Spatial and Temporal Patterns of Malaria in Phu Yen Province, Vietnam, from 2005 to 2016.
Ontology highlight
ABSTRACT:
SUBMITTER: Wangdi K
PROVIDER: S-EPMC7543816 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundThe transition from malaria control to elimination requires understanding and targeting interventions among high-risk populations. In Vietnam, forest-goers are often difficult to test, treat and follow-up for malaria because they are highly mobile. If undiagnosed, forest-goers can maintain parasite reservoirs and contribute to ongoing malaria transmission.MethodsA case-control study was conducted to identify malaria risk factors associated with forest-goers in three communes in Phu Yen Province, Vietnam. Cases (n = 81) were residents from the study area diagnosed with malaria and known to frequent forest areas. Controls (n = 94) were randomly selected forest-going residents from within the study area with no identified malaria infection. Participants were interviewed face-to-face using a standard questionnaire to identify malaria risk factors. Logistic regression was used to calculate odds ratios (ORs) and 95% CI for risk factors after adjusting for socio-demographic characteristics.ResultsAmong the cases, malaria infection varied by species: 66.7% were positive for Plasmodium falciparum, 29.6% for Plasmodium vivax, and 3.7% were diagnosed as mixed infection. Cases were less likely than controls to use treated nets (aOR = 0.31; 95% CI 0.12-0.80), work after dark (aOR = 2.93; 95% CI 1.35, 6.34), bath in a stream after dark (aOR = 2.44; 95% CI 1.02-5.88), and collect water after dark (aOR = 1.99; 95% CI 1.02-3.90).ConclusionsAs Vietnam moves toward malaria elimination, these findings can inform behaviour change communication and malaria prevention strategies, incorporating the risk of after-dark and water-related activities, in this priority and difficult-to-access population group.
| S-EPMC6923829 | biostudies-literature
Project description:BackgroundThe resurgence of mumps around the world occurs frequently in recent years. As the country with the largest number of cases in the world, the status of mumps epidemics in China is not yet clear. This study, taking the relatively serious epidemic province of Guangxi as the example, aimed to examine the spatiotemporal pattern and epidemiological characteristics of mumps, and provide a scientific basis for the effective control of this disease and formulation of related health policies.MethodsGeographic information system (GIS)-based spatiotemporal analyses, including spatial autocorrelation analysis, Kulldorff's purely spatial and space-time scan statistics, were applied to detect the location and extent of mumps high-risk areas. Spatial empirical Bayesian (SEB) was performed to smoothen the rate for eliminating the instability of small-area data.ResultsA total of 208,470 cases were reported during 2005 and 2016 in Guangxi. Despite the fluctuations in 2006 and 2011, the overall mumps epidemic continued to decline. Bimodal seasonal distribution (mainly from April to July) were found and students aged 5-9 years were high-incidence groups. Though results of the global spatial autocorrelation based on the annual incidence largely varied, the spatial distribution of the average annual incidence of mumps was nonrandom with the significant Moran's I. Spatial cluster analysis detected high-value clusters, mainly located in the western, northern and central parts of Guangxi. Spatiotemporal scan statistics identified almost the same high-risk areas, and the aggregation time was mainly concentrated in 2009-2012.ConclusionThe incidence of mumps in Guangxi exhibited spatial heterogeneity in 2005-2016. Several spatial and spatiotemporal clusters were identified in this study, which might assist the local government to develop targeted health strategies, allocate health resources reasonably and increase the efficiency of disease prevention.
| S-EPMC6090846 | biostudies-literature
Project description:Helminths are among the most-commonly encountered disease-causing agents in dogs all over the world, including Vietnam. A cross-sectional survey was carried out between March and December 2019 in four dog slaughter-slabs in Yen Lap District, Phu Tho Province, northern Vietnam, aiming to reveal the occurrence of gastrointestinal helminths in dogs. Worms were collected from 350 dog samples and identified by morphological techniques followed by molecular methods for tapeworms and hookworms. Nine species, including Taenia hydatigena, Spirometra erinaceieuropaei, Dipylidium caninum, Spirocerca lupi, Ancylostoma ceylanicum, Ancylostoma caninum, Toxocara canis, Toxascaris leonina and Trichuris vulpis, were found to occur in this area. Infection with at least one worm species was found in 272 dogs (77.7%; 95% Wilson score CI: 73.1-81.8%). The most common species was A. ceylanicum, with a prevalence of 45.1% (95% Wilson score CI: 40.0-50.4%) while T. hydatigena had a very low prevalence of 0.3% (95% Exact CI: 0.0-1.6%). Co-infection with hookworms and D. caninum was common. Five of the recovered helminth species have a zoonotic potential. Control of these parasites is necessary in order to protect human and animal health.
