Effective Public Health Interventions Research Articles

Impact of Climate Change on Human Lactation: Biological, Socioeconomic, and Public Health Implications

Global climate change poses significant challenges to human health and nutrition, particularly affecting lactating individuals and their infants. This review examines the impact of climate change on lactation, focusing on physiological, socioeconomic, and public health implications. Rising temperatures and heat stress can reduce milk production and alter milk composition, while malnutrition and water scarcity exacerbate these effects. Vulnerable populations face heightened risks, emphasizing the need for targeted mitigation strategies. Recommendations include enhancing nutritional support, securing water supplies, reducing environmental toxin exposure, and integrating lactation considerations into climate policies. Employing a social-ecological lens, this review underscores the necessity of a holistic approach to support lactating individuals in a warming world. Future research should explore the complex interactions between climate stressors and lactation to develop effective public health interventions.

Environmental Health Assessment by Local Environmental Justice Experts for Evidence-based Decision-making in an Agricultural Community of Northern California.

Environmental justice research driven by academics and policymakers often overlooks the valuable insights and leadership of the communities most impacted by environmental hazards. When institution-led research approaches are employed, inadequate community ownership and limited institutional accountability hinder the effectiveness of environmental public health interventions. In contrast, a community-owned and -managed approach to environmental justice research can guide community members in developing evidence-based interventions. This paper outlines a community-led environmental health assessment survey (sample= 100) and resulting community actions over six years (2017 to 2023) in a Northern California farmworker community with a perceived high prevalence of cancer and exposure to environmental hazards in households, neighborhoods, and job sites. Local resident experts in Knights Landing, CA, documented community risk factors and exposures in collaboration with interdisciplinary undergraduate and graduate student-researchers. The survey instrument focused on environmental hazards identified by local resident experts including vehicular and agricultural pollution, occupational pesticide contact, and sun exposure. Survey findings highlighted the need for targeted interventions to reduce environmental health risks, such as academic outreach programs, county investments in public services, and community-led mutual aid initiatives. Despite academic reservations about our non-random sampling method and data collection by local resident experts, our project sparked substantial actions and investments with minimal personnel and financial resources. Local leaders working with student-researchers developed more effective environmental public health interventions through a community-owned and -managed approach that went beyond the efforts of local regulatory and research institutions.

P021 PROPORTION AND GEOGRAPHIC DISTRIBUTION OF HYPERTENSION PATIENTS AND THEIR ASSOCIATED SOCIODEMOGRAPHIC FACTORS IN COLOMBIA, 2019-2023

Background and Objectives: A substantial underreporting of hypertension data in Colombia has been documented, with a lack of recent updates. Access to updated data is crucial for planning effective public health interventions in our country. We aimed to determine both the proportion and the geographic distribution of patients with hypertension in Colombia, along with their associated demographic and regional factors Methods: We conducted a descriptive, cross-sectional, observational study. Data on the frequency and geographic distribution of individuals diagnosed with hypertension from 2019 to 2023 were collected, along with sociodemographic characteristics like age, gender, and socioeconomic status. These data were extracted from the SISPRO platform based on diagnoses coded according to the International Statistical Classification of Diseases and Related Health Problems (ICD-10). Results: During the study period, we identified a hypertension prevalence of 12.86% (6,719,193 individuals) across Colombia. Regionally, Bogotá D.C. and Antioquia reported the highest proportion of hypertension—19.21% (1,290,882 people) and 16.15% (1,085,420 people), respectively. Conversely, the lowest rates were found in Vaupés (0.01%, corresponding to 960 people) and Guainía (0.02%, corresponding to 1,592 people). The prevalence was significantly higher in females (60,80%) compared to males (39,20%). Additionally, the highest prevalence was found among individuals aged 55 to 74, accounting for 69,67% of cases. The highest frequency of diagnosis was noted in the contributory regime (68.95%), with the lowest in displaced persons, irrespective of their insurance status (0.33%). Conclusions: The prevalence of hypertension is notably higher in Colombia's major urban centers, while significantly lower in remote regions and among vulnerable populations, suggesting significant underreporting. This underreporting likely results in untreated conditions and increased future health complications. It is imperative to develop strategies to enhance the recording and diagnosis of hypertension in Colombia, particularly in remote and vulnerable groups.

