International Journal of Health Geographics最新文献

{"title":"Designing a clustering algorithm for optimizing health station locations.","authors":"Pasi Fränti, Sami Sieranoja, Tiina Laatikainen","doi":"10.1186/s12942-025-00390-1","DOIUrl":"https://doi.org/10.1186/s12942-025-00390-1","url":null,"abstract":"<p><p>In this paper, we define the optimization of health station locations as a clustering problem. We design a robust algorithm for the problem using a pre-calculated overhead graph for fast distance calculations and apply a robust clustering algorithm called random swap to provide accurate optimization results. We study the effect of three cost functions (Euclidean distance, squared Euclidean distance, travel cost) using real patient locations in North Karelia, Finland. We compare the optimization results with the existing health station locations. We found that the algorithm optimized the locations beyond administrative borders and strongly utilized the transport network. The results can provide additional insight for the decision-makers.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"24 1","pages":"4"},"PeriodicalIF":3.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Different environmental factors predict the occurrence of tick-borne encephalitis virus (TBEV) and reveal new potential risk areas across Europe via geospatial models.","authors":"Patrick H Kelly, Rob Kwark, Harrison M Marick, Julie Davis, James H Stark, Harish Madhava, Gerhard Dobler, Jennifer C Moïsi","doi":"10.1186/s12942-025-00388-9","DOIUrl":"10.1186/s12942-025-00388-9","url":null,"abstract":"<p><strong>Background: </strong>Tick-borne encephalitis (TBE) is the most serious tick-borne viral disease in Europe. Identifying TBE risk areas can be difficult due to hyper focal circulation of the TBE virus (TBEV) between mammals and ticks. To better define TBE hazard risks and elucidate regional-specific environmental factors that drive TBEV circulation, we developed two machine-learning (ML) algorithms to predict the habitat suitability (maximum entropy), and occurrence of TBEV (extreme gradient boosting) within distinct European regions (Central Europe, Nordics, and Baltics) using local variables of climate, habitat, topography, and animal hosts and reservoirs.</p><p><strong>Methods: </strong>Geocoordinates that reported the detection of TBEV in ticks or rodents and anti-TBEV antibodies in rodent reservoirs in 2000 or later were extracted from published and grey literature. Region-specific ML models were defined via K-means clustering and trained according to the distribution of extracted geocoordinates relative to explanatory variables in each region. Final models excluded colinear variables and were evaluated for performance.</p><p><strong>Results: </strong>521 coordinates (455 ticks; 66 rodent reservoirs) of TBEV occurrence (2000-2022) from 100 records were extracted for model development. The models had high performance across regions (AUC: 0.72-0.92). The strongest predictors of habitat suitability and TBEV occurrence in each region were associated with different variable categories: climate variables were the strongest predictors of habitat suitability in Central Europe; rodent reservoirs and elevation were strongest in the Nordics; and animal hosts and land cover contributed most to the Baltics. The models predicted several areas with few or zero reported TBE incidence as highly suitable (≥ 60%) TBEV habitats or increased probability (≥ 25%) of TBEV occurrence including western Norway coastlines, northern Denmark, northeastern Croatia, eastern France, and northern Italy, suggesting potential capacity for locally-acquired autochthonous TBEV infections or possible underreporting of TBE cases based on reported human surveillance data.</p><p><strong>Conclusions: </strong>This study shows how varying environmental factors drive the occurrence of TBEV within different European regions and identifies potential new risk areas for TBE. Importantly, we demonstrate the utility of ML models to generate reliable insights into TBE hazard risks when trained with sufficient explanatory variables and to provide high resolution and harmonized risk maps for public use.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"24 1","pages":"3"},"PeriodicalIF":3.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11908066/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143634887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial equity and factors that influence the distribution of elderly care institutions in China.","authors":"Xiaohan Li, Weishan Qin, Hongqiang Jiang, Fengxun Qi, Zhiqi Han","doi":"10.1186/s12942-025-00389-8","DOIUrl":"10.1186/s12942-025-00389-8","url":null,"abstract":"<p><strong>Background: </strong>With China becoming an aging society, the number of elderly care institutions (ECIs) is continuously increasing in response to the growing population of older persons. However, regional disparities may lead to an uneven distribution of ECIs, which could affect equity in care. This study identified the limiting factors in the development of ECIs across different regions, thereby promoting equity in accessing care for the older population.