Exposure to alcohol outlets, alcohol access, and alcohol consumption among adolescents.

BACKGROUND: Adolescents who live near more alcohol outlets tend to consume more alcohol, despite laws prohibiting alcohol purchases for people aged <21 years. We examined relationships between adolescents' exposure to alcohol outlets, the sources through which they access alcohol, and their alcohol consumption. METHODS: Participants for this longitudinal study (n = 168) were aged 15-18 years and were from 10 cities in the San Francisco Bay Area. We collected survey data to measure participant characteristics, followed by 1 month of GPS tracking to measure exposure to alcohol outlets (separated into exposures near home and away from home for bars, restaurants, and off-premise outlets). A follow-up survey approximately 1 year later measured alcohol access (through outlets, family members, peers aged <21 years, peers aged ≥21 years) and alcohol consumption (e.g. count of drinking days in last 30). Generalized structural equation models related exposure to alcohol outlets, alcohol access, and alcohol consumption. RESULTS: Exposure to bars and off-premise outlets near home was positively associated with accessing alcohol from peers aged <21, and in turn, accessing alcohol from peers aged <21 was positively associated with alcohol consumption. There was no direct association between exposure to alcohol outlets near home or away from home and alcohol consumption. CONCLUSIONS: Interventions that reduce adolescents' access through peers aged <21 may reduce adolescents' alcohol consumption.

Feasibility and Validity of Geographically Explicit Ecological Momentary Assessment With Recall-Aided Space-Time Budgets.

We employ data from the Adolescent Health and Development in Context Study-a representative sample of urban youth ages 11-17 in and around the Columbus, OH area-to investigate the feasibility and validity of smartphone-based geographically explicit ecological momentary assessment (GEMA). Age, race, household income, familiarity with smartphones, and self-control were associated with missing global positioning systems (GPS) coverage, whereas school day was associated with discordance between percent of time at home based on GPS-only versus recall-aided space-time budget data. Fatigue from protocol compliance increases missing GPS across the week, which results in more discordance. Although some systematic differences were observed, these findings offer evidence that smartphone-based GEMA is a viable method for the collection of activity space data on urban youth.

Using data from online geocoding services for the assessment of environmental obesogenic factors: a feasibility study.

BACKGROUND: The increasing prevalence of obesity is a major public health problem in many countries. Built environment factors are known to be associated with obesity, which is an important risk factor for type 2 diabetes. Online geocoding services could be used to identify regions with a high concentration of obesogenic factors. The aim of our study was to examine the feasibility of integrating information from online geocoding services for the assessment of obesogenic environments. METHODS: We identified environmental factors associated with obesity from the literature and translated these factors into variables from the online geocoding services Google Maps and OpenStreetMap (OSM). We tested whether spatial data points can be downloaded from these services and processed and visualized on maps. True- and false-positive values, false-negative values, sensitivities and positive predictive values of the processed data were determined using search engines and in-field inspections within four pilot areas in Bavaria, Germany. RESULTS: Several environmental factors could be identified from the literature that were either positively or negatively correlated with weight outcomes in previous studies. The diversity of query variables was higher in OSM compared with Google Maps. In each pilot area, query results from Google showed a higher absolute number of true-positive hits and of false-positive hits, but a lower number of false-negative hits during the validation process. The positive predictive value of database hits was higher in OSM and ranged between 81 and 100% compared with a range of 63-89% for Google Maps. In contrast, sensitivities were higher in Google Maps (between 59 and 98%) than in OSM (between 20 and 64%). CONCLUSIONS: It was possible to operationalize obesogenic factors identified from the literature with data and variables available from geocoding services. The validity of Google Maps and OSM was reasonable. The assessment of environmental obesogenic factors via geocoding services could potentially be applied in diabetes surveillance.

Development of a spatial sampling protocol using GIS to measure health disparities in Bobo-Dioulasso, Burkina Faso, a medium-sized African city.

BACKGROUND: Many cities in developing countries experience an unplanned and rapid growth. Several studies have shown that the irregular urbanization and equipment of cities produce different health risks and uneven exposure to specific diseases. Consequently, health surveys within cities should be carried out at the micro-local scale and sampling methods should try to capture this urban diversity. METHODS: This article describes the methodology used to develop a multi-stage sampling protocol to select a population for a demographic survey that investigates health disparities in the medium-sized city of Bobo-Dioulasso, Burkina Faso. It is based on the characterization of Bobo-Dioulasso city typology by taking into account the city heterogeneity, as determined by analysis of the built environment and of the distribution of urban infrastructures, such as healthcare structures or even water fountains, by photo-interpretation of aerial photographs and satellite images. Principal component analysis and hierarchical ascendant classification were then used to generate the city typology. RESULTS: Five groups of spaces with specific profiles were identified according to a set of variables which could be considered as proxy indicators of health status. Within these five groups, four sub-spaces were randomly selected for the study. We were then able to survey 1045 households in all the selected sub-spaces. The pertinence of this approach is discussed regarding to classical sampling as random walk method for example. CONCLUSION: This urban space typology allowed to select a population living in areas representative of the uneven urbanization process, and to characterize its health status in regards to several indicators (nutritional status, communicable and non-communicable diseases, and anaemia). Although this method should be validated and compared with more established methods, it appears as an alternative in developing countries where geographic and population data are scarce.

Role of spatial tools in public health policymaking of Bangladesh: opportunities and challenges.

