Public Health Planning Research Articles

The Impact of Public Health on Health Planning

This study investigates the complex relationship between public health and health planning, specifically how public health influences the design of health systems and services. It looks into the use of public health data and frameworks in health planning to address health inequities, socioeconomic factors, and community-level health issues. Case studies demonstrate effective models in which public health initiatives influence health planning, stressing stakeholder involvement, interdisciplinary methods, and policy frameworks. The study continues by discussing the obstacles and potential for better integrating public health into comprehensive health planning. Keywords: Public health, health planning, socio-economic determinants, health disparities, community health, policy integration.

Metabarcoding study of potential pathogens and zoonotic risks associated with dog feces in Seoul, South Korea.

A significant portion of South Korea's population, approximately a quarter, owns pets, with dogs being the most popular choice among them. However, studies analyzing the fecal organism communities of dogs in South Korea are lacking, and limited efforts have been exerted to identify pathogens with potential zoonotic implications. Therefore, this study aimed to investigate potential pathogens using metabarcoding analysis and evaluate the risk of zoonotic diseases in dog feces in Seoul, South Korea. Fecal samples were collected from both pet and stray dogs in the Mapo district of Seoul. Next-generation sequencing (NGS) was utilized, employing 16S rRNA amplicon sequencing to identify prokaryotic pathogens, and 18S rRNA amplicon sequencing for eukaryotic pathogens. The data obtained from the QIIME2 pipeline were subjected to various statistical analyses to identify different putative pathogens and their compositions. Significant variations in microbiota composition were found between stray and pet dogs, and putative prokaryotic and eukaryotic pathogens were identified. The most prevalent putative bacterial pathogens were Fusobacterium, Helicobacter, and Campylobacter. The most prevalent putative eukaryotic pathogens were Giardia, Pentatrichomonas, and Cystoisospora. Interestingly, Campylobacter, Giardia, and Pentatrichomonas were found to be significantly more prevalent in stray dogs than in pet dogs. The variation in the prevalence of potential pathogens in dog feces could be attributed to environmental factors, including dietary variances and interactions with wildlife, particularly in stray dogs. These factors likely contributed to the observed differences in pathogen occurrence between stray and pet dogs. This study offers valuable insights into the zoonotic risks associated with dog populations residing in diverse environments. By identifying and characterizing putative pathogens in dog feces, this research provides essential information on the impact of habitat on dog-associated pathogens, highlighting the importance of public health planning and zoonotic risk management.

Temporal trends in Parkinson's disease among older adults in the United States from 1999 to 2020: Retrospective analysis from CDC WONDER database

This retrospective study assessed the mortality trends related to Parkinson's Disease (PD) between 1999 and 2020. We assessed individuals aged 65 years and older and a total of 831,793 deaths were identified. Of these total number of deaths, place of death was accessible for 830,176 cases. Majority of the deaths occurred in nursing homes of long-term care facilities (367,633), followed by at home (212,886), medical facilities (165,450), other locations (44,506), and hospice (39,701). Analysis of age-adjusted mortality rates (AAMR) revealed an overall rise from 1999 to 2020, 88.9 to 119.6 per 100,000 population. AAMR showed an initial decline between 1999 and 2013, followed by a slight increase between 2013 and 2018 and then a significant rise from 2018 to 2020. Gender-based analysis showed a constantly higher AAMR for older men compared to older women. Variations in AAMR based on race and ethnicity revealed that Non-Hispanic White population had the highest AAMRs. Geographic disparities among states showed that Nebraska, Vermont, Minnesota, Utah, and Idaho had a significantly higher AAMR than Hawaii, Florida, Nevada. New York, and District of Columbia. Midwest region had a consistently higher AAMR followed by West, South, and Northeast. Additionally, nonmetropolitan areas had a higher AAMR than metropolitan areas. These findings offer valuable insights into mortality patterns related to PD among the elderly, highlighting the significance of incorporating demographic and geographic variables into public health planning and interventions.

