| Autor: |
Meredith HR; Duke Global Health Institute, Durham, North Carolina, USA., Wesolowski A; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA., Okoth D; Department of Health Services and Sanitation, Turkana County, Kenya., Maraga L; Academic Model Providing Access to Healthcare, Eldoret, Uasin Gishu, Kenya., Ambani G; Academic Model Providing Access to Healthcare, Eldoret, Uasin Gishu, Kenya., Chepkwony T; Academic Model Providing Access to Healthcare, Eldoret, Uasin Gishu, Kenya., Abel L; Academic Model Providing Access to Healthcare, Eldoret, Uasin Gishu, Kenya., Kipkoech J; Academic Model Providing Access to Healthcare, Eldoret, Uasin Gishu, Kenya., Lokoel G; Department of Health Services and Sanitation, Turkana County, Kenya., Esimit D; Department of Health Services and Sanitation, Turkana County, Kenya., Lokemer S; Department of Health Services and Sanitation, Turkana County, Kenya., Maragia J; Department of Health Services and Sanitation, Turkana County, Kenya., O'Meara WP; Duke Global Health Institute, Durham, North Carolina, USA.; School of Public Health, Moi University College of Health Sciences, Eldoret, Uasin Gishu, Kenya.; School of Medicine, Duke University, Durham, North Carolina, USA., Obala AA; School of Medicine, Moi University College of Health Sciences, Eldoret, Uasin Gishu, Kenya. |
| Abstrakt: |
While many studies have characterized mobility patterns and disease dynamics of individuals from settled populations, few have focused on more mobile populations. Highly mobile groups are often at higher disease risk due to their regular movement that may increase the variability of their environments, reduce their access to health care, and limit the number of intervention strategies suitable for their lifestyles. Quantifying the movements and their associated disease risks will be key to developing intervention strategies more suitable for mobile populations. Here, we worked with four semi-nomadic communities in Central Turkana, Kenya to 1) characterize mobility patterns of travelers from semi-nomadic communities and 2) test the hypothesis that semi-nomadic individuals are at greater risk of exposure to malaria during seasonal migrations than when staying at their semi-permanent settlements. From March-October, 2021, we conducted a study in semi-nomadic households (n=250) where some members traveled with their herd while others remained at the semi-permanent settlement. Participants provided medical and travel histories, demographics, and a dried blood spot for malaria testing before and after the travel period. Further, a subset of travelers was given GPS loggers to document their routes. Four travel patterns emerged from the logger data, Long Term, Transient, Day trip, and Static, with only Long Term and Transient trips being associated with malaria cases detected in individuals who carried GPS devices. After completing their trips, travelers had a higher prevalence of malaria than those who remained at the household (9.2% vs 4.4%), regardless of gender, age group, and catchment area. These findings highlight the need to develop intervention strategies amenable to mobile lifestyles that can ultimately help prevent the transmission of malaria. |
