Mobility evaluation by GPS tracking in a rural, low-income population in Cambodia.
Ontology highlight
ABSTRACT:
SUBMITTER: Pepey A
PROVIDER: S-EPMC9106150 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature
Publications
Mobility evaluation by GPS tracking in a rural, low-income population in Cambodia.
Pepey Anaïs A Obadia Thomas T Kim Saorin S Sovannaroth Siv S Mueller Ivo I Witkowski Benoit B Vantaux Amélie A Souris Marc M
PloS one 20220513 5
Global Positioning System (GPS) technology is an effective tool for quantifying individuals' mobility patterns and can be used to understand their influence on infectious disease transmission. In Cambodia, mobility measurements have been limited to questionnaires, which are of limited efficacy in rural environments. In this study, we used GPS tracking to measure the daily mobility of Cambodian forest goers, a population at high risk of malaria, and developed a workflow adapted to local constrain ...[more]
Similar Datasets
Project description:BackgroundThe Great Mekong Subregion has attained a major decline in malaria cases and fatalities over the last years, but residual transmission hotspots remain, supposedly fueled by forest workers and migrant populations. This study aimed to: (i) characterize the fine-scale mobility of forest-goers and understand links between their daily movement patterns and malaria transmission, using parasites detection via real time polymerase chain reaction (RT PCR) and the individual exposure to Anopheles bites by quantification of anti-Anopheles saliva antibodies via enzyme-linked immunosorbent assay; (ii) assess the concordance of questionnaires and Global Positioning System (GPS) data loggers for measuring mobility.MethodsTwo 28 day follow-ups during dry and rainy seasons, including a GPS tracking, questionnaires and health examinations, were performed on male forest goers representing the population at highest risk of infection. Their time spent in different land use categories and demographic data were analyzed in order to understand the risk factors driving malaria in the study area.ResultsMalaria risk varied with village forest cover and at a resolution of only a few kilometers: participants from villages outside the forest had the highest malaria prevalence compared to participants from forest fringe's villages. The time spent in a specific environment did not modulate the risk of malaria, in particular the time spent in forest was not associated with a higher probability to detect malaria among forest-goers. The levels of antibody response to Anopheles salivary peptide among participants were significantly higher during the rainy season, in accordance with Anopheles mosquito density variation, but was not affected by sociodemographic and mobility factors. The agreement between GPS and self-reported data was only 61.9% in reporting each kind of visited environment.ConclusionsIn a context of residual malaria transmission which was mainly depicted by P. vivax asymptomatic infections, the implementation of questionnaires, GPS data-loggers and quantification of anti-saliva Anopheles antibodies on the high-risk group were not powerful enough to detect malaria risk factors associated with different mobility behaviours or time spent in various environments. The joint implementation of GPS trackers and questionnaires allowed to highlight the limitations of both methodologies and the benefits of using them together. New detection and follow-up strategies are still called for.
| S-EPMC10936104 | biostudies-literature
Project description:The home address is a common spatial proxy for exposure assessment in epidemiological studies but mobility may introduce exposure misclassification. Mobility can be assessed using self-reports or objectively measured using GPS logging but self-reports may not assess the same information as measured mobility. We aimed to assess mobility patterns of a rural population in the Netherlands using GPS measurements and self-reports and to compare GPS measured to self-reported data, and to evaluate correlates of differences in mobility patterns.In total 870 participants filled in a questionnaire regarding their transport modes and carried a GPS-logger for 7 consecutive days. Transport modes were assigned to GPS-tracks based on speed patterns. Correlates of measured mobility data were evaluated using multiple linear regression. We calculated walking, biking and motorised transport durations based on GPS and self-reported data and compared outcomes. We used Cohen's kappa analyses to compare categorised self-reported and GPS measured data for time spent outdoors.Self-reported time spent walking and biking was strongly overestimated when compared to GPS measurements. Participants estimated their time spent in motorised transport accurately. Several variables were associated with differences in mobility patterns, we found for instance that obese people (BMI > 30 kg/m2) spent less time in non-motorised transport (GMR 0.69-0.74) and people with COPD tended to travel longer distances from home in motorised transport (GMR 1.42-1.51).If time spent walking outdoors and biking is relevant for the exposure to environmental factors, then relying on the home address as a proxy for exposure location may introduce misclassification. In addition, this misclassification is potentially differential, and specific groups of people will show stronger misclassification of exposure than others. Performing GPS measurements and identifying explanatory factors of mobility patterns may assist in regression calibration of self-reports in other studies.
| S-EPMC5551017 | biostudies-other
Project description:Welfare dynamics studies are useful in understanding how individuals, families, society, and a country are organised. For the last two decades, Ethiopia's economic reports on income disparity, poverty, and other welfare metrics have been hopeful and controversial. It is crucial to understand how rural households of various income levels perform over time and income mobility. Income mobility can be observed as a change in position over time between two income vectors, with some climbing and others sliding down and changing places at various rates. This study, therefore, explored the rural households' income mobility in Ethiopia using three waves of the Living Standards Measurement Study-Integrated Survey on Agriculture (LSMS-ISA) collected from 2011 to 2016. The Shorrocks rigidity index, transition probability matrix, Fields, and Ok methods were employed to analyse the relative and absolute income mobility. The logit model with conditional fixed effect was used to assess the drivers of individual households' income mobility and the multinomial logit model with conditional fixed effect as an alternative model. Based on the finding of this study, it is suggested to implement different policies targeting income growth to shorten mobility gaps and address factors contributing to downward income mobility in rural households in Ethiopia are necessary.
| S-EPMC10501615 | biostudies-literature
Project description:Cysticercosis and Trichinella spp. infection are parasitic zoonoses prevalent among pigs in Southeast Asia, where pork is the most important source of meat. In rural Cambodia, many pigs are raised extensively in family backyards, and information regarding the prevalence in rural small-scale pig production is very limited. This study was conducted in four provinces in north-eastern Cambodia to determine the seroprevalence of porcine cysticercosis and Trichinella spp. infection in rural villages, and to identify possible risk factors. Only households with less than 10 pigs above three months old were eligible. In total, 139 households participated, and 242 blood samples were collected. Farmers were interviewed about food and hygiene habits, disease knowledge and practices. The serum samples were analysed by ELISA to determine antigens to Taenia spp. cysticerci or antibodies to Trichinella spp. muscle larvae. Positivity among the pigs was 11.2% (95% CI 7.5-15.8) for Taenia spp. cysts and 2.5% (95% CI 0.9-5.4) for Trichinella spp. Cysticerci were more common in the province Preah Vihear (p < 0.001) than in the other provinces. Risk factors associated with porcine cysticercosis were management systems for the pigs and access to human faeces (p < 0.001). Trichinella spp. infection in pigs was more common in the province Ratanakiri (p = 0.001). The main risk factor associated with Trichinella spp. transmission was feeding pigs with food waste (p = 0.048). More men had heard about cysticercosis than women (p = 0.002), and men also consumed undercooked pork meat to a greater extent (p = 0.004). Although the present study is relatively small, several risk factors could be identified for porcine infection with Taenia spp. and Trichinella spp., which can be used to guide future interventions to improve both porcine and human health in these provinces.
| S-EPMC8293377 | biostudies-literature
Project description:This paper examines the effects of population growth and decline on county-level income inequality in the rural United States from 1980 to 2016. Findings from previous research have shown that population growth is positively associated with income inequality. However, these studies are largely motivated by theories of urbanization and growth in metropolitan areas, and do not explicitly test for differences between the impacts of population growth and decline. Examining the effects of both forms of population change on income inequality is particularly important in rural counties of the United States, the majority of which are experiencing population decline. We analyze county-level data (N=11,320 county-decades) from the U.S. Decennial Census and American Community Survey, applying fixed-effects regression models to estimate the respective effects of population growth and decline on income inequality within rural counties. We find that both forms of population change have significant effects on income inequality relative to stable growth. Population decline is associated with increases in income inequality, while population growth is marginally associated with decreases in inequality. These relationships are consistent across a variety of model specifications, including models that account for counties' employment, sociodemographic, and ethno-racial composition. We also find that the relationship between income inequality and population change varies by counties' geographic region, baseline level of inequality, and baseline population size, suggesting that the links between population change and income inequality are not uniform across rural America.