| S-EPMC9167857 | biostudies-literature
Project description:BackgroundAlthough anthrax is endemic in Lesotho, limited information is available on the patterns of the disease among livestock animals. This study investigated temporal patterns of anthrax outbreaks and cases among livestock animals in Lesotho.MethodsSecondary data of anthrax outbreaks reported to the Department of Livestock Services between January 2005 and December 2016 was used for this study. Proportions of anthrax outbreaks and cases, and their corresponding 95% confidence interval were calculated and compared across year, season, month and region using the Chi-square or Fisher's exact test. The autoregression model was used to evaluate annual trends of anthrax outbreaks and cases.ResultsA total of 38 outbreaks were reported in the Lowlands districts of Lesotho. District was significantly (p<0.0001) associated with outbreaks and cases, with the highest proportions of outbreaks (52.6%) and cases (70.2%) reported in Maseru. Significantly (p = 0.0004) higher proportions of anthrax outbreaks (78.9%) and cases (95.1%) were reported in the rainy-hot season compared to the dry-cold season. Five hundred and twenty-six (n = 526) anthrax cases were reported with significantly (p<0.0001) higher proportion of cases (70.3%) in cattle compared to other species. Higher proportion of anthrax cases (35.9%) were reported in 2008 and during the months of February (30.8%) and April (30.2%). There was no significant annual trend in anthrax outbreaks (r = 0.0282; p = 0.6213) and cases (r = 0.0873; p = 0.3512) over the study period.ConclusionThe burden of anthrax in Lesotho is significantly higher in cattle. Anthrax outbreaks occur only in the lowland districts and follow a seasonal pattern. Therefore, more effort should be targeted at curbing the disease in cattle and the lowlands districts. Furthermore, there should be heightened monitoring of cases in the rainy season to ensure that resultant carcasses are disposed of appropriately to minimise future outbreaks.
| S-EPMC6200195 | biostudies-literature
Project description:BACKGROUND:Although anthrax occurs globally, the burden of the disease remains particularly high in Africa. Furthermore, the disease anthrax has significant public health and economic implications. However, sufficient attention has not been given to the geographic distribution of anthrax outbreaks and cases in Lesotho. Therefore, this study investigates the spatial patterns of anthrax outbreaks and cases among livestock in Lesotho from 2005 to 2016. METHODS:A cross-sectional study design was adopted to realise the objectives of this study using retrospective data of anthrax outbreaks and cases recorded by the Department of Livestock Services (DLS) between 2005 and 2016. Anthrax outbreaks were geo-coded at village level and aggregated at district level. Proportions and 95% CI of anthrax outbreaks and cases by village and district were calculated. Cartographic maps displaying the distribution of anthrax outbreaks and cases at village and district level were constructed. RESULTS:A total of 38 outbreaks were reported over the study period, and they were clustered in the Lowlands districts of Lesotho. Most outbreaks (52.6%, 20/38) in livestock were reported in the Maseru district. The Leribe district reported the lowest proportions of outbreaks (5.3%, 2/38) and cases (0.6%, 3/526). At the village level, 18% (7/38) of outbreaks were in Maseru Urban, followed by Ratau (16%, 6/38) and Mofoka (13%, 5/38). The Maseru district reported the highest (1.3%, 369/29,070) proportion of cases followed by Mafeteng (0.9%, 73/8530). The village with the most cases was Kolo (10.5%, 21/200), followed by Thaba-Chitja (7.7%, 33/430). CONCLUSION:Anthrax outbreaks and cases exclusively occur in the Lowlands districts of Lesotho, with villages such as Mahobong, Pitseng, Kolo, and Thaba-Chitja having a higher risk of anthrax disease. Findings of the present study have serious public health implications in light of the fact that between 2003 and 2008 Lesotho's main abattoir was closed; hence, most of the meat in Lesotho was imported and/or sourced from the informal slaughter facilities. Much larger studies are needed to further investigate factors contributing to spatial disparities in anthrax outbreaks and cases observed in this study. Findings of the present study can be used to guide the formulation of a policy on prevention and control of anthrax in Lesotho.
