations of logistic regressions run at different times to illustrate how arbitrary timeframes impact the estimates of a logistic model of graduation. Conversely, we illustrate how separately running and analyzing all the distinct logistic regressions provides insights that are unlikely to come from a survival model.

Delivering Systematic and Repeatable Area-Based Conservation Assessments: From Global to Local Scales

Protected and conserved areas are a key area-based strategy to address the biodiversity and climate change crises. Indicators are fundamental to understanding performance over time. The Digital Observatory for Protected Areas (DOPA) was born in 2013 as a set of open-access web services and applications to be used to assess, monitor, and report on protected and conserved areas. For over a decade, it has delivered over 50 indicators to support policy processes, national and regional governments, and practitioners. DOPA has also developed a versatile and efficient back-end approach that is widely applicable in other area-based conservation contexts. Here, we describe the methods and workflows behind DOPA’s back end and provide examples of policy relevant questions it can answer. We discuss the key advantages and limitations of this systematic and replicable approach and explore the use of this back-end architecture to inform progress in area-based conservation targets for the following decades. This approach, embedded in multiple services provided by the Knowledge Centre for Biodiversity of the European Commission (KCBD), can also support the implementation and monitoring of area-based targets of the Kunming Montreal Global Biodiversity Framework at international, regional, and national levels.

Developing an integrated HIV clinical and population database in rural south Africa

BackgroundHIV cohort data are crucial for understanding the effects of HIV infection and treatment. Linkage between HIV clinical and population records unlocks potential to understand the cause and consequences of social-economic factors on HIV-care seeking behaviors and mortality outcomes. We created HIV data infrastructure by linking HIV treatment records with routinely collected records. MethodsWe linked HIV cohort data(n=119,434) to Health and Demographic Surveillance(n=256,425), laboratory tests(n=617,314), clinic visits and hospital admissions(n=170,325) from KwaZulu-Natal, an HIV-endemic setting in rural South Africa. We used graph-based probabilistic record linkage algorithm to link the data for HIV patients accessing care at 17 clinics within the Hlabisa sub-district. Africa Health Research Institute (AHRI) has collected health and demographic surveillance data since 2000. AHRI has set up a hospital information system in the district to capture ICD-10 code admissions and discharges. ResultsFrom the 5% sample of 462,524 linkage clusters, the computed F-score was 0.88, sensitivity was 88% and positive predictive value was 91%. 38.5% of the patient ID clusters contained records linked across multiple databases. 36% of the HIV patient ID clusters contained records linked with laboratory tests. 19.5% of the HIV patient ID clusters contained data on demographic surveillance. 12.5% of the HIV patient ID clusters contained records linked with clinic visits and hospital admissions. 4% of patient IDs contained records across all four databases. ConclusionWe have successfully created a cross-sectorial HIV cohort through probabilistic record linkage methods. The cohort provides a platform to answer policy-relevant questions.

Toward a Sociology of Plasma Products.

Over the past 20 years, plasma has become a medical treatment characterized as "liquid gold" to signal its lifesaving potential. Through a manufacturing process termed fractionation, plasma, collected through blood donation, is turned into Plasma Derived Medical Products (PDMPs). The World Health Organization (WHO) has underlined the importance of PDMPs for global health care, including a number of PDMPs on the WHO Model List of Essential Medicines. The process of collecting plasma from a donor, manufacturing plasma derived treatments, and distributing those treatments globally requires the coordination of multiple social actors operating in different social, political and economic contexts, but has received little attention in scholarly literature on public policy or the social sciences. This paper will introduce a set of analytic questions and concepts that can direct a sociology of plasma products. We build on the behavioral turn in the policy sciences to identify relevant policy questions emerging from this field and offer the analytic tools necessary to investigate how different social actors in this space make meaning of plasma. To do this, we will draw on key concepts in the sociology of health and illness.

The Medicines Intelligence Data Platform: A Population-Based Data Resource From New South Wales, Australia.

