Impact Of Public Health Interventions Research Articles

From agent-based simulations of disease dynamics to expert support on public health interventions

Abstract Agent-based epidemiological simulators have long been used to predict the dynamics of infectious diseases. They allow to combine compartment approaches with representing contact networks derived from real or synthetic populations. Often, scenario techniques are applied to model the impact of public health interventions. During the COVID-19 pandemics the agent-based simulator COVASIM has been established for modelling the spreading of SARS-CoV-2. It includes four different contact networks related to homes, schools, workplaces and environment. During the pandemic it became obvious that the current simulation techniques were not sufficient to cover important needs in public health administrations and society. While most simulations were based on scenarios predicting expected numbers for the infected, hospitalized or critically ill, these studies could not provide easily strategic options on a variety of equivalent public health interventions, which are all suitable to keep disease dynamics within acceptable bounds, set e.g. by hospital capacity. Knowing about a variety of equivalent options, however, is of crucial importance to enable a fair burden sharing throughout all groups of society. Therefore, we applied machine learning techniques to create an inverse model for epidemiological simulations based on COVASIM. For given transmission rates it outputs a range of quantitative interventions which are equivalently suitable to keep epidemiological dynamics within predefined bounds. Currently, it covers interventions like school and workplace closings and other mobility restrictions. Future work will be directed to include other interventions, like vaccination campaigns, and we will work to make the approach more generalizable for other infectious diseases. The tool may turn out helpful fur public health administrations and stimulate the public debate on the best way a society may take in a pandemic event or other situations where public health action has to be imposed. Key messages • Machine Learning allows to invert agent based simulators. • Instead of disease dynamics simulations a set of equivalent public health interventions consistent with pre-defined constraints can be calculated.

An interdisciplinary approach: Exploring the five dimensions of the tools deployed by EUVABECO

Abstract To effectively address the complexities of vaccination across the EU, EUVABECO’s 14 partners are implementing a comprehensive, interdisciplinary approach. Twelve pilot projects will be implemented from September 2024 to August 2025 in six MS (BE, DE, EL, LV, LU, PL, PT). These pilots will test innovative tools across five domains-medical, social, industrial, modelling and forecasting, and digital-to optimize vaccination outcomes: Medical/Clinical Tool: A decision support system that aids patients and healthcare professionals by providing current, evidence-based information tailored to individuals, thereby facilitating informed vaccination decisions. Social Tool: Data linkage on an individual level, to enhance the targeting and monitoring of vaccine campaigns, and ensuring equitable access to vaccines, especially for vulnerable populations. Industrial Tool: An electronic vaccine leaflet that boosts the flexibility and responsiveness of vaccine distribution by offering dynamic, real-time information in local languages to both healthcare providers and patients - regardless of the source and point of use of the vaccine. Modelling and Forecasting Tool: A disease simulator that models infection dynamics and evaluates the impact of public health interventions, crucial for strategic planning and resource allocation. Digital Tool: A citizen-held digital vaccination card that enables individuals to maintain a comprehensive, accessible record of their own vaccination histories, enhancing continuity of care across borders.

The spatial-temporal variations and influencing factors of COVID-19 case fatality rate: a worldwide study in 30 countries from February 2021 to May 2022.

To evaluate the variations in COVID-19 case fatality rates (CFRs) across different regions and waves, and the impact of public health interventions, social and economic characteristics, and demographic factors on COVID-19 CFRs, we collected data from 30 countries with the highest incidence rate in three waves. We summarized the CFRs of different countries and continents in each wave through meta-analysis. Spearman's correlation and multiple linear regression were employed to estimate the correlation between influencing factors and reduction rates of CFRs. Significant differences in CFRs were observed among different regions during the three waves (P<0.001). An association was found between the changes in fully vaccinated rates (r s =0.41), population density (r s =0.43), the proportion of individuals over 65years old (r s =0.43), and the reduction rates of case fatality rate. Compared to Wave 1, the reduction rates in Wave 2 were associated with population density (β=0.19, 95%CI: 0.05-0.33) and smoking rates (β=-4.66, 95%CI: -8.98 - -0.33), while in Wave 3 it was associated with booster vaccine rates (β=0.60, 95%CI: 0.11-1.09) and hospital beds per thousand people (β=4.15, 95%CI: 1.41-6.89). These findings suggest that the COVID-19 CFRs varied across different countries and waves, and promoting booster vaccinations, increasing hospital bed capacity, and implementing tobacco control measures can help reduce CFRs.

