Public Health Decision Research Articles

Impact of COVID-19 vaccination on preventive behavior: The importance of confounder adjustment in observational studies.

The COVID-19 pandemic has underscored the critical role of observational studies in evaluating the effectiveness of public health strategies. However, although many studies have explored the true impact of vaccination on preventive behavior, their results may be skewed by potential biases and confounding variables. This study examines the application of covariate adjustment and propensity score (PS) estimation, particularly through inverse probability treatment weighting (IPTW), to assess their performance in reducing bias in a framework featuring ordinal outcomes and cumulative logistic regression models, as commonly used in observational studies related to social sciences and psychology. Before applying these methods to the case study, we conducted a simulation study that accounted for the presence or absence of model misspecification in an observational scenario with ordinal outcomes, binary treatment, and a continuous confounder. Our findings demonstrate the effectiveness of combining covariate adjustment with PS methods in reducing bias and improving causal inference. These methods were subsequently applied to an Italian observational study on COVID-19 vaccine hesitancy conducted during the initial phase of the vaccination campaign (April-May 2021). Our analysis revealed that vaccination status had a limited short-term impact on the adoption of preventive measures. This study highlights the importance of employing appropriate adjustment techniques in observational research, particularly when evaluating complex behavioral outcomes. The results support the combined use of covariate adjustment and PS methods to enhance the reliability of findings, ultimately contributing to more informed public health decision-making.

Harnessing Deep Learning for Real-Time Water Quality Assessment: A Sustainable Solution

This study presents an innovative approach utilizing artificial intelligence (AI) for the prediction and classification of water quality parameters based on physico-chemical measurements. The primary objective was to enhance the accuracy, speed, and accessibility of water quality monitoring. Data collected from various water samples in Algeria were analyzed to determine key parameters such as conductivity, turbidity, pH, and total dissolved solids (TDS). These measurements were integrated into deep neural networks (DNNs) to predict indices such as the sodium adsorption ratio (SAR), magnesium hazard (MH), sodium percentage (SP), Kelley’s ratio (KR), potential salinity (PS), exchangeable sodium percentage (ESP), as well as Water Quality Index (WQI) and Irrigation Water Quality Index (IWQI). The DNNs model, optimized through the selection of various activation functions and hidden layers, demonstrated high precision, with a correlation coefficient (R) of 0.9994 and a low root mean square error (RMSE) of 0.0020. This AI-driven methodology significantly reduces the reliance on traditional laboratory analyses, offering real-time water quality assessments that are adaptable to local conditions and environmentally sustainable. This approach provides a practical solution for water resource managers, particularly in resource-limited regions, to efficiently monitor water quality and make informed decisions for public health and agricultural applications.

The value of environmental surveillance for pandemic response

Environmental sampling surveillance (ESS) technologies, such as wastewater genomic surveillance and air sensors, have been increasingly adopted during the COVID-19 pandemic to provide valuable information for public health response. However, ESS coverage is not universal, and public health decision-makers need support to choose whether and how to expand and sustain ESS efforts. This paper introduces a model and approach to quantify the value of ESS systems that provide leading epidemiological indicators for pandemic response. Using the COVID-19 pandemic as a base-case scenario, we quantify the value of ESS systems in the first year of a new pandemic and demonstrate how the value of ESS systems depends on biological and societal parameters. Under baseline assumptions, an ESS system that provides a 5-day early warning relative to syndromic surveillance could reduce deaths from 149 (95% prediction interval: 136–169) to 134 (124–144) per 100,000 population during the first year of a new COVID-19-like pandemic, resulting in a net monetary benefit of $1,450 ($609-$2,740) per person. The system’s value is higher for more transmissible and deadly pathogens but hinges on the effectiveness of public health interventions. Our findings also suggest that ESS systems would provide net-positive benefits even if they were permanently maintained and pathogens like SARS-Cov-2 emerged once every century or less frequently. Our results can be used to prioritize pathogens for ESS, decide whether and how to expand systems to currently uncovered populations, and determine how to scale surveillance systems’ coverage over time.