In spite of the increasing efforts to gather spatial data in developing countries, the use of maps is mostly for visualization of health indicators rather than informed decision-making. Various spatial tools can aid policymakers to allocate resources effectively, predict patterns in communicable or infectious diseases, and provide insights into geographical factors which are associated with utilization or adequacy of health services. In Bangladesh, the launch of District Health Information System 2, along with recent efforts to gather spatial data of facilities location, provides an interesting opportunity to study the current landscape and the potential barriers in advancing the use of spatial tools for informed decision making. This study assessed the current level of map usage and spatial tools for health sector planning in Bangladesh, focusing on investigating why map usage and spatial tools remained at a basic level for the purpose of health policy. The study design involved in-depth interviews, followed by an expert survey (n = 39) obtained through snowball sampling.Our survey revealed that assessing areas with shortage of community health workers emerged as the top most for basic map usage or primarily for visualization purpose, while planning for emergency and obstetric care services, and disease mapping was the most frequent category for intermediate and advanced map usage, respectively. Furthermore, we found lack of inter-institutional collaboration, lack of continuous availability of trained personnel, and lack of awareness on the use of geographic information system (GIS) as a decision-making tool as three most critical barriers in the current landscape. Our findings highlight the barriers in increasing the adoption of spatial tools for health policymaking and planning in Bangladesh.

Prediction of high-risk areas for visceral leishmaniasis using socioeconomic indicators and remote sensing data.

Spatial heterogeneity in the incidence of visceral leishmaniasis (VL) is an important aspect to be considered in planning control actions for the disease. The objective of this study was to predict areas at high risk for visceral leishmaniasis (VL) based on socioeconomic indicators and remote sensing data. We applied classification and regression trees to develop and validate prediction models. Performance of the models was assessed by means of sensitivity, specificity and area under the ROC curve. The model developed was able to discriminate 15 subsets of census tracts (CT) with different probabilities of containing CT with high risk of VL occurrence. The model presented, respectively, in the validation and learning samples, sensitivity of 79% and 52%, specificity of 75% and 66%, and area under the ROC curve of 83% and 66%. Considering the complex network of factors involved in the occurrence of VL in urban areas, the results of this study showed that the development of a predictive model for VL might be feasible and useful for guiding interventions against the disease, but it is still a challenge as demonstrated by the unsatisfactory predictive performance of the model developed.

Geographic information systems (GIS): an emerging method to assess demand and provision for rehabilitation services.

PURPOSE: To illustrate the application of geographic information systems (GIS) as a tool to assess rehabilitation service delivery by presenting results from research recently conducted to assess demand and provision for community rehabilitation service delivery in Ontario, Canada. METHODS: Secondary analysis of data obtained from existing sources was used to establish demand and provision profiles for community rehabilitation services. These data were integrated using GIS software. RESULTS: A number of descriptive maps were produced that show the geographical distribution of service provision variables (location of individual rehabilitation health care providers and location of private and publicly funded community rehabilitation clinics) in relation to the distribution of demand variables (location of the general population; location of specific populations (i.e., residents age 65 and older) and distribution of household income). CONCLUSIONS: GIS provides a set of tools for describing and understanding the spatial organization of the health of populations and the distribution of health services that can aid the development of health policy and answer key research questions with respect to rehabilitation health services delivery. Implications for Rehabilitation It is important to seek out alternative and innovative methods to examine rehabilitation service delivery. GIS is a computer-based program that takes any data linked to a geographically referenced location and processes it through a software system that manages, analyses and displays the data in the form of a map, allowing for an alternative level of analysis. GIS provides a set of tools for describing and understanding the spatial organization of population health and health services that can aid the development of health policy and answer key research questions with respect to rehabilitation health services delivery.

Free and simple GIS as appropriate for health mapping in a low resource setting: a case study in eastern Indonesia.

BACKGROUND: Despite the demonstrated utility of GIS for health applications, there are perceived problems in low resource settings: GIS software can be expensive and complex; input data are often of low quality. This study aimed to test the appropriateness of new, inexpensive and simple GIS tools in poorly resourced areas of a developing country. GIS applications were trialled in pilot studies based on mapping of health resources and health indicators at the clinic and district level in the predominantly rural province of Nusa Tenggara Timur in eastern Indonesia. The pilot applications were (i) rapid field collection of health infrastructure data using a GPS enabled PDA, (ii) mapping health indicator data using open source GIS software, and (iii) service availability mapping using a free modelling tool. RESULTS: Through contextualised training, district and clinic staff acquired skills in spatial analysis and visualisation and, six months after the pilot studies, they were using these skills for advocacy in the planning process, to inform the allocation of some health resources, and to evaluate some public health initiatives. CONCLUSIONS: We demonstrated that GIS can be a useful and inexpensive tool for the decentralisation of health data analysis to low resource settings through the use of free and simple software, locally relevant training materials and by providing data collection tools to ensure data reliability.

Scaling up malaria control in Africa: an economic and epidemiological assessment.

This paper estimates the number of people at risk of contracting malaria in Africa using GIS methods and the disease's epidemiologic characteristics. It then estimates yearly costs of covering the population at risk with the package of interventions (differing by level of malaria endemicity and differing for rural and urban populations) for malaria as recommended by the UN Millennium Project. These projected costs are calculated assuming a ramp-up of coverage to full coverage by 2008, and then projected out through 2015 to give a year-by-year cost of meeting the Millennium Development Goal for reducing the burden of malaria by 75%. We conclude that the cost of comprehensive malaria control for Africa is US $3.0 billion per year on average, or around US $4.02 per African at risk.