Utilizing the CFIR framework for mapping the facilitators and barriers of implementing teachers led school mental health programs - a scoping review.

Ample evidence has been generated regarding the effectiveness of school-based mental health interventions as part of the continuum of care for children and capacity building of teachers to deal with the emotional and behavioral challenges of students. The increasing trend of utilization of teachers as the natural support system of children and the huge financial impact of public health interventions highlights the need to review all available evidence regarding multilevel factors that facilitate or pose a challenge to the provision of School Mental Health Programs (SMHP) using teachers as providers. The current review aims to map extracted evidence under the Consolidated Framework for Implementation Research (CFIR) domains to support future implementation research on school-based mental health services. The scoping review included experimental, qualitative studies, and systematic reviews involving teacher-led mental health programs conducted in the school setting to improve the socio-emotional well-being of children and adolescents irrespective of time and geographical limitations. All (published and unpublished) evidence in English from Pubmed, Cochrane database of systematic reviews and clinical trials, Scopus, and Science Direct was searched using keywords and Boolean combinations and extracted using study designs, place of study, year of publication, sample size, and target population. A qualitative analysis of implementation facilitators and barriers cited by the studies was carried out and mapped on CFIR. This review identified barriers and facilitators of implementation across school-based mental health Programs in 29 studies. The major emerging themes were those related to the inner setting of the organization, the process of implementation, and the characteristics of individuals involved in implementation. These included the availability of structural characteristics, positive school culture, organizational readiness, committed leadership, and beliefs of the providers. Findings highlight the need for early assessment of contextual factors acting as barriers and facilitators and careful execution following realistic planning and stakeholders' engagement to ensure the success of SMHP.

Estimating Time-Varying Exposure Effects Through Continuous-Time Modelling in Mendelian Randomization.

Mendelian randomization is an instrumental variable method that utilizes genetic information to investigate the causal effect of a modifiable exposure on an outcome. In most cases, the exposure changes over time. Understanding the time-varying causal effect of the exposure can yield detailed insights into mechanistic effects and the potential impact of public health interventions. Recently, a growing number of Mendelian randomization studies have attempted to explore time-varying causal effects. However, the proposed approaches oversimplify temporal information and rely on overly restrictive structural assumptions, limiting their reliability in addressing time-varying causal problems. This article considers a novel approach to estimate time-varying effects through continuous-time modelling by combining functional principal component analysis and weak-instrument-robust techniques. Our method effectively utilizes available data without making strong structural assumptions and can be applied in general settings where the exposure measurements occur at different timepoints for different individuals. We demonstrate through simulations that our proposed method performs well in estimating time-varying effects and provides reliable inference when the time-varying effect form is correctly specified. The method could theoretically be used to estimate arbitrarily complex time-varying effects. However, there is a trade-off between model complexity and instrument strength. Estimating complex time-varying effects requires instruments that are unrealistically strong. We illustrate the application of this method in a case study examining the time-varying effects of systolic blood pressure on urea levels.

Prevalence and Control of Malaria in Uganda: Epidemiological Trends and Public Health Interventions

Malaria is a critical public health issue in Uganda, a country that bears one of the highest burdens of the disease globally. Despite significant advancements in medical treatments and preventive measures, malaria continues to pose a substantial threat to the health and economic well-being of Ugandans. This review provides a comprehensive analysis of the prevalence and control of malaria in Uganda, examining epidemiological trends and assessing the impact of public health interventions. Recent data indicate that while the overall incidence of malaria has declined due to intensified control efforts, pockets of high transmission persist, particularly in rural and hard-to-reach areas. Seasonal variations and demographic factors further influence malaria transmission, with children under five and pregnant women being particularly vulnerable. Public health interventions such as insecticide-treated nets (ITNs), indoor residual spraying (IRS), rapid diagnostic testing (RDT), and artemisinin-based combination therapies (ACTs) have been instrumental in reducing malaria prevalence, though challenges such as insecticide resistance and health system constraints remain. The methodology used in writing this paper involves a comprehensive review of existing literature on malaria prevalence and control strategies in Uganda. Future directions include integrated vector management, strengthening surveillance systems, and investing in research and innovation to develop new tools and strategies. Addressing these challenges through a multifaceted approach is essential for sustainable malaria control and ultimately eliminating the disease in Uganda. Keywords: Malaria Control, Epidemiological Trends, Public Health Interventions, Insecticide Resistance, Integrated Vector Management (IVM).