Detection of Real-Time Changes in Direction of COVID-19 Transmission Using National- and State-Level Epidemic Trends Based on Rt Estimates - United States Overall and New Mexico, April-October 2024.

Public health practitioners rely on timely surveillance data for planning and decision-making; however, surveillance data are often subject to delays. Epidemic trend categories, based on time-varying effective reproductive number (Rt) estimates that use nowcasting methods, can mitigate reporting lags in surveillance data and detect changes in community transmission before reporting is completed. CDC analyzed the performance of epidemic trend categories for COVID-19 during summer 2024 in the United States and at the state level in New Mexico. COVID-19 epidemic trend categories were estimated and released in real time based on preliminary data, then retrospectively compared with final emergency department (ED) visit data to determine their ability to detect or confirm real-time changes in subsequent ED visits. Across the United States and in New Mexico, epidemic trend categories were an early indicator of increases in COVID-19 community transmission, signifying increases in COVID-19 community transmission in May, and a confirmatory indicator that decreasing COVID-19 ED visits reflected actual decreases in COVID-19 community transmission in September, rather than incomplete reporting. Public health decision-makers can use epidemic trend categories, in combination with other surveillance indicators, to understand whether COVID-19 community transmission and subsequent ED visits are increasing, decreasing, or not changing; this information can guide communications decisions.

Comparative risk assessment modeling of cardiovascular and all-cause burden attributable to sitting time and physical inactivity: evidence from Argentina.

Although there is evidence that sitting time (ST) and insufficient physical activity (PA) are associated with premature mortality, the burden of cardiovascular disease (CVD) and all-cause mortality (ACM) attributable to the combined effects of ST and PA in counterfactual scenarios is limited. Potential impact fractions (PIFs) were used to calculate premature deaths (PDs) and disability-adjusted life years (DALYs). Cause-eliminated life tables were utilized to estimate health-adjusted life expectancy (HALE) gains. Monte Carlo simulations were performed for uncertainty analysis. The theoretical minimum risk exposure level (ST < 4 $\mathrm{h} \cdot{\mathrm{d}}^{-1}$, PA > 65 $\mathrm{min} \cdot{\mathrm{d}}^{-1}$) could prevent 16.7% of CVD deaths and 12.3% of all-cause deaths annually. This would save 669 to 2,630 DALYs per 100,000 and increase healthy life years by 0.57 to 2.94. Increasing PA to > 65 $\mathrm{min} \cdot{\mathrm{d}}^{-1}$ while maintaining ST could yield gains in HALE from 0.49 (CVD) to 2.60 (ACM) years. Reducing ST to < 4 $\mathrm{h}\cdot{\mathrm{d}}^{-1}$ while keeping PA constant could lead to gains in HALE from 0.07 (CVD) to 0.34 (ACM) years. A 50% reduction in suboptimal ST (≥ 4 $\mathrm{h}\cdot{\mathrm{d}}^{-1}$) doubled HALE gains, ranging from 0.11 to 0.63years. Public health decision-makers should prioritize vulnerable populations, including older adults and individuals with inadequate PA levels.

Unlocking data: Decision-maker perspectives on cross-sectoral data sharing and linkage as part of a whole-systems approach to public health policy and practice.

