927 research outputs found
Can mobile phone data improve emergency response to natural disasters?
No full textDisaster management requires accurate information and must link data collection and analysis to an immediate decision-making process. Existing approaches to assessing population movements in the immediate aftermath of disasters, such as transport surveys and manual registration of individuals at emergency-relief hubs, are often inadequate: while important for record-keeping purposes, both are slow and may exclude those groups who are unreachable and most vulnerable. Proxy analysis via aerial or even satellite reconnaissance has a potentially useful role, but can provide only a coarse geographical picture of moving populations. In practice, the most readily available sources of information are from eye-witness or media reports. Although timely, such reports are not accumulated systematically and can constitute a biased representation of events.<br/
Information for decision making from imperfect national data: tracking major changes in health care use in Kenya using geostatistics
No full textBackground: most Ministries of Health across Africa invest substantial resources in some form of health management information system (HMIS) to coordinate the routine acquisition and compilation of monthly treatment and attendance records from health facilities nationwide. Despite the expense of these systems, poor data coverage means they are rarely, if ever, used to generate reliable evidence for decision makers. One critical weakness across Africa is the current lack of capacity to effectively monitor patterns of service use through time so that the impacts ofchanges in policy or service delivery can be evaluated. Here, we present a new approach that, for the first time, allows national changes in health service use during a time of major health policy change to be tracked reliably using imperfect data from a national HMIS.Methods: monthly attendance records were obtained from the Kenyan HMIS for 1 271 government-run and 402 faith-based outpatient facilities nationwide between 1996 and 2004. Aspace-time geostatistical model was used to compensate for the large proportion of missing records caused by non-reporting health facilities, allowing robust estimation of monthly and annualuse of services by outpatients during this period.Results: we were able to reconstruct robust time series of mean levels of outpatient utilisation of health facilities at the national level and for all six major provinces in Kenya. These plots revealed reliably for the first time a period of steady nationwide decline in the use of health facilities in Kenyabetween 1996 and 2002, followed by a dramatic increase from 2003. This pattern was consistent across different causes of attendance and was observed independently in each province.Conclusion: the methodological approach presented can compensate for missing records in health information systems to provide robust estimates of national patterns of outpatient service use. This represents the first such use of HMIS data and contributes to the resurrection of these hugely expensive but underused systems as national monitoring tools. Applying this approach to Kenya has yielded output with immediate potential to enhance the capacity of decision makers in monitoring nationwide patterns of service use and assessing the impact of changes in health policy and service deliver
Identification of specific tree species in ancient semi-natural woodland from digital areal sensor imagery
No full textRemote sensing has great potential as a source of information on tree species. The classification approaches used commonly to extract species information from remotely sensed imagery typically aim to optimize the overall accuracy of species identification, a target which need not satisfy the requirements of a particular user. Often users are interested in a specific species or subset of species, and these may not be accurately identified in a conventional classification. Here, a two-phase classification approach was used to map specific species from aerial sensor imagery of an ancient British woodland. Particular attention was focused on the identification of sycamore since this is displacing the native ash and information on its distribution would enhance basic understanding and management activities. The results show that the classification approach can be adapted to focus on a specific species of interest and used to increase classification accuracy significantly. For example, sycamore was classified to a low accuracy when a conventional approach to classification with a neural network was used (46.6–63.6%, depending on perspective), but the adoption of the two-phase approach increased its accuracy significantly (82.3–93.3%). The results demonstrate the ability to map specific class(es) of interest accurately from remotely sensed imagery. The approach used also highlights the ability to tailor an analysis to the specific requirements of the ecological study in hand and is of broad applicability