The Medicines Intelligence (MedIntel) Data Platform is an anonymised linked data resource designed to generate real-world evidence on prescribed medicine use, effectiveness, safety, costs and cost-effectiveness in Australia. The platform comprises Medicare-eligible people who are ≥18 years and residing in New South Wales (NSW), Australia, any time during 2005-2020, with linked administrative data on dispensed prescription medicines (Pharmaceutical Benefits Scheme), health service use (Medicare Benefits Schedule), emergency department visits (NSW Emergency Department Data Collection), hospitalisations (NSW Admitted Patient Data Collection) plus death (National Death Index) and cancer registrations (NSW Cancer Registry). Data are currently available to 2022, with approval to update the cohort and data collections annually. The platform includes 7.4 million unique people across all years, covering 36.9% of the Australian adult population; the overall population increased from 4.8 M in 2005 to 6.0 M in 2020. As of 1 January 2019 (the last pre-pandemic year), the cohort had a mean age of 48.7 years (51.1% female), with most people (4.4 M, 74.7%) residing in a major city. In 2019, 4.4 M people (73.3%) were dispensed a medicine, 1.2 M (20.5%) were hospitalised, 5.3 M (89.4%) had a GP or specialist appointment, and 54 003 people died. Anti-infectives were the most prevalent medicines dispensed to the cohort in 2019 (43.1%), followed by nervous system (32.2%) and cardiovascular system medicines (30.2%). The MedIntel Data Platform creates opportunities for national and international research collaborations and enables us to address contemporary clinically- and policy-relevant research questions about quality use of medicines and health outcomes in Australia and globally.

Data analysis plan of the OECD PaRIS survey: leveraging a multi-level approach to analyse data collected from people living with chronic conditions and their primary care practices in 20 countries

ObjectiveIn view of the increasing number of people with (multiple) chronic conditions, the Organisation for Economic Co-operation and Development (OECD) initiated the International Survey of People Living with Chronic Conditions (PaRIS survey), which aims to provide insight in patient-reported outcomes and experiences of chronic care provided by primary care practices to support policy development. The objective of this research note is to describe the structure of the data, collected in the PaRIS survey and how the data will be analysed in a multilevel approach for cross-country comparison.Analysis planThe data structure of the PaRIS survey represents three levels: countries/health systems, primary care practices and patients. Multilevel analysis is used because of its accuracy in estimating country-level outcomes, its flexibility in modelling relationships, and its opportunities in connecting to relevant policy questions. Country-level outcomes will be estimated to facilitate cross-country comparison and (future) within-country comparison over time. Characteristics of patients that potentially explain variation in patient-reported outcomes and experiences can be linked to primary care practice and country/health system characteristics. This makes it possible to address policy-relevant questions relating, e.g., to the impact of chronic care management on patients with a specific chronic condition.

Improving Diagnosis and Care for Patients With Sarcoma: Do Real-World General Practitioners Data and Prospective Data Collections Have a Place Next to Clinical Trials?

There has been growing interest in the use of real-world data (RWD) to address clinically and policy-relevant (research) questions that cannot be answered with data from randomized controlled trials (RCTs) alone. This is, for example, the case in rare malignancies such as sarcomas as limited patient numbers pose challenges in conducting RCTs within feasible timeliness, a manageable number of collaborators, and statistical power. This narrative review explores the potential of RWD to generate real-world evidence (RWE) in sarcoma research, elucidating its application across different phases of the patient journey, from prediagnosis to the follow-up/survivorship phase. For instance, examining electronic health records (EHRs) from general practitioners (GPs) enables the exploration of consultation frequency and presenting symptoms in primary care before a sarcoma diagnosis. In addition, alternative study designs that integrate RWD with well-designed observational RCTs may offer relevant information on the effectiveness of clinical treatments. As, especially in cases of ultrarare sarcomas, it can be an extreme challenge to perform well-powered randomized prospective studies. Therefore, it is crucial to support the adaptation of novel study designs. Regarding the follow-up/survivorship phase, examining EHR from primary and secondary care can provide valuable insights into identifying the short- and long-term effects of treatment over an extended follow-up period. The utilization of RWD also comes with several challenges, including issues related to data quality and privacy, as described in this study. Notwithstanding these challenges, this study underscores the potential of RWD to bridge, at least partially, gaps between evidence and practice and holds promise in contributing to the improvement of sarcoma care.

Global environmental change policy priorities from the Americas and opportunities to bridge the science-policy gap

ABSTRACT Governments and intergovernmental organizations support scientific research to produce the knowledge and tools needed to monitor and mitigate global environmental changes (GEC). However, GEC-related policy decisions are often not based on scientific evidence, and GEC research is often not based on policy-relevant questions, resulting in a science-policy gap. Assessing the GEC policy priorities of researchers and policymakers is an essential step towards closing this gap. This task was undertaken by the Inter-American Institute for Global Change Research (IAI), an intergovernmental organization pursuing science and capacity building to reach the vision of a sustainable Americas. The assessment included survey consultations, listening sessions, and an analysis of policy documents for 17 countries of the Americas. Three key findings emerged from this assessment. First, the top current priority for policymakers was Climate action, and Biodiversity and ecosystem services for researchers, with a poor alignment between the priorities of these social actors at the country level. Second, clusters of non-neighboring countries had a profile of GEC priorities more similar than clusters of neighboring countries, although there were some sub-regional clusters around particular GEC goals. Third, researchers and policymakers agreed that the lack of cross-sectoral collaboration and communication between technical and non-technical actors are important barriers. A key opportunity for policymakers was the growing funding and international cooperation for GEC, while for researchers, the growing body of evidence to inform GEC decision-making. These findings have implications for the design of research and capacity-building actions targeted to the priorities and needs of the region.

People’s beliefs about pronouns reflect both the language they speak and their ideologies.

People’s beliefs about pronouns reflect both the language they speak and their ideologies.

Is There Enough Housing Production? It Matters Which Indicators Are Used to Answer

McClure and Schwartz argue that there is no US housing shortage for the 2000-2020 period because: (1) we added more dwellings than households; (2) vacancy rates are not low by historical standards; and (3) current headship rates are unrelated to the ratio of recent growth in dwellings to growth in households. I argue that these indicators offer misleading guides for addressing policy-relevant questions related to the supply of housing because household formation is endogenous to changes in the housing stock. I demonstrate the shortcomings of these indicators with a stylized graphic/numerical analysis of short-term and long-term housing market equilibrium and adjustment dynamics in two hypothetical metropolitan areas. Changes in household size (adjusted for demographic and income distributional alterations) and changes in housing rents and prices are more appropriate indicators for making housing policy choices, implying that current concerns over the responsiveness (long-run elasticity) of housing supply are indeed justified.

Healthcare workforce equity for health equity: An overview of its importance for the level of primary health care.

Healthcare workforce crises often stem from healthcare workers' inequities. This study provides an overview of the main PHC workforce policy questions related to health equity, offering examples of evidence necessary to support the implementation of policies and strategies that increase equity in the health workforce and access to the PHC workforce and services. The equity-related policies in PHC and workforce were linked with the indicators listed in the Global Health Workforce Network Data and Evidence Hub and guidelines for health workforce management. The policy-relevant questions in PHC cover many workforce issues such as the optimal size, equitable distribution, relevant competencies to ensure equitable healthcare access, and equitable approaches for retention, training, recruitment, benefits and incentive schemes and governance. This will require intersectionality evidence of the optimised staffing to PHC workload, that PHC practitioners' training demonstrates evidence-based knowledge aligned with locally relevant expertise. Critical for equitable PHC access and health equity is the establishment of efficient measurement of PHC workforce equity and its implications for population health. Using indicators that measure health and workforce equity in research, policy, and practices may improve recruitment and retention, and respond more effectively to the PHC workforce crises.

Behavioral spillovers from green purchases: comparing impacts on consumption and policy support

Green labels and green advertising aim to promote products associated with less energy demands and lower carbon emissions. However, there are indications that purchasing green products may trigger negative spillover effects to subsequent pro-environmental behaviors. One policy-relevant question is whether spillovers from green purchases vary between different types of pro-environmental behaviors, notably consumption vs. policy support, and what determines any differences. Another important question is whether the price of green products affects the magnitude and direction of such spillover effects. Using an experiment among US citizens (N = 1,985), we show that spillovers from green purchases differ significantly between pro-environmental behaviors. In particular we find that spillovers are positive for green consumption and negative for climate policy support. Regarding price effects, we do not find robust evidence on their moderating role but only a tendency for cheap prices to exacerbate the direction of the spillover: negative for policy support and positive for green consumption. Finally, pro-environmental identity, previously suggested in the literature as a central moderator of spillover effects from green purchases, appears to be less determinant.