Investigating Modifiable Risk Factors Across Dementia Subtypes: Insights from the UK Biobank.

This study investigates the relationship between modifiable risk factors and dementia subtypes using data from 460,799 participants in the UK Biobank. Utilizing univariate Cox proportional hazards regression models, we examined the associations between 83 modifiable risk factors and the risks of all-cause dementia (ACD), Alzheimer's disease (AD), and vascular dementia (VD). Composite scores for different domains were generated by aggregating risk factors associated with ACD, AD, and VD, respectively, and their joint associations were assessed in multivariable Cox models. Additionally, population attributable fractions (PAF) were utilized to estimate the potential impact of eliminating adverse characteristics of the risk domains. Our findings revealed that an unfavorable medical history significantly increased the risk of ACD, AD, and VD (hazard ratios (HR) = 1.88, 95% confidence interval (95% CI): 1.74-2.03, p < 0.001; HR = 1.80, 95% CI: 1.54-2.10, p < 0.001; HR = 2.39, 95% CI: 2.10-2.71, p < 0.001, respectively). Blood markers (PAF = 12.1%; 17.4%) emerged as the most important risk domain for preventing ACD and VD, while psychiatric factors (PAF = 18.3%) were the most important for preventing AD. This study underscores the potential for preventing dementia and its subtypes through targeted interventions for modifiable risk factors. The distinct insights provided by HR and PAF emphasize the importance of considering both the strength of the associations and the population-level impact of dementia prevention strategies. Our research provides valuable guidance for developing effective public health interventions aimed at reducing the burden of dementia, representing a significant advancement in the field.

‘I’ve just heard that there are people who feel like they need to exercise’: a photo-elicitation study of values and priorities influencing physical activity in a socioeconomically disadvantaged neighbourhood in Uppsala, Sweden

ObjectivePeople living in socioeconomically disadvantaged neighbourhoods in Sweden engage less in physical activity compared with the general population, contributing to an elevated risk of cardiometabolic diseases. To inform targeted and...

"It spreads like wildfire": mothers' gatherings for vaccine acceptance.

This case study describes the design, implementation, and evaluation of an initiative to increase COVID-19 vaccine confidence and uptake among refugee and immigrant women in Clarkston, Georgia. Applying the principles and practices of human-centered design, Mothers x Mothers was co-created by Refugee Women's Network and IDEO.org as a series of gatherings for refugee and immigrant mothers to discuss health issues, beginning with the COVID-19 vaccine. The gatherings included both vaccinated and unvaccinated mothers and used a peer support model, with facilitation focused on creating a trusting environment and supporting mothers to make their own health decisions. The facilitators for Mothers x Mothers gatherings were community health workers (CHWs) recruited and trained by Refugee Women's Network. Notably, these CHWs were active in every phase of the initiative, from design to implementation to evaluation, and the CHWs' professional development was specifically included among the initiative's goals. These elements and others contributed to an effective public health intervention for community members who, for a variety of reasons, did not get sufficient or appropriate COVID-19 vaccine information through other channels. Over the course of 8 Mothers x Mothers gatherings with 7 distinct linguistic/ethnic groups, 75% of the unvaccinated participants decided to get the COVID-19 vaccine and secured a vaccine referral.

Development and Psychometric Validation of Population-Based Knowledge Attitudes and Practices-Questionnaire on Fluoride (PBKAP-QF).

Fluoride exposure is a global public health concern. Understanding the knowledge, attitudes, and practices (KAP) of affected populations is essential for effective community management. This study aimed to develop and validate a KAP questionnaire to assess fluoride and its risk in general population.An extensive literature review and focus group discussions were conducted to construct the questionnaire. Content validity was assessed using the Content Validity Index (CVI) based on expert feedback. Factor analysis was performed for final tool validation, and item characteristics were analyzed using IBM SPSS v. 27 and IBM AMOS v. 26.A total of 300 responses were collected. Initially, 41 items were included in the questionnaire, which were reduced to 25 after expert review. The final version included 19 items, with an I-CVI ranging from 0.80 to 1.00, indicating no issues with item difficulty or discrimination. Cronbach's alpha ranged from 0.88 to 0.90, demonstrating good internal consistency. The Kaiser-Meyer-Olkin (KMO) value was 0.848, and Bartlett's test (χ2 = 6860.978, df = 156, p < 0.01) confirmed data suitability for factor analysis. Three constructs were extracted with factor loadings greater than 0.5. Confirmatory factor analysis demonstrated a good model fit.This study developed and validated a robust 19-item KAP questionnaire for assessing knowledge, attitudes, and practices related to fluoride exposure. The tool demonstrated excellent reliability, validity, and internal consistency, supporting its use in guiding effective community-level management and public health interventions in fluoride-endemic areas.

Transmission chains and molecular characterizations of extended-spectrum β-lactamase producing Enterobacteriaceae at a veterinary hospital in Chengdu, China

The rapid emergence of Extended-spectrum β-lactamase producing Enterobacteriaceae (ESBL-E) is a major global public health concern. Previous studies have identified that intensive medical care of dogs and cats in veterinary hospitals have accelerated the infections and spread of ESBL-E. To investigate the spread of ESBL-E in a veterinary hospital, a total of 202 samples including hospitalized animals, veterinary healthcare workers and environment were collected from a veterinary hospital in Chengdu, China. ESBL-E were identified by antimicrobial susceptibility testing and 16 s rRNA sequencing and were further conducted on ESBL gene detection and multilocus sequence typing (MLST). At last, strains with transmission potential were analyzed by whole genome sequencing (WGS). Our results showed that the overall prevalence of ESBL-positive isolates was 34.7% (70/202), with 55.3% (26/47) in animals, 29.3% (12/41) in healthcare workers and 28.1% (32/114) in environment swabs. Twenty diverse MLST types were detected, with ST744, ST231 as the most prevalent ones. Transmission chains of two ESBL-E.coli (ST744 blaCTX-M-18, blaTEM-1) from cat_21 to cat_14, and two ESBL-Kp (ST231 blaCTX-M-27, blaTEM-1, blaSHV-1) from cat_20 to cat_37 were further confirmed by WGS. Furthermore, interdisciplinary investigation and cooperation of AMR are needed to better limit the transmissions of high-risk strains and to implement effective public health interventions.

Impact of Atmospheric Parameters on Human Respiratory Tract Infections (RTIs)

Respiratory tract infections (RTIs) are a significant public health issue globally, with varying degrees of severity and implications. Environmental factors, particularly atmospheric parameters, can exacerbate these health conditions. Understanding the relationship between atmospheric conditions and RTIs is crucial for effective public health interventions. This systematic literature review analyzes research on the impact of atmospheric parameters on RTIs through a systematic search across multiple electronic databases using different keyword combinations. The retrieved articles were categorized based on atmospheric parameters investigated, such as temperature, humidity, air pollution, and rainfall. The review findings indicate that various atmospheric factors, including wind speed, air pollution (PM10, PM2.5, CO, and O3), air temperature, atmospheric pressure, and humidity, influence the occurrence and severity of respiratory infections. Low wind speed and high air pollution were identified as risk factors for severe acute respiratory infection (SARI). In children, meteorological factors like air temperature, atmospheric pressure, rainfall, sunlight hours, wind speed, and relative humidity were associated with lower respiratory tract infections. Pollutants like PM2.5, carbon monoxide, sulfur dioxide, and tropospheric ozone directly impact pediatric respiratory infections. Climate variables affect the seasonal activity of pathogens like respiratory syncytial virus (RSV). These findings underscore the importance of considering atmospheric parameters in understanding and preventing RTIs. Addressing these challenges requires a comprehensive approach integrating environmental and public health interventions. This approach can better mitigate the effects of atmosphere-related risks on respiratory health.

Forecasting infection fatality rate of COVID-19: measuring the efficiency of several hybrid models

The main goal of this paper is to delve into a crucial epidemiological metric the daily infection fatality rate in the context of the ongoing COVID-19 pandemic. The significance of understanding this metric lies in its potential to provide insights into the severity and impact of the virus on a daily basis. Methods: To achieve this overarching objective, we employ a comprehensive approach by applying various hybrid models that hybridize both machine learning and statistical techniques. In our pursuit of a deeper understanding, we leverage advanced machine learning algorithms, including Support Vector Machine and Random Forest. These techniques allow us to capture intricate patterns and relationships within the data, contributing to a more nuanced analysis of the infection fatality rate. The application of machine-learning models in epidemiological studies has gained prominence due to their ability to adapt to complex and evolving patterns inherent in infectious disease dynamics. Complementing our machine-learning arsenal, we integrate traditional statistical models such as ARIMA (AutoRegressive Integrated Moving Average), fractional ARIMA, and BATS (Bayesian Structural Time Series). Results. To assess the performance of these models, we employ key evaluation metrics, including Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). These metrics serve as critical benchmarks, allowing us to quantify the accuracy and reliability of our models in predicting the daily infection fatality rate. A meticulous evaluation of model performance is crucial for ensuring the validity and of our findings. According to these measures, we see that hybrid models performed well especially ARIMA-RF model RMSE: 0.29, MSE: 0.084, MAE: 0.215 for the horizon 60 and for horizon 120 ARIMA-RF still the best performance, RMSE: 0.268, MSE: 0.071, MAE: 0.183, we get these results due to the capacity of this approach to handle complex patterns contrarily to other model ARIMA, BATS, RF and SVM. Conclusion. This work adopted this approach in order to build a model to predict infection fatality rate, we aspire to provide a nuanced understanding of the factors influencing the severity of the virus, ultimately contributing to the ongoing discourse on effective public health interventions and mitigation strategies.

Prevalence of human papilloma virus among cervical cancer patients in India: A systematic review and meta-analysis.

Cervical cancer, predominantly caused by the human papillomavirus (HPV), is a major health challenge in India, with high morbidity and mortality rates. Given India's vast geographic and socio-economic diversity, understanding regional variations in HPV prevalence is crucial for developing targeted and effective public health interventions. This systematic review and meta-analysis were conducted to elucidate the prevalence of HPV among cervical cancer patients in India. A literature search was executed across PubMed, EMBASE, and Web of Science up to December 07, 2023. Observational studies reporting HPV prevalence among cervical cancer patients in India are included. A Modified Newcastle-Ottawa scale was used for quality assessment. A random-effects meta-analysis was used to determine pooled HPV prevalence, and heterogeneity was evaluated using the I² statistic. Subgroup and sensitivity analyses were performed to assess result stability and investigate heterogeneity sources. All statistical analyses were performed using R software version 4.3. The meta-analysis included 17 studies with a total of 2529 cervical cancer cases, of which 1977 were HPV-positive. The pooled HPV prevalence was 85% (95% CI: 71-92%), with substantial heterogeneity (I² = 94%). Subgroup analysis by geographic zones showed notable differences: South (88%, 95% CI: 76-95%), North (73%, 95% CI: 1-100%), East (99%, 95% CI: 1-100%), Central (71%, 95% CI: 54-84%), and West (77%, 95% CI: 0-100%). Sensitivity analysis demonstrated the consistency of the results, and a reanalysis, excluding influential studies, yielded a prevalence of 82% (95% CI: 67-91%). Our analysis reveals a high prevalence of HPV in cervical cancer patients in India, with significant regional variations. The observed heterogeneity highlights the complexity of HPV epidemiology in India and necessitates further research to explore underlying causes and regional characteristics. Future studies should aim to expand geographic representation and deepen understanding of the factors contributing to the variability in HPV prevalence.

Effect of homophily on coupled behavior-disease dynamics near a tipping point

Understanding the interplay between social activities and disease dynamics is crucial for effective public health interventions. Recent studies using coupled behavior-disease models assumed homogeneous populations. However, heterogeneity in population, such as different social groups, cannot be ignored. In this study, we divided the population into social media users and non-users, and investigated the impact of homophily (the tendency for individuals to associate with others similar to themselves) and online events on disease dynamics. Our results reveal that homophily hinders the adoption of vaccinating strategies, hastening the approach to a tipping point after which the population converges to an endemic equilibrium with no vaccine uptake. Furthermore, we find that online events can significantly influence disease dynamics, with early discussions on social media platforms serving as an early warning signal of potential disease outbreaks. Our model provides insights into the mechanisms underlying these phenomena and underscores the importance of considering homophily in disease modeling and public health strategies.

Impact of deforestation and climate on spatio-temporal spread of dengue fever in Mexico

Dengue prevalence results from the interaction of multiple socio-environmental variables which influence its spread. This study investigates the impact of forest loss, precipitation, and temperature on dengue incidence in Mexico from 2010 to 2020 using a Bayesian hierarchical spatial model. Three temporal structures—AR1, RW1, and RW2—were compared, with RW2 showing superior performance. Findings indicate that a 1 % loss of municipal forest cover correlates with a 16.9 % increase in dengue risk. Temperature also significantly affects the vectors' ability to initiate and maintain outbreaks, highlighting the significant role of environmental factors. The research emphasizes the importance of multilevel modeling, finer temporal data resolution, and understanding deforestation causes to enhance the predictability and effectiveness of public health interventions. As dengue continues affecting global populations, particularly in tropical and subtropical regions, this study contributes insights, advocating for an integrated approach to health and environmental policy to mitigate the impact of vector-borne diseases.

A Geospatial Framework of Food Demand Mapping

Spatial mapping of food demand is essential for understanding and addressing disparities in food accessibility, which significantly impact public health and nutrition. This research presents an innovative geospatial framework designed to map food demand, integrating individual dietary behaviors with advanced spatial analysis techniques. This study analyzes the spatial distribution of eating habits across Lithuania using a geospatial approach. The methodology involves dividing Lithuania into 60,000 points and interpolating survey data with Shepard’s operator, which relies on a weighted average of values at data points. This flexible approach allows for adjusting the number of points based on spatial resolution and sample size, enhancing the reliability and applicability of the generated maps. The procedure includes generating a structured grid system, incorporating measurements into the grid, and applying Shepard’s operator for interpolation, resulting in precise representations of food demand. This framework provides a comprehensive understanding of dietary behaviors, informing targeted policy interventions to improve food accessibility and nutrition. Traditional food spatial mapping approaches are often limited to specific polygons and lack the flexibility to achieve high granular detail. By applying advanced interpolation techniques and ensuring respondent location data without breaching privacy concerns, this study creates high-resolution maps that accurately represent regional differences in eating habits. The methodology’s flexibility allows for adjustments in spatial resolution and sample size, enhancing the maps’ validity and applicability. This novel approach facilitates the creation of detailed food demand maps at any granular level, providing valuable insights for policymakers and stakeholders. These insights enable the development of targeted strategies to improve food accessibility and nutrition. Additionally, the obtained information can be used for computer simulations to further analyze and predict food demand scenarios. By leveraging spatial data integration, this study contributes to a deeper understanding of the complex dynamics of food demand, identifying critical areas such as food deserts and swamps, and paving the way for more effective public health interventions and policies aimed at achieving equitable food distribution and better nutritional outcomes.

Creating, publishing, and spreading processes of health-related contents in internet news sites: evaluation of the opinions of actors in health communication.

The accuracy and reliability of health information disseminated through news is crucial, as it directly impacts both individual and societal health outcomes. This study aims to analyze the publication process of health content in Türkiye and its implications for public health. By examining the perspectives of various health communication stakeholders, the study seeks to identify existing issues and propose potential solutions. The research uses a mixed-methods approach, including baseline content analysis of 846 news by 133 criteria, quantitative research with 78 participants encompassing bureaucrats, academics, journalists, and health association members, and 15 in-depth interviews for comprehensive insights. The content analysis indicated that 23.2% of the analyzed news articles lacked credible sources, while 63% did not mention the author's name. A striking 96.2% of respondents stated that inaccurate health news poses a risk to public health, emphasizing the urgent need for standardized reporting practices. The majority (90.9%) pinpointed the media as the primary catalysts for infodemic spread, with 93.5% citing gatekeepers as barriers to accurate information. Eroding trust in media, fueled by unethical practices, harms both media credibility and effective public health interventions. The study underscores the necessity for a collaborative approach among public institutions, academia, and media, focusing on responsibility, regulation, and sanctions against the infodemic. The research advocates for a balanced approach that prioritizes health rights and press freedom within a stakeholder-driven framework, highlighting that legislation alone cannot fully enhance the digital information ecosystem.

The importance of the incubation time distribution in compartmental epidemiological models

This study investigates the utilization of various mathematical models for comprehending and managing outbreaks of infectious diseases, with a specific focus on how different distributions of incubation times influence predictions regarding epidemics. Two methodologies are examined: a compartmental SEnIR ODE model, which represents an enhanced version of the mean-field SEIR model, and a stochastic agent-based complex network model. Our findings demonstrate that the selection of diverse incubation time distributions can result in noteworthy discrepancies in critical epidemic forecasts, highlighting the crucial role of precise modeling in shaping effective public health interventions. The research underscores the necessity of integrating authentic distribution patterns into epidemic modeling to increase its reliability and applicability.

Enhancing Citizen Health Literacy Through Digital Technology.

Contemporary society faces significant public health challenges with prevalent unhealthy behaviors and environmental risks leading non-communicable diseases. This study assesses health awareness campaigns'to effectiveness in a specific Taiwanese municipality, focusing on the impact of a health care management system. Objectives include enhancing public health literacy and evaluating community health promotion. The proposed system includes a personalized assistant app, personnel education, and events covering topics like hypertension, diabetes, diet, exercise, and chronic disease prevention. In summary, the research advocates for effective public health interventions, integrating digital technologies, personalized health management, and community engagement to promote health literacy and healthier lifestyles.

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.

Effect of School-Based Educational Intervention on Childhood Obesity in Croatian Urban and Rural Settings.

Childhood obesity is a global public health concern. Development of effective public health interventions represents the only viable option for decreasing the prevalence of childhood obesity. The objective of this study was to assess the effectiveness of a school-based "PETICA-Play for Health" obesity prevention program in urban and rural areas in Croatia. This before-and-after study included 28 elementary schools in Croatia focused on pupils (n = 753, 2nd grade) and their guardians (n = 753) during the school year 2022/2023. "PETICA" multicomponent lessons and workshops (10) were implemented in the school curriculum and home settings (parents) as part of the intervention. Knowledge and lifestyle habit changes were evaluated via the questionnaire on knowledge and habits regarding nutrition and physical activity pre-/post-education. The Wilcoxon test for paired samples, Student's t-test (dependent and independent samples), multiple Mann Whitney U tests (p < 0.05), Spearman's correlation, and heatmaps were used. The analysis showed a significant increase in acquired knowledge among pupils (both settings) and habits in the rural setting (increase in sports activities participation, breakfast, and vegetables consumption (p < 0.001, p = 0.003, p = 0.004)), decrease of sweets and salty snacks intake (p = 0.027, p = 0.011), and also, the link between parents' and children's physical activity levels (p = 0.028 vs. p = 0.022), emphasizing the importance of parents as healthy lifestyle role-models. The study shows that "PETICA" is an efficient school-based educational model that contributes to positive changes in children's knowledge and lifestyle habits that are a prerequisite for childhood obesity prevention.

Maternal Malaria and the Risk of Subsequent Pregnancy Complications

Maternal malaria, primarily caused by Plasmodium falciparum, has well-documented immediate effects on pregnancy, including preterm birth, low birth weight, and placental dysfunction. However, its impact extends beyond the current pregnancy, potentially influencing outcomes in subsequent pregnancies. This review examines how maternal malaria affects future pregnancies, focusing on the mechanisms through which previous malaria infections contribute to subsequent complications. Key mechanisms include chronic placental damage, immune system alterations, and persistent inflammation, which can disrupt placental function and increase the risk of adverse outcomes such as preterm labor and placental abruption in future pregnancies. The review highlights evidence that women with a history of maternal malaria are at an elevated risk for complications in subsequent pregnancies, including an increased likelihood of preterm birth and low birth weight. Persistent placental damage and immune dysregulation resulting from past malaria infections contribute to these risks. Understanding these long-term effects is crucial for developing effective public health strategies and interventions aimed at mitigating the risks of adverse pregnancy outcomes in malaria-endemic regions. Keywords: Maternal malaria, Plasmodium falciparum, pregnancy complications, placental damage, immune dysregulation, chronic inflammation, preterm birth