Secondary data from different policy sectors can provide unique insights into the social, environmental, economic and political determinants of health. This is especially pertinent in the context of whole-systems approaches to healthy public policy, which typically combine cross-sectoral collaboration with the application of theoretical insights from systems science. However, the sharing and linkage of data between different sectors are still relatively rare. Previous research has documented the perspectives of researchers and members of the public on data sharing, especially healthcare data, but has not engaged with relevant policy and practice decision-makers. We sought to work collaboratively with decision-makers relevant to healthy public policy and practice in Scotland to identify practical ways that cross-sectoral data sharing and linkage could be used to best effect to improve health and reduce health inequalities. We facilitated three sequential stakeholder workshops with 20 participants from local and central government, public health teams, Health and Social Care Partnerships, the third sector, organisations which support data-intensive research and public representatives from across Scotland. Workshops were informed by two scoping reviews (carried out in June 2021) and three case studies of existing cross-sectoral linkage projects. Workshop activities included brainstorming of factors that would help participants make better decisions in their current role; reflective questions on lessons learnt from the case studies; and identifying and prioritising recommendations for change. Findings were synthesised using thematic analysis. Scotland; public and third sector data. Based on the workshops, and supported by the reviews and case studies, we created a visual representation of the use of evidence, and secondary data in particular, in decision-making for healthy public policy and practice. This covered three key overarching themes: differing understandings of evidence; diverse functions of evidence; and factors affecting use (such as technical, political and institutional, workforce and governance). Building on this, workshop participants identified six guiding principles for cross-sectoral data sharing and linkage: it should be pragmatic; participatory; ambitious; fair; iterative; with holistic and proportionate governance. Participants proposed 21 practical actions to this end, including: a strategic approach to identifying and sharing key data sets; streamlining governance processes (e.g. through standardised data sharing agreements; central data repositories; and a focus on reusable data resources) and building workforce capacity. To make these possible, participants identified a need for strong political and organisational leadership as well as a transparent and inclusive public conversation. Participation from some stakeholders was limited by workload pressures associated with the COVID-19 pandemic. No consensus was reached on the impact, effort, and/or timing of some recommendations. Findings were closely informed by the Scottish context but are nonetheless likely to be relevant to other jurisdictions. There is broad consensus among key stakeholders that linked cross-sectoral data can be used far more extensively for public health decision-making than it is at present. No single change will lead to improved use of such data: a range of technical, organisational and political constraints must be addressed. This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme as award number NIHR133585.

Enhancing geographical access to cardiovascular disease healthcare services in Lagos State, Nigeria

Abstract Cardiovascular diseases (CVDs) are the leading cause of global mortality, and three-quarters of the world’s deaths from CVDs occur in low- and middle-income countries. Improving spatial access to care facilities is a way of reducing the disease burden through early detection and treatment. Determining the number of facilities to provide and the locations in order to improve accessibility is central to healthcare facility planning. The maximal covering location problem (MCLP) was used in the identification and determination of where to place additional CVD treatment facilities in Lagos State for better accessibility. The primary objective of this study was to optimize the placement of CVD facilities to maximize coverage for both patients and non-patients alike. The optimization model utilized data on the population of Lagos State, the locations of CVD patients, the location of existing CVD care facilities, and the locations of general hospitals (GHs) within the state. The MCLP implementation was carried out using the maxcovr. The results of the models indicated that the addition of two facilities at the Isolo and Ile-Epo GHs would enhance CVD patient coverage from 70 to 87.4%, reducing the average travel distance from 9,038 to 6,466 m. Similarly, the introduction of two facilities at the Ikorodu and Ajegunle GHs improved population coverage from 40.3 to 70.8%, reducing the average travel distance of 18,788 m by over 5,000 m. These findings are expected to offer valuable insights for public health decision-makers who face the challenge of prioritizing limited budgets.

Theoretical perspectives on biostatistics and its multifaceted applications in global health studies

