Feasibility study of geospatial mapping of chronic disease risk to inform public health commissioning.
ABSTRACT: To explore the feasibility of producing small-area geospatial maps of chronic disease risk for use by clinical commissioning groups and public health teams.Cross-sectional geospatial analysis using routinely collected general practitioner electronic record data.Tower Hamlets, an inner-city district of London, UK, characterised by high socioeconomic and ethnic diversity and high prevalence of non-communicable diseases.The authors used type 2 diabetes as an example. The data set was drawn from electronic general practice records on all non-diabetic individuals aged 25-79 years in the district (n=163 275). The authors used a validated instrument, QDScore, to calculate 10-year risk of developing type 2 diabetes. Using specialist mapping software (ArcGIS), the authors produced visualisations of how these data varied by lower and middle super output area across the district. The authors enhanced these maps with information on examples of locality-based social determinants of health (population density, fast food outlets and green spaces). Data were piloted as three types of geospatial map (basic, heat and ring). The authors noted practical, technical and information governance challenges involved in producing the maps.Usable data were obtained on 96.2% of all records. One in 11 adults in our cohort was at 'high risk' of developing type 2 diabetes with a 20% or more 10-year risk. Small-area geospatial mapping illustrated 'hot spots' where up to 17.3% of all adults were at high risk of developing type 2 diabetes. Ring maps allowed visualisation of high risk for type 2 diabetes by locality alongside putative social determinants in the same locality. The task of downloading, cleaning and mapping data from electronic general practice records posed some technical challenges, and judgement was required to group data at an appropriate geographical level. Information governance issues were time consuming and required local and national consultation and agreement.Producing small-area geospatial maps of diabetes risk calculated from general practice electronic record data across a district-wide population was feasible but not straightforward. Geovisualisation of epidemiological and environmental data, made possible by interdisciplinary links between public health clinicians and human geographers, allows presentation of findings in a way that is both accessible and engaging, hence potentially of value to commissioners and policymakers. Impact studies are needed of how maps of chronic disease risk might be used in public health and urban planning.
Project description:Measles vaccination campaigns are conducted regularly in many low- and middle-income countries to boost measles control efforts and accelerate progress towards elimination. National and sometimes first-level administrative division campaign coverage may be estimated through post-campaign coverage surveys (PCCS). However, these large-area estimates mask significant geographic inequities in coverage at more granular levels. Here, we undertake a geospatial analysis of the Nigeria 2017-18 PCCS data to produce coverage estimates at 1 × 1 km resolution and the district level using binomial spatial regression models built on a suite of geospatial covariates and implemented in a Bayesian framework via the INLA-SPDE approach. We investigate the individual and combined performance of the campaign and routine immunization (RI) by mapping various indicators of coverage for children aged 9-59 months. Additionally, we compare estimated coverage before the campaign at 1 × 1 km and the district level with predicted coverage maps produced using other surveys conducted in 2013 and 2016-17. Coverage during the campaign was generally higher and more homogeneous than RI coverage but geospatial differences in the campaign's reach of previously unvaccinated children are shown. Persistent areas of low coverage highlight the need for improved RI performance. The results can help to guide the conduct of future campaigns, improve vaccination monitoring and measles elimination efforts. Moreover, the approaches used here can be readily extended to other countries.
Project description:We assessed district-level geospatial trends in precision weighted prevalence and absolute wealth disparity in stunting, underweight, wasting, low birthweight, and anemia among children under five in India. The largest wealth disparities were found for anthropometric failures and substantial variation existed across states. We identified statistically significant (p < 0.001) geospatial patterns in district-wide wealth disparities for all outcomes, which differed from geospatial patterns for the overall prevalence. We characterized each district as either a "Disparity", "Pitfall", "Intensity", or "Prosperity" area based on its overall burden and wealth disparity, as well as discuss the importance of considering both measures for geographically-targeted public health interventions to improve health equity.
Project description:The Geospatial Technologies like Remote Sensing (RS) and Geographic Information System (GIS) have been playing vital role in capable forecasting and management of imperative groundwater resources in the emerging nations. In recent times, the geospatial technologies like RS, GIS and Multi Influence Factor (MIF) methodology are helpful in identifying groundwater potential zone. For the present study, the geospatial technology is used to prepare various thematic maps such as Land Slope, Geomorphology, Geology, Soil, Drainage Density, Lineament Density, Landuse/Landcover, Hydrogeomorphology, and Annual consideration of the valuation of groundwater assets for the semi-arid region in and around Bommanahal Mandal of Anantapur District in Andhra Pradesh, Southern India. As a part of the study eight thematic layers and their functions have been designed applicable weights at the Saaty?s scale according to their comparative connotation in groundwater occurrence. The designed weights are normalized by using AHP (Analytic Hierarchy Process) MIF techniques and eigenvector method to various thematic layers and their features. Further to create a groundwater potential map the chosen thematic maps are integrated by weighted linear grouping method in a GIS environment. Based on the groundwater potential index values, the study area is classified into four different groundwater potential zones such as 'good', 'moderate to good', 'moderate' and 'poor'. The new recharge structures have proposed to fulfill the demand of groundwater to expand the scope of groundwater for future generations. Considering the overlay analysis of geomorphology and drainage layer execution through GIS technologies, the appropriate sites for artificial recharge structures have been identified.
Project description:Commercial geospatial data resources are frequently used to understand healthcare utilisation. Although there is widespread evidence of a digital divide for other digital resources and infra-structure, it is unclear how commercial geospatial data resources are distributed relative to health need.To examine the distribution of commercial geospatial data resources relative to health needs, we assembled coverage and quality metrics for commercial geocoding, neighbourhood characterisation, and travel time calculation resources for 183 countries. We developed a country-level, composite index of commercial geospatial data quality/availability and examined its distribution relative to age-standardised all-cause and cause specific (for three main causes of death) mortality using two inequality metrics, the slope index of inequality and relative concentration index. In two sub-national case studies, we also examined geocoding success rates versus area deprivation by district in Eastern Region, Ghana and Lagos State, Nigeria.Internationally, commercial geospatial data resources were inversely related to all-cause mortality. This relationship was more pronounced when examining mortality due to communicable diseases. Commercial geospatial data resources for calculating patient travel times were more equitably distributed relative to health need than resources for characterising neighbourhoods or geocoding patient addresses. Countries such as South Africa have comparatively high commercial geospatial data availability despite high mortality, whilst countries such as South Korea have comparatively low data availability and low mortality. Sub-nationally, evidence was mixed as to whether geocoding success was lowest in more deprived districts.To our knowledge, this is the first global analysis of commercial geospatial data resources in relation to health outcomes. In countries such as South Africa where there is high mortality but also comparatively rich commercial geospatial data, these data resources are a potential resource for examining healthcare utilisation that requires further evaluation. In countries such as Sierra Leone where there is high mortality but minimal commercial geospatial data, alternative approaches such as open data use are needed in quantifying patient travel times, geocoding patient addresses, and characterising patients' neighbourhoods.
Project description:PURPOSE:Geospatial, contextual, and multilevel research is integral to cancer prevention and control. NCI-designated Cancer Centers are at the forefront of cancer research; therefore, this paper sought to review the geospatial, contextual, and multilevel research at these cancer centers. METHODS:Investigators used PubMed and Web of Science to compile geospatial publications from 1971 to February 2016 with cancer center-affiliated authors. Relevant abstracts were pulled and classified by six geospatial approaches, eight geospatial scales, and eight cancer sites. RESULTS:The searches identified 802 geospatial, contextual, and multilevel publications with authors affiliated at 60 of the 68 NCI-designated Cancer Centers. Over 90% were published after 2000. Five cancer centers accounted for approximately 50% of total publications, and 30 cancer centers accounted for over 85% of total publications. Publications covered all geospatial approaches and scales to varying degrees, and 90% dealt with cancer. CONCLUSIONS:The NCI-designated Cancer Center network is increasingly pursuing geospatial, contextual, and multilevel cancer research, although many cancer centers still conduct limited to no research in this area. Expanding geospatial efforts to research programs across all cancer centers will further enrich cancer prevention and control. Similar reviews may benefit other domestic and international cancer research institutions.
Project description:Since 1996 when Highly Pathogenic Avian Influenza type H5N1 first emerged in southern China, numerous studies sought risk factors and produced risk maps based on environmental and anthropogenic predictors. However little attention has been paid to the link between the level of intensification of poultry production and the risk of outbreak. This study revised H5N1 risk mapping in Central and Western Thailand during the second wave of the 2004 epidemic. Production structure was quantified using a disaggregation methodology based on the number of poultry per holding. Population densities of extensively- and intensively-raised ducks and chickens were derived both at the sub-district and at the village levels. LandSat images were used to derive another previously neglected potential predictor of HPAI H5N1 risk: the proportion of water in the landscape resulting from floods. We used Monte Carlo simulation of Boosted Regression Trees models of predictor variables to characterize the risk of HPAI H5N1. Maps of mean risk and uncertainty were derived both at the sub-district and the village levels. The overall accuracy of Boosted Regression Trees models was comparable to that of logistic regression approaches. The proportion of area flooded made the highest contribution to predicting the risk of outbreak, followed by the densities of intensively-raised ducks, extensively-raised ducks and human population. Our results showed that as little as 15% of flooded land in villages is sufficient to reach the maximum level of risk associated with this variable. The spatial pattern of predicted risk is similar to previous work: areas at risk are mainly located along the flood plain of the Chao Phraya river and to the south-east of Bangkok. Using high-resolution village-level poultry census data, rather than sub-district data, the spatial accuracy of predictions was enhanced to highlight local variations in risk. Such maps provide useful information to guide intervention.
Project description:Traditional methods of health surveillance often under-represent racial and ethnic minorities. Our objective was to use geospatial analysis and emergency claims data to estimate local chronic disease prevalence separately for specific racial and ethnic groups. We also performed a regression analysis to identify associations between median household income and local disease prevalence among Black, Hispanic, Asian, and White adults in New York City. The study population included individuals who visited an emergency department at least once from 2009 to 2013. Our main outcomes were geospatial estimates of diabetes, hypertension, and asthma prevalence by Census tract as stratified by race and ethnicity. Using emergency claims data, we identified 4.9 million unique New York City adults with 28.5% of identifying as Black, 25.2% Hispanic, and 6.1% Asian. Age-adjusted disease prevalence was highest among Black and Hispanic adults for diabetes (13.4 and 13.1%), hypertension (28.7 and 24.1%), and asthma (9.9 and 10.1%). Correlation between disease prevalence maps demonstrated moderate overlap between Black and Hispanic adults for diabetes (0.49), hypertension (0.57), and asthma (0.58). In our regression analysis, we found that the association between low income and high disease prevalence was strongest for Hispanic adults, whereas increases in income had more modest reductions in disease prevalence for Black adults, especially for diabetes. Our geographically detailed maps of disease prevalence generate actionable evidence that can help direct health interventions to those communities with the highest health disparities. Using these novel geographic approaches, we reveal the underlying epidemiology of chronic disease for a racially and culturally diverse population.
Project description:AIMS/HYPOTHESIS:Our aim was to investigate the geospatial distribution of diabetic foot ulceration (DFU), lower extremity amputation (LEA) and mortality rates in people with diabetes in small geographical areas with varying levels of multiple deprivation. METHODS:We undertook a population cohort study to extract the health records of 112,231 people with diabetes from the Scottish Care Information - Diabetes Collaboration (SCI-Diabetes) database. We linked this to health records to identify death, LEA and DFU events. These events were geospatially mapped using multiple deprivation maps for the geographical area of National Health Service (NHS) Greater Glasgow and Clyde. Tests of spatial autocorrelation and association were conducted to evaluate geographical variation and patterning, and the association between prevalence-adjusted outcome rates and multiple deprivation by quintile. RESULTS:Within our health board region, people with diabetes had crude prevalence-adjusted rates for DFU of 4.6% and for LEA of 1.3%, and an incidence rate of mortality preceded by either a DFU or LEA of 10.5 per 10,000 per year. Spatial autocorrelation identified statistically significant hot spot (high prevalence) and cold spot (low prevalence) clusters for all outcomes. Small-area maps effectively displayed near neighbour clustering across the health board geography. Disproportionately high numbers of hot spots within the most deprived quintile for DFU (p <?0.001), LEA (p <?0.001) and mortality (p <?0.001) rates were found. Conversely, a disproportionately higher number of cold spots was found within the least deprived quintile for LEA (p <?0.001). CONCLUSIONS/INTERPRETATION:In people with diabetes, DFU, LEA and mortality rates are associated with multiple deprivation and form geographical neighbourhood clusters.
Project description:We introduce a method called neighbor-based bootstrapping (NB2) that can be used to quantify the geospatial variation of a variable. We applied this method to an analysis of the incidence rates of disease from electronic medical record data (International Classification of Diseases, Ninth Revision codes) for ?100 million individuals in the United States over a period of 8 years. We considered the incidence rate of disease in each county and its geospatially contiguous neighbors and rank ordered diseases in terms of their degree of geospatial variation as quantified by the NB2 method. We show that this method yields results in good agreement with established methods for detecting spatial autocorrelation (Moran's I method and kriging). Moreover, the NB2 method can be tuned to identify both large area and small area geospatial variations. This method also applies more generally in any parameter space that can be partitioned to consist of regions and their neighbors.
Project description:GOALS OF THIS STUDY WERE TO: (1) develop distributional maps of modern rodent genera throughout the countries of South Africa, Lesotho, and Swaziland by georeferencing museum specimens; (2) assess habitat preferences for genera by cross-referencing locality position with South African vegetation; and (3) identify mean annual precipitation and temperature range where the genera are located. Conterminous South Africa including the countries of Lesotho and Swaziland Digital databases of rodent museum specimens housed in the Ditsong National Museum of Natural History, South Africa (DM), and the Division of Mammals, National Museum of Natural History, Smithsonian Institution, United States (NMNH), were acquired and then sorted into a subset of specimens with associated coordinate data. The coordinate data were then used to develop distributional maps for the rodent genera present within the study area. Percent habitat occupation and descriptive statistics for six climatic variables were then determined for each genus by cross-referencing locality positions with vegetation and climatic maps. This report presents a series of maps illustrating the distribution of 35 rodent genera based on 19,471 geo-referenced specimens obtained from two major collections. Inferred habitat use by taxon is provided for both locality and specimen percent occurrence at three hierarchical habitat levels: biome, bioregion, and vegetation unit. Descriptive statistics for six climatic variables are also provided for each genus based on locality and specimen percent incidence. As rodent faunas are commonly used in paleoenvironmental reconstructions, an accurate assessment of rodent environmental tolerance ranges is necessary before confidence can be placed in an actualistic model. While the data presented here represent only a subset of the modern geographic distributions for many of the taxa examined, a wide range of environmental regimes are observed, suggesting that more research is necessary in order to accurately reconstruct an environmental signature when these taxa are found in the fossil record.