Virtual Research Prioritization: Innovations for Research Agenda Development With Impacted Communities

Involving communities in research prioritization is an important component of developing relevant research, policy, and healthcare questions. The coronavirus disease 2019 (COVID-19) pandemic has led to an increased need for research prioritization methods which align with public health protections. The Research Prioritization by Affected Communities (RPAC) protocol is a participatory method which directly involves historically excluded communities in developing and prioritizing research questions. We adapted the RPAC protocol for the virtual environment (RPAC-ve) to understand the research, healthcare delivery, and policy priorities of greatest importance to communities affected by the COVID-19 pandemic in relation to pregnancy in the United States. RPAC-ve included the use of a web-based platform, sharable documents for listing and ranking research questions, and a scribe to capture a visual representation of RPAC-ve sessions. RPAC-ve is a flexible and effective priority setting method which can be used to engage high risk populations.

Impact of Primary Care Exception Expansion on Family Medicine Resident Billing During the COVID-19 Pandemic.

The Medicare Primary Care Exception (PCE) permits indirect supervision of residents performing lower-complexity visits in primary care settings. During the COVID-19 pandemic, Medicare expanded the PCE to all patient visits regardless of complexity. This study investigates how PCE expansion changed resident billing practices at a family medicine residency during calendar year 2020. We hypothesized that residents not constrained by the PCE would bill more high-level visits. We queried billing codes from attendings' and residents' established evaluation and management visits associated with the University of Washington Family Medicine Residency (UWFMR) from January to December 2020. We used χ2 tests to compare resident and attending physicians' use of low/moderate and high-level codes by quarter. Resident high-complexity code use increased after PCE expansion in Q4 (odds ratio [OR] 3.50 [2.34-5.23]) compared to Q1. No change was observed among attending physicians (OR 1.05 [0.86-1.28]). Resident and attending billing patterns became more similar following PCE expansion. With the PCE expansion, senior family medicine resident physicians at UWFMR used higher-complexity billing codes at a rate approximating that of attending physicians. The findings of this study have implications regarding the financial well-being and sustainability of primary care residency training and raise a relevant policy question about whether the PCE expansion should persist. More research is needed to determine whether these findings were replicated in other primary care residency practices, the impact on resident education, and the impact on patient outcomes.

Framework for federated causal inference based on real-world observational data sources (BY-COVID)

Abstract Background Causal inference techniques help researchers and policy-makers to evaluate public health interventions. Approaching causal inference by re-using routinely collected observational data across different regions is challenging and guidance is currently lacking. With the aim of filling this gap and allowing a rapid response in the case of a next pandemic, a methodological framework to estimate causal effects in a federated research infrastructure is showcased in the baseline use case of the European BY-COVID project. Methods A framework, implementing existing methodologies and expertise, is proposed, enabling federated research based on routinely collected sensitive individual-level data. The framework includes step-by-step guidelines, from defining a research question, to establishing a causal model, identifying and specifying data requirements in a data model, and developing an interoperable and reproducible analytical pipeline for distributed deployment. Using open-source software, a complete workflow implementing federated causal inference was prototyped to assess the real-world effectiveness of SARS-CoV-2 primary vaccination in preventing infection in populations spanning different countries, integrating a data quality assessment, imputation of missing values, matching of exposed to unexposed based on identified confounders and a survival analysis within the matched population. Results The proposed methodological framework was successfully demonstrated within the BY-COVID use case, illustrating its feasibility and value. Different Findable, Accessible, Interoperable and Reusable (FAIR) research objects were produced, such as a study protocol, a data management plan, data model and interoperable analytical pipeline. Conclusions The framework provides a systematic approach to address policy-relevant causal research questions in a privacy-preserving way. The methodology and derived research objects can be re-used and contribute to pandemic preparedness. Key messages • Estimating causal effects using observational data in a privacy-preserving way was demonstrated to be achievable and can help policy-makers to evaluate public health interventions. • The methodology, constituted of Findable, Accessible, Interoperable and Reusable (FAIR) research objects, is generalizable to other research questions and contributes to pandemic preparedness.