Biostatistics plays a crucial role in global health studies by providing the tools necessary for analyzing complex health data and informing evidence-based public health decisions. This theoretical review explores the foundations of biostatistics and its diverse applications in the context of global health. At its core, biostatistics utilizes mathematical and statistical principles to analyze health data, enabling researchers to identify patterns, determine risk factors, assess interventions, and predict health outcomes across populations. Key theoretical concepts, including probability theory, statistical inference, and regression models, form the backbone of biostatistical analysis and are integral to understanding the dynamic relationships between health determinants and outcomes. The review highlights the various applications of biostatistics in global health, focusing on areas such as disease surveillance, epidemiology, and public health intervention evaluation. Biostatistical techniques are essential for analyzing epidemiological data, monitoring disease trends, and evaluating the effectiveness of health policies and interventions on a global scale. Furthermore, advanced statistical methods, including machine learning and Bayesian approaches, are becoming increasingly important in global health research. These methods allow for the analysis of large-scale data, predictive modeling, and more precise risk assessments, particularly in the context of emerging diseases, genetic research, and environmental health studies. Despite its strengths, biostatistics faces challenges in global health, including issues related to data quality, ethical considerations, and cross-cultural variations in health outcomes. Addressing these challenges is critical for ensuring accurate and equitable health assessments. Looking forward, biostatistics continues to evolve with innovations in data science, big data analytics, and real-time health data collection, making it an indispensable tool for improving global health outcomes and supporting health policies. This review emphasizes the theoretical underpinnings of biostatistics while highlighting its transformative potential in global health research and practice. Keywords: Theoretical Perspectives, Biostatistics, Multifaceted Applications, Global Health Studies.

Trends in Hepatocellular Carcinoma Mortality Rates in the US and Projections Through 2040

ImportanceThe burden of liver cancer varies worldwide. An upward trend in both hepatocellular carcinoma (HCC) incidence and mortality in the past 2 decades has been observed.ObjectiveTo assess observed HCC-related age-standardized mortality rates (ASMRs) in the US for 2006 to 2022 and provide ASMR projections through 2040.Design, Setting, and ParticipantsThis cross-sectional study used data from the National Vital Statistics System, which is accessible through the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research website. Data on deaths attributed to HCC (from January 1, 2006, to December 31, 2022) were obtained for adults 25 years or older and were stratified by liver disease etiology, age, sex, and race and ethnicity. Etiologies included alcohol-associated liver disease (ALD), hepatitis B virus (HBV), hepatitis C virus (HCV), and metabolic dysfunction–associated steatotic liver disease (MASLD).Main Outcomes and MeasuresThe main outcomes were (1) observed ASMRs of HCC per 100 000 persons using Joinpoint regression (National Cancer Institute) to assess trends during 2006 to 2022 and (2) ASMRs projected for 2023 to 2040 using Prophet and AutoARIMA modeling.ResultsThis study included 188 280 HCC-related deaths from 2006 to 2022. Most deaths occurred among males (77.4%). The annual percentage change was 4.1% (95% CI, 2.2% to 7.7%) for 2006 to 2009 and decreased to 1.8% (95% CI, 0.7% to 2.0%) for 2009 to 2022, with an overall observed ASMR of 5.03 per 100 000 persons in 2022 and a projected ASMR of 6.39 per 100 000 persons by 2040, with consistent trends for both sexes. By etiology, ASMRs decreased for HCV- and HBV-related mortality but increased for ALD- and MASLD-related mortality. In 2022, MASLD surpassed HBV as the third-leading cause of HCC-related death and was projected to overtake HCV in 2032 as the second-leading cause; ALD was projected to be the leading cause of HCC-related death in 2026. In 2022, the ASMR was higher among individuals aged 65 years or older compared with those aged 25 to 64 years (18.37 vs 1.79 per 100 000 persons). The American Indian or Alaska Native population had the largest increase in projected ASMR by 2040 (14.71 per 100 000 persons) compared with the Asian population (3.03 per 100 000 persons).Conclusions and RelevanceIn this cross-sectional study, ASMRs for ALD- and MASLD-related HCC death increased rapidly from 2006 to 2022; ALD-related HCC was projected to be the leading cause by 2026, with MASLD as the second-leading cause by 2032. These findings may serve as a reference for public health decision-making and timely identification of groups at high risk of HCC death.

