Dataset Information

Estimating the spatial risk of tuberculosis distribution in Gurage zone, southern Ethiopia: a geostatistical kriging approach.

ABSTRACT: BACKGROUND:In low-income countries it is difficult to obtain complete data that show spatial heterogeneity in the risk of tuberculosis within-and-between smaller administrative units. This may contribute to the partial effectiveness of tuberculosis control programs. The aim of this study was to estimate the spatial risk of tuberculosis distribution in Gurage Zone, Southern Ethiopia using limited spatial datasets. METHODS:A total of 1601 patient data that were retrieved from unit tuberculosis registers were included in the final analyses. The population and geo-location data were obtained from the Central Statistical Agency of Ethiopia. Altitude data were extracted from ASTER Global Digital Elevation Model Version 2. Aggregated datasets from sample of 169(40%), 254(60%) and 338(80%) kebeles were used to estimate the spatial risk of TB distribution in the Gurage Zone by using a geostatistical kriging approach. The best set of input parameters were decided based on the lowest prediction error criteria of the cross-validation technique. ArcGIS 10.2 was used for the spatial data analyses. RESULTS:The best semivariogram models were the Pentaspherical, Rational Quadratic, and K-Bessel for the 40, 60 and 80% spatial datasets, respectively. The predictive accuracies of the models have improved with the true anisotropy, altitude and latitude covariates, the change in detrending pattern from local to global, and the increase in size of spatial dataset. The risk of tuberculosis was estimated to be higher at western, northwest, southwest and southeast parts of the study area, and crossed between high and low at west-central parts. CONCLUSION:This study has underlined that the geostatistical kriging approach can be applied to estimate the spatial risk of tuberculosis distribution in data limited settings. The estimation results may help local public health authorities measure burden of the disease at all locations, identify geographical areas that require more attention, and evaluate the impacts of intervention programs.

SUBMITTER: Tadesse S

PROVIDER: S-EPMC6019714 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Estimating the spatial risk of tuberculosis distribution in Gurage zone, southern Ethiopia: a geostatistical kriging approach.

Tadesse Sebsibe S Enqueselassie Fikre F Gebreyesus Seifu Hagos SH

BMC public health 20180625 1

<h4>Background</h4>In low-income countries it is difficult to obtain complete data that show spatial heterogeneity in the risk of tuberculosis within-and-between smaller administrative units. This may contribute to the partial effectiveness of tuberculosis control programs. The aim of this study was to estimate the spatial risk of tuberculosis distribution in Gurage Zone, Southern Ethiopia using limited spatial datasets.<h4>Methods</h4>A total of 1601 patient data that were retrieved from unit t ...[more]

PMID: 29940913

Similar Datasets

Project description:BACKGROUND:Tuberculosis (TB) is a disease of public health concern, with a varying distribution across settings depending on socio-economic status, HIV burden, availability and performance of the health system. Ethiopia is a country with a high burden of TB, with regional variations in TB case notification rates (CNRs). However, TB program reports are often compiled and reported at higher administrative units that do not show the burden at lower units, so there is limited information about the spatial distribution of the disease. We therefore aim to assess the spatial distribution and presence of the spatio-temporal clustering of the disease in different geographic settings over 10 years in the Sidama Zone in southern Ethiopia. METHODS:A retrospective space-time and spatial analysis were carried out at the kebele level (the lowest administrative unit within a district) to identify spatial and space-time clusters of smear-positive pulmonary TB (PTB). Scan statistics, Global Moran's I, and Getis and Ordi (Gi*) statistics were all used to help analyze the spatial distribution and clusters of the disease across settings. RESULTS:A total of 22,545 smear-positive PTB cases notified over 10 years were used for spatial analysis. In a purely spatial analysis, we identified the most likely cluster of smear-positive PTB in 192 kebeles in eight districts (RR= 2, p<0.001), with 12,155 observed and 8,668 expected cases. The Gi* statistic also identified the clusters in the same areas, and the spatial clusters showed stability in most areas in each year during the study period. The space-time analysis also detected the most likely cluster in 193 kebeles in the same eight districts (RR= 1.92, p<0.001), with 7,584 observed and 4,738 expected cases in 2003-2012. CONCLUSION:The study found variations in CNRs and significant spatio-temporal clusters of smear-positive PTB in the Sidama Zone. The findings can be used to guide TB control programs to devise effective TB control strategies for the geographic areas characterized by the highest CNRs. Further studies are required to understand the factors associated with clustering based on individual level locations and investigation of cases.

