Public Health Policy Decisions Research Articles

The Urban Factor: Meta-analysis of Air Pollution and Cancer Incidence

Background: Urban air pollution is a significant environmental health concern associated with various adverse health outcomes, including cancer. Understanding the relationship between urban air pollution exposure and cancer incidence is crucial for informing public health interventions and policy decisions. Methods: A systematic review and meta-analysis will be conducted to investigate the association between urban air pollution exposure and cancer incidence. Eligible studies will be identified through comprehensive searches of electronic databases. Inclusion criteria encompassed studies reporting quantitative data on air pollution exposure and cancer outcomes. Data extraction and quality assessment will be performed according to predefined criteria. Results: The systematic search will be yielded X studies meeting the inclusion criteria for the meta-analysis. These studies, representing diverse geographic regions, employed various exposure assessment methods and included cohort, case-control, and cross-sectional designs. The meta-analysis will reveal a statistically significant association between urban air pollution exposure and overall cancer incidence (p < 0.05). The pooled effect will estimate indicated a X% increased risk of cancer per unit increase in air pollution exposure. Conclusion: Our meta-analysis provides robust evidence of a significant association between urban air pollution exposure and cancer incidence. These findings underscore the urgent need for targeted interventions to reduce air pollution levels in urban areas and mitigate cancer risks. Public health policies focusing on environmental quality improvements are crucial for promoting population health and well-being.

Perspectives and challenges in developing and implementing integrated dengue surveillance tools and technology in Thailand: a qualitative study.

Dengue remains a persistent public health concern, especially in tropical and sub-tropical countries like Thailand. The development and utilization of quantitative tools and information technology show significant promise for enhancing public health policy decisions in integrated dengue control. However, the effective implementation of these tools faces multifaceted challenges and barriers that are relatively underexplored. This qualitative study employed in-depth interviews to gain a better understanding of the experiences and challenges of quantitative tool development and implementation with key stakeholders involved in dengue control in Thailand, using a phenomenological framework. A diverse range of participants, including public health workers and dengue control experts, participated in these interviews. The collected interview data were systematically managed and investigated using thematic analysis to extract meaningful insights. The ability to collect dengue surveillance data and conduct ongoing analyses were contingent upon the availability of individuals possessing essential digital literacy and analytical skills, which were often in short supply. Furthermore, effective space-time early warning and precise data collection were hindered by the absence of user-friendly tools, efficient reporting systems, and complexities in data integration. Additionally, the study underscored the importance of the crucial role of community involvement and collaboration among organizations involved in integrated dengue surveillance, control and quantitative tool development. This study employed a qualitative approach to gain a deeper understanding of the contextual intricacies surrounding the development and implementation of quantitative tools, which, despite their potential for strengthening public health policy decisions in dengue control, remain relatively unexplored in the Thai context. The findings yield valuable insights and recommendations for the development and utilization of quantitative tools to support dengue control in Thailand. This information also has the potential to support use of such tools to exert impact beyond dengue to a broader spectrum of diseases.

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

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

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

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

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

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

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

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

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

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

Nutrients and environmental toxicants: effect on placental function and fetal growth

Understanding the mechanisms by which environmental factors impact reproductive health is crucial for informing public health interventions and policy decisions. By elucidating the pathways through which environmental stressors exert their effects, we can develop targeted strategies to mitigate risks and promote reproductive well-being. In this lecture, we will delve into the latest research findings and emerging trends in the field of environmental reproductive health. By exploring the intricate interplay between environmental exposures and reproductive outcomes, we aim to broaden our understanding of this complex relationship and its implications for human health. Through collaborative efforts across disciplines, we can work towards safeguarding reproductive health for current and future generations.

Differences in physical and mental health symptoms among residents living near municipal solid waste sites: a cross sectional study in the Ashanti Region, Ghana

ObjectivePhysical and mental health concerns and symptoms, including sleep problems, low mood, extreme tiredness, and appetite loss are prevalent among people living near waste sites. This research examines differences in health symptoms among residents living near municipal solid waste sites in the Ashanti Region, Ghana.MethodsThe study used cross-sectional data from 827 residents living near three municipal waste sites, including Besease, Asokore, and Dompoase sites in the Ashanti Region, Ghana. Descriptive statistics, Pearson’s chi-square, and binary logistic regressions were performed to examine the differences and associations between the variables.ResultsHealth symptoms, including sleep problems/insomnia, frequent extreme tiredness, low mood, loss of appetite, stress, anxiety, and depression, were reported by the majority of the participants. Residents near open dumpsites (Besease and Asokore) exhibit significantly higher likelihoods of experiencing various health symptoms such as extreme fatigue, depression, psychological disorders, thinking and concentration problems, low mood, loss of appetite, and anxiety compared to those near the engineered Oti landfill in Dompoase.ConclusionWhile emphasizing the importance of proper landfill design and management in Ghana, this study underscores the need for further longitudinal and clinical investigations. Clinically establishing the link between dumpsites and health symptoms is imperative for informed public health interventions and policy decisions aimed at mitigating the potential adverse health effects of landfills on residents' well-being.

Systems Are Overstretched from the COVID-19 Pandemic: An Interpretive Description of Disabled People’s Access to Healthcare and Disability Support in New Zealand

The COVID-19 pandemic disrupted healthcare and support services, creating challenges for disabled people. New Zealand implemented a range of policies to prevent and limit viral transmission of COVID-19. This study investigates disabled people’s experiences accessing healthcare and disability support services during the COVID-19 pandemic, and based on this analysis, the implications of public health policy decisions on disabled people’s experiences during the pandemic in New Zealand are explicated. A qualitative design underpinned by interpretive description methodology guided this study. A total of 64 disabled people or parents of disabled children participated in semi-structured interviews. The team of health services and disability researchers then engaged in an iterative thematic approach to analysis, which led to three key themes: (1) protective personal factors, which assisted disabled people to access healthcare and support services, (2) immediate pandemic policy impacts, including policy and legislative changes, which created additional access barriers for disabled people, and (3) exacerbating factors, including compounding vulnerabilities, overstretched systems, and the impact of the vaccine mandate, which worsened the already limited access to healthcare and disability services for disabled people. The pandemic overwhelmed an already stretched healthcare and disability support system, resulting in service disruptions with negative consequences for disabled people’s health and wellbeing. Future policy development needs to be disability-centred in its inclusion of people with lived experience and consideration of the support needs of disabled populations. A first step in this process could include pandemic planning and policy co-design to ensure a continuum of healthcare services and support availability for individuals when services are disrupted. In addition, access to formal and informal support for disabled people should be recognised as a fundamental human right when accessing healthcare and disability support services.

Biomathematics and its Applications in Modeling COVID-19 Infections

The emerging field of biomathematics utilizes mathematical techniques to gain quantitative insights into biological systems. Mathematical modeling and computational simulations are becoming increasingly vital tools for elucidating complex infectious disease dynamics. The COVID-19 pandemic has highlighted the applicability of biomathematical methods for informing public health policy decisions. Here we provide an overview of major modeling approaches used to analyze SARS-CoV-2 transmission, forecast COVID-19 trajectories, evaluate intervention strategies, and understand viral evolution. Key model types discussed include compartmental models, agent-based models, and immunological models. We highlight critical model parameters and how their estimation from noisy data presents core challenges. The continued integration of biological mechanisms and epidemiological evidence through iteratively refined models promises more realistic representations of the intricate system interactions underlying the COVID-19 pandemic. While assumptions and uncertainties pervade, mathematical abstractions have already provided invaluable guidance for real-time decision-making in this global health emergency. Collaborative development of versatile biomathematical frameworks for emerging infectious diseases can equip policymakers with prescient, science-based insights for navigating future outbreaks.

Preferences of pregnant women toward a future maternal Group B Streptococcus vaccine in China: A cross-sectional survey with a discrete choice experiment

ABSTRACT Group B streptococcus (GBS) is a major cause of neonatal death worldwide. A GBS vaccine for pregnant women is under development and is expected to be available in the near future. The perceptions and preferences of pregnant women in China of GBS vaccines has not been investigated, and this study aimed to investigate pregnant women‘s awareness of GBS and their potential preferences for the GBS vaccine. A discrete choice experiment was conducted among pregnant women in hospitals from Shaanxi, Hunan, and Zhejiang provinces located in Western, Central, and Eastern China, respectively. A conditional logit model was used to analyze the data and calculate willingness to pay values and choice probabilities of different GBS vaccine programs. A total of 354 pregnant women were included in the final analysis, 45.8% of whom were willing to receive a GBS vaccine if it were licensed. Vaccine safety was the most important attribute of a future vaccine, while cost was the least important attribute. Compared with no vaccination, pregnant women had a strong preference for future GBS vaccination (ASC = 1.267, p < .001). Pregnant women’s decisions were highly influenced by those of other pregnant women. Improving the safety, efficacy, and vaccination rate of the GBS vaccine in China is of great significance for future GBS vaccine development and vaccination. Compared to other variable options, the cost of a GBS vaccine was of the least importance among pregnant women in mainland China. These findings can inform public health policy decisions related to GBS vaccination in China.

From respiratory diseases to nervous system disorders: Unraveling the certified causes of influenza-associated deaths in Poland from 2000 to 2019.

This study aims to accurately estimate influenza-associated deaths in Poland and their certified cause of death, due to significant discrepancies between official numbers and expected impact. Excess influenza-associated mortality in Poland from 2000 to 2019 was calculated using Seasonal-Trend Decomposition Procedure based on LOESS (STL), which can detect non-linear trends and non-sinusoidal cycles. Excess mortality was then used as an explanatory variable in a model predicting monthly fluctuations of officially recorded causes of death from 2010 to 2019. A total of 142,000 conservative estimates of influenza-associated deaths were identified, representing 1.86% of overall mortality. Only 0.61% of influenza-associated deaths were officially recorded as influenza. Nearly half of certified influenza deaths were attributed to the seasonal baseline mortality, potentially doubling estimated impact based solely on influenza peaks. Influenza-associated deaths were frequently recorded as respiratory diseases (24.36%), with majority attributed to underlying conditions such as cardiovascular diseases (45.31%), cancer (9.06%), or diabetes (2.66%). Influenza-associated deaths were more commonly certified as nervous system diseases (1.84%) or mental disorders (1.04%), rather than influenza itself. There was a noticeable impact of influenza on secondary infections, such as meningococcal and gastrointestinal infections. These findings highlight the importance of improved estimation for informing public health policy decisions.

Big data-driven public health policy making: Potential for the healthcare industry

The use of healthcare data analytics is anticipated to play a significant role in future public health policy formulation. Therefore, this study examines how big data analytics (BDA) may be methodically incorporated into various phases of the health policy cycle for fact-based and precise health policy decision-making. So, this study explores the potential of BDA for accurate and rapid policy-making processes in the healthcare industry. A systematic review of literature spanning 22 years (from January 2001 to January 2023) has been conducted using the PRISMA approach to develop a conceptual framework. The study introduces the emerging topic of BDA in healthcare policy, goes over the advantages, presents a framework, advances instances from the literature, reveals difficulties and provides recommendations. This study argues that BDA has the ability to transform the conventional policy-making process into data-driven process, which helps to make accurate health policy decision. In addition, this study contends that BDA is applicable to the different stages of health policy cycle, namely policy identification, agenda setting as well as policy formulation, implementation and evaluation. Currently, descriptive, predictive and prescriptive analytics are used for public health policy decisions on data obtained from several common health-related big data sources like electronic health reports, public health records, patient and clinical data, and government and social networking sites. To effectively utilize all of the data, it is necessary to overcome the computational, algorithmic and technological obstacles that define today's extremely heterogeneous data landscape, as well as a variety of legal, normative, governance and policy limitations. Big data can only fulfill its full potential if data are made available and shared. This enables public health institutions and policymakers to evaluate the impact and risk of policy changes at the population level.

The importance of utilizing travel history metadata for informative phylogeographical inferences: a case study of early SARS-CoV-2 introductions into Australia.

Inferring the spatiotemporal spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) via Bayesian phylogeography has been complicated by the overwhelming sampling bias present in the global genomic dataset. Previous work has demonstrated the utility of metadata in addressing this bias. Specifically, the inclusion of recent travel history of SARS-CoV-2-positive individuals into extended phylogeographical models has demonstrated increased accuracy of estimates, along with proposing alternative hypotheses that were not apparent using only genomic and geographical data. However, as the availability of comprehensive epidemiological metadata is limited, many of the current estimates rely on sequence data and basic metadata (i.e. sample date and location). As the bias within the SARS-CoV-2 sequence dataset is extensive, the degree to which we can rely on results drawn from standard phylogeographical models (i.e. discrete trait analysis) that lack integrated metadata is of great concern. This is particularly important when estimates influence and inform public health policy. We compared results generated from the same dataset, using two discrete phylogeographical models: one including travel history metadata and one without. We utilized sequences from Victoria, Australia, in this case study for two unique properties. Firstly, the high proportion of cases sequenced throughout 2020 within Victoria and the rest of Australia. Secondly, individual travel history was collected from returning travellers in Victoria during the first wave (January to May) of the coronavirus disease 2019 (COVID-19) pandemic. We found that the implementation of individual travel history was essential for the estimation of SARS-CoV-2 movement via discrete phylogeography models. Without the additional information provided by the travel history metadata, the discrete trait analysis could not be fit to the data due to numerical instability. We also suggest that during the first wave of the COVID-19 pandemic in Australia, the primary driving force behind the spread of SARS-CoV-2 was viral importation from international locations. This case study demonstrates the necessity of robust genomic datasets supplemented with epidemiological metadata for generating accurate estimates from phylogeographical models in datasets that have significant sampling bias. For future work, we recommend the collection of metadata in conjunction with genomic data. Furthermore, we highlight the risk of applying phylogeographical models to biased datasets without incorporating appropriate metadata, especially when estimates influence public health policy decision making.

A Comparative Study on Deep Learning Models for COVID-19 Forecast.

The COVID-19 pandemic has led to a global health crisis with significant morbidity, mortality, and socioeconomic disruptions. Understanding and predicting the dynamics of COVID-19 are crucial for public health interventions, resource allocation, and policy decisions. By developing accurate models, informed public health strategies can be devised, resource allocation can be optimized, and virus transmission can be reduced. Various mathematical and computational models have been developed to estimate transmission dynamics and forecast the pandemic's trajectories. However, the evolving nature of COVID-19 demands innovative approaches to enhance prediction accuracy. The machine learning technique, particularly the deep neural networks (DNNs), offers promising solutions by leveraging diverse data sources to improve prevalence predictions. In this study, three typical DNNs, including the Long Short-Term Memory (LSTM) network, Physics-informed Neural Network (PINN), and Deep Operator Network (DeepONet), are employed to model and forecast COVID-19 spread. The training and testing data used in this work are the global COVID-19 cases in the year of 2021 from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. A seven-day moving average as well as the normalization techniques are employed to stabilize the training of deep learning models. We systematically investigate the effect of the number of training data on the predicted accuracy as well as the capability of long-term forecast in each model. Based on the relative L2 errors between the predictions from deep learning models and the reference solutions, the DeepONet, which is capable of learning hidden physics given the training data, outperforms the other two approaches in all test cases, making it a reliable tool for accurate forecasting the dynamics of COVID-19.

Arboviral Risk Associated with Solid Organ and Hematopoietic Stem Cell Grafts: The Prophylactic Answers Proposed by the French High Council of Public Health in a National Context.

Diseases caused by arboviruses are on the increase worldwide. In addition to arthropod bites, most arboviruses can be transmitted via accessory routes. Products of human origin (labile blood products, solid organs, hematopoietic stem cells, tissues) present a risk of contamination for the recipient if the donation is made when the donor is viremic. Mainland France and its overseas territories are exposed to a complex array of imported and endemic arboviruses, which differ according to their respective location. This narrative review describes the risks of acquiring certain arboviral diseases from human products, mainly solid organs and hematopoietic stem cells, in the French context. The main risks considered in this study are infections by West Nile virus, dengue virus, and tick-borne encephalitis virus. The ancillary risks represented by Usutu virus infection, chikungunya, and Zika are also addressed more briefly. For each disease, the guidelines issued by the French High Council of Public Health, which is responsible for mitigating the risks associated with products of human origin and for supporting public health policy decisions, are briefly outlined. This review highlights the need for a "One Health" approach and to standardize recommendations at the international level in areas with the same viral epidemiology.

The evolution of SARS-CoV-2 seroprevalence in Canada: a time-series study, 2020-2023.

During the first year of the COVID-19 pandemic, the proportion of reported cases of COVID-19 among Canadians was under 6%. Although high vaccine coverage was achieved in Canada by fall 2021, the Omicron variant caused unprecedented numbers of infections, overwhelming testing capacity and making it difficult to quantify the trajectory of population immunity. Using a time-series approach and data from more than 900 000 samples collected by 7 research studies collaborating with the COVID-19 Immunity Task Force (CITF), we estimated trends in SARS-CoV-2 seroprevalence owing to infection and vaccination for the Canadian population over 3 intervals: prevaccination (March to November 2020), vaccine roll-out (December 2020 to November 2021), and the arrival of the Omicron variant (December 2021 to March 2023). We also estimated seroprevalence by geographical region and age. By November 2021, 9.0% (95% credible interval [CrI] 7.3%-11%) of people in Canada had humoral immunity to SARS-CoV-2 from an infection. Seroprevalence increased rapidly after the arrival of the Omicron variant - by Mar. 15, 2023, 76% (95% CrI 74%-79%) of the population had detectable antibodies from infections. The rapid rise in infection-induced antibodies occurred across Canada and was most pronounced in younger age groups and in the Western provinces: Manitoba, Saskatchewan, Alberta and British Columbia. Data up to March 2023 indicate that most people in Canada had acquired antibodies against SARS-CoV-2 through natural infection and vaccination. However, given variations in population seropositivity by age and geography, the potential for waning antibody levels, and new variants that may escape immunity, public health policy and clinical decisions should be tailored to local patterns of population immunity.

Promoting health in the digital environment: health policy experts' responses to on-demand delivery in Aotearoa New Zealand.

Services offering on-demand delivery of unhealthy commodities, such as fast food, alcohol and smoking/vaping products have proliferated in recent years. It is well known that the built environment can be health promoting or harmful to health, but there has been less consideration of the digital environment. Increased availability and accessibility of these commodities may be associated with increased consumption, with harmful public health implications. Policy regulating the supply of these commodities was developed before the introduction of on-demand services and has not kept pace with the digital environment. This paper reports on semi-structured interviews with health policy experts on the health harms of the uptake in on-demand delivery of food, alcohol and smoking/vaping products, along with their views on policies that might mitigate these harms. We interviewed 14 policy experts from central and local government agencies and ministries, health authorities, non-Government Organisations (NGOs) and university research positions in Aotearoa New Zealand using a purposive sampling strategy. Participants concerns over the health harms from on-demand services encompassed three broad themes-the expansion of access to and availability of unhealthy commodities, the inadequacy of existing restrictions and regulations in the digital environment and the expansion of personalized marketing and promotional platforms for unhealthy commodities. Health policy experts' proposals to mitigate harms included: limiting access and availability, updating regulations and boosting enforcement and limiting promotion and marketing. Collectively, these findings and proposals can inform future research and public health policy decisions to address harms posed by on-demand delivery of unhealthy commodities.

Excess mortality during the COVID-19 pandemic (2020-2021) in an urban community of Bangladesh.

Measuring COVID-19-related mortality is vital for making public health policy decisions. The magnitude of COVID-19-related mortality is largely unknown in low- and middle-income countries (LMICs), including Bangladesh, due to inadequate COVID-19 testing capacity and a lack of robust civil registration and vital statistics systems. Even with the lack of data, cemetery-based death records in LMICs may provide insightful information on potential COVID-19-related mortality rates; nevertheless, there is a dearth of research employing cemetery-based death records. This study aimed to assess the excess mortality during the COVID-19 pandemic in an urban setting in Bangladesh using a cemetery-based death registration dataset. A total of 6,271 deaths recorded between January 2015 and December 2021 were analysed using a Bayesian structural time series model. Exploratory analysis found that the average monthly number of deaths was 69 during the pre-COVID-19 period (January 2015-February 2020), but significantly increased to 92 during the COVID-19 period (March 2020-December 2021). The increase in male deaths was twice as large as the increase in female deaths. Model-based results were not statistically significant (relative effect 17%, 95% credible interval: -18%, 57%), but there was an overall increasing trend during the COVID-19 period, and specific months or shorter periods had a substantial increase. This first-of-its-kind study in Bangladesh has assessed the excess mortality in an urban community during the COVID-19 pandemic. Cemetery-based death registration appears to aid in tracking population mortality, especially in resource-limited countries where collecting data on the ground is challenging during crisis periods; however, additional large-scale research is required.