Managing Information Overload in a Pandemic: Influence of Social Media on Vaccine Attitudes in the Digital Age

Purpose: This study examined how social media influences public health decisions, focusing on how information overload during a pandemic impacts vaccine hesitancy and acceptance across demographics. It analyzed conflicting vaccine information to understand social media's role in shaping attitudes and identify strategies to mitigate its negative effects. Methodology: This study employed a multi-stage sampling method to gather data from diverse age groups, education levels, and occupations. This approach combines various techniques at different stages, making it suitable for large, geographically dispersed populations. By segmenting the population into manageable units, multi-stage sampling improves the accuracy, reliability, and representativeness of the data collected. Findings: The findings reveal that most respondents consider vaccine information on social media unreliable, underscoring the need for greater trust in online discussions. Many feel overwhelmed by conflicting messages, highlighting the necessity for clearer communication. Social media significantly shapes attitudes toward COVID-19 vaccines, with many shifting views after engaging with online content. Additionally, participants report mental health impacts from the constant stream of vaccine information, illustrating information overload. Unique Contributions to Theory, Policy and Practice: This study advances the information overload model by illustrating how excessive and conflicting vaccine information on social media disrupts comprehension and harms mental health. It emphasizes social media's role in shaping health attitudes and advocates for interdisciplinary approaches that incorporate psychological resilience into public health strategies. Findings highlight the need for regulations against misinformation to improve the credibility of online health information and promote trusted sources, particularly verified health organizations. The study also calls for health literacy initiatives to empower the public in navigating misinformation and suggests strategies for managing information overload through social media disengagement. Targeted initiatives that leverage trusted information sources can effectively combat vaccine hesitancy and boost community engagement in public health campaigns, ultimately improving health outcomes.

QUALIDADE PARA GARANTIR A CONFIABILIDADE DOS RESULTADOS DOS TESTES DA COVID 19

Objective: To analyze and identify the main quality criteria essential to ensure the reliability of COVID-19 test results performed in clinical laboratories. Literature Review: Ensuring the quality of COVID-19 testing is vital for the effectiveness of pandemic control strategies. This involves meticulous care with the selection and implementation of tests, the formation and training of teams, and adherence to standards and good practices. Only with an ongoing commitment to quality and accuracy can we ensure that test results reflect the true situation of COVID-19 infection and aid in public health decision-making.Conclusion: To ensure the reliability of COVID-19 test results, it is essential to take a comprehensive approach that encompasses test accuracy, sample quality, rigorous validation, staff qualification, and transparency.

Bayesian analysis of the COVID-19 pandemic using a Poisson process with change-points

Abstract Analyzing COVID-19 data presents a challenge in Bayesian computations of the Poisson process because the experimental conditions are not under control. This lack of homogeneity can lead to inconsistent model parameters, which violates the assumptions of Bayesian inference. In this paper, we study the multiple change-point detection problem from this viewpoint for a non-homogeneous sample path of the Poisson process as the response variable. The rate parameters are linked to some explanatory using a generalized linear model. The number of change-points is considered to be unknown as well as their locations. We introduce a Bayesian paradigm to estimate the number and location of change-points. We also present an adaptive RJMCMC algorithm to generate pseudo-random samples from the posterior distributions. We apply the proposed model to analyze the COVID-19 infection curves from different countries and identify patterns of cases. We also assess the efficacy of interventions, such as vaccination and public health emergency responses, implemented by different countries. The results of the analysis provide valuable insights into the spread of COVID-19 and the effectiveness of interventions. The proposed model can be used to inform public health decision-making and help to improve the management of the pandemic.

Development of an Integrated Surveillance System to Improve Preparedness for Arbovirus Outbreaks in a Dengue Endemic Setting: Descriptive Study.