The effects of spatial population dataset choice on population at risk of disease estimates
No full textBackground: The spatial modeling of infectious disease distributions and dynamics is increasingly being undertaken for health services planning and disease control monitoring, implementation, and evaluation. Where risks are heterogeneous in space or dependent on person-to-person transmission, spatial data on human population distributions are required to estimate infectious disease risks, burdens, and dynamics. Several different modeled human population distribution datasets are available and widely used, but the disparities among them and the implications for enumerating disease burdens and populations at risk have not been considered systematically. Here, we quantify some of these effects using global estimates of populations at risk (PAR) of P. falciparum malaria as an example.Methods: The recent construction of a global map of P. falciparum malaria endemicity enabled the testing of different gridded population datasets for providing estimates of PAR by endemicity class. The estimated population numbers within each class were calculated for each country using four different global gridded human population datasets: GRUMP (~1 km spatial resolution), LandScan (~1 km), UNEP Global Population Databases (~5 km), and GPW3 (~5 km). More detailed assessments of PAR variation and accuracy were conducted for three African countries where census data were available at a higher administrative-unit level than used by any of the four gridded population datasets.Results: The estimates of PAR based on the datasets varied by more than 10 million people for some countries, even accounting for the fact that estimates of population totals made by different agencies are used to correct national totals in these datasets and can vary by more than 5% for many low-income countries. In many cases, these variations in PAR estimates comprised more than 10% of the total national population. The detailed country-level assessments suggested that none of the datasets was consistently more accurate than the others in estimating PAR. The sizes of such differences among modeled human populations were related to variations in the methods, input resolution, and date of the census data underlying each dataset. Data quality varied from country to country within the spatial population datasets.Conclusions: Detailed, highly spatially resolved human population data are an essential resource for planning health service delivery for disease control, for the spatial modeling of epidemics, and for decision-making processes related to public health. However, our results highlight that for the low-income regions of the world where disease burden is greatest, existing datasets display substantial variations in estimated population distributions, resulting in uncertainty in disease assessments that utilize them. Increased efforts are required to gather contemporary and spatially detailed demographic data to reduce this uncertainty, particularly in Africa, and to develop population distribution modeling methods that match the rigor, sophistication, and ability to handle uncertainty of contemporary disease mapping and spread modeling. In the meantime, studies that utilize a particular spatial population dataset need to acknowledge the uncertainties inherent within them and consider how the methods and data that comprise each will affect conclusions. © 2011 Tatem et al; licensee BioMed Central Ltd.SCOPUS: ar.jinfo:eu-repo/semantics/publishe
A local space–time kriging approach applied to a national outpatient malaria data set
No full textIncreases in the availability of reliable health data are widely recognised as essential for efforts to strengthen health-care
systems in resource-poor settings worldwide. Effective health-system planning requires comprehensive and up-to-date
information on a range of health metrics and this requirement is generally addressed by a Health Management
Information System (HMIS) that coordinates the routine collection of data at individual health facilities and their
compilation into national databases. In many resource-poor settings, these systems are inadequate and national databases
often contain only a small proportion of the expected records. In this paper, we take an important health metric in Kenya
(the proportion of outpatient treatments for malaria (MP)) from the national HMIS database and predict the values of MP
at facilities where monthly records are missing. The available MP data were densely distributed across a spatiotemporal
domain and displayed second-order heterogeneity. We used three different kriging methodologies to make cross-validation
predictions of MP in order to test the effect on prediction accuracy of (a) the extension of a spatial-only to a space–time
prediction approach, and (b) the replacement of a globally stationary with a locally varying random function model.
