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Showing papers in "International Journal of Health Geographics in 2013"


Journal ArticleDOI
TL;DR: It was found that both malaria and anaemia were more prevalent in rural communities compared to urban areas and GIS presentation of ecological health data could provide an efficient means of translating this knowledge to lay audiences.
Abstract: Malaria and anaemia are important health problems among children globally. Iron deficiency anaemia may offer protection against malaria infection and iron supplementation may increase the risk of malaria-related hospitalization and mortality. The nature and mechanism of these relationships, however, remain largely unresolved, resulting in concern and uncertainty around policies for non-selective iron supplementation in malaria endemic areas. Use of geographical information systems (GIS) to investigate this disease-disease interaction could contribute important new information for developing safe and effective anaemia and malaria interventions. To assess the current state of knowledge we conducted a systematic review of peer-reviewed and grey literature. Our primary objective was to qualitatively assess the application and utility of geographical concepts or spatial analyses in paediatric global health research. The secondary objective was to identify geographical factors that may be associated with anaemia and malaria prevalence or incidence among children 0–5 years of age living in low- and middle-income countries. Evaluation tools for assessing the quality of geographical data could not be found in the peer-reviewed or grey literature, and thus adapted versions of the STROBE (Strengthening The Reporting of Observational Studies in Epidemiology) and GRADE (Grades of Recommendation, Assessment, Development and Evaluation) methods were used to create reporting, and overall evidence quality scoring systems. Among the 20 included studies, we found that both malaria and anaemia were more prevalent in rural communities compared to urban areas. Geographical factors associated with malaria prevalence included regional transmission stability, and proximity to a mosquito breeding area. The prevalence of anaemia tended to vary inversely with greater or poorer access to community services such as piped water. Techniques for investigating geographic relationships ranged from simple descriptive mapping of spatial distribution patterns, to more complex statistical models that incorporated environmental factors such as seasonal temperature and rain fall. Including GIS in paediatric global health research may be an effective approach to explore relationships between childhood diseases and contribute key evidence for safe implementation of anaemia control programs in malaria endemic areas. Further, GIS presentation of ecological health data could provide an efficient means of translating this knowledge to lay audiences.

139 citations


Journal ArticleDOI
TL;DR: The proposed kernel-based algorithm outperformed the traditional algorithm on most criteria associated to activity place detection, and offered a stronger resilience to GPS noise, managing to detect up to 92.3% of actual stops and estimating stop duration within 5% error margins at all tested noise levels.
Abstract: Background Health studies and mHealth applications are increasingly resorting to tracking technologies such as Global Positioning Systems (GPS) to study the relation between mobility, exposures, and health. GPS tracking generates large sets of geographic data that need to be transformed to be useful for health research. This paper proposes a method to test the performance of activity place detection algorithms, and compares the performance of a novel kernel-based algorithm with a more traditional time-distance cluster detection method.

135 citations


Journal ArticleDOI
TL;DR: European areas with current and future climatic suitability of Chikungunya transmission are identified and the highest risk of transmission by the end of the 21st century was projected for France, Northern Italy and the Pannonian Basin (East-Central Europe).
Abstract: Chikungunya was, from the European perspective, considered to be a travel-related tropical mosquito-borne disease prior to the first European outbreak in Northern Italy in 2007. This was followed by cases of autochthonous transmission reported in South-eastern France in 2010. Both events occurred after the introduction, establishment and expansion of the Chikungunya-competent and highly invasive disease vector Aedes albopictus (Asian tiger mosquito) in Europe. In order to assess whether these outbreaks are indicative of the beginning of a trend or one-off events, there is a need to further examine the factors driving the potential transmission of Chikungunya in Europe. The climatic suitability, both now and in the future, is an essential starting point for such an analysis. The climatic suitability for Chikungunya outbreaks was determined by using bioclimatic factors that influence, both vector and, pathogen. Climatic suitability for the European distribution of the vector Aedes albopictus was based upon previous correlative environmental niche models. Climatic risk classes were derived by combining climatic suitability for the vector with known temperature requirements for pathogen transmission, obtained from outbreak regions. In addition, the longest potential intra-annual season for Chikungunya transmission was estimated for regions with expected vector occurrences. In order to analyse spatio-temporal trends for risk exposure and season of transmission in Europe, climate change impacts are projected for three time-frames (2011–2040, 2041–2070 and 2071–2100) and two climate scenarios (A1B and B1) from the Intergovernmental Panel on Climate Change (IPCC). These climatic projections are based on regional climate model COSMO-CLM, which builds on the global model ECHAM5. European areas with current and future climatic suitability of Chikungunya transmission are identified. An increase in risk is projected for Western Europe (e.g. France and Benelux-States) in the first half of the 21st century and from mid-century onwards for central parts of Europe (e.g. Germany). Interestingly, the southernmost parts of Europe do not generally provide suitable conditions in these projections. Nevertheless, many Mediterranean regions will persist to be climatically suitable for transmission. Overall, the highest risk of transmission by the end of the 21st century was projected for France, Northern Italy and the Pannonian Basin (East-Central Europe). This general tendency is depicted in both, the A1B and B1 climate change scenarios. In order to guide preparedness for further outbreaks, it is crucial to anticipate risk as to identify areas where specific public health measures, such as surveillance and vector control, can be implemented. However, public health practitioners need to be aware that climate is only one factor driving the transmission of vector-borne disease.