Can a nature walk change your brain? Investigating hippocampal brain plasticity after one hour in a forest

In cities, the incidence of mental disorders is higher, while visits to nature have been reported to benefit mental health and brain function. However, there is a lack of knowledge about how exposure to natural and urban environments affects brain structure. To explore the causal relationship between exposure to these environments and the hippocampal formation, 60 participants were sent on a one hour walk in either a natural (forest) or an urban environment (busy street), and high-resolution hippocampal imaging was performed before and after the walks. We found that the participants who walked in the forest had an increase in subiculum volume, a hippocampal subfield involved in stress response inhibition, while no change was observed after the urban walk. However, this result did not withstand Bonferroni correction for multiple comparisons. Furthermore, the increase in subiculum volume after the forest walk was associated with a decrease in self-reported rumination. These results indicate that visits to nature can lead to observable alterations in brain structure, with potential benefits for mental health and implications for public health and urban planning policies.

Integrated analysis of remote sensing with meteorological and health data for allergic rhinitis forecasting in Tianjin.

Long time series of vegetation monitoring can be carried out by remote sensing data, the level of urban greening is objectively described, and the spatial characteristics of plant pollen are indirectly understood. Pollen is the main allergen in patients with seasonal allergic rhinitis. Meteorological factors affect the release and diffusion of pollen. Therefore, studying of the complex relationship between meteorological factors and allergic rhinitis is essential for effective prevention and treatment of the disease. In this study, we leverage remote sensing data for a comprehensive decade-long analysis of urban greening in Tianjin, which exhibits an annual increase in vegetative cover of 0.51 per annum, focusing on its impact on allergic rhinitis through changes in pollen distribution. Utilizing high-resolution imagery, we quantify changes in urban Fractional Vegetation Coverage (FVC) and its correlation with pollen types and allergic rhinitis cases. Our analysis reveals a significant correlation between FVC trends and pollen concentrations, with a surprising value of 0.71, highlighting the influence of urban greenery on allergenic pollen levels. We establish a robust connection between the seasonal patterns of pollen outbreaks and allergic rhinitis consultations, with a noticeable increase in consultations during high pollen seasons. our findings indicate a higher allergenic potential of herbaceous compared to woody vegetation. This nuanced understanding underscores the importance of pollen sensitivity, alongside concentration, in driving allergic rhinitis incidents. Utilizing a Generalized Linear Model, significant features influencing the number of visits for allergic rhinitis (P < 0.05) were identified. Both GLM and LSTM models were employed to forecast the visitation volumes for rhinitis during the spring and summer-autumn of 2022. Upon validation, it was found that the R² values between the simulated and actual values for both GLM and LSTM models surpassed the 95% confidence threshold. Moreover, the R² values for the summer-autumn seasons (GLM: 0.56, LSTM: 0.72) were higher than those for spring (GLM: 0.22, LSTM: 0.47). Comparing the errors between the simulated and actual values of GLM and LSTM models, LSTM exhibited higher simulation precision in both spring and summer-autumn seasons, demonstrating superior simulation performance. Overall, our study pioneers the integration of remote sensing with meteorological and health data for allergic rhinitis forecasting. This integrative approach provides valuable insights for public health planning, particularly in urban settings, and lays the groundwork for advanced, location-specific allergenic pollen forecasting and mitigation strategies.

Correlation of Cause of Death Issued in Medico Legal Deaths in Hospitals and Cause of Death Found at Autopsy

Background:The death certificate is essential for legal investigations and public health planning. This study examines theaccuracy of causes of death reported by hospitals versus post-mortem findings and evaluates the completeness of deathcertificates.Method: A prospective, descriptive study was conducted in 99 cases, with hospital-issued death certificates, broughtfor medico-legal autopsy to the Department of Forensic Medicine, Institute of Medicine, Maharajgunj Medical Campus.Cases which were brought dead to the hospital but still were issued with death certificates and cases where the cause ofdeath remained undetermined even after autopsy were excluded. Data was recorded in a pro-forma and analyzed using theSPSS software Version 20.Result: The study involved individuals aged 13 to 30 years, mostly male. Private hospitals issued 66.7% of death certificates,with 55.6% by house officers. Only 62.6% met WHO standards. Autopsies were performed within 24 hours in 67.7% ofcases, revealing blunt force head injury as the leading cause of death (26.3%). There was a 67.7% discrepancy betweenhospital and autopsy causes of death, with house officers and government hospitals showing notable inconsistencies.Conclusion: Factors like the use of standard or non-standard death certificates, the designation of doctors, and the typeof hospital issuing the certificates influence the discrepancies between the causes of death listed by hospitals and thosedetermined after autopsies. This study aims to enhance the existing knowledge on the accuracy of death certificates and islikely the first systematic study of its kind in Nepal, laying the groundwork for future research.

Predicting Particulate Matter (PM 10) Levels in Morocco: A 5-Day Forecast Using the Analog Ensemble Method.

Accurate prediction of Particulate Matter (PM 10) levels, an indicator of natural pollutants such as those resulting from dust storms, is crucial for public health and environmental planning. This study aims to provide accurate forecasts of PM 10 over Morocco for five days. The Analog Ensemble (AnEn) and the Bias Correction (AnEnBc) techniques were employed to post-process PM 10 forecasts produced by the Copernicus Atmosphere Monitoring Service (CAMS) global atmospheric composition forecasts, using CAMS reanalysis data as a reference. The results show substantial prediction improvements: the Root Mean Square Error (RMSE) decreased from 63.83 μg/m 3 in the original forecasts to 44.73 μg/m 3 with AnEn and AnEnBc, while the Mean Absolute Error (MAE) reduced from 36.70 μg/m 3 to 24.30 μg/m 3. Additionally, the coefficient of determination (R 2) increased more than twofold from 29.11% to 65.18%, and the Pearson correlation coefficient increased from 0.61 to 0.82. This is the first use of this approach for Morocco and the Middle East and North Africa and has the potential for translation into early and more accurate warnings of PM 10 pollution events. The application of such approaches in environmental policies and public health decision making can minimize air pollution health impacts.

A Systematic Review on Post Covid-19 Complications and Risk Factors

Abstract: That should be because the COVID-19 pandemic has been very crucial to society, global health, and the lives of all human beings, underscoring the feature of a viral germ leading to infirmities of a serious nature that still affect several sick people even beyond the acute phase of infection. This review is intended to be a lighthouse regarding the risk factors and possibly associated outcomes of several post-COVID-19 issues affecting recovered patients. Respiratory system— Persistent cough, chronic respiratory insufficiency, and pulmonary fibrosis are examples of post-COVID-19 consequences that can impair long-term respiratory function. The current research also identifies a significant association between COVID-19 and deleterious cardiovascular effects, including myocarditis, heart failure, and thromboembolic events that are also studied as part of the cardiovascular consequences. The neurological manifestations of encephalopathy, stroke, persistent cognitive impairment, and mental health concerns deserve broad consideration. The more long-term effects on brain function going forward are pointed out. For the assessment of the immediate and long-term impacts of COVID-19, research into its after-effects becomes almost imperative. It aims to identify and assess the diverse health issues that people can encounter even when they have apparently recovered from acute illness. Research into these problems can help healthcare. Research into the after-effects of COVID-19 is also critical to public health planning and resource allocation. Other than an estimation of the potential cost of treatment and care for people suffering from sequelae, this research helps project health needs for affected persons and communities. In other words, research into COVID-19-related issues is the way to conduct public health initiatives, treat patients more comprehensively, and advance science to mitigate and prevent the long-lasting consequences of such a global health crisis

Economic evaluation of the recent French tobacco control policy: a model-based approach

BackgroundOne in four French adults smoked daily in 2021, compared with one in six in Organisation for Economic Co-operation and Development (OECD) countries. To strengthen its tobacco control policy, in...

Colombian ocular diseases epidemiology study (CODES): glaucoma incidence and sociodemographic characterisation 2015–2020

ABSTRACT Clinical relevance Glaucoma encompasses a group of eye diseases that progressively damage the optic nerve, leading to vision loss and blindness. Understanding the incidence and demographic distribution is vital for public health planning and patient care. Background Previous small-scale studies in Colombia have reported the prevalence of glaucoma in individuals over 40 years between 1.1% and 5.29%. These data may not fully capture the true extent of the disease. Furthermore, with the ageing population, the incidence of glaucoma is likely to increase, highlighting the need for up-to-date incidence data to guide healthcare planning. Methods Data were obtained from the Social Protection Information System, the primary health record repository of Colombia, using the International Classification of Disease 10 for glaucoma. Crude incidence rates were calculated and adjusted for sex, age, and gender. The 2020 incidence was compared with estimated rates from previous years to assess the impact of the coronavirus disease 2019 lockdown. A standardised morbidity map was created to show regional variations. Results The average incidence of glaucoma in Colombia was 17.36 per 1,000 inhabitants, increasing from 12.29 in 2015 to 24.22 in 2019. Of the 212,700 reported cases, 60.75% were females. Primary open-angle glaucoma was the most prevalent type, with significant incidence in the elderly over 70 years of age, irrespective of sex. Bogotá, Antioquia, and Valle del Cauca had the highest numbers of new cases. Conclusion The increasing incidence of glaucoma and its demographic and geographical variations necessitate the awareness of ophthalmologists and optometrists. This study emphasises the need for continuous monitoring, effective interventions, and developing healthcare strategies to address the escalating challenge of glaucoma in Colombia.

Decision-making regarding accessing paediatric unscheduled healthcare during the COVID-19 pandemic: a mixed methods rapid review and thematic synthesis

ObjectiveReductions in paediatric unscheduled healthcare utilisation were seen during the COVID-19 pandemic, with concerns around their impact on children’s health. The reasons for these changes are not well described. This...

Repeated Exposure to COVID-19 Misinformation: A Longitudinal Analysis of Prevalence and Predictors in a Community Sample.

Belief in health misinformation can affect individual health decisions and actions. Repeated exposure to the same misinformation strengthens its impact, yet little is known about how commonly repeated exposure occurs. To estimate the prevalence, we tracked exposure to 5 inaccurate COVID-19 claims every week for up to 23 consecutive weeks in a racially diverse panel of adults (n=213). Repeated exposure was common: across the 5 claims, 10%-43% of respondents reported hearing the misinformation in≥3 different weeks. Frontline workers were more likely than other community members to experience repeated exposure, with adjusted incidence rate ratios (IRRs) ranging from 1.8 to 4.9 across the 4 items. Repeated exposure was most common among older adults. Adjusted IRR for those ages≥50 versus 18-29years ranged from 1.8 to 2.5 per misinformation claim. Public health planning efforts to counter health misinformation should anticipate multiple exposures to the same false claim, especially in certain subgroups.

Understanding the Transmission Dynamics of the Chikungunya Virus in Africa.

The Chikungunya virus (CHIKV) poses a significant global public health concern, especially in Africa. Since its first isolation in Tanzania in 1953, CHIKV has caused recurrent outbreaks, challenging healthcare systems in low-resource settings. Recent outbreaks in Africa highlight the dynamic nature of CHIKV transmission and the challenges of underreporting and underdiagnosis. Here, we review the literature and analyse publicly available cases, outbreaks, and genomic data, providing insights into the epidemiology, genetic diversity, and transmission dynamics of CHIKV in Africa. Our analyses reveal the circulation of geographically distinct CHIKV genotypes, with certain regions experiencing a disproportionate burden of disease. Phylogenetic analysis of sporadic outbreaks in West Africa suggests repeated emergence of the virus through enzootic spillover, which is markedly different from inferred transmission dynamics in East Africa, where the virus is often introduced from Asian outbreaks, including the recent reintroduction of the Indian Ocean lineage from the Indian subcontinent to East Africa. Furthermore, there is limited evidence of viral movement between these two regions. Understanding the history and transmission dynamics of outbreaks is crucial for effective public health planning. Despite advances in surveillance and research, diagnostic and surveillance challenges persist. This review and secondary analysis highlight the importance of ongoing surveillance, research, and collaboration to mitigate the burden of CHIKV in Africa and improve public health outcomes.

Real-time forecast of temperature-related excess mortality at small-area level: towards an operational framework

The development of innovative tools for real-time monitoring and forecasting of environmental health impacts is central to effective public health interventions and resource allocation strategies. Though a need for such generic tools has been previously echoed by public health planners and regional authorities responsible for issuing anticipatory alerts, a comprehensive, robust and scalable real-time system for predicting temperature-related excess deaths at a local scale has not been developed yet. Filling this gap, we propose a flexible operational framework for coupling publicly available weather forecasts with temperature-mortality risk functions specific to small census-based zones, the latter derived using state-of-the-art environmental epidemiological models. Utilising high-resolution temperature data forecast by a leading European meteorological centre, we demonstrate a real-time application to forecast the excess mortality during the July 2022 heatwave over England and Wales. The output, consisting of expected temperature-related excess deaths at small geographic areas on different lead times, can be automated to generate maps at various spatio-temporal scales, thus facilitating preventive action and allocation of public health resources in advance. While the real-case example discussed here demonstrates an application for predicting (expected) heat-related excess deaths, the framework can also be adapted to other weather-related health risks and to different geographical areas, provided data on both meteorological exposure and the underlying health outcomes are available to calibrate the associated risk functions. The proposed framework addresses an urgent need for predicting the short-term environmental health burden on public health systems globally, especially in low- and middle-income regions, where rapid response to mitigate adverse exposures and impacts to extreme temperatures are often constrained by available resources.

Planning healthy food environments: An analysis of local government municipal public health and wellbeing policy in regional Victoria.

While there has been an increased focus on how local governments can support the creation of healthy food environments through healthy public policy, little is known about how this is reflected in public health policy in regional areas. The aim of this study was to understand how improvements to the food environment are prioritised, implemented and evaluated by local governments in regional Victoria. Using a qualitative descriptive approach, content analysis was undertaken of Municipal Public Health and Wellbeing Plans and Council Plans from 10 regional local governments in Victoria, Australia. Seventeen policy documents were analysed including 10 Council Plans, 6 Municipal Public Health and Wellbeing Plans and 1 Food Systems Strategy. Findings highlight regional public health and wellbeing plans have very few concrete actions in place to improve food environments. There is an opportunity for Australian regional local governments to include in their public health and wellbeing plans a greater emphasis on supporting healthy food behaviours, and therefore seeking opportunities to improve food environments through clearly aligned priorities, objectives, actions and measures of success. SO WHAT?: Improving the quality of public health and wellbeing plans can support local government to use policy to drive improvements in food environments leading to improved health and wellbeing for regional communities.

Flexible Bayesian estimation of incubation times.

The incubation period is of paramount importance in infectious disease epidemiology as it informs about the transmission potential of a pathogenic organism and helps to plan public health strategies to keep an epidemic outbreak under control. Estimation of the incubation period distribution from reported exposure times and symptom onset times is challenging as the underlying data is coarse. We develop a new Bayesian methodology using Laplacian-P-splines that provides a semi-parametric estimation of the incubation density based on a Langevinized Gibbs sampler. A finite mixture density smoother informs a set of parametric distributions via moment matching and an information criterion arbitrates between competing candidates. Algorithms underlying our method find a natural nest within the EpiLPS package, which has been extended to cover estimation of incubation times. Various simulation scenarios accounting for different levels of data coarseness are considered with encouraging results. Applications to real data on COVID-19, MERS and Mpox reveal results that are in alignment with what has been obtained in recent studies. The proposed flexible approach is an interesting alternative to classic Bayesian parametric methods for estimation of the incubation distribution.

Integrating socio-psychological factors in the SEIR model optimized by a genetic algorithm for COVID-19 trend analysis

The global spread of COVID-19 has profoundly affected health and economies, highlighting the need for precise epidemic trend predictions for effective interventions. In this study, we used infectious disease models to simulate and predict the trajectory of COVID-19. An SEIR (susceptible, exposed, infected, removed) model was established using Wuhan data to reflect the pandemic. We then trained a genetic algorithm-based SEIR (GA-SEIR) model using data from a specific U.S. region and focused on individual susceptibility and infection dynamics. By integrating socio-psychological factors, we achieved a significant enhancement to the GA-SEIR model, leading to the development of an optimized version. This refined GA-SEIR model significantly improved our ability to simulate the spread and control of the epidemic and to effectively track trends. Remarkably, it successfully predicted the resurgence of COVID-19 in mainland China in April 2023, demonstrating its robustness and reliability. The refined GA-SEIR model provides crucial insights for public health authorities, enabling them to design and implement proactive strategies for outbreak containment and mitigation. Its substantial contributions to epidemic modelling and public health planning are invaluable, particularly in managing and controlling respiratory infectious diseases such as COVID-19.

Analysis of COVID-19 Virus Spread in Jakarta Using Multiple Linear Regression

COVID-19, first identified in Wuhan, China in December 2019, quickly spread worldwide and was declared a pandemic by WHO in March 2020. Indonesia reported its first case on March 2, 2020, and the pandemic has had a significant impact on the country's economic, social, and health sectors. This study aims to predict the death rate due to COVID-19 in Jakarta using multiple linear regression method. The dataset collected from Andra Farm - Go Green website includes COVID-19 cases recorded in all sub-districts in Jakarta on November 1, 2023. Pre-processing was performed to improve the quality and accuracy of the model. The method used was multiple linear regression. The analysis results show that variables such as total travel and discarded trip have a significant influence in predicting the number of positive cases. The study found that lowering the correlation threshold for selecting independent variables reduced the mean squared error (MSE) and improved model performance, highlighting the importance of variable selection in developing accurate predictive models. These findings provide important insights for the government in making informed decisions regarding post-pandemic healthcare. This research underscores the value of robust data processing and variable selection techniques in enhancing predictive accuracy for public health planning.

Four Years And More Than 200,000 Deaths Later: Lessons Learned From The COVID-19 Pandemic In US Nursing Homes.

Nursing home residents and staff were disproportionately affected by the COVID-19 pandemic, drawing attention to long-standing challenges of poor infection control, understaffing, and substandard quality of care in many facilities. Evolving practices and policies during the pandemic often focused on these challenges, with little effect. Despite the emergence of best practices to mitigate transmission of the virus, even the highest-quality facilities experienced outbreaks, indicating a larger systemic problem, rather than a quality problem at the facility level. Here we present a narrative review and discussion of the evolution of policies and practices and their effectiveness, drawing on evidence from the United States that was published during 2020-23. The lessons learned from this experience point to the need for more fundamental and nuanced changes to avoid similar outcomes from a future pandemic: greater integration of long-term care into public health planning, and ultimately a shift in the physical structure of nursing homes. More incremental measures such as vaccination mandates, higher staffing, and balancing infection control with resident quality of life will avoid some adverse outcomes, but without more systemic change, nursing home residents and staff will remain at substantial risk for repetition of the poor outcomes from the COVID-19 pandemic.

Malaria prevalence and associated population and ecological risk factors among women and children under 5 years in Rwanda

BackgroundMalaria continues to pose a substantial public health concern in Rwanda, despite substantial progress in recent years. Little is known about effect of ecological factors and their interaction in malaria transmission. Understanding the prevalence and identifying risk factors, both population-based and ecological such as zooprophylaxis and irrigation are crucial for targeted intervention strategies. MethodsThis study analyzed the 6th iteration of the Demographic and Health Survey conducted in Rwanda between 2019 and 2020. The study employed a nationally representative sample, utilizing rapid diagnostic tests and blood smear microscopy to determine malaria prevalence among women and under 5 years old children. Logistic regression analysis was used in R version 4.3.1 to evaluate population and ecological risk factors that are associated with malaria prevalence. Additionally, interactive effects of ecological factors on malaria were evaluated. ResultsThe analysis revealed a notable malaria prevalence in Rwanda, emphasizing the continued significance of malaria control efforts. Approximately 1.79 % of the population tested positive for malaria. Proximity to irrigation sites and lowland were identified as significant risk factors to malaria with adjusted odds ratio (AOR) 1.47(1.00–2.15) and AOR 5.44(4.01–8.61) respectively however cattle ownership exhibited a protective effect AOR 0.41(0.23–0.72). Interactive effects of livestock and irrigation on malaria prevalence were revealed. Additionally, population-based risk factors, including age, household wealth, utilization of Insecticide Treated Nets, were associated with varying malaria risks. ConclusionThis study underscores the persistent challenge of malaria in Rwanda and the importance of tailored intervention strategies. To effectively combat malaria, efforts must consider the interplay of ecological factors, such as high cattle density, and demographic factors, targeting high-risk populations, especially those living in proximity to lowlands and irrigation areas. These findings provide critical insights for advancing malaria elimination efforts in Rwanda and serve as a basis for comprehensive public health planning and action.