Project description:BackgroundIn 2011, the World Health Organization recognized podoconiosis as one of the neglected tropical diseases. Nonetheless, the number of people with podoconiosis and the geographical distribution of the disease is poorly understood. Based on a nationwide mapping survey and geostatistical modelling, we predict the prevalence of podoconiosis and estimate the number of cases across Ethiopia.MethodsWe used nationwide data collected in Ethiopia between 2008 and 2013. Data were available for 141,238 individuals from 1,442 villages in 775 districts from all nine regional states and two city administrations. We developed a geostatistical model of podoconiosis prevalence among adults (individuals aged 15 years or above), by combining environmental factors. The number of people with podoconiosis was then estimated using a gridded map of adult population density for 2015.ResultsPodoconiosis is endemic in 345 districts in Ethiopia: 144 in Oromia, 128 in Southern Nations, Nationalities and People's [SNNP], 64 in Amhara, 4 in Benishangul Gumuz, 4 in Tigray and 1 in Somali Regional State. Nationally, our estimates suggest that 1,537,963 adults (95% confidence intervals, 290,923-4,577,031 adults) were living with podoconiosis in 2015. Three regions (SNNP, Oromia and Amhara) contributed 99% of the cases. The highest proportion of individuals with podoconiosis resided in the SNNP (39%), while 32% and 29% of people with podoconiosis resided in Oromia and Amhara Regional States, respectively. Tigray and Benishangul Gumuz Regional States bore lower burdens, and in the remaining regions, podoconiosis was almost non-existent. Discussion: The estimates of podoconiosis cases presented here based upon the combination of currently available epidemiological data and a robust modelling approach clearly show that podoconiosis is highly endemic in Ethiopia. Given the presence of low cost prevention, and morbidity management and disability prevention services, it is our collective responsibility to scale-up interventions rapidly.

Project description:BackgroundTuberculosis remains a serious public health concern globally. The enormous social, economic, and health impacts of the diseases are attributed to the lack of updated data on the prevalence, geospatial distribution, population structures, and genotypic variants of the circulating M. tuberculosis.MethodsStructured questionnaire, mycobacterial culture, and standard 24-Mycobacterial Interspersed Repeated Units-Variable Number Tandem Repeats (MIRU-VNTR) were employed to collect sociodemographic characters, residence linked information, and genotype the isolates. The retrospective discrete Bernoulli model was used to identify the hot spot districts of sputum smear positivity, and Web-based Miru-VNTRPlus were used for the identification of lineages and sublineages.ResultsOut of 832 presumptive pulmonary tuberculosis (PTB) suspects, 119 (14.3%) were smear-positive. In the multivariate binary logistic model, PTB suspected patients in the age groups of 7-25 and 25-34 and those from rural residents were 4.53 (AOR = 4.53; 95% CI 2.25-9.13), 3.00 (AOR = 3.00; 95% CI 1.41-6.35) and 1.65 (AOR = 1.65; 95% CI 1.01-2.70) times at higher risk of turning smear-positive. Eleven (47.8%) districts of Arsi Zone were shown to have a high rate of clustering (RR = 2.27; 95% CI 1.62-3.2) of smear-positive PTB. Of 72 isolates queried for the lineage assignment, 59 (81.9%) were classified into the previously known lineages and 13 (18.1%) were not assigned to any known lineages. Overall, 42 (58.3%) belong to M. tuberculosis lineage 4 (Euro-American), 16 (22.2%) M. tuberculosis lineage 3 (Delhi/CAS), and 1 (1.4%) M. tuberculosis Lineage 1 (Indo-Oceanic/ East Africa Indian). Further classification to the sublineage indicates that the predominant lineage was Delhi/CAS comprising 16 (22.2%) isolates followed by 15 (20.8%) isolates belonging to Haarlem. The remaining isolates were distributed as 13 (18.1%) TUR, 6 (8.3%) LAM, 4 (5.5%) URAL, 4 (4.5%) NEW-1 and 1 (1.4%) EAI.ConclusionOur study showed higher smear-positive results among PTB suspected patients and remarkable spatial variation across districts of Arsi Zone in smear-positive PTB. This information together with the genotypic features could be used as input for the efforts of designing control strategies.

Project description:BackgroundCervical cancer is a malignant tumor of the lower-most part of the uterus and major cause of morbidity and mortality among women's in the world. Its high mortality rate in the globe can be reduced through comprehensive approaches' that include; primary prevention, early diagnosis, effective screening, and treatment packages. This study was aimed to assess the knowledge and practice of cervical cancer screening and its associated factors among reproductive age group women in districts of Gurage zone, Southern Ethiopia, 2019.MethodsA community-based cross-sectional study design was conducted from March 1-30, 2019. A total of 268 respondents were selected using a systematic sampling technique. Data was collected using pretested, semi-structured, and interviewer-administered questionnaires. Data were entered into Epi data version 3.1software and exported to SPSS 24 for analysis. Bivariate and multivariate analyses with a 95% confidence level was done and variables (P <0.05) were deemed statistically significant.ResultA total of 260 respondents participated in the study with a response rate of 97%. About 3.8% of the respondents had experiences cervical cancer screening and 26.2% of respondents had good knowledge. Early age at first sex [AOR = 6.05 (95%CI; 1.167-31.36)], having information about cervical cancer [(AOR = 10.2 (95% CI 1.9-96.4)], and multiple sexual partners [AOR = 3.96 (95% CI; 1.48-10.58)] were factors affecting the practice of cervical cancer screening. Being uneducated [AOR = 15.5 (95%CI; 3.82-62.967)], family history of cervical cancer [AOR = 14.158 (95%CI;3.88-51.7)], having plans to screen for cervical cancer [AOR = 0.352 (95%CI;.175-.710)], menarcheal age [AOR = 2.63 (95%CI;1.28-5.37)] and age at first sex [AOR = 3.17 (95%CI;1.283-7.837)] were factors affecting knowledge of cervical screening.ConclusionThe study findings indicate that respondents' practice and knowledge of cervical cancer is mainly affected by early age at first sex, having information about cervical cancer, multiple sexual partners, Educational status, family history of cervical cancer, having plans to screen for cervical cancer, age at first sex and age of menarche. Therefore, all concerned bodies need to focus on women in the reproductive age group to increase the level of knowledge and practice of cervical cancer screening through appropriate interventions.