134 citations


Journal ArticleDOI
TL;DR: This paper reviews different examples of GPS exergames and of gamified geosocial apps and gadgets (mobile, location-aware apps and devices with social and gamification features), and briefly discusses some of the issues surrounding their use.
Abstract: Large numbers of children and adolescents in Canada, UK and USA are not getting their recommended daily dose of moderate to vigorous physical activity, and are thus more prone to obesity and its ill health effects. Exergames (video games that require physical activity to play) are rapidly gaining user acceptance, and may have the potential to increase physical activity levels among young people. Mobile exergames for GPS (global positioning system)-enabled smartphones and mini-tablets take players outdoors, in the open air, unlike console exergames, e.g., Xbox 360 Kinect exergames, which limit players to playing indoors in front of a TV set. In this paper and its companion ‘Additional file 1’, we review different examples of GPS exergames and of gamified geosocial apps and gadgets (mobile, location-aware apps and devices with social and gamification features), and briefly discuss some of the issues surrounding their use. Further research is needed to document best practices in this area, quantify the exact health and fitness benefits of GPS exergames and apps (under different settings and scenarios), and find out what is needed to improve them and the best ways to promote their adoption by the public.

121 citations


Journal ArticleDOI
TL;DR: By decomposing identified vulnerability “hotspots” into their underlying factors, this approach provides valuable information on both the location of neighborhoods, and (2) vulnerability factors that should be given priority in the context of targeted intervention strategies, to help to reduce the burden of vector-borne diseases.
Abstract: Background As a result of changes in climatic conditions and greater resistance to insecticides, many regions across the globe, including Colombia, have been facing a resurgence of vector-borne diseases, and dengue fever in particular. Timely information on both (1) the spatial distribution of the disease, and (2) prevailing vulnerabilities of the population are needed to adequately plan targeted preventive intervention. We propose a methodology for the spatial assessment of current socioeconomic vulnerabilities to dengue fever in Cali, a tropical urban environment of Colombia.

114 citations


Journal ArticleDOI
TL;DR: Positive associations between LAN and BC incidence are suggested, especially among whites, and the consistency of the findings with previous studies suggests that there could be fundamental biological links between exposure to artificial LAN and increased BC incidence.
Abstract: Literature has identified detrimental health effects from the indiscriminate use of artificial nighttime light. We examined the co-distribution of light at night (LAN) and breast cancer (BC) incidence in Georgia, with the goal to contribute to the accumulating evidence that exposure to LAN increases risk of BC. Using Georgia Comprehensive Cancer Registry data (2000–2007), we conducted a case-referent study among 34,053 BC cases and 14,458 lung cancer referents. Individuals with lung cancer were used as referents to control for other cancer risk factors that may be associated with elevated LAN, such as air pollution, and since this cancer type was not previously associated with LAN or circadian rhythm disruption. DMSP-OLS Nighttime Light Time Series satellite images (1992–2007) were used to estimate LAN levels; low (0–20 watts per sterradian cm2), medium (21–41 watts per sterradian cm2), high (>41 watts per sterradian cm2). LAN levels were extracted for each year of exposure prior to case/referent diagnosis in ArcGIS. Odds ratios (OR) and 95% confidence intervals (CI) were estimated using logistic regression models controlling for individual-level year of diagnosis, race, age at diagnosis, tumor grade, stage; and population-level determinants including metropolitan statistical area (MSA) status, births per 1,000 women aged 15–50, percentage of female smokers, MSA population mobility, and percentage of population over 16 in the labor force. We found that overall BC incidence was associated with high LAN exposure (OR = 1.12, 95% CI [1.04, 1.20]). When stratified by race, LAN exposure was associated with increased BC risk among whites (OR = 1.13, 95% CI [1.05, 1.22]), but not among blacks (OR = 1.02, 95% CI [0.82, 1.28]). Our results suggest positive associations between LAN and BC incidence, especially among whites. The consistency of our findings with previous studies suggests that there could be fundamental biological links between exposure to artificial LAN and increased BC incidence, although additional research using exposure metrics at the individual level is required to confirm or refute these findings.

86 citations


Journal ArticleDOI
TL;DR: This study provides direct evidence for strong geographic clustering of HIV infection across SSA, and identifies priority geographic areas for HIV programming, and support the need for spatially targeted interventions in order to maximize the impact on the epidemic in SSA.
Abstract: The geographical structure of an epidemic is ultimately a consequence of thedrivers of the epidemic and the population susceptible to the infection. The‘know your epidemic’ concept recognizes this geographicalfeature as a key element for identifying populations at higher risk of HIVinfection where prevention interventions should be targeted. In an effort toclarify specific drivers of HIV transmission and identify prioritypopulations for HIV prevention interventions, we conducted a comprehensivemapping of the spatial distribution of HIV infection across sub-SaharanAfrica (SSA). The main source of data for our study was the Demographic and Health Surveyconducted in 20 countries from SSA. We identified and compared spatialclusters with high and low numbers of HIV infections in each country usingKulldorff spatial scan test. The test locates areas with higher and lowernumbers of HIV infections than expected under spatial randomness. For eachidentified cluster, a likelihood ratio test was computed. A P-valuewas determined through Monte Carlo simulations to evaluate the statisticalsignificance of each cluster. Our results suggest stark geographic variations in HIV transmission patternswithin and across countries of SSA. About 14% of the population in SSA islocated in areas of intense HIV epidemics. Meanwhile, another 16% of thepopulation is located in areas of low HIV prevalence, where some behavioralor biological protective factors appear to have slowed HIV transmission. Our study provides direct evidence for strong geographic clustering of HIVinfection across SSA. This striking pattern of heterogeneity at themicro-geographical scale might reflect the fact that most HIV epidemics inthe general population in SSA are not far from their epidemic threshold. Ourfindings identify priority geographic areas for HIV programming, and supportthe need for spatially targeted interventions in order to maximize theimpact on the epidemic in SSA.

81 citations


Journal ArticleDOI
TL;DR: Questions are asked about the spatio-temporal stability in the GSV date stamp and how frequently and where is imagery from different time periods woven together to represent environmental conditions in a particular place?
Abstract: Background: Recently, Google Street View (GSV) has been examined as a tool for remotely conducting systematic observation of the built environment. Studies have found it offers benefits over in-person audits, including efficiency, safety, cost, and the potential to expand built environment research to larger areas and more places globally. However, one limitation has been the lack of documentation on the date of imagery collection. In 2011, Google began placing a date stamp on images which now enables investigation of this concern. This study questions the spatio-temporal stability in the GSV date stamp. Specifically, is the imagery collected contemporaneously? If not, how frequently and where is imagery from different time periods woven together to represent environmental conditions in a particular place. Furthermore, how much continuity exists in imagery for a particular time period? Answering these questions will provide guidance on the use of GSV as a tool for built environment audits. Methods: GSV was used to virtually “drive” five sites that are a part of the authors’ ongoing studies. Each street in the sites was “driven” one mouse-click at a time while observing the date stamp on each image. Every time the date stamp changed, this “disruption” was marked on the map. Every street segment in the site was coded by the date the imagery for that segment was collected. Spatial query and descriptive statistics were applied to understand the spatio-temporal patterns of imagery dates. Results: Spatio-temporal instability is present in the dates of GSV imagery. Of the 353 disruptions, 82.4% occur close to (<25 m) intersections. The remainder occurs inconsistently in other locations. The extent of continuity for a set of images collected with the same date stamp ranged from 3.13 m to 3373.06 m, though the majority of continuous segments were less than 400 m. Conclusion: GSV offers some benefits over traditional built environment audits. However, this investigation empirically identifies a previously undocumented limitation in its application for research. Imagery dates can change often and without warning. Caution should be used at intersections where these disruptions are most likely to occur, though caution should be used everywhere when using GSV as a data collection tool.

80 citations


Journal ArticleDOI
TL;DR: Density and proximity metrics were largely comparable, with some exceptions, and future comparability could be ensured by moving towards a more standardised set of environmental metrics, where appropriate, lessening the potential pitfalls of methodological variation between studies.
Abstract: The use of Geographical Information Systems (GIS) to objectively measure ‘obesogenic’ food environment (foodscape) exposure has become common-place. This increase in usage has coincided with the development of a methodologically heterogeneous evidence-base, with subsequent perceived difficulties for inter-study comparability. However, when used together in previous work, different types of food environment metric have often demonstrated some degree of covariance. Differences and similarities between density and proximity metrics, and within methodologically different conceptions of density and proximity metrics need to be better understood. Frequently used measures of food access were calculated for North East England, UK. Using food outlet data from local councils, densities of food outlets per 1000 population and per km2 were calculated for small administrative areas. Densities (counts) were also calculated based on population-weighted centroids of administrative areas buffered at 400/800/1000m street network and Euclidean distances. Proximity (street network and Euclidean distances) from these centroids to the nearest food outlet were also calculated. Metrics were compared using Spearman’s rank correlations. Measures of foodscape density and proximity were highly correlated. Densities per km2 and per 1000 population were highly correlated (rs = 0.831). Euclidean and street network based measures of proximity (rs = 0.865) and density (rs = 0.667-0.764, depending on neighbourhood size) were also highly correlated. Density metrics based on administrative areas and buffered centroids of administrative areas were less strongly correlated (rs = 0.299-0.658). Density and proximity metrics were largely comparable, with some exceptions. Whilst results suggested a substantial degree of comparability across existing studies, future comparability could be ensured by moving towards a more standardised set of environmental metrics, where appropriate, lessening the potential pitfalls of methodological variation between studies. The researchers’ role in creating their own obesogenic ‘reality’ should be better understood and acknowledged.

77 citations


Journal ArticleDOI
TL;DR: It seems that promoting park use might be a promising strategy to increase physical activity in low-income populations, known to be at higher risk for overweight and obesity.
Abstract: Background Public parks can be an important setting for physical activity promotion, but to increase park use and the activity levels of park users, the crucial attributes related to active park use need to be defined. Not only user characteristics and structural park attributes, but also characteristics of the surrounding neighborhood are important to examine. Furthermore, internationally comparable studies are needed, to find out if similar intervention strategies might be effective worldwide. The main aim of this study was to examine whether the overall number of park visitors and their activity levels depend on study site, neighborhood walkability and neighborhood income.

75 citations


Journal ArticleDOI
TL;DR: Comparing self-perceived unmet needs across Italian regions and assessing how the reported reasons - grouped into the categories of availability, accessibility and acceptability – vary geographically suggest that some population groups are more vulnerable than others to experiencing unmet health needs and to reporting some categories of reasons.
Abstract: Background: Unmet health needs should be, in theory, a minor issue in Italy where a publicly funded and universally accessible health system exists. This, however, does not seem to be the case. Moreover, in the last two decades responsibilities for health care have been progressively decentralized to regional governments, which have differently organized health service delivery within their territories. Regional decision-making has affected the use of health care services, further increasing the existing geographical disparities in the access to care across the country. This study aims at comparing self-perceived unmet needs across Italian regions and assessing how the reported reasons - grouped into the categories of availability, accessibility and acceptability – vary geographically. Methods: Data from the 2006 Italian component of the European Union Statistics on Income and Living Conditions are employed to explore reasons and predictors of self-reported unmet medical needs among 45,175 Italian respondents aged 18 and over. Multivariate logistic regression models are used to determine adjusted rates for overall unmet medical needs and for each of the three categories of reasons. Results: Results show that, overall, 6.9% of the Italian population stated having experienced at least one unmet medical need during the last 12 months. The unadjusted rates vary markedly across regions, thus resulting in a clear-cut north–south divide (4.6% in the North-East vs. 10.6% in the South). Among those reporting unmet medical needs, the leading reason was problems of accessibility related to cost or transportation (45.5%), followed by acceptability (26.4%) and availability due to the presence of too long waiting lists (21.4%). In the South, more than one out of two individuals with an unmet need refrained from seeing a physician due to economic reasons. In the northern regions, working and family responsibilities contribute relatively more to the underutilization of medical services. Logistic regression results suggest that some population groups are more vulnerable than others to experiencing unmet health needs and to reporting some categories of reasons. Adjusting for the predictors resulted in very few changes in the rank order of macro-area rates. Conclusions: Policies to address unmet health care needs should adopt a multidimensional approach and be tailored so as to consider such geographical heterogeneities.

Journal ArticleDOI
TL;DR: In settings with clearly distinguishable individual structures, the remote, imagery-based method had reasonable accuracy for the purposes of rapid estimation, was simple and quick to implement, and would likely perform better in more current application, however, it may have insurmountable limitations in settings featuring connected buildings or shelters, a complex pattern of roofs and multi-level buildings.
Abstract: Estimating the size of forcibly displaced populations is key to documenting their plight and allocating sufficient resources to their assistance, but is often not done, particularly during the acute phase of displacement, due to methodological challenges and inaccessibility. In this study, we explored the potential use of very high resolution satellite imagery to remotely estimate forcibly displaced populations. Our method consisted of multiplying (i) manual counts of assumed residential structures on a satellite image and (ii) estimates of the mean number of people per structure (structure occupancy) obtained from publicly available reports. We computed population estimates for 11 sites in Bangladesh, Chad, Democratic Republic of Congo, Ethiopia, Haiti, Kenya and Mozambique (six refugee camps, three internally displaced persons’ camps and two urban neighbourhoods with a mixture of residents and displaced) ranging in population from 1,969 to 90,547, and compared these to “gold standard” reference population figures from census or other robust methods. Structure counts by independent analysts were reasonably consistent. Between one and 11 occupancy reports were available per site and most of these reported people per household rather than per structure. The imagery-based method had a precision relative to reference population figures of <10% in four sites and 10–30% in three sites, but severely over-estimated the population in an Ethiopian camp with implausible occupancy data and two post-earthquake Haiti sites featuring dense and complex residential layout. For each site, estimates were produced in 2–5 working person-days. In settings with clearly distinguishable individual structures, the remote, imagery-based method had reasonable accuracy for the purposes of rapid estimation, was simple and quick to implement, and would likely perform better in more current application. However, it may have insurmountable limitations in settings featuring connected buildings or shelters, a complex pattern of roofs and multi-level buildings. Based on these results, we discuss possible ways forward for the method’s development.

Journal ArticleDOI
TL;DR: Evidence is found that income-related inequalities in exposure to ambient PM10 may contribute to Europe-wide mortality inequalities, and to those in Eastern but not Western European regions, where lower-income regions were more susceptible to the health effects of PM10.
Abstract: Background: Environmental disparities may underlie the unequal distribution of health across socioeconomic groups. However, this assertion has not been tested across a range of countries: an important knowledge gap for a transboundary health issue such as air pollution. We consider whether populations of low-income European regions were a) exposed to disproportionately high levels of particulate air pollution (PM10) and/or b) disproportionately susceptible to pollution-related mortality effects. Methods: Europe-wide gridded PM10 and population distribution data were used to calculate population-weighted average PM10 concentrations for 268 sub-national regions (NUTS level 2 regions) for the period 2004–2008. The data were mapped, and patterning by mean household income was assessed statistically. Ordinary least squares regression was used to model the association between PM10 and cause-specific mortality, after adjusting for regional-level household income and smoking rates. Results: Air quality improved for most regions between 2004 and 2008, although large differences between Eastern and Western regions persisted. Across Europe, PM10 was correlated with low household income but this association primarily reflected East–West inequalities and was not found when Eastern or Western Europe regions were considered separately. Notably, some of the most polluted regions in Western Europe were also among the richest. PM10 was more strongly associated with plausibly-related mortality outcomes in Eastern than Western Europe, presumably because of higher ambient concentrations. Populations of lower-income regions appeared more susceptible to the effects of PM10, but only for circulatory disease mortality in Eastern Europe and male respiratory mortality in Western Europe. Conclusions: Income-related inequalities in exposure to ambient PM10 may contribute to Europe-wide mortality inequalities, and to those in Eastern but not Western European regions. We found some evidence that lowerincome regions were more susceptible to the health effects of PM10.

Journal ArticleDOI
TL;DR: The typhoid occurrence/risk prediction equation was used to develop the first risk map showing areas of Dhaka Metropolitan Area whose inhabitants are at greater or lesser risk of typhoid infection, and has the potential to advise public health professionals on developing prevention strategies such as targeted vaccination.
Abstract: Background: Developing countries in South Asia, such as Bangladesh, bear a disproportionate burden of diarrhoeal diseases such as Cholera, Typhoid and Paratyphoid. These seem to be aggravated by a number of social and environmental factors such as lack of access to safe drinking water, overcrowdedness and poor hygiene brought about by poverty. Some socioeconomic data can be obtained from census data whilst others are more difficult to elucidate. This study considers a range of both census data and spatial data from other sources, including remote sensing, as potential predictors of typhoid risk. Typhoid data are aggregated from hospital admission records for the period from 2005 to 2009. The spatial and statistical structures of the data are analysed and Principal Axis Factoring is used to reduce the degree of co-linearity in the data. The resulting factors are combined into a Quality of Life index, which in turn is used in a regression model of typhoid occurrence and risk. Results: The three Principal Factors used together explain 87% of the variance in the initial candidate predictors, which eminently qualifies them for use as a set of uncorrelated explanatory variables in a linear regression model. Initial regression result using Ordinary Least Squares (OLS) were disappointing, this was explainable by analysis of the spatial autocorrelation inherent in the Principal factors. The use of Geographically Weighted Regression caused a considerable increase in the predictive power of regressions based on these factors. The best prediction, determined by analysis of the Akaike Information Criterion (AIC) was found when the three factors were combined into a quality of life index, using a method previously published by others, and had a coefficient of determination of 73%. Conclusions: The typhoid occurrence/risk prediction equation was used to develop the first risk map showing areas of Dhaka Metropolitan Area whose inhabitants are at greater or lesser risk of typhoid infection. This, coupled with seasonal information on typhoid incidence also reported in this paper, has the potential to advise public health professionals on developing prevention strategies such as targeted vaccination.

Journal ArticleDOI
TL;DR: A sample-splitting method embedded within a population growth model is used to estimate a level of population density above which policies to socially distance, redistribute or quarantine populations are likely to be more effective than they are for areas with population densities that lie below the threshold.
Abstract: Geographic variables play an important role in the study of epidemics. The role of one such variable, population density, in the spread of influenza is controversial. Prior studies have tested for such a role using arbitrary thresholds for population density above or below which places are hypothesized to have higher or lower mortality. The results of such studies are mixed. The objective of this study is to estimate, rather than assume, a threshold level of population density that separates low-density regions from high-density regions on the basis of population loss during an influenza pandemic. We study the case of the influenza pandemic of 1918–19 in India, where over 15 million people died in the short span of less than one year. Using data from six censuses for 199 districts of India (n=1194), the country with the largest number of deaths from the influenza of 1918–19, we use a sample-splitting method embedded within a population growth model that explicitly quantifies population loss from the pandemic to estimate a threshold level of population density that separates low-density districts from high-density districts. The results demonstrate a threshold level of population density of 175 people per square mile. A concurrent finding is that districts on the low side of the threshold experienced rates of population loss (3.72%) that were lower than districts on the high side of the threshold (4.69%). This paper introduces a useful analytic tool to the health geographic literature. It illustrates an application of the tool to demonstrate that it can be useful for pandemic awareness and preparedness efforts. Specifically, it estimates a level of population density above which policies to socially distance, redistribute or quarantine populations are likely to be more effective than they are for areas with population densities that lie below the threshold.

Journal ArticleDOI
TL;DR: The development and validation of a new activity location questionnaire which can be useful in accounting for multiple environmental influences in large population health investigations is described and can be used to accurately collect data on regular activity spaces in terms of locations regularly visited.
Abstract: Background Place and health researchers are increasingly interested in integrating individuals’ mobility and the experience they have with multiple settings in their studies. In practice, however, few tools exist which allow for rapid and accurate gathering of detailed information on the geographic location of places where people regularly undertake activities. We describe the development and validation of a new activity location questionnaire which can be useful in accounting for multiple environmental influences in large population health investigations.