Dengue fever, transmitted by Aedes aegypti and Aedes albopictus mosquitoes, poses a significant public health challenge in tropical and subtropical regions. Dengue surveillance involves monitoring the incidence, distribution, and trends of infections through systematic data collection, analysis, interpretation, and dissemination. It supports public health decision-making, guiding interventions like vector control, vaccination campaigns, and public education. Herein, we report the development of a surveillance system already in use to support public health managers against dengue transmission in Foz do Iguaçu, a dengue-endemic Brazilian city located in the Triple Border with Argentina and Paraguay. We present data encompassing the fieldwork organization of more than 100 health agents; epidemiological and entomological data were gathered from November 2022 to April 2024, totalizing 18 months of data collection. By registering health agents, we were able to provide support for those facing issues to fill their daily milestone of inspecting 16 traps per working day. We filtered dengue transmission in the city by patient age, gender, and reporting units, as well as according to dengue virus serotype. The entomological indices presented a strong seasonal pattern, as expected. Several longtime established routines in Foz do Iguaçu have been directly impacted by the adoption of Vigilância Integrada com Tecnologia (VITEC). The implementation of VITEC has enabled more efficient and accurate diagnostics of local transmission risk, leading to a better understanding of operational activity patterns and risks. Lately, local public health managers can easily identify hot spots of dengue transmission and optimize interventions toward those highly sensitive areas.

A generalized multinomial probabilistic model for SARS-COV-2 infection prediction and public health intervention assessment in an indoor environment.

SARS-CoV-2 Omicron and its sub-lineages have become the predominant variants globally since early 2022. As of January 2023, over 664 million confirmed cases and over 6.7 million deaths had been reported globally. Current infection models are limited by the need for large datasets or calibration to specific contexts, making them difficult to apply to different settings. This study aims to develop a generalized multinomial probabilistic model of airborne infection to assist public health decision-makers in evaluating the effectiveness of public health interventions (PHIs) across a broad spectrum of scenarios. The proposed model systematically incorporates group characteristics, epidemiology, viral loads, social activities, environmental conditions, and PHIs. Assumptions about social distance and contact duration that estimate infectivity during short-term group gatherings have been made. The study is differentiated from earlier works on probabilistic infection modeling in the following ways: (1) predicting new cases arising from more than one infectious person in a gathering, (2) incorporating additional key infection factors, and (3) evaluating the effectiveness of multiple PHIs on SARS-CoV-2 infection simultaneously. Although the results show that limiting group size has an impact on infection, improving ventilation has a much greater positive health impact. The proposed model is versatile and can flexibly accommodate other scenarios or airborne diseases by modifying the parameters allowing new factors to be added.

Effective method to mitigate impact of rain or snowmelt sewer flushing events on wastewater-based surveillance measurements

Wastewater-based surveillance (WBS) is increasingly used for monitoring disease targets in wastewaters around the world. This study, performed in Ottawa, Canada, identifies a decrease in SARS-CoV-2 wastewater measurements during snowmelt-induced sewer flushing events. Observations first revealed a correlation between suppressed viral measurements and periods of increased sewage flowrates, air temperatures above 0 °C during winter months, and solids mass flux increases. These correlations suggest that high sewage flowrates from snowmelt events or intense precipitation events lead to the scouring of previously settled solids in sewers and the subsequent entrainment of these solids into the transported wastewaters. Collection of WBS samples during flushing events hence contains a heterogeneous mixture of solids, including resuspended solids with varying degrees of decay. Therefore flushing events can present a challenge for accurately measuring disease target viral signals when using solids-based analytical methods. This study demonstrates that resuspended solids entrained in the wastewaters during flushing events retain PMMoV signal while the SARS-CoV-2 signal is significantly reduced due to the slower decay rate of pepper mild mottle virus (PMMoV) compared to SARS-CoV-2 within wastewaters. Hence current normalization methods using PMMoV are shown to be ineffective in correcting for flushing events and the associated resuspension of settled solids, as the PMMoV signal of settled solids within sewers does not account for the differential decay rates experiences by SARS-CoV-2 signal in settled solids. Instead, this study identifies RNA to PMMoV correction factor as an effective approach to correct for flushing events and to realign SARS-CoV-2 signal with COVID-19 hospital admission rates within communities. As such, the study highlights the key physicochemical parameters necessary to identify flushing events that affect SARS-CoV-2 WBS measurements and introduces a novel RNA to PMMoV correction factor approach for solids-based analysis of SARS-CoV-2 during flushing events, enhancing the accuracy of WBS data for public health decision-making.