| S-EPMC7588925 | biostudies-literature
Project description:BackgroundThe objective of this study is to analyze the spatial and temporal patterns of malaria incidence as to determine the means by which climatic factors such as temperature, rainfall and humidity affect its distribution in Maputo province, Mozambique.MethodsThis study presents a model of malaria that evolves in space and time in Maputo province-Mozambique, over a ten years period (1999-2008). The model incorporates malaria cases and their relation to environmental variables. Due to incompleteness of climatic data, a multiple imputation technique is employed. Additionally, the whole province is interpolated through a Gaussian process. This method overcomes the misalignment problem of environmental variables (available at meteorological stations--points) and malaria cases (available as aggregates for every district--area). Markov Chain Monte Carlo (MCMC) methods are used to obtain posterior inference and Deviance Information Criteria (DIC) to perform model comparison.ResultsA Bayesian model with interaction terms was found to be the best fitted model. Malaria incidence was associated to humidity and maximum temperature. Malaria risk increased with maximum temperature over 28 °C (relative risk (RR) of 0.0060 and 95% Bayesian credible interval (CI) of 0.00033-0.0095) and humidity (relative risk (RR) of 0.00741 and 95% Bayesian CI 0.005141-0.0093). The results would suggest that additional non-climatic factors including socio-economic status, elevation, etc. also influence malaria transmission in Mozambique.ConclusionsThese results demonstrate the potential of climate predictors particularly, humidity and maximum temperature in explaining malaria incidence risk for the studied period in Maputo province. Smoothed maps obtained as monthly average of malaria incidence allowed to visualize months of initial and peak transmission. They also illustrate a variation on malaria incidence risk that might not be related to climatic factors. However, these factors are still determinant for malaria transmission and intensity in the region.
| S-EPMC3161914 | biostudies-literature
Project description:Dengue remains an important public health problem in Timor-Leste, with several major epidemics occurring over the last 10 years. The aim of this study was to identify dengue clusters at high geographical resolution and to determine the association between local environmental characteristics and the distribution and transmission of the disease.Notifications of dengue cases that occurred from January 2005 to December 2013 were obtained from the Ministry of Health, Timor-Leste. The population of each suco (the third-level administrative subdivision) was obtained from the Population and Housing Census 2010. Spatial autocorrelation in dengue incidence was explored using Moran's I statistic, Local Indicators of Spatial Association (LISA), and the Getis-Ord statistics. A multivariate, Zero-Inflated, Poisson (ZIP) regression model was developed with a conditional autoregressive (CAR) prior structure, and with posterior parameters estimated using Bayesian Markov chain Monte Carlo (MCMC) simulation with Gibbs sampling.The analysis used data from 3206 cases. Dengue incidence was highly seasonal with a large peak in January. Patients ? 14 years were found to be 74% [95% credible interval (CrI): 72-76%] less likely to be infected than those < 14 years, and females were 12% (95% CrI: 4-21%) more likely to suffer from dengue as compared to males. Dengue incidence increased by 0.7% (95% CrI: 0.6-0.8%) for a 1 °C increase in mean temperature; and 47% (95% CrI: 29-59%) for a 1 mm increase in precipitation. There was no significant residual spatial clustering after accounting for climate and demographic variables.Dengue incidence was highly seasonal and spatially clustered, with positive associations with temperature, precipitation and demographic factors. These factors explained the observed spatial heterogeneity of infection.
| S-EPMC5755460 | biostudies-literature
Project description:BackgroundTuberculosis (TB) makes a big challenge to public health, especially in high TB burden counties of China and Greater Mekong Subregion (GMS). The aim of this study was to identify the spatial-temporal dynamic process and high-risk region of notified pulmonary tuberculosis (PTB), sputum smear-positive tuberculosis (SSP-TB) and sputum smear-negative tuberculosis (SSN-TB) cases in Yunnan, the south-western of China between years of 2005 to 2018. Meanwhile, to evaluate the similarity of prevalence pattern for TB among GMS.MethodsData for notified PTB were extracted from the China Information System for Disease Control and Prevention (CISDCP) correspond to population information in 129 counties of Yunnan between 2005 to 2018. Seasonally adjusted time series defined the trend cycle and seasonality of PTB prevalence. Kulldorff's space-time scan statistics was applied to identify temporal, spatial and spatial-temporal PTB prevalence clusters at county-level of Yunnan. Pearson correlation coefficient and hierarchical clustering were applied to define the similarity of TB prevalence among borders with GMS.ResultThere were a total of 381,855 notified PTB cases in Yunnan, and the average prevalence was 59.1 per 100,000 population between 2005 to 2018. A declined long-term trend with seasonality of a peak in spring and a trough in winter for PTB was observed. Spatial-temporal scan statistics detected the significant clusters of PTB prevalence, the most likely cluster concentrated in the northeastern angle of Yunnan between 2011 to 2015 (RR = 2.6, P < 0.01), though the most recent cluster for PTB and spatial cluster for SSP-TB was in borders with GMS. There were six potential TB prevalence patterns among GMS.ConclusionThis study detected aggregated time interval and regions for PTB, SSP-TB, and SSN-TB at county-level of Yunnan province. Similarity prevalence pattern was found in borders and GMS. The localized prevention strategy should focus on cross-boundary transmission and SSN-TB control.
| S-EPMC6925503 | biostudies-literature
Project description:BackgroundDengue, a viral disease transmitted by Aedes mosquitoes, is an important public health concern throughout Thailand. Climate variables are potential predictors of dengue transmission. Associations between climate variables and dengue have usually been performed on large-scale first-level national administrative divisions, i.e. provinces. Here we analyze data on a finer spatial resolution in one province, which is often more relevant for effective disease control design. The objective of this study was to investigate the effect of seasonal variations, monthly climate variability, and to identify local clusters of symptomatic disease at the sub-district level based on reported dengue cases.MethodsData on dengue cases were retrieved from the national communicable disease surveillance system in Thailand. Between 2006 and 2016, 15,167 cases were recorded in 199 sub-districts of Khon Kaen Province, northeastern Thailand. Descriptive analyses included demographic characteristics and temporal patterns of disease and climate variables. The association between monthly disease incidence and climate variations was analyzed at the sub-district level using Bayesian Poisson spatial regression. A hotspot analysis was used to assess the spatial patterns (clustered/dispersed/random) of dengue incidence.ResultsDengue was predominant in the 5-14 year-old age group (51.1%). However, over time, dengue incidence in the older age groups (> 15 years) gradually increased and was the most affected group in 2013. Dengue outbreaks coincide with the rainy season. In the spatial regression model, maximum temperature was associated with higher incidence. The hotspot analysis showed clustering of cases around the urbanized area of Khon Kaen city and in rural areas in the southwestern portion of the province.ConclusionsThere was an increase in the number of reported dengue cases in older age groups over the study period. Dengue incidence was highly seasonal and positively associated with maximum ambient temperature. However, climatic variables did not explain all the spatial variation of dengue in the province. Further analyses are needed to clarify the detailed effects of urbanization and other potential environmental risk factors. These results provide useful information for ongoing prediction modeling and developing of dengue early warning systems to guide vector control operations.
| S-EPMC6708185 | biostudies-literature
Project description:ObjectiveThis study attempts to analyze the spatial clustering and spatial-temporal distribution characteristics of hepatitis E (HE) at the county (city and district) level in Jiangsu province to provide a scientific basis for the prevention and control of HE.MethodThe information on HE cases reported in the Chinese Center for Disease Control and Prevention Information System from 2005 to 2020 was collected for spatial autocorrelation analysis and spatial-temporal clustering analysis.ResultFrom 2005 to 2020, 48,456 HE cases were reported in Jiangsu province, with an average annual incidence rate of 3.87/100,000. Male cases outnumbered female cases (2.46:1), and the incidence was highest in the 30-70 years of age group (80.50%). Farmers accounted for more than half of all cases (59.86%), and in terms of the average annual incidence, the top three cities were all in Zhenjiang city. Spatial autocorrelation analysis showed that Global Moran's I of HE incidence varied from 0.232 to 0.513 for the years. From 2005 to 2020, 31 counties (cities and districts) had high and statistically significant HE incidence, and two clustering areas were detected by spatial-temporal scanning.ConclusionHE incidence in Jiangsu province from 2005 to 2020 was stable, with age and gender differences, regional clustering, and spatial-temporal clustering. Further investigation of HE clustering areas is necessary to formulate corresponding targeted prevention and control measures.
| S-EPMC10442811 | biostudies-literature