| S-EPMC8570540 | biostudies-literature
Project description:BackgroundOn January 21, 2020, the World Health Organization reported the first case of severe acute respiratory syndrome coronavirus 2, which rapidly evolved to the COVID-19 pandemic. Since then, the virus has also rapidly spread among Latin American, Caribbean, and African countries.ObjectiveThe first aim of this study is to identify new emerging COVID-19 clusters over time and space (from January 21 to mid-May 2020) in Latin American, Caribbean, and African regions, using a prospective space-time scan measurement approach. The second aim is to assess the impact of real-time population mobility patterns between January 21 and May 18, 2020, under the implemented government interventions, measurements, and policy restrictions on COVID-19 spread among those regions and worldwide.MethodsWe created a global COVID-19 database, of 218 countries and territories, merging the World Health Organization daily case reports with other measures such as population density and country income levels for January 21 to May 18, 2020. A score of government policy interventions was created for low, intermediate, high, and very high interventions. The population's mobility patterns at the country level were obtained from Google community mobility reports. The prospective space-time scan statistic method was applied in five time periods between January and May 2020, and a regression mixed model analysis was used.ResultsWe found that COVID-19 emerging clusters within these five periods of time increased from 7 emerging clusters to 28 by mid-May 2020. We also detected various increasing and decreasing relative risk estimates of COVID-19 spread among Latin American, Caribbean, and African countries within the period of analysis. Globally, population mobility to parks and similar leisure areas during at least a minimum of implemented intermediate-level control policies (when compared to low-level control policies) was related to accelerated COVID-19 spread. Results were almost consistent when regional stratified analysis was applied. In addition, worldwide population mobility due to working during high implemented control policies and very high implemented control policies, when compared to low-level control policies, was related to positive COVID-19 spread.ConclusionsThe prospective space-time scan is an approach that low-income and middle-income countries could use to detect emerging clusters in a timely manner and implement specific control policies and interventions to slow down COVID-19 transmission. In addition, real-time population mobility obtained from crowdsourced digital data could be useful for current and future targeted public health and mitigation policies at a global and regional level.
| S-EPMC8204939 | biostudies-literature
Project description:ObjectivesAdolescent childbirth is associated with older adult adverse health outcomes that negatively affect mobility function, but these associations have not been studied globally in large samples of reproductive-age women. This study examines the association between age at first childbirth and mobility disability in national surveys from low-income and middle-income countries, and hypotheses that adolescent childbirth is associated with mobility disability.DesignCross-sectional analysis.SettingPopulation health surveys from 2013 to 2018 containing mobility disability measures among ever-pregnant women ages 15-49. These included 13 Demographic Health Surveys from Haiti, Pakistan, Uganda, Cambodia, Colombia, South Africa, Timor-Leste, Albania, Gambia, Maldives, Peru, Senegal and Yemen and 1 Maternal Health Survey from Ghana.ParticipantsThe sample included 157 988 women ages 15-49 years.Primary outcome measureAdolescent childbirth was defined as 10-19 years of age. Poisson regression models were used to estimate prevalence ratios (PRs) of mobility disability among women who first gave birth during adolescence and in adult life (ages 20-45 years) in each country and across the whole sample. Countries were also analysed according to the use of standard and non-standard mobility disability measures. Covariates included current age, urban/rural residence, education and household wealth.ResultsPrevalence of adolescent childbirth (17.5%-66.2%) and mobility disability (0.32%-21.45%) varied widely across countries. Adolescent childbirth was significantly (p<0.05) associated with greater mobility disability in six of eight countries using standard disability measures. Among the six countries that did not use standard disability measures, none showed a statistically significant association between adolescent childbirth and mobility disability. Considering the whole sample and adjusting for all covariates, women who gave birth during adolescence had greater prevalence of mobility disability (pooled PR 1.19, 95% CI 1.06-1.31).ConclusionsThis analysis suggests a moderate and consistent association of adolescent childbearing with subsequent mobility disability.
