Effective Public Health Interventions Research Articles

From neighborhood contexts to human behaviors: Cellphone-based place visitation data contribute to estimating neighborhood-level depression prevalence in the United States

The elucidation of neighborhood-level mental illness is pivotal to effective community need assessment and public health interventions. However, aggregating individual behaviors linked to mental health at the neighborhood level has proven to be a challenge. In this study, we collected place visitation data from extensive mobile phone records as a proxy measure of health behaviors to investigate whether and how the place visitation data can contribute to improving the estimation of nationwide neighborhood-level depression prevalence. Using nationwide place visitation data from 2019, we measured eight types of health behaviors at the neighborhood level in the United States, including positive and negative health behaviors (PNHB) and health service utilization behaviors (HSUB). The study revealed that visitations to different types of places of interest (POI) (i.e., fitness visitation, drinking place (alcoholic beverages) visitation, pharmacy visitation, general hospital visitation, and specialty hospital visitation) were significantly associated with neighborhood-level depression. Incorporating cellphone-based place visitation data (i.e., the proxy of health behaviors) into the models enhanced modeling fitness, with the model that included neighborhood context variables exhibiting the strongest fitness, followed by PNHB, POI features, and HSUB variables. These improvements are greater in models for self-reported mental health status compared to depression. The model fitness exhibits spatial differences, with smaller differences between actual and predicted values in urban areas (2.834) compared to rural areas (2.956) and the Midwest (2.212) compared to other regions. Overall, this study represents the first nationwide investigation of the role of cellphone-based place visitation data in estimating neighborhood-level depression prevalence. It expands a novel conceptual framework for explicating neighborhood-level depression prevalence by incorporating neighborhood-level health behaviors.

Short-term effectiveness of the national German quitline for smoking cessation: results of a randomized controlled trial

BackgroundThe objective of the present study was to examine the short-term effectiveness of the national German quitline for smoking cessation.MethodsA parallel-group, two-arm, superiority, randomized controlled trial with data collection at baseline and post-intervention (three months from baseline) was conducted. Individuals were randomized to either the intervention group, receiving up to six telephone counselling calls, or the control group, receiving an active control intervention (self-help brochure). The primary outcome was the seven-day point prevalence abstinence at post-assessment. Secondary outcomes included changes in smoking-related cognitions and coping strategies from pre- to post-assessment, the perceived effectiveness of intervention components, and the satisfaction with the intervention.ResultsA total of n = 905 adult daily smokers were assigned to either the intervention group (n = 477) or the control group (n = 428). Intention-to-treat analyses demonstrated that individuals allocated to the telephone counselling condition were more likely to achieve seven-day point prevalence abstinence at post-assessment compared to those allocated to the self-help brochure condition (41.1% vs. 23.1%; OR = 2.3, 95% CI [1.7, 3.1]). Participants who received the allocated intervention in both study groups displayed significant improvements in smoking-related cognitions and coping strategies with the intervention group showing greater enhancements than the control group. This pattern was also found regarding the perceived effectiveness of intervention components and the satisfaction with the intervention.ConclusionThe present study provides first empirical evidence on the short-term effectiveness of the national German quitline for smoking cessation, highlighting its potential as an effective public health intervention to reduce the burden of disease associated with smoking.Trial registrationThis study is registered in the German Clinical Trials Register (DRKS00025343). Date of registration: 2021/06/07.

Sociodemographic factors associated with Kyasanur forest disease in India - a retrospective study