Addressing the Gap: Racial Disparities and Public Health Strategies in the Epidemiology of Gastrointestinal Stromal Tumors.

Background Gastrointestinal stromal tumors (GISTs) represent the most common mesenchymal neoplasms of the gastrointestinal tract, arising from the interstitial cells of Cajal. These tumors bridge the nervous system and muscular layers of the gastrointestinal tract, playing a crucial role in the digestive process. The incidence of GISTs demonstrates notable variations across different racial and ethnic groups, underscoring the need for in-depth analysis to understand the interplay of genetic, environmental, and socioeconomic factors behind these disparities. Linear regression analysis is a pivotal statistical tool in such epidemiological studies, offering insights into the temporal dynamics of disease incidence and the impact of public health interventions. Methodology This investigation employed a detailed dataset from 2009 to 2020, documenting GIST incidences across Asian, African American, Hispanic, and White populations. A meticulous preprocessing routine prepared the dataset for analysis, which involved data cleaning, normalization of racial terminologies, and aggregation by year and race. Linear regression models and Pearson correlation coefficients were applied to analyze trends and correlations in GIST incidences across the different racial groups, emphasizing an understanding of temporal patterns and racial disparities in disease incidence. Results The study analyzed GIST cases among four racial groups, revealing a male predominance (53.19%) and an even distribution of cases across racial categories: Whites (27.66%), Hispanics (25.53%), African Americans (24.47%), and Asians (22.34%). Hypertension was the most common comorbidity (32.98%), followed by heart failure (28.72%). The linear regression analysis for Asians showed a decreasing trend in GIST incidences with a slope of -0.576, an R-squared value of 0.717, and a non-significant p-value of 0.153. A significant increasing trend was observed for Whites, with a slope of 0.581, an R-squared value of 0.971, and a p-value of 0.002. African Americans exhibited a moderate positive slope of 0.277 with an R-squared value of 0.470 and a p-value of 0.201, indicating a non-significant increase. Hispanics showed negligible change over time with a slope of -0.095, an R-squared value of 0.009, and a p-value of 0.879, suggesting no significant trend. Conclusions This study examines GIST incidences across racial groups, revealing significant disparities. Whites show an increasing trend (p = 0.002), while Asians display a decreasing trend (p = 0.153), with stable rates in African Americans and Hispanics. Such disparities suggest a complex interplay of genetics, environment, and socioeconomic factors, highlighting the need for targeted research and interventions that address these differences and the systemic inequalities influencing GIST outcomes.

Real-Time Use of a Dynamic Model To Measure the Impact of Public Health Interventions on Measles Outbreak Size and Duration - Chicago, Illinois, 2024.

Measles is a highly infectious, vaccine-preventable disease that can cause severe illness, hospitalization, and death. A measles outbreak associated with a migrant shelter in Chicago occurred during February-April 2024, in which a total of 57 confirmed cases were identified, including 52 among shelter residents, three among staff members, and two among community members with a known link to the shelter. CDC simulated a measles outbreak among shelter residents using a dynamic disease model, updated in real time as additional cases were identified, to produce outbreak forecasts and assess the impact of public health interventions. As of April 8, the model forecasted a median final outbreak size of 58 cases (IQR=56-60 cases); model fit and prediction range improved as more case data became available. Counterfactual analysis of different intervention scenarios demonstrated the importance of early deployment of public health interventions in Chicago, with a 69% chance of an outbreak of 100 or more cases had there been no mass vaccination or active case-finding compared with only a 1% chance when those interventions were deployed. This analysis highlights the value of using real-time, dynamic models to aid public health response, set expectations about outbreak size and duration, and quantify the impact of interventions. The model shows that prompt mass vaccination and active case-finding likely substantially reduced the chance of a large (100 or more cases) outbreak in Chicago.

Modeling the Impact of Misinformation on the Transmission Dynamics of COVID-19

The threat posed by the COVID-19 pandemic has been accompanied by an epidemic of misinformation, causing confusion and mistrust among the public. Misinformation about COVID-19 whether intentional or unintentional takes many forms, including conspiracy theories, false treatments, and inaccurate information about the origins and spread of the virus. Though the pandemic has brought to light the significant impact of misinformation on public health, mathematical modeling emerged as a valuable tool for understanding the spread of COVID-19 and the impact of public health interventions. However, there has been limited research on the mathematical modeling of the spread of misinformation related to COVID-19. In this paper, we present a mathematical model of the spread of misinformation related to COVID-19. The model highlights the challenges posed by misinformation, in that rather than focusing only on the reproduction number that drives new infections, there is an additional threshold parameter that drives the spread of misinformation. The equilibria of the model are analyzed for both local and global stability, and numerical simulations are presented. We also discuss the model’s potential to develop effective strategies for combating misinformation related to COVID-19.

Machine learning forecasts for seasonal epidemic peaks: Lessons learnt from an atypical respiratory syncytial virus season.

Seasonal peaks in infectious disease incidence put pressures on health services. Therefore, early warning of the timing and magnitude of peak activity during seasonal epidemics can provide information for public health practitioners to take appropriate action. Whilst many infectious diseases have predictable seasonality, newly emerging diseases and the impact of public health interventions can result in unprecedented seasonal activity. We propose a Machine Learning process for generating short-term forecasts, where models are selected based on their ability to correctly forecast peaks in activity, and can be useful during atypical seasons. We have validated our forecasts using typical and atypical seasonal activity, using respiratory syncytial virus (RSV) activity during 2019-2021 as an example. During the winter of 2020/21 the usual winter peak in RSV activity in England did not occur but was 'deferred' until the Spring of 2021. We compare a range of Machine Learning regression models, with alternate models including different independent variables, e.g. with or without seasonality or trend variables. We show that the best-fitting model which minimises daily forecast errors is not the best model for forecasting peaks when the selection criterion is based on peak timing and magnitude. Furthermore, we show that best-fitting models for typical seasons contain different variables to those for atypical seasons. Specifically, including seasonality in models improves performance during typical seasons but worsens it for the atypical seasons.

Impact of hypertension prevalence trend on mortality and burdens of dementia and disability in England and Wales to 2060: a simulation modelling study

Previous estimates of the impact of public health interventions targeting hypertension usually focus on one health outcome. This study aims to consider the effects of change in future hypertension prevalence on mortality, dementia, and disability simultaneously. We modelled three plausible scenarios based on observed trends of hypertension prevalence from 2003 to 2017 in England: observed trends continue (baseline scenario); 2017 prevalence remains unchanged; and 2017 prevalence decreases by 50% by 2060. We used a probabilistic Markov model to integrate calendar trends in incidence of cardiovascular disease, dementia, disability, and mortality to forecast their future occurrence in the population of England and Wales. Assuming the hypertension prevalence trend modifies health transition probabilities, we compared mortality outcomes and the burden of dementia and disability to 2060 for the scenarios. If the decline in hypertension prevalence stops, there would be a slight increase in the number of additional deaths to 2060 (22·9 [95% uncertainty interval 19·0-26·6] more deaths per 100 000 population), although the burdens of disability and dementia in absolute terms would change little. Alternatively, if the downward hypertension prevalence trend accelerates (with prevalence falling by 50% between 2017 and 2060), there would be a modest additional reduction in deaths (57·0 [50·4-63·5] fewer deaths per 100 000 population), a small increase in dementia burden (9·0 [5·1-13·2] more cases per 100 000 population), no significant effect on disability burden, and an 8% gain in healthy life expectancy at age 65 years from 2020 to 2060 (5·3 years vs 4·9 years) compared with the baseline scenario. The major future impact of alternative hypertension prevention strategies appears to be on future life expectancy. The salutary effect of lower population blood pressure distribution on incidence of dementia and disability might not offset expansion of the susceptible population due to reduced mortality. British Heart Foundation and UK Economic and Social Research Council.

Using Relative and Absolute Measures for Socioeconomic Inequalities in Health: Experiences from a Retrospective Cohort Study on COVID-19.