| S-EPMC10360427 | biostudies-literature
Project description:BackgroundUrban living affects children's nutrition and growth, which are determinants of their survival, cognitive development, and lifelong health. Little is known about urban-rural differences in children's height and weight, and how these differences have changed over time. We aimed to investigate trends in children's height and weight in rural and urban settings in low-income and middle-income countries, and to assess changes in the urban-rural differentials in height and weight over time.MethodsWe used comprehensive population-based data and a Bayesian hierarchical mixture model to estimate trends in children's height-for-age and weight-for-age Z scores by rural and urban place of residence, and changes in urban-rural differentials in height and weight Z scores, for 141 low-income and middle-income countries between 1985 and 2011. We also estimated the contribution of changes in rural and urban height and weight, and that of urbanisation, to the regional trends in these outcomes.FindingsUrban children are taller and heavier than their rural counterparts in almost all low-income and middle-income countries. The urban-rural differential is largest in Andean and central Latin America (eg, Peru, Honduras, Bolivia, and Guatemala); in some African countries such as Niger, Burundi, and Burkina Faso; and in Vietnam and China. It is smallest in southern and tropical Latin America (eg, Chile and Brazil). Urban children in China, Chile, and Jamaica are the tallest in low-income and middle-income countries, and children in rural areas of Burundi, Guatemala, and Niger the shortest, with the tallest and shortest more than 10 cm apart at age 5 years. The heaviest children live in cities in Georgia, Chile, and China, and the most underweight in rural areas of Timor-Leste, India, Niger, and Bangladesh. Between 1985 and 2011, the urban advantage in height fell in southern and tropical Latin America and south Asia, but changed little or not at all in most other regions. The urban-rural weight differential also decreased in southern and tropical Latin America, but increased in east and southeast Asia and worldwide, because weight gain of urban children outpaced that of rural children.InterpretationFurther improvement of child nutrition will require improved access to a stable and affordable food supply and health care for both rural and urban children, and closing of the the urban-rural gap in nutritional status.FundingBill & Melinda Gates Foundation, Grand Challenges Canada, UK Medical Research Council.
| S-EPMC4547325 | biostudies-literature
Project description:AimMobility applications have the potential to support low-income older adults in facing mobility challenges. However, there is a generally lower uptake of technology in this segment. To understand factors affecting the intention to use a mobility app, we drew upon the Protection Motivation Theory, and tested a model of low-income older adults' technology adoption.MethodsA cross-sectional survey was conducted across seven states in Malaysia among community-dwelling low-income older adults aged ≥60 years old (n = 282). Measurement items were adapted from pre-validated scales and 7-point Likert Scales were used. Partial least squares structural equation modeling was utilized to assess the hypothesized model.ResultsMobility technology awareness was found to shape an individual's threat and coping appraisals associated with their intention to use a mobility app. The decision of a low-income older adult to adopt a mobility app as a protective action is not a direct function of threat and coping appraisals but is indirect, and mediated by the underlying cost-benefit perceptions of non-adoption and adoption of the mobility app. In terms of technology perceptions, perceived usefulness is a significant predictor, but not perceived ease of use.ConclusionsThis study entails a new model by uncovering the psychological factors encompassing mobility technology awareness, threat-coping appraisals, and cost-benefit perceptions on Technology Acceptance Model studies. These insights have important implications for the development and implementation of a mobility app among low-income older adults. Geriatr Gerontol Int 2024; 24: 342-350.
| S-EPMC11503575 | biostudies-literature
Project description:BackgroundPolicymakers need accurate data to develop efficient interventions to promote transport physical activity. Given the imprecise assessment of physical activity in trips, our aim was to illustrate novel advances in the measurement of walking in trips, including in trips incorporating non-walking modes.MethodsWe used data of 285 participants (RECORD MultiSensor Study, 2013-2015, Paris region) who carried GPS receivers and accelerometers over 7 days and underwent a phone-administered web mobility survey on the basis of algorithm-processed GPS data. With this mobility survey, we decomposed trips into unimodal trip stages with their start/end times, validated information on travel modes, and manually complemented and cleaned GPS tracks. This strategy enabled to quantify walking in trips with different modes with two alternative metrics: distance walked and accelerometry-derived number of steps taken.ResultsCompared with GPS-based mobility survey data, algorithm-only processed GPS data indicated that the median distance covered by participants per day was 25.3 km (rather than 23.4 km); correctly identified transport time vs. time at visited places in 72.7% of time; and correctly identified the transport mode in 67% of time (and only in 55% of time for public transport). The 285 participants provided data for 8983 trips (21,163 segments of observation). Participants spent a median of 7.0% of their total time in trips. The median distance walked per trip was 0.40 km for entirely walked trips and 0.85 km for public transport trips (the median number of accelerometer steps were 425 and 1352 in the corresponding trips). Overall, 33.8% of the total distance walked in trips and 37.3% of the accelerometer steps in trips were accumulated during public transport trips. Residents of the far suburbs cumulated a 1.7 times lower distance walked per day and a 1.6 times lower number of steps during trips per 8 h of wear time than residents of the Paris core city.ConclusionsOur approach complementing GPS and accelerometer tracking with a GPS-based mobility survey substantially improved transport mode detection. Our findings suggest that promoting public transport use should be one of the cornerstones of policies to promote physical activity.
| S-EPMC6781383 | biostudies-literature