ObjectivesKyasanur forest disease (KFD) is a tick-borne disease in India affecting humans and two local non-human primate species. A critical knowledge gap in the scientific literature is the lack of information on how people's sociodemographic factors influence KFD occurrence. MethodsWe analyzed available data on KFD from three data sources: (a) 104 peer-reviewed articles using keyword searches on PubMed Central and Google Scholar, (b) 116 Program for Monitoring Emerging Diseases reports, and (c) an acute febrile illness surveillance data set on KFD from a report by the government of India. We performed statistical analyses to calculate the prevalence of KFD by state and differences in KFD cases by sex and age group. ResultsAll three data sets used indicate that KFD cases and deaths have occurred predominantly in the 15-64 years age group (literature: 87% cases and 95% deaths, Program for Monitoring Emerging Diseases: 78% cases and 78% deaths, acute febrile illness: 96% cases [no breakdown for acute febrile illness death data]). Data reporting varies across states and is non-standardized. ConclusionsThe inconsistent reporting of sociodemographic data on KFD in India has created a gap in our understanding of its impact on different social groups. Collecting and reporting data on sociodemographic factors is critical to understanding the epidemiology of KFD and designing effective public health interventions.

Enhancing enteric pathogen detection: implementation and impact of multiplex PCR for improved diagnosis and surveillance

BackgroundSyndromic surveillance of acute gastroenteritis plays a significant role in the diagnosis and management of gastrointestinal infections that are responsible for a substantial number of deaths globally, especially in developing countries. In Lebanon, there is a lack of national surveillance for acute gastroenteritis, and limited data exists regarding the prevalence of pathogens causing diarrhea. The one-year study aims to investigate the epidemiology of common gastrointestinal pathogens and compare our findings with causative agents of diarrhea reported by our study collaborative centers.MethodsA multicenter, cross-sectional study was conducted over a one-year period. A total of 271 samples were obtained from outpatients and inpatients presenting with symptoms of acute gastroenteritis at various healthcare facilities. The samples were then analyzed using Allplex gastrointestinal assay that identifies a panel of enteric pathogens.ResultsOverall, enteropathogens were detected in 71% of the enrolled cases, 46% of those were identified in patients as single and 54% as mixed infections. Bacteria were observed in 48%, parasites in 12% and viruses in 11%. Bacterial infections were the most prevalent in all age groups. Enteroaggregative E. coli (26.5%), Enterotoxigenic E. coli (23.2%) and Enteropathogenic E. coli (20.3%) were the most frequently identified followed by Blastocystis hominis (15.5%) and Rotavirus (7.7%). Highest hospitalization rate occurred with rotavirus (63%), Enterotoxigenic E. coli (50%), Blastocystis hominis (45%) and Enteropathogenic E. coli (43%). Enteric pathogens were prevalent during summer, fall and winter seasons.ConclusionsThe adoption of multiplex real-time PCR assays in the diagnosis of gastrointestinal infections has identified gaps and improved the rates of detection for multiple pathogens. Our findings highlight the importance of conducting comprehensive surveillance to monitor enteric infections. The implementation of a syndromic testing panel can therefore provide healthcare professionals with timely and accurate information for more effective treatment and public health interventions.

Natural Language Processing-Powered Real-Time Monitoring Solution for Vaccine Sentiments and Hesitancy on Social Media: System Development and Validation.

Vaccines serve as a crucial public health tool, although vaccine hesitancy continues to pose a significant threat to full vaccine uptake and, consequently, community health. Understanding and tracking vaccine hesitancy is essential for effective public health interventions; however, traditional survey methods present various limitations. This study aimed to create a real-time, natural language processing (NLP)-based tool to assess vaccine sentiment and hesitancy across 3 prominent social media platforms. We mined and curated discussions in English from Twitter (subsequently rebranded as X), Reddit, and YouTube social media platforms posted between January 1, 2011, and October 31, 2021, concerning human papillomavirus; measles, mumps, and rubella; and unspecified vaccines. We tested multiple NLP algorithms to classify vaccine sentiment into positive, neutral, or negative and to classify vaccine hesitancy using the World Health Organization's (WHO) 3Cs (confidence, complacency, and convenience) hesitancy model, conceptualizing an online dashboard to illustrate and contextualize trends. We compiled over 86 million discussions. Our top-performing NLP models displayed accuracies ranging from 0.51 to 0.78 for sentiment classification and from 0.69 to 0.91 for hesitancy classification. Explorative analysis on our platform highlighted variations in online activity about vaccine sentiment and hesitancy, suggesting unique patterns for different vaccines. Our innovative system performs real-time analysis of sentiment and hesitancy on 3 vaccine topics across major social networks, providing crucial trend insights to assist campaigns aimed at enhancing vaccine uptake and public health.

How Socio-economic Inequalities Cluster People with Diabetes in Malaysia: Geographic Evaluation of Area Disparities Using a Non-parameterized Unsupervised Learning Method

Accurate assessments of epidemiological associations between health outcomes and routinely observed proximal and distal determinants of health are fundamental for the execution of effective public health interventions and policies. Methods to couple big public health data with modern statistical techniques offer greater granularity for describing and understanding data quality, disease distributions, and potential predictive connections between population-level indicators with areal-based health outcomes. This study applied clustering techniques to explore patterns of diabetes burden correlated with local socio-economic inequalities in Malaysia, with a goal of better understanding the factors influencing the collation of these clusters. Through multi-modal secondary data sources, district-wise diabetes crude rates from 271,553 individuals with diabetes sampled from 914 primary care clinics throughout Malaysia were computed. Unsupervised machine learning methods using hierarchical clustering to a set of 144 administrative districts was applied. Differences in characteristics of the areas were evaluated using multivariate non-parametric test statistics. Five statistically significant clusters were identified, each reflecting different levels of diabetes burden at the local level, each with contrasting patterns observed under the influence of population-level characteristics. The hierarchical clustering analysis that grouped local diabetes areas with varying socio-economic, demographic, and geographic characteristics offer opportunities to local public health to implement targeted interventions in an attempt to control the local diabetes burden.

Metabolic Insights into Caffeine's Anti-Adipogenic Effects: An Exploration through Intestinal Microbiota Modulation in Obesity.

Obesity, a chronic condition marked by the excessive accumulation of adipose tissue, not only affects individual well-being but also significantly inflates healthcare costs. The physiological excess of fat manifests as triglyceride (TG) deposition within adipose tissue, with white adipose tissue (WAT) expansion via adipocyte hyperplasia being a key adipogenesis mechanism. As efforts intensify to address this global health crisis, understanding the complex interplay of contributing factors becomes critical for effective public health interventions and improved patient outcomes. In this context, gut microbiota-derived metabolites play an important role in orchestrating obesity modulation. Microbial lipopolysaccharides (LPS), secondary bile acids (BA), short-chain fatty acids (SCFAs), and trimethylamine (TMA) are the main intestinal metabolites in dyslipidemic states. Emerging evidence highlights the microbiota's substantial role in influencing host metabolism and subsequent health outcomes, presenting new avenues for therapeutic strategies, including polyphenol-based manipulations of these microbial populations. Among various agents, caffeine emerges as a potent modulator of metabolic pathways, exhibiting anti-inflammatory, antioxidant, and obesity-mitigating properties. Notably, caffeine's anti-adipogenic potential, attributed to the downregulation of key adipogenesis regulators, has been established. Recent findings further indicate that caffeine's influence on obesity may be mediated through alterations in the gut microbiota and its metabolic byproducts. Therefore, the present review summarizes the anti-adipogenic effect of caffeine in modulating obesity through the intestinal microbiota and its metabolites.

Assessment of Factors Related to COVID-19 Preventive Health Behaviours Using a Health Belief Model

Abstract Non-pharmaceutical interventions, including physical distancing, hand hygiene, mask wearing, are some of the most effective public health interventions against COVID-19 spread. Adoption of these measures can vary in different countries and even in different populations within a country. The goal of our study was to investigate factors that influence adoption of these preventive health behaviours in the Latvian population within the Health Belief Model framework, while also expanding on the models cues to action dimension by testing if evaluation of COVID-19-related government actions and belief in COVID-19 related conspiracy theories could be used as such. Our quantitative cross-sectional study that was carried out in Latvia before the second wave of COVID-19 pandemic included two samples of major population language groups (nLatvian = 452; nRussian = 190) and showed differences that could potentially improve adoption of preventive health behaviours in Latvia if regarded in informing and educating the public. It was also found that evaluation of COVID-19-related government actions has statistically significant impact on preventive health behaviour and could be used as cues to action within the HBM, while belief in COVID-19-related conspiracy theories had no association with preventive health behaviour.