One approach to reducing the burden of diseases can be to identify socioeconomically vulnerable groups. We aimed to estimate the socioeconomic inequality of in-hospital deaths using relative and absolute indices of socioeconomic inequality. In this retrospective cohort study on Covid-19 patients; age, gender, marital status, education level, date of admission, diagnostic method, and final condition were measured. Socioeconomic inequality in inhospital death was assessed using three approaches. We used the relative index of inequality (RII) to measure relative inequality. We used two approaches to evaluate absolute inequality: the slope index of inequality (SII) and the concentration index (ci). Overall, 587 patients' data were collected and 42 (7.2%) of these patients died in the hospital. There were statistically significant differences between the case-fatality rates of different levels of education (P<0.001). In addition, all the inequality indices showed that the distribution of COVID-19-related deaths was higher among the lower education levels. Accordingly, after controlling the effect of age, gender, and comorbidities the RII indicated that the case fatality rate in the lowest education level was 9.42 (95% CI: 2.23 to 39.01, P<0.001) times compared to the case fatality rate in the highest level of education. The results of all three approaches indicate considerable education inequality in CFR in favor of groups of high education levels. These results can improve the prioritization and impact of public health interventions, including prevention and diagnosis of Covid-19 in favor of vulnerable groups.

Routine Pediatric Surgical Emergencies: Incidence, Morbidity, and Mortality During the 1st 8000 Days of Life—A Narrative Review

BackgroundMany potentially treatable non-congenital and non-traumatic surgical conditions can occur during the first 8000 days of life and an estimated 85% of children in low- and middle-income countries (LMICs) will develop one before 15 years old. This review summarizes the common routine surgical emergencies in children from LMICs and their effects on morbidity and mortality.MethodsA narrative review was undertaken to assess the epidemiology, treatment, and outcomes of common surgical emergencies that present within the first 8000 days (or 21.9 years) of life in LMICs. Available data on pediatric surgical emergency care in LMICs were aggregated.ResultsOutside of trauma, acute appendicitis, ileal perforation secondary to typhoid fever, and intestinal obstruction from intussusception and hernias continue to be the most common abdominal emergencies among children in LMICs. Musculoskeletal infections also contribute significantly to the surgical burden in children. These “neglected” conditions disproportionally affect children in LMICs and are due to delays in seeking care leading to late presentation and preventable complications. Pediatric surgical emergencies also necessitate heavy resource utilization in LMICs, where healthcare systems are already under strain.ConclusionsDelays in care and resource limitations in LMIC healthcare systems are key contributors to the complicated and emergent presentation of pediatric surgical disease. Timely access to surgery can not only prevent long-term impairments but also preserve the impact of public health interventions and decrease costs in the overall healthcare system.

Opioid-related harms and care impacts of conventional and AI-based prescription management strategies: insights from leveraging agent-based modeling and machine learning.

Like its counterpart to the south, Canada ranks among the top five countries with the highest rates of opioid prescriptions. With many suffering from opioid use disorder first having encountered opioids via prescription routes, practitioners and health systems have an enduring need to identify and effectively respond to the problematic use of opioid prescription. There are strong challenges to successfully addressing this need: importantly, the patterns of prescription fulfillment that signal opioid abuse can be subtle and difficult to recognize, and overzealous enforcement can deprive those with legitimate pain management needs the appropriate care. Moreover, injudicious responses risk shifting those suffering from early-stage abuse of prescribed opioids to illicitly sourced street alternatives, whose varying dosage, availability, and the risk of adulteration can pose grave health risks. This study employs a dynamic modeling and simulation to evaluate the effectiveness of prescription regimes employing machine learning monitoring programs to identify the patients who are at risk of opioid abuse while being treated with prescribed opioids. To this end, an agent-based model was developed and implemented to examine the effect of reduced prescribing and prescription drug monitoring programs on overdose and escalation to street opioids among patients, and on the legitimacy of fulfillments of opioid prescriptions over a 5-year time horizon. A study released by the Canadian Institute for Health Information was used to estimate the parameter values and assist in the validation of the existing agent-based model. The model estimates that lowering the prescription doses exerted the most favorable impact on the outcomes of interest over 5 years with a minimum burden on patients with a legitimate need for pharmaceutical opioids. The accurate conclusion about the impact of public health interventions requires a comprehensive set of outcomes to test their multi-dimensional effects, as utilized in this research. Finally, combining machine learning and agent-based modeling can provide significant advantages, particularly when using the latter to gain insights into the long-term effects and dynamic circumstances of the former.