Examining research systems and models for local government: a systematic review

Background: Local authorities (LA) are key in improving population health, and LA public health decision makers need support from appropriately organised research capacity; however, few models of LA research systems are known to exist. Aims and objectives: To explore potential and existing models of LA-based research systems. Methods: This mapping review and time-constrained systematic review synthesises conceptual and empirical literature from 12 health and social science databases, grey literature and reference/citation tracking. Three reviewers screened titles, abstracts and full texts of retrieved records, and extracted key data from included papers. Evidence was synthesised based on characteristics of research systems and quality-assessed for relevance, rigour and richness. Findings: Nine models were examined in depth. From these, we developed a typology of research systems. Few models were specifically designed for LA research activity; as a Whole System approach, the Local Authority Champions of Research model offers a potential blueprint. Useful lessons may be learned from UK Collaborations for Leadership in Applied Health Research, Academic Collaborative Centres in the Netherlands, local Research and Development units in Sweden, and generic University-Community partnerships. Discussion and conclusions: An optimal research system requires the coexistence of multiple systems including Centre, Partnership, Collaboration, Network and Community types. The review is UK-focused, but the models appear to have wider relevance. Our classification offers those planning an LA research system the opportunity to choose an approach that meets their requirements and resources. A Whole System approach is optimal, with egalitarian input from the LA and academia.

Integrating Contact Tracing Data to Enhance Outbreak Phylodynamic Inference: A Deep Learning Approach.

Phylodynamics is central to understanding infectious disease dynamics through the integration of genomic and epidemiological data. Despite advancements, including the application of deep learning to overcome computational limitations, significant challenges persist due to data inadequacies and statistical unidentifiability of key parameters. These issues are particularly pronounced in poorly resolved phylogenies, commonly observed in outbreaks such as SARS-CoV-2. In this study, we conducted a thorough evaluation of PhyloDeep, a deep learning inference tool for phylodynamics, assessing its performance on poorly resolved phylogenies. Our findings reveal the limited predictive accuracy of PhyloDeep (and other state-of-the-art approaches) in these scenarios. However, models trained on poorly resolved, realistically simulated trees demonstrate improved predictive power, despite not being infallible, especially in scenarios with superspreading dynamics, whose parameters are challenging to capture accurately. Notably, we observe markedly improved performance through the integration of minimal contact tracing data, which refines poorly resolved trees. Applying this approach to a sample of SARS-CoV-2 sequences partially matched to contact tracing from Hong Kong yields informative estimates of superspreading potential, extending beyond the scope of contact tracing data alone. Our findings demonstrate the potential for enhancing phylodynamic analysis through complementary data integration, ultimately increasing the precision of epidemiological predictions crucial for public health decision-making and outbreak control.

Addressing bias due to measurement error of an outcome with unknown sensitivity in database epidemiological studies.

In epidemiological database studies, the occurrence of an event is measured with error through an indicator whose specificity is often maximised, at the expense of sensitivity. However, if the indicator has low sensitivity, measures of occurrence are underestimated. In association studies, risk difference is biased, and risk ratio may be biased as well, in either direction, if the sensitivity is differential across exposure groups. In this work, we show that if an auxiliary screening indicator can be defined to complement the main indicator, estimates of the positive predictive value of both indicators provide tools to estimate the sensitivity of the primary indicator, or a lower bound thereof. This mitigates bias in estimating the number of cases, prevalence, cumulative incidence, rate (particularly when the event is rare), and in association studies, risk ratio and risk difference. They also allow testing for non-differential sensitivity. While direct estimation of sensitivity is often infeasible, this novel methodology improves evidence based on data obtained from re-use of existing databases, which may prove critical for regulatory and public health decisions.