Space–time kriging was found to produce predictions with 98.4% less mean bias and 14.8% smaller mean imprecision than
conventional spatial-only kriging. A modification of space–time kriging that allowed space–time variograms to be
recalculated for every prediction location within a spatially local neighbourhood resulted in a larger decrease in mean
imprecision over ordinary kriging (18.3%) although the mean bias was reduced less (87.5%)
Using remotely sensed night-time light as a proxy for poverty in Africa
No full textBACKGROUND: Population health is linked closely to poverty. To assess the effectiveness of health interventions it is critical to monitor the spatial and temporal changes in the health indicators of populations and outcomes across varying levels of poverty. Existing measures of poverty based on income, consumption or assets are difficult to compare across geographic settings and are expensive to construct. Remotely sensed data on artificial night time lights (NTL) have been shown to correlate with gross domestic product in developed countries. METHODS: Using national household survey data, principal component analysis was used to compute asset-based poverty indices from aggregated household asset variables at the Administrative 1 level (n = 338) in 37 countries in Africa. Using geographical information systems, mean brightness of and distance to NTL pixels and proportion of area covered by NTL were computed for each Administrative1 polygon. Correlations and agreement of asset-based indices and the three NTL metrics were then examined in both continuous and ordinal forms. RESULTS: At the Administrative 1 level all the NTL metrics distinguished between the most poor and least poor quintiles with greater precision compared to intermediate quintiles. The mean brightness of NTL, however, had the highest correlation coefficient with the asset-based wealth index in continuous (Pearson correlation = 0.64, p < 0.01) and ordinal (Spearman correlation = 0.79, p < 0.01; Kappa = 0.64) forms. CONCLUSION: Metrics of the brightness of NTL data offer a robust and inexpensive alternative to asset-based poverty indices derived from survey data at the Administrative 1 level in Africa. These could be used to explore economic inequity in health outcomes and access to health interventions at sub-national levels where household assets data are not available at the required resolution
Geographical access to care at birth in Ghana: a barrier to safe motherhood
No full textBackground: appropriate facility-based care at birth is a key determinant of safe motherhood but geographical access remains poor in many high burden regions. Despite its importance, geographical access is rarely audited systematically, preventing integration in national-level maternal health system assessment and planning. In this study, we develop a uniquely detailed set of spatially-linked data and a calibrated geospatial model to undertake a national-scale audit of geographical access to maternity care at birth in Ghana, a high-burden country typical of many in sub-Saharan Africa.Methodology and findings: we assembled detailed spatial data on health facilities, roads, rivers, and other landscape features influencing journeys. These were used in a geospatial model to estimate journey-time for all women of childbearing age (WoCBA) to their nearest health facility offering differing levels of care at birth, taking into account different transport types and availability. We calibrated the model using data on actual journeys made by women seeking care at birth. We found that a third of women (34%) in Ghana live beyond the clinically significant two-hour threshold from any facility likely to offer emergency obstetric and neonatal care (EmONC) classed at the ‘partial’ standard or better. Nearly half (45%) live that distance or further from ‘comprehensive’ EmONC facilities, offering life-saving blood transfusion and surgery. In the most remote regions these figures rose to 63% and 81%, respectively. Poor levels of access were found in many regions that meet international targets based on facilities-per-capita ratios.Conclusions and significance: detailed data assembly combined with geospatial modelling can provide nation-wide audits of geographical access to care at birth to support systemic maternal health planning, human resource deployment, and strategic targeting. Current international benchmarks of maternal health care provision are inadequate for these purposes because they fail to take account of the location and accessibility of services relative to the women they serve<br/
Empirical modelling of government health service use by children with fevers in Kenya
No full textAn understanding of spatial patterns of health facility use allows a more informed approach to the modelling of catchment populations. In the absence of patient use data, an intuitive and commonly used approach to the delineation of facility catchment areas is Thiessen polygons. This study presents a series of methods by which the validity of these assumptions can be tested directly and hence the suitability of a Thiessen polygon catchment model explicitly assessed. These methods are applied to paediatric out-patient origin data from a sample of 81 government health facilities in four districts of Kenya. A geographical information system was used to predict the location of the catchment boundary along a transect between each pair of neighbouring facilities based on patient choice patterns. The mean location of boundaries between facilities of different type was found to be significantly displaced from the Thiessen boundary towards the lower-order facility. The effect of distance on within-catchment utilization rate was assessed by using exclusion buffers to remove the effect of neighbouring facilities. Utilization rate was found to exhibit a slight but steady decrease with distance up to 6 kmfrom a facility. The accuracy of the future modelling of unsampled facility catchments can be increased by the incorporation of these trends
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