Epidemiological impact of public health interventions against diabetes in Qatar: mathematical modeling analyses.

To predict the epidemiological impact of specific, and primarily structural public health interventions that address lifestyle, dietary, and commuting behaviors of Qataris as well as subsidies and legislation to reduce type 2 diabetes mellitus (T2DM) burden among Qataris. A deterministic population-based mathematical model was used to investigate the impact of public health interventions on the epidemiology of T2DM among Qataris aged 20-79 years, which is the age range typically used by the International Diabetes Federation for adults. The study evaluated the impact of interventions up to 2050, a three-decade time horizon, to allow for the long-term effects of different types of interventions to materialize. The impact of each intervention was evaluated by comparing the predicted T2DM incidence and prevalence with the intervention to a counterfactual scenario without intervention. The model was parameterized using representative data and stratified by sex, age, T2DM risk factors, T2DM status, and intervention status. All intervention scenarios had an appreciable impact on reducing T2DM incidence and prevalence. A lifestyle management intervention approach, specifically applied to those who are categorized as obese and ≥35 years old, averted 9.5% of new T2DM cases by 2050. An active commuting intervention approach, specifically increasing cycling and walking, averted 8.5% of new T2DM cases by 2050. Enhancing consumption of healthy diets including fruits and vegetables, specifically a workplace intervention involving dietary modifications and an educational intervention, averted 23.2% of new T2DM cases by 2050. A subsidy and legislative intervention approach, implementing subsidies on fruits and vegetables and taxation on sugar-sweetened beverages, averted 7.4% of new T2DM cases by 2050. A least to most optimistic combination of interventions averted 22.8-46.9% of new T2DM cases by 2050, respectively. Implementing a combination of individual-level and structural public health interventions is critical to prevent T2DM onset and to slow the growing T2DM epidemic in Qatar.

Population psychoprophylaxis and informational psychohygiene: basic directions

The relevance of the development of a preventive direction in the field of mental health care is beyond doubt. Mental health is an integral part and the most important component of the quality of health and includes not only the absence of mental disorders and various forms of disability caused by them, but also the state of mental well-being, in which the individual realizes his individual abilities, can withstand stress loads and be productive in everyday and professional activities. In this respect, the preservation of mental health is at the core not only of the well-being of the individual and his microsociety, but also of the functioning of society as a whole. Mental health care not only concerns the early diagnosis and effective treatment of mental disorders, but also focuses on broad aspects of population psychoprophylaxis and informational psychohygiene, contributing to the promotion of mental health and reducing the prevalence of the most relevant mental disorders. There is evidence of the positive impact of public health interventions on the prevalence of mental disorders. The general principles of the system of psychoprophylaxis imply the organization of three components: psychohygienic, psychoprophylactic and public mental health monitoring. The main directions of implementation of population psychoprophylaxis are: educational, socio-economic, environmental, humanistic and informational. Informational hygiene is a basic element of strengthening the mental health of the population, because a number of effects of informational influence can be predictors of mental ill-being of the population. Approaches to the assessment of informational content for its monitoring for destructive and manipulative content are proposed.

Impact of public health interventions on COVID-19 control in Lahore

Background: The COVID-19 pandemic has had a significant impact on public health, with governments and organizations implementing various interventions to control its spread. This study aimed to evaluate the impact of public health interventions on controlling the spread of COVID-19 in Lahore, Pakistan. Methods: Data on COVID-19 cases and public health interventions were collected from March 2020 to March 2022. The data were analyzed to evaluate the impact of each intervention on controlling the spread of COVID-19 in Lahore. Results: From March 2020 to March 2022, 6,710,142 COVID-19 tests were conducted in Lahore, with 518,393 cases testing positive for the virus. The contact tracing strategy involved tracing an average of 6.83 contacts per positive case in 2020, which increased over the year, and an average of 6.80 contacts per positive case in 2021, with a decrease in the second quarter of the year. The cordoning-off strategy was used to contain the spread of COVID-19 in over 340 areas of Lahore. Shutdowns of non-essential businesses and schools, guidelines for social distancing, and the use of PPE were also effective in reducing the transmission of COVID-19.