Age-Period-Cohort Analysis of Long-Term Trends in Ischemic Stroke Mortality in China Caused by Specific Risk Factors from 1990 to 2019

Objective: The objective of this study was to study the primary risk factors for the long-term trends of mortality rates in ischemic stroke (IS) in China. Methods: Using the Global Burden of Disease Study 2019 (GBD 2019) database, research was conducted on the 11 primary risk factors for the mortality rates of IS in China from 1990 to 2019. This study employed joinpoint regression software and the age-period-cohort method to evaluate the trends of mortality rates divided by age, period, and cohort over time. Results: From 1990 to 2019, the age-standardized mortality rate (ASMR) caused by a diet high in red meat and high body mass index in China showed an upward trend. ASMR increased first and then decreased due to smoking, diet high in sodium, particulate matter pollution, high fasting plasma glucose, and high systolic blood pressure. Low-density lipoprotein cholesterol (LDL-C), kidney dysfunction, low temperature, and lead exposure remained relatively stable during this period. In the 35–45 age group, the mortality rate of IS due to high LDL-C was up to about 60%, and smoking affected men more than women. Overall, high LDL-C, high systolic blood pressure, and particulate matter pollution were the most common risk factors in patients with IS. The risk of death rose with age. The period and cohort relative risks showed that metabolic risk factors had the greatest impact on the mortality of IS. Conclusion: Metabolic risk factors have become the primary risk factors for the ASMR of IS in China. Relevant authorities should pay attention to their long-term effects on IS. Effective public health policies and interventions should be implemented to reduce the burden of IS.

Effects of public health interventions and zero COVID policy on paediatric diseases: A Southern China study.

With the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in schools and communities, clinical evidence is needed to determine the impact of the pandemic and public health interventions under the zero coronavirus disease policy on the occurrence of common infectious diseases and non-infectious diseases among children. The current study was designed to analyse the occurrence of common infectious diseases before and after the pandemic outbreak in southern China. Data was obtained for 1 801 728 patients admitted into children's hospitals in Guangzhou between January 2017 and July 2022. Regression analysis was performed for data analysis. The annual occurrence of common paediatric infectious diseases remarkably decreased after the pandemic compared to the baseline before the pandemic and the monthly occurrence. Cases per month of common paediatric infectious diseases were significantly lower in five periods during the local outbreak when enhanced public health measures were in place. Cases of acute non-infectious diseases such as bone fractures were not reduced. Non-pharmaceutical interventions decreased annual and monthly cases of paediatric respiratory and intestinal infections during the coronavirus disease 2019 (COVID-19) pandemic, especially when enhanced public health interventions were in place. Our findings provide clinical evidence that public health interventions under the dynamic zero COVID policy in the past three years had significant impacts on the occurrence of common respiratory and intestinal infectious diseases among children and adolescents but little impact on reducing non-infectious diseases such as leukaemia and bone fracture.

Haze weather and mortality in China from 2014 to 2020: Definitions, vulnerability, and effect modification by haze characteristics

Haze weather, characterized by low visibility due to severe air pollution, has aroused great public concern. However, haze definitions are inconclusive, and multicentre studies on the health impacts of haze are scarce. We collected data on the daily number of deaths and environmental factors in 190 Chinese cities from 2014 to 2020. The city-specific association was estimated using quasi-Poisson regression and then pooled using meta-analysis. We found a negative association between daily visibility and non-accidental deaths, and mortality risk sharply increased when visibility was < 10 km. Haze weather, defined as a daily average visibility of < 10 km without a limit for humidity, produced the best model fitness and greatest effect on mortality. A haze day was associated with an increase of 2.53% (95% confidence interval [CI]:1.96, 3.10), 2.84 (95% CI: 2.13, 3.56), and 2.99% (95% CI: 1.94, 4.04) in all non-accident, cardiovascular and respiratory mortality, respectively. Haze had the greatest effect on lung cancer mortality. The haze-associated risk of mortality increased with age. Severe haze (visibility <2 km) and damp haze (haze with relative humidity >90%) had greater health impacts. Our findings can help in the development of early warning systems and effective public health interventions for haze.

Ern: An [Formula: see text] package to estimate the effective reproduction number using clinical and wastewater surveillance data.

The effective reproduction number, [Formula: see text], is an important epidemiological metric used to assess the state of an epidemic, as well as the effectiveness of public health interventions undertaken in response. When [Formula: see text] is above one, it indicates that new infections are increasing, and thus the epidemic is growing, while an [Formula: see text] is below one indicates that new infections are decreasing, and so the epidemic is under control. There are several established software packages that are readily available to statistically estimate [Formula: see text] using clinical surveillance data. However, there are comparatively few accessible tools for estimating [Formula: see text] from pathogen wastewater concentration, a surveillance data stream that cemented its utility during the COVID-19 pandemic. We present the [Formula: see text] package ern that aims to perform the estimation of the effective reproduction number from real-world wastewater or aggregated clinical surveillance data in a user-friendly way.

Adverse Events Following Immunization (AEFI): Current Trends, Latest Autopsy Protocol, Indian Scenario, and Statistics

Vaccination has been one of the most effective public health interventions in human history, saving countless lives and reducing the burden of infectious diseases. However, as with any medical intervention, vaccines can sometimes lead to adverse events following immunization (AEFI). This article explores the current trends, the latest autopsy protocols, the Indian scenario, and statistical insights into AEFI. Understanding and addressing AEFI is essential to maintain public trust in vaccination programs. Vaccine related deaths need to be investigated properly and exact cause of death can be determined with autopsy

Global, regional, and national burden of dengue, 1990–2021: Findings from the global burden of disease study 2021

BackgroundDengue is an acute viral infectious disease caused by the dengue virus and transmitted through mosquitoes. Although numerous studies have examined the global burden of dengue, comprehensive and systematic global analyses remain limited. This study uses data from the Global Burden of Disease (GBD) Study 2021 to systematically analyze the global epidemiological trends and disease burden of dengue from 1990 to 2021. MethodsThis study utilized data from the GBD Study 2021 Study to analyze trends in the incidence, prevalence, mortality, and disability-adjusted life years (DALYs) of dengue across 204 countries and territories, stratified by age, sex, and Socio-demographic Index (SDI) levels. In addition, a Bayesian age-period-cohort model was employed to predict the future burden of dengue. The average annual percent change (AAPC) was used to describe the overall trend in rates or counts of indicators between 1990 and 2021. ResultsIn 2021, the global ASIR of dengue was 752.04 per 100,000 population (95 % UI: 196.33–1363.35), and ASMR was 0.38 per 100,000 population (95 % UI: 0.23–0.51). From 1990 to 2021, both the ASIR (AAPC = 7.89, 95 % CI: 7.89–8.91) and ASMR (AAPC = 0.01, 95 % CI: 0.00–0.01) showed an increasing trend. Adolescents under 14 years had the highest ASIR, age-standardized prevalence rate (ASPR), and age-standardized disability-adjusted life years (DALYs) for dengue. By 2035, the projected ASIR of dengue is 862.23 per 100,000 population (95 % CI: 627.84–1096.62 per 100,000 population), the ASPR is 51.60 per 100,000 population (95 % CI: 37.70–65.50 per 100,000 population), and the ASMR is 0.43 per 100,000 population (95 % CI: 0.29–0.56 per 100,000 population). The ASIR, ASPR, ASMR, and age-standardized DALYs for dengue are expected to continue rising in the next 10 years. ConclusionThe global burden of dengue is projected to continue rising in the coming years, underscoring the urgent need for comprehensive control measures, including enhanced vector control, public education, vaccination, and drug development. These findings provide crucial scientific evidence for the formulation of effective public health strategies and interventions aimed at reducing the global threat posed by dengue.

A Complex Interrelationship of Childhood Obesity, Dental Caries and Malocclusion Amidst the COVID-19 Pandemic and Social Media Amplification: A Narrative Review

Recent research has highlighted the link between childhood obesity, dental caries, and malocclusion, and how the Coronavirus Disease - 2019 (COVID-19) pandemic and social media have worsened these health issues. Increased screentime and decreased physical activity during the pandemic have significantly contributed to these conditions among children. Studies emphasise the need to address common risk factors such as unhealthy diets, limited healthcare access, and poor public health policies to reduce their negative effects. This review explores the complex relationship between childhood obesity, dental caries, and malocclusion, with a focus on their global importance. It examines the connections and shared risk factors between these conditions, particularly in the context of the pandemic and the growing influence of social media on children. The goal is to provide insights for developing effective interventions and management strategies to improve children's health worldwide. The present review stresses the urgent need for comprehensive strategies that target both individual and collective risk factors, while also considering broader socioeconomic and digital influences on children's health behaviours. Promoting healthy lifestyles, access to nutritious foods, physical activity, and better dental care can help to alleviate these conditions. Understanding the interplay between these health issues and the digital environment is crucial to creating effective and adaptable public health interventions.

Unmet need for family planning among pastoralist community of West Omo zone, Ethiopia: A community based cross-sectional study

Introduction: Family planning is among the best suggested strategies to decrease the high maternal mortality and morbidity prevailing among pastoral communities. Despite this fact, there is no sufficient information regarding the unmet need for family planning in the study area. Therefore, this study was intended to assess the prevalence of unmet need for family planning and its associated factors among currently married women residing in pastoralist community of West Omo zone, Ethiopia. Methods: Community-based cross-sectional study was conducted from 01 January to 01 April 2021 in West Omo zone. Five-hundred-sixty currently married women aged 15–49 were interviewed, and the study participants were randomly selected by employing a modified random walk approach. Data were checked manually for completeness and consistency, then entered into Epidata 3.1 and exported to SPSS version 22 for analysis. Multivariate binary logistic regression was used to identify the associated factors, and Adjusted Odds Ratio (AOR) at 95% CI with p-value &lt; 0.05 was considered as significant variables. Result: A total of 560 currently married women agreed to participate in this study making a response rate of 93.17%. Two hundred three, that is, about 36.3% (95% CI: 32.1, 40.2%) currently married women had an unmet need for family planning, with 119 (58.6%) wishing to delay childbirth and 84 (41.4%) seeking to limit. Have no formal education (AOR = 2.86 (95% CI: 1.61, 5.10)), having poor knowledge of family planning (AOR = 2.83 (95% CI: 1.45, 5.54)), and lack of husband support of family planning (AOR = 2.38 (95% CI: 1.22, 4.67)) are positively associated with unmet need for family planning. Conclusion: The magnitude of unmet need for family planning in this study is fairly high as compared to previous studies done among non-pastoral community. Hence, it’s important to consider the above identified factors while designing effective public health intervention.

The 'lifeworld' of health and disease and the design of public health interventions.

The consequences of the COVID-19 pandemic are still working through health systems worldwide, and further reflections about the nature of health and disease, and about how to design and implement effective public health interventions are much needed. For numerous diseases and conditions, as well as for COVID-19, our knowledge base is rich. We know a lot about the biology of the disease, and we have plenty of statistics that relate health to socio-economic factors. In this paper, we argue that we need to add a third dimension to this knowledge base, namely a thorough description of the lifeworld of health and disease, in terms of the mixed biosocial mechanisms that operate in it. We present the concepts of lifeworld and of mixed mechanisms, and then illustrate how they can be operationalised and measured through mixed methodologies that combine qualitative and quantitative approaches. Finally, we explain the complementarity of our approach with the biological and statistical dimensions of health and disease for the design of public health interventions.

Evaluating the impact of local alcohol licensing decisions on outcomes for the community: a systematic review

BackgroundInternational evidence reviews suggest that reducing the availability of alcohol positively impacts both levels of alcohol consumption and associated harms. To understand the impact of recent changes to alcohol licensing...

Maternal Perception and Attitude towards Television Food Advertisements Targeting Children: A Cross-sectional Questionnaire Study from Pune, India

Introduction: Television food advertisements targeting children are recognised as a significant health risk, influencing their dietary choices and contributing to the increasing prevalence of Non Communicable Diseases (NCDs) among children in Low and Middle-income Countries (LMICs). Understanding maternal perceptions and attitudes towards these advertisements is crucial for designing effective public health interventions. Aim: To evaluate maternal perceptions and attitudes toward television food advertisements targeting children, specifically among mothers of children aged 4-11 years. Materials and Methods: A cross-sectional study was conducted in the Department of Paediatric and Preventive Dentistry, Dr. D. Y. Patil Dental College and Hospital, Pune, Maharashtra, India, from My 2023 to November 2023. A total of 332 mothers of children aged 4-11 years were divided into two groups: Group 1 (mothers of children aged 4-7 years) with 159 participants, and group 2 (mothers of children aged 8-11 years) with 173 participants. A prevalidated, closed-ended questionnaire consisting of 24 questions was administered to the mothers. The reliability of the questionnaire was ensured through the preliminary exploratory study and expert reviews, achieving a Cronbach’s Alpha value of 0.84. Data analysis was performed using descriptive statistics and Chi-square tests to compare perceptions of mothers in both groups. Results: The study received 332 responses from mothers with children aged 4-11 years. Significant differences between the two age groups were observed. In group 1, 64 (52%) mothers mothers agreed, while 41 (57.7%) mothers in group 2 strongly agreed that their children show a keen interest in watching food advertisements. Additionally, 80 (55.9%) mothers in group 1 agreed, while 39 (44.8%) mothers in group 2 strongly agreed that their children do not understand the persuasive intent of food advertisements, with statistical significance (p-value=0.001). Most mothers, 75 (52.4%) in group 1 and 68 (47.6%) in group 2, agreed that reducing exposure to food advertisements would improve children’s eating behaviour (p-value=0.01). Moreover, 61 (64.9%) mothers in group 1 strongly agreed, and 77 (58.3%) mothers in group 2 agreed that seeing advertisements persuaded children to want unhealthy foods, highlighting significant differences between the age groups (p-value=0.004). Conclusion: Mothers recognise the negative impact of TV food advertisements on their children’s eating habits, particularly noting the use of cartoons and celebrities to promote unhealthy snacks and low-nutrition food that their children struggle to understand. The present study emphasises the urgent need for regulatory measures to protect young viewers and promote healthier dietary choices across different age groups.

Trends in food and nutrition literacy in obesity: A bibliometric analysis

Background Low food and nutrition literacy is a significant factor in obesity prevalence, adversely impacting economic and individual health outcomes, with limited research impeding effective public health interventions. Objectives This study seeks to provide a comprehensive overview of scientific research conducted over the past three decades on food and nutrition literacy in relation to obesity. The objective is to evaluate the thematic focus of studies in this domain. Methods A bibliometric analysis methodology, known as a quantitative technique, was employed to investigate scientific studies published in the Web of Science database between 1995 and 2023. The analysis, conducted using the Bibliometrix library in R StudioIDE, considers key variables such as publication year, authorship, countries of origin, high-impact journals, citations, collaborations, and keywords. Results The research encompasses 755 articles, revealing a notable surge in publications post-2007. Remarkably, studies predominantly concentrate on the food and nutrition literacy of children and adolescents. The most cited author is Velardo, whose 2015 article focuses on defining nutritional literacy. The total number of publications from the 10 countries with the most productive authors in this field is 590, accounting for 78.15% of the dataset. The integration of digital media tools among this demographic has sparked innovative research trends. Conclusions Analysis of the data underscores a shortage of research in the literature, emphasizing the need for future studies to prioritize investigating the connection between food and nutrition literacy and obesity in children and adolescents. Bridging this knowledge gap is imperative for developing effective strategies to address and mitigate the obesity epidemic in these specific age groups.