Project description:BackgroundImmunization is essential to prevent between 2 and 3 million deaths globally each year and it is widely accepted that it is one of the most cost-effective health interventions. Despite all its advantages, immunization in Ethiopia is still far from the target set by the United Nations Sustainable Development Goals to achieve universal immunization by all countries in 2030. The 2016 Ethiopian Demographic and Health Survey (EDHS) reported an overall full immunization rate of only 38.3%. The objective of this study was to evaluate the spatial distribution of under immunization in 12 to 23 months old children and further identify the determinants of under immunization clustering in the Butajira Health and Demographic Surveillance Site (HDSS).MethodsWe conducted a community based sectional survey from March to April, 2016 in Butajira HDSS. We collected data on immunization status from a total of 482 children between the age of 12 to 23 months. We randomly selected household and interviewed mothers and /or observed vaccination cards when available to collect data on child's immunization status. We also collected the geographic location of all villages within the ten Kebeles using a Handheld Global Positioning System (GPS) (Garmin GPSMAP®). We analyzed the spatial distribution of under immunization and clustering using the SatScan® software which employs a purely spatial Bernoulli's model. We also ran a logistic regression model to help evaluate the causes of clustering.ResultsWe found that only 22.4% [95% CI: 18.9, 26.4%] of children were fully immunized. This study identified one significant cluster of under immunization among children 12-23 months of age within the Butajira HDSS (relative risk (RR) = 1.24,P < 0·01). We found that children residing in this cluster had more than 1.24 times risk of under immunization compared with children residing outside of the identified cluster. We found significant differences with regard to Maternal Tetanus Toxoid immunization status and place of delivery between cases found within a spatial cluster and cases found outside the cluster. For example, the odds of home delivery is more than two times [AOR 2.21: 95%CI; 1.06, 4.63] among children within an identified spatial cluster than the odds among children found outside the identified cluster.ConclusionsUnder immunization of 12-23 months old children and under immunization with specific vaccines such as Polio, BCG, DPT (1-3) and Measles clustered geographically. Spatial studies could be effective in identifying geographic areas of under immunization for targeted intervention like in this study to gear health education to the specific locality.

Project description:Smoothing methods have been developed to improve the reliability of risk cancer estimates from sparsely populated geographical entities. Filtering local details of the spatial variation of the risk leads however to the detection of larger clusters of low or high cancer risk while most spatial outliers are filtered out. Static maps of risk estimates and the associated prediction variance also fail to depict the uncertainty attached to the spatial distribution of risk values and does not allow its propagation through local cluster analysis. This paper presents a geostatistical methodology to generate multiple realizations of the spatial distribution of risk values. These maps are then fed into spatial operators, such as in local cluster analysis, allowing one to assess how risk spatial uncertainty translates into uncertainty about the location of spatial clusters and outliers. This novel approach is applied to age-adjusted breast and pancreatic cancer mortality rates recorded for white females in 295 US counties of the Northeast (1970-1994). A public-domain executable with example datasets is provided.Geostatistical simulation generates risk maps that are more variable than the smooth risk map estimated by Poisson kriging and reproduce better the spatial pattern captured by the risk semivariogram model. Local cluster analysis of the set of simulated risk maps leads to a clear visualization of the lower reliability of the classification obtained for pancreatic cancer versus breast cancer: only a few counties in the large cluster of low risk detected in West Virginia and Southern Pennsylvania are significant over 90% of all simulations. On the other hand, the cluster of high breast cancer mortality in Niagara county, detected after application of Poisson kriging, appears on 60% of simulated risk maps. Sensitivity analysis shows that 500 realizations are needed to achieve a stable classification for pancreatic cancer, while convergence is reached for less than 300 realizations for breast cancer.The approach presented in this paper enables researchers to generate a set of simulated risk maps that are more realistic than a single map of smoothed mortality rates and allow the propagation of cancer risk uncertainty through local cluster analysis. Coupled with visualization and querying capabilities of geographical information systems, animated display of realizations can highlight areas that depart consistently from the general behavior observed across the region, guiding further investigation and control activities.

Dataset Information

Estimating the spatial risk of tuberculosis distribution in Gurage zone, southern Ethiopia: a geostatistical kriging approach.

Publications

Estimating the spatial risk of tuberculosis distribution in Gurage zone, southern Ethiopia: a geostatistical kriging approach.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets