Redefining of “EPSG” and “PROJ” for current State Coordinate Reference System of the Republic of North Macedonia

Idrizi, Bashkim

doi:10.5194/ica-proc-4-45-2021

Cited by 1 publication

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“…Also, the prevalence of malaria in each village was added to the map canvas after each village's malaria prevalence and fitted into the attribute table of a village map layer. Maps coordinates were generated by using World Geodetic System 1984 (WGS84) [41] and enhanced by the European Petroleum Survey Group (EPSG: 4326) as a database of coordinate system information and related documents on projections and datum [42]. The results showing the associations between independent and dependent variables were presented using odd ratios (OR) with 95% confidence intervals (CI).…”

Section: Data Management and Analysismentioning

confidence: 99%

Prevalence and risk factors associated with malaria infections at a micro-geographic level in three villages of Muheza district, north-eastern Tanzania

Challe,

Francis,

Seth

et al. 2024

Preprint

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BackgroundDespite a recent reduction in malaria morbidity and mortality, the disease remains a major cause of morbidity and mortality in Tanzania. However, the malaria burden is heterogeneous with a higher burden in some regions compared to others, suggesting that stratification of malaria burden and risk/predictors of infections is critical to guide the proper use of the current and future interventions. This study assessed the prevalence and predictors of /risk factors associated with malaria infections at micro-geographic levels in three villages of Muheza district, Tanga region, north-eastern Tanzania.MethodsA cross-sectional community survey was conducted in three villages; Magoda, Mpapayu, and Mamboleo in Muheza district, Tanga region, north-eastern Tanzania in June 2021. Participants’ demographic, anthropometric, clinical, and malaria protection data were collected during the survey and combined with census data collected in 2013 including housing conditions and socio-economic status (SES). Finger prick blood samples were taken for parasite detection using both microscopy and rapid diagnostic tests (RDT). A generalised estimating equation (GEE) was used to determine the association between the prevalence and predictors/risk factors of malaria infections.ResultsThe survey covered 1,134 individuals from 380 households and most of them (95.2%) reported that they slept under bed nets the night before the survey. By both microscopy and RDT, the prevalence of malaria infections was 19.2% and 24.3%, respectively. The prevalence was significantly higher among school children (aged >5 – 15 years, with 27.3% by microscopy and 37.6% by RDTs) compared to under-fives and adults (aged ≥15 years (p<0.001)). Individuals with a history of fever within 48 hours before the survey and those with fever at presentation (auxiliary temperature ≥37.50C) were more likely to have malaria infections by microscopy (AOR = 1.16; 95% CI, 1.10 – 1.22; p<0.001) and RDTs (AOR = 1.18; 95% CI, 1.13 – 1.23; p<0.001). Participants with high SES and living in good houses (with closed eaves and/or closed windows) were less likely to be infected by malaria parasites as detected by microscopy (AOR =0.97; 95% CI, 0.92 - 1.02; p=0.205) and RDTs (AOR = 0.91; 95% CI, 0.85 - 0.97; p<0.001). Among the three villages, the prevalence of malaria by microscopy ranged from 14.7% to 24.6% and varied significantly but without any clear patterns across villages indicating high heterogeneity and random distribution of malaria at micro-geographic levels (p=0.001).ConclusionThe villages had high prevalence and predictor/risk factors risk of malaria infections including age, sex (male), fever, SES, and housing conditions. High prevalence and risk were among school children (aged ≥5 - 14 years), males, individuals with low SES and a history of fever within 48 hours before the survey, or fever at presentation (with auxiliary temperature ≥37.50C). The prevalence varied over short distances at micro-geographic levels suggesting that causes of such variations need to be established and considered when designing and implementing targeted malaria control interventions.

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Section: Data Management and Analysismentioning

confidence: 99%