</p><p><strong>Methods: </strong>This study utilised point-of-interest data on ECIs in China from 2018 to 2022. The spatiotemporal distribution of ECIs and the causes of disparities were assessed along four dimensions-economy, population, society, and environment-using research methods such as the standard deviation ellipse, rank-size rule, and multiscale geographically weighted regression.</p><p><strong>Results: </strong>There were significant differences between the ECIs of the eastern and western regions in China. The eastern region had a denser distribution and higher concentrations in primary cities. The proportion of the older population, regional economic development, and household income are crucial for a balanced distribution of ECIs, whereas the environmental impact is relatively minor.</p><p><strong>Conclusions: </strong>The number of ECIs in China continues to increase, but improvements in regional disparities remain insignificant. The construction of ECIs is influenced by various factors; in underdeveloped regions, government initiatives are crucial for promoting equity in care for older persons.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"24 1","pages":"2"},"PeriodicalIF":3.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11877872/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of individual Google Location History data to identify consumer encounters with food outlets.","authors":"Olufunso Oje, Ofer Amram, Perry Hystad, Assefaw Gebremedhin, Pablo Monsivais","doi":"10.1186/s12942-025-00387-w","DOIUrl":"10.1186/s12942-025-00387-w","url":null,"abstract":"<p><strong>Background: </strong>Addressing key behavioral risk factors for chronic diseases, such as diet, requires innovative methods to objectively measure dietary patterns and their upstream determinants, notably the food environment. Although GIS techniques have pushed the boundaries by mapping food outlet availability, they often simplify food access dynamics to the vicinity of home addresses, possibly misclassifying neighborhood effects. Leveraging Google Location History Timeline (GLH) data offers a novel approach to assess long-term patterns of food outlet utilization at an individual level, providing insights into the relationship between food environment interactions, diet quality, and health outcomes.</p><p><strong>Methods: </strong>We leveraged GLH data previously collected from a sub-set of participants in the Washington State Twin Registry (WSTR). GLH included more than 287 million location records from 357 participants. We developed methods to identify visits to food outlets using outlet-specific buffer zones applied to the InfoUSA data on food outlet locations. This methodology involved the application of minimum and maximum stay durations, along with revisit intervals. We calculated metrics from the GLH data to detect frequency of visits to different food outlet classifications (e.g. grocery stores, fast food, convenience stores) important to health. Several sensitivity analyses were conducted to examine the robustness of our food outlet metrics and to examine visits occurring within 1 and 2.5 km of residential locations.</p><p><strong>Results: </strong>We identified 156,405 specific food outlet visits for the 357 study participants. 60% were full-service restaurants, 15% limited-service restaurants, and 16% supermarkets. Mean visits per person per month to any food outlet was 12.795. Only 8, 10 and 11% of full-service restaurants, limited-service restaurants, and supermarkets, respectively, occurred within 1 km of residential locations.</p><p><strong>Conclusions: </strong>GLH data presents a novel method to assess individual-level food utilization behaviors.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"24 1","pages":"1"},"PeriodicalIF":3.0,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11830192/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143426527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial analysis and mapping of malaria risk areas using geospatial technology in the case of Nekemte City, western Ethiopia.","authors":"Dechasa Diriba, Shankar Karuppannan, Teferi Regasa, Melion Kasahun","doi":"10.1186/s12942-024-00386-3","DOIUrl":"10.1186/s12942-024-00386-3","url":null,"abstract":"<p><strong>Background: </strong>Malaria is a major public health issue in Nekemte City, western Ethiopia, with various environmental and social factors influencing transmission patterns. Effective control and prevention strategies require precise identification of high-risk areas. This study aims to map malaria risk zones in Nekemte City using geospatial technologies, including remote sensing and Geographic Information Systems (GIS), to support targeted interventions and resource allocation.</p><p><strong>Methods: </strong>The study integrated environmental and social factors to assess malaria risk in the city. Environmental factors, including climatic and geographic characteristics, such as elevation, rainfall patterns, temperature, slope, and proximity to river, were selected based on experts' opinions and literature review. These factors were weighted using the analytic hierarchy process according to their relative influence on malaria hazard susceptibility. Social factors considered within the GIS framework focused on human settlements and access to resources. These included population density, proximity to health facilities, and proximity to roads. The malaria risk analysis incorporated hazard and vulnerability layers, along with Land use/cover (LULC) data. A weighted overlay analysis method combined these layers and generate the final malaria risk map.</p><p><strong>Results: </strong>The malaria risk map identified that 18.2% (10.5 km²) of the study area was at very high risk, 18.8% (10.9 km²) at high risk, 30.4% (17.8 km²) at moderate risk, 19.8% (11.5 km²) at low risk, and 12.6% (7.3 km²) at very low risk. A combined 37% (21.4 km²) of Nekemte City was classified as at high to very high malaria risk, highlighting key areas for intervention.</p><p><strong>Conclusions: </strong>This malaria risk map offers a valuable tool for malaria control and elimination efforts in Nekemte City. By identifying high-risk areas, the map provides actionable insights that can guide local health strategies, optimize resource distribution, and improve the efficiency of interventions. These findings contribute to enhanced public health planning and can support future regional malaria control initiatives.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"23 1","pages":"27"},"PeriodicalIF":3.0,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11660687/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142865943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial dynamics of Culex quinquefasciatus abundance: geostatistical insights from Harris County, Texas.","authors":"Morgan Jibowu, Melissa S Nolan, Ryan Ramphul, Heather T Essigmann, Abiodun O Oluyomi, Eric L Brown, Maximea Vigilant, Sarah M Gunter","doi":"10.1186/s12942-024-00385-4","DOIUrl":"10.1186/s12942-024-00385-4","url":null,"abstract":"<p><p>Mosquito-borne diseases pose a significant public health threat, prompting the need to pinpoint high-risk areas for targeted interventions and environmental control measures. Culex quinquefasciatus is the primary vector for several mosquito-borne pathogens, including West Nile virus. Using spatial analysis and modeling techniques, we investigated the geospatial distribution of Culex quinquefasciatus abundance in the large metropolis of Harris County, Texas, from 2020 to 2022. Our geospatial analysis revealed clusters of high mosquito abundance, predominantly located in central Houston and the north-northwestern regions of Harris County, with lower mosquito abundance observed in the western and southeastern areas. We identified persistent high mosquito abundance in some of Houston's oldest neighborhoods, highlighting the importance of considering socioeconomic factors, the built environment, and historical urban development patterns in understanding vector ecology. Additionally, we observed a positive correlation between mosquito abundance and neighborhood-level socioeconomic status with the area deprivation index explaining between 22 and 38% of the variation in mosquito abundance (p-value < 0.001). This further underscores the influence of the built environment on vector populations. Our study emphasizes the utility of spatial analysis, including hotspot analysis and geostatistical interpolation, for understanding mosquito abundance patterns to guide resource allocation and surveillance efforts. Using geostatistical analysis, we discerned fine-scale geospatial patterns of Culex quinquefasciatus abundance in Harris County, Texas, to inform targeted interventions in vulnerable communities, ultimately reducing the risk of mosquito exposure and mosquito-borne disease transmission. By integrating spatial analysis with epidemiologic risk assessment, we can enhance public health preparedness and response efforts to prevent and control mosquito-borne disease.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"23 1","pages":"26"},"PeriodicalIF":3.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11619097/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142786594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Light at night exposure and risk of dementia conversion from mild cognitive impairment in a Northern Italy population.","authors":"Tommaso Filippini, Sofia Costanzini, Annalisa Chiari, Teresa Urbano, Francesca Despini, Manuela Tondelli, Roberta Bedin, Giovanna Zamboni, Sergio Teggi, Marco Vinceti","doi":"10.1186/s12942-024-00384-5","DOIUrl":"10.1186/s12942-024-00384-5","url":null,"abstract":"<p><strong>Background: </strong>A few studies have suggested that light at night (LAN) exposure, i.e. lighting during night hours, may increase dementia risk. We evaluated such association in a cohort of subjects diagnosed with mild cognitive impairment (MCI).</p><p><strong>Methods: </strong>We recruited study participants between 2008 and 2014 at the Cognitive Neurology Clinic of Modena Hospital, Northern Italy and followed them for conversion to dementia up to 2021. We collected their residential history and we assessed outdoor artificial LAN exposure at subjects' residences using satellite imagery data available from the Visible Infrared Imaging Radiometer Suite (VIIRS) for the period 2014-2022. We assessed the relation between LAN exposure and cerebrospinal fluid biomarkers. We used a Cox-proportional hazards model to compute the hazard ratio (HR) of dementia with 95% confidence interval (CI) according to increasing LAN exposure through linear, categorical, and non-linear restricted-cubic spline models, adjusting by relevant confounders.</p><p><strong>Results: </strong>Out of 53 recruited subjects, 34 converted to dementia of any type and 26 converted to Alzheimer's dementia. Higher levels of LAN were positively associated with biomarkers of tau pathology, as well as with lower concentrations of amyloid β_1-42 assessed at baseline. LAN exposure was positively associated with dementia conversion using linear regression model (HR 1.04, 95% CI 1.01-1.07 for 1-unit increase). Using as reference the lowest tertile, subjects at both intermediate and highest tertiles of LAN exposure showed increased risk of dementia conversion (HRs 2.53, 95% CI 0.99-6.50, and 3.61, 95% CI 1.34-9.74). In spline regression analysis, the risk linearly increased for conversion to both any dementia and Alzheimer's dementia above 30 nW/cm²/sr of LAN exposure. Adding potential confounders including traffic-related particulate matter, smoking status, chronic diseases, and apolipoprotein E status to the multivariable model, or removing cases with dementia onset within the first year of follow-up did not substantially alter the results.</p><p><strong>Conclusion: </strong>Our findings suggest that outdoor artificial LAN may increase dementia conversion, especially above 30 nW/cm²/sr, although the limited sample size suggests caution in the interpretation of the results, to be confirmed in larger investigations.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"23 1","pages":"25"},"PeriodicalIF":3.0,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11585219/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of an approach to forecast future takeaway outlet growth around schools and population exposure to takeaways in England.","authors":"Bochu Liu, Oliver Mytton, John Rahilly, Ben Amies-Cull, Nina Rogers, Tom Bishop, Michael Chang, Steven Cummins, Daniel Derbyshire, Suzan Hassan, Yuru Huang, Antonieta Medina-Lara, Bea Savory, Richard Smith, Claire Thompson, Martin White, Jean Adams, Thomas Burgoine","doi":"10.1186/s12942-024-00383-6","DOIUrl":"10.1186/s12942-024-00383-6","url":null,"abstract":"<p><strong>Background: </strong>Neighbourhood exposure to takeaways can contribute negatively to diet and diet-related health outcomes. Urban planners within local authorities (LAs) in England can modify takeaway exposure through denying planning permission to new outlets in management zones around schools. LAs sometimes refer to these as takeaway \"exclusion zones\". Understanding the long-term impacts of this intervention on the takeaway retail environment and health, an important policy question, requires methods to forecast future takeaway growth and subsequent population-level exposure to takeaways. In this paper we describe a novel two-stage method to achieve this.</p><p><strong>Methods: </strong>We used historic data on locations of takeaways and a time-series auto-regressive integrated moving average (ARIMA) model, to forecast numbers of outlets within management zones to 2031, based on historical trends, in six LAs with different urban/rural characteristics across England. Forecast performance was evaluated based on root mean squared error (RMSE) and mean absolute scaled error (MASE) scores in time-series cross-validation. Using travel-to-work data from the 2011 UK census, we then translated these forecasts of the number of takeaways within management zones into population-level exposures across home, work and commuting domains.</p><p><strong>Results: </strong>Our ARIMA models outperformed exponential smoothing equivalents according to RMSE and MASE. The model was able to forecast growth in the count of takeaways up to 2031 across all six LAs, with variable growth rates by RUC (min-max: 39.4-79.3%). Manchester (classified as a non-London urban with major conurbation LA) exhibited the highest forecast growth rate (79.3%, 95% CI 61.6, 96.9) and estimated population-level takeaway exposure within management zones, increasing by 65.5 outlets per capita to 148.2 (95% CI 133.6, 162.7) outlets. Overall, urban (vs. rural) LAs were forecast stronger growth and higher population exposures.</p><p><strong>Conclusions: </strong>Our two-stage forecasting approach provides a novel way to estimate long-term future takeaway growth and population-level takeaway exposure. While Manchester exhibited the strongest growth, all six LAs were forecast marked growth that might be considered a risk to public health. Our methods can be used to model future growth in other types of retail outlets and in other areas.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"23 1","pages":"24"},"PeriodicalIF":3.0,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11550555/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142630355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using spatial video and deep learning for automated mapping of ground-level context in relief camps.","authors":"Jayakrishnan Ajayakumar, Andrew J Curtis, Felicien M Maisha, Sandra Bempah, Afsar Ali, Naveen Kannan, Grace Armstrong, John Glenn Morris","doi":"10.1186/s12942-024-00382-7","DOIUrl":"10.1186/s12942-024-00382-7","url":null,"abstract":"<p><strong>Background: </strong>The creation of relief camps following a disaster, conflict or other form of externality often generates additional health problems. The density of people in a highly stressed environment with questionable safe food and water access presents the potential for infectious disease outbreaks. These camps are also not static data events but rather fluctuate in size, composition, and level and quality of service provision. While contextualized geospatial data collection and mapping are vital for understanding the nature of these camps, various challenges, including a lack of data at the required spatial or temporal granularity, as well as the issue of sustainability, can act as major impediments. Here, we present the first steps toward a deep learning-based solution for dynamic mapping using spatial video (SV).</p><p><strong>Methods: </strong>We trained a convolutional neural network (CNN) model on a SV dataset collected from Goma, Democratic Republic of Congo (DRC) to identify relief camps from video imagery. We developed a spatial filtering approach to tackle the challenges associated with spatially tagging objects such as the accuracy of global positioning system and positioning of camera. The spatial filtering approach generates smooth surfaces of detection, which can further be used to capture changes in microenvironments by applying techniques such as raster math.</p><p><strong>Results: </strong>The initial results suggest that our model can detect temporary physical dwellings from SV imagery with a high level of precision, recall, and object localization. The spatial filtering approach helps to identify areas with higher concentrations of camps and the web-based tool helps to explore these areas. The longitudinal analysis based on applying raster math on the detection surfaces revealed locations, which had a considerable change in the distribution of tents over space and time.</p><p><strong>Conclusions: </strong>The results lay the groundwork for automated mapping of spatial features from imagery data. We anticipate that this work is the building block for a future combination of SV, object identification and automatic mapping that could provide sustainable data generation possibilities for challenging environments such as relief camps or other informal settlements.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"23 1","pages":"23"},"PeriodicalIF":3.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11536618/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The influence of malaria control interventions and climate variability on changes in the geographical distribution of parasite prevalence in Kenya between 2015 and 2020.","authors":"Bryan O Nyawanda, Sammy Khagayi, Eric Ochomo, Godfrey Bigogo, Simon Kariuki, Stephen Munga, Penelope Vounatsou","doi":"10.1186/s12942-024-00381-8","DOIUrl":"10.1186/s12942-024-00381-8","url":null,"abstract":"<p><strong>Background: </strong>The burden of malaria in Kenya was showing a declining trend, but appears to have reached a plateau in recent years. This study estimated changes in the geographical distribution of malaria parasite risk in the country between the years 2015 and 2020, and quantified the contribution of malaria control interventions and climatic/ environmental factors to these changes.</p><p><strong>Methods: </strong>Bayesian geostatistical models were used to analyse the Kenyan 2015 and 2020 Malaria Indicator Survey (MIS) data. Bivariate models were fitted to identify the most important control intervention indicators and climatic/environmental predictors of parasitaemia risk by age groups (6-59 months and 5-14 years). Parasitaemia risk and the number of infected children were predicted over a 1 × 1 km² grid. The probability of the decline in parasitaemia risk in 2020 compared to 2015 was also evaluated over the gridded surface and factors associated with changes in parasitaemia risk between the two surveys were evaluated.</p><p><strong>Results: </strong>There was a significant decline in the coverage of most malaria indicators related to Insecticide Treated Nets (ITN) and Artemisinin Combination Therapies (ACT) interventions. Overall, there was a 31% and 26% reduction in malaria prevalence among children aged < 5 and 5-14 years, respectively. Among younger children, the highest reduction (50%) and increase (41%) were in the low-risk and semi-arid epi zones, respectively; while among older children there was increased risk in both the low-risk (83%) and semi-arid (100%) epi zones. Increase in nightlights and the proportion of individuals using ITNs in 2020 were associated with reduced parasitaemia risk.</p><p><strong>Conclusion: </strong>Increased nightlights and ITN use could have led to the reduction in parasitaemia risk. However, the reduction is heterogeneous and there was increased risk in northern Kenya. Taken together, these results suggest that constant surveillance and re-evaluation of parasite and vector control measures in areas with increased transmission is necessary. The methods used in this analysis can be employed in other settings.</p>","PeriodicalId":48739,"journal":{"name":"International Journal of Health Geographics","volume":"23 1","pages":"22"},"PeriodicalIF":3.0,"publicationDate":"2024-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11514743/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142511033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}