In praise of Prais-Winsten: An evaluation of methods used to account for autocorrelation in interrupted time series.

Interrupted time series are increasingly being used to assess the population impact of public health interventions. These data are usually correlated over time (auto correlated) and this must be accounted for in the analysis. Typically, this is done using either the Prais-Winsten method, the Newey-West method, or autoregressive-moving-average (ARMA) modeling. In this paper, we illustrate these methods via a study of pneumococcal vaccine introduction and explore their performance under 20 simulated autocorrelation scenarios with sample sizes ranging between 20 and 300. We show that in terms of mean square error, the Prais-Winsten and ARMA methods perform best, while in terms of coverage the Prais-Winsten method generally performs better than other methods. All three methods are unbiased. As well as having good statistical properties, the Prais-Winsten method is attractive because it is decision-free and produces a single measure of autocorrelation that can be compared between studies and used to guide sample size calculations. We would therefore encourage analysts to consider using this simple method to analyze interrupted time series.

COVID-19 epidemic and public health interventions in Shanghai, China: Statistical analysis of transmission, correlation and conversion.

The Shanghai COVID-19 epidemic is an important example of a local outbreak and of the implementation of normalized prevention and disease control strategies. The precise impact of public health interventions on epidemic prevention and control is unknown. We collected information on COVID-19 patients reported in Shanghai, China, from January 30 to May 31, 2022. These newly added cases were classified as local confirmed cases, local asymptomatic infections, imported confirmed cases and imported asymptomatic infections. We used polynomial fitting correlation analysis and illustrated the time lag plot in the correlation analysis of local and imported cases. Analyzing the conversion of asymptomatic infections to confirmed cases, we proposed a new measure of the conversion rate (C r ). In the evolution of epidemic transmission and the analysis of intervention effects, we calculated the effective reproduction number (R t ). Additionally, we used simulated predictions of public health interventions in transmission, correlation, and conversion analyses. (1) The overall level of R t in the first three stages was higher than the epidemic threshold. After the implementation of public health intervention measures in the third stage, R t decreased rapidly, and the overall R t level in the last three stages was lower than the epidemic threshold. The longer the public health interventions were delayed, the more cases that were expected and the later the epidemic was expected to end. (2) In the correlation analysis, the outbreak in Shanghai was characterized by double peaks. (3) In the conversion analysis, when the incubation period was short (3 or 7 days), the conversion rate fluctuated smoothly and did not reflect the effect of the intervention. When the incubation period was extended (10 and 14 days), the conversion rate fluctuated in each period, being higher in the first five stages and lower in the sixth stage. Effective public health interventions helped slow the spread of COVID-19 in Shanghai, shorten the outbreak duration, and protect the healthcare system from stress. Our research can serve as a positive guideline for addressing infectious disease prevention and control in China and other countries and regions.

The impact of public health interventions in a developing nation: an overview

Introduction: Around 80% of factors that determine population health sit outside the control of health services. It is essential we influence these factors in addition to those within the remit of health services in order to improve and protect the health of population in a developing country. Public health functions encompass working across the domains that constitute population health systems with various partners. The objective of this article is to give an overview of public health interventions that can improve the health of the population of a developing nation.&#x0D; Method: A descriptive study, based on a review of the literature of key public health frameworks and interventions that are likely to have significant impacts on population health. Some selected public health interventions and case studies are highlighted to illustrate the importance of priority areas in developing countries.&#x0D; Results: Various public health frameworks recognise the importance of wider determinants of health (socio-economic factors), effective healthcare, healthy behaviours, working with communities as critical to securing population health. Another framework adopts a life-course model of intervention starting from public health interventions during preconception period and childhood, adolescence, working life and older age. For many developing countries, the author identified some examples of priority areas for interventions such as stopping and preventing wars; improving child health, including free school meals; achieving universal healthcare through integrated primary health care; addressing commercial determinants of health; embracing new technologies; and measuring and monitoring population health.&#x0D; Conclusion: In order to improve the health of a population in a developing country, attention needs to go beyond health services to influence the wider determinants of health, health behaviours and adopting the World Health Organisation’s roadmap on essential public health functions.