Journal ArticleDOI
TL;DR: It is suggested that it is useful to take into account the exposure to transportation noise in the residential neighborhood rather than only at the residence, different percentiles of noise exposure in the Residential neighborhood, and the socioeconomic characteristics of the residential Neighborhood to explain variations in annoyance due to road traffic in the neighborhood.
Abstract: Road traffic and related noise is a major source of annoyance and impairment to health in urban areas. Many areas exposed to road traffic noise are also exposed to rail and air traffic noise. The resulting annoyance may depend on individual/neighborhood socio-demographic factors. Nevertheless, few studies have taken into account the confounding or modifying factors in the relationship between transportation noise and annoyance due to road traffic. In this study, we address these issues by combining Geographic Information Systems and epidemiologic methods. Street network buffers with a radius of 500 m were defined around the place of residence of the 7290 participants of the RECORD Cohort in Ile-de-France. Estimated outdoor traffic noise levels (road, rail, and air separately) were assessed at each place of residence and in each of these buffers. Higher levels of exposure to noise were documented in low educated neighborhoods. Multilevel logistic regression models documented positive associations between road traffic noise and annoyance due to road traffic, after adjusting for individual/neighborhood socioeconomic conditions. There was no evidence that the association was of different magnitude when noise was measured at the place of residence or in the residential neighborhood. However, the strength of the association between neighborhood noise exposure and annoyance increased when considering a higher percentile in the distribution of noise in each neighborhood. Road traffic noise estimated at the place of residence and road traffic noise in the residential neighborhood (75th percentile) were independently associated with annoyance, when adjusted for each other. Interactions of effects indicated that the relationship between road traffic noise exposure in the residential neighborhood and annoyance was stronger in affluent and high educated neighborhoods. Overall, our findings suggest that it is useful to take into account (i) the exposure to transportation noise in the residential neighborhood rather than only at the residence, (ii) different percentiles of noise exposure in the residential neighborhood, and (iii) the socioeconomic characteristics of the residential neighborhood to explain variations in annoyance due to road traffic in the neighborhood.

Journal ArticleDOI
TL;DR: This paper advances the understanding of the nexus between place, health and SES by providing an objective spatially informed SES measure for testing health outcomes and reported a robust association between RLF and several health measures.
Abstract: Residential property is reported as the most valuable asset people will own and therefore provides the potential to be used as a socio-economic status (SES) measure. Location is generally recognised as the most important determinant of residential property value. Extending the well-established relationship between poor health and socio-economic disadvantage and the role of residential property in the overall wealth of individuals, this study tested the predictive value of the Relative Location Factor (RLF), a SES measure designed to reflect the relationship between location and residential property value, and six cardiometabolic disease risk factors, central obesity, hypertriglyceridemia, reduced high density lipoprotein (HDL), hypertension, impaired fasting glucose, and high low density lipoprotein (LDL). These risk factors were also summed and expressed as a cumulative cardiometabolic risk (CMR) score. RLF was calculated using a global hedonic regression model from residential property sales transaction data based upon several residential property characteristics, but deliberately blind to location, to predict the selling price of the property. The predicted selling price was divided by the actual selling price and the results interpolated across the study area and classified as tertiles. The measures used to calculate CMR were collected via clinic visits from a population-based cohort study. Models with individual risk factors and the cumulative cardiometabolic risk (CMR) score as dependent variables were respectively tested using log binomial and Poisson generalised linear models. A statistically significant relationship was found between RLF, the cumulative CMR score and all but one of the risk factors. In all cases, participants in the most advantaged and intermediate group had a lower risk for cardio-metabolic diseases. For the CMR score the RR for the most advantaged was 19% lower (RR = 0.81; CI 0.76-0.86; p <0.0001) and the middle group was 9% lower (RR = 0.91; CI 0.86-0.95; p <0.0001) than the least advantaged group. This paper advances the understanding of the nexus between place, health and SES by providing an objective spatially informed SES measure for testing health outcomes and reported a robust association between RLF and several health measures.