Outcomes of integrated knowledge translation: A comparative, multi-country case study

Abstract Background Integrated knowledge translation (IKT) seeks to establish continuous relationships between researchers and knowledge users, in particular decision-makers, to enhance evidence-informed decision-making in public health and healthcare. IKT is, however, difficult to evaluate and outcome evaluations remain scarce. The German-African Collaboration for Evidence-based Healthcare and Public Health in Africa (CEBHA+) undertook research on IKT and non-communicable diseases in Ethiopia, Malawi, Rwanda, South Africa, and Uganda. Over six years, we implemented an IKT approach and evaluated it according to a pre-defined programme theory. Methods We conducted qualitative interviews and surveys with CEBHA+ researchers and their decision-making partners at two stages (3/2020-2/2021; 9/2022-5/2023) and collected IKT-related documents (see protocol: https://rdcu.be/dyfBP). Transcripts and documents were analysed using qualitative content analysis and surveys were analysed descriptively, with subsequent integration, cross-case analysis, and revision of the programme theory. Results We collected 92 IKT-related documents; and 55 researchers and decision-makers participated in surveys, focus groups or interviews. Proximal outcomes were collaborative research; capacity-building related to research skills and evidence use; and relationship-building, establishing trust and partnerships. Intermediate outcomes were changes in attitudes and knowledge; decision-makers became attuned to research and researchers increased their understanding of the decision-making context, and developed a vision for research impact. Distal outcomes were the production of applicable research and the consideration of evidence by decision-makers. High opportunity costs associated with IKT implementation constituted unintended effects. Conclusions Our study presents rich, theory-informed insights into IKT outcomes, based in particular on the experience of researchers implementing the CEBHA+ IKT approach. Key messages • A multi-country research collaboration offered an opportunity to examine IKT implementation and investigate proximal, intermediate, and distal IKT outcomes based on a pre-defined programme theory. • Beyond our initial conceptualisation, the IKT approach was linked to researchers developing a vision for ‘research impact’ and gaining a better understanding of the decision-making context.

The normalised mortality, a new measure to ensure valid conclusions from cardiovascular mortality

Abstract Background The quality and efficacy of health systems is assessed by comparing age-standardised mortality (ASM) from cardiovascular diseases (CVD), as they are regarded avoidable by health care. Differences in CVD mortality in West-Europe are surprising and hard to explain: in 2018, ASM for CVD in Germany was much higher (women: 102, men: 155/100,000) than in the Netherlands (65 and 97). Yet, these differences do not root in health system quality, but in different shares of CVD deaths among all deaths (DE: 39%, NL: 25%), which strongly effect CVD mortality. We developed a mortality measure less sensible to differences in shares of CVD deaths to increase plausibility of comparisons and validity of conclusions. Methods WHO data on sex-specific all and CVD deaths, ASM for CVD and all-causes for 2000-2018 for Western-European countries was used to calculate the share of CVD deaths among all deaths. ASM for CVD was divided by the share of CVD deaths resulting in a mortality measure normalized for 1% of CVD deaths. ASM and normalized mortality for CVD were used in comparative analyses. Strength of linear association between total and (normalized) ASM for CVD was estimated (adj. R²). Results Normalised CVD mortality ranked from 2.0 (CHE in women) to 4.9 (SVN in men) in 2018. While ASM for CVD was lowest in Danish women and Dutch men, Switzerland leads both rankings using the normalised CVD mortality. While ASM for CVD was much higher in Germany than in the Netherlands, the countries had the same normalized CVD mortality (2.6). ASM for CVD was moderately associated with all-cause mortality only in men (adj. R² 0.33; in women: -0.06). Using the normalized CVD mortality yielded an adj R² of 0.66 in women and 0.94 in men. The normalised CVD mortality ensures reliable comparisons of CVD mortality eliminating the effect of different shares of CVD deaths. It yields plausible results and enables valid conclusions about health system quality and better decisions for public health. Key messages • Age-standardised CVD mortality rates are effected by different shares of CVD deaths in countries and hinder valid conclusions on health systems efficacy and quality of health systems. • The normalised mortality rate eliminates the effect of different shares of CVD deaths in countries and allows to validly compare CVD mortality.