Journal ArticleDOI
TL;DR: Neighbourhood-level socio-economic-related risks are found to have direct effects on low birth weight and preterm birth and the evidence supports both the materialist and psycho-social conceptualizations and the pathways that describe them, although the magnitude of the former is greater than the latter.
Abstract: Background Although socio-economic factors have been identified as one of the most important groups of neighbourhood-level risks affecting birth outcomes, uncertainties still exist concerning the pathways through which they are transferred to individual risk factors. This poses a challenge for setting priorities and developing appropriate community-oriented public health interventions and planning guidelines to reduce the level of adverse birth outcomes.

Journal ArticleDOI
TL;DR: The NEWS-Nigeria demonstrated acceptable measurement properties among Nigerian adults and may be useful for evaluation of the built environment in Nigeria.
Abstract: Background: The development of reliable and culturally sensitive measures of attributes of the built and social environment is necessary for accurate analysis of environmental correlates of physical activity in low-income countries, that can inform international evidence-based policies and interventions in the worldwide prevention of physical inactivity epidemics. This study systematically adapted the Neighborhood Environment Walkability Scale (NEWS) for Nigeria and evaluated aspects of reliability and validity of the adapted version among Nigerian adults. Methods: The adaptation of the NEWS was conducted by African and international experts, and final items were selected for NEWS-Nigeria after a cross-validation of the confirmatory factor analysis structure of the original NEWS. Participants (N = 386; female = 47.2%) from two cities in Nigeria completed the adapted NEWS surveys regarding perceived residential density, land use mix – diversity, land use mix – access, street connectivity, infrastructure and safety for walking and cycling, aesthetics, traffic safety, and safety from crime. Self-reported activity for leisure, walking for different purposes, and overall physical activity were assessed with the validated International Physical Activity Questionnaire (long version). Results: The adapted NEWS subscales had moderate to high test-retest reliability (ICC range 0.59 –0.91). Construct validity was good, with residents of high-walkable neighborhoods reporting significantly higher residential density, more land use mix diversity, higher street connectivity, more traffic safety and more safety from crime, but lower infrastructure and safety for walking/cycling and aesthetics than residents of low-walkable neighborhoods. Concurrent validity correlations were low to moderate (r = 0.10 –0.31) with residential density, land use mix diversity, and traffic safety significantly associated with most physical activity outcomes. Conclusions: The NEWS-Nigeria demonstrated acceptable measurement properties among Nigerian adults and may be useful for evaluation of the built environment in Nigeria. Further adaptation and evaluation in other African countries is needed to create a version that could be used throughout the African region.

Journal ArticleDOI
TL;DR: Of the neighbourhood built environment exposure variables measured in this study, the three that were the most highly associated with inactivity were walkability, the density of cul-de-sacs, and park space.
Abstract: We investigated the independent association between several neighbourhoodbuilt environment features and physical inactivity within a national sampleof Canadian youth, and estimated the proportion of inactivity within thepopulation that was attributable to these built environment features. This was a cross-sectional study of 6626 youth aged 11–15 yearsfrom 272 schools across Canada. Participants resided within 1 km oftheir school. Walkability, outdoor play areas (parks, wooded areas, yards athome, cul-de-sacs on roads), recreation facilities, and aesthetics weremeasured objectively within each school neighbourhood using geographicinformation systems. Physical inactivity (<5 days/week of60 minutes of moderate-to-vigorous physical activity) was assessed byquestionnaire. Multilevel logistic regression analyses, which controlled forseveral covariates, examined relationships between built environmentfeatures and physical inactivity. The final regression model indicated that, by comparison to youth living inthe least walkable neighbourhoods, the risks for physical inactivity were28-44% higher for youth living in neighbourhoods in the remaining threewalkability quartiles. By comparison to youth living in neighbourhoods withthe highest density of cul-de-sacs, risks for physical inactivity were28-32% higher for youth living in neighbourhoods in the lowest twoquartiles. By comparison to youth living in neighbourhoods with the leastamount of park space, risks for physical inactivity were 28-37% higher foryouth living in the neighbourhoods with a moderate to high (quartiles 2 and3) park space. Population attributable risk estimates suggested that 23% ofphysical inactivity within the population was attributable to living inwalkable neighbourhoods, 16% was attributable to living in neighbourhoodswith a low density of cul-de-sacs, and 15% was attributable to living inneighbourhoods with a moderate to high amount of park space. Of the neighbourhood built environment exposure variables measured in thisstudy, the three that were the most highly associated with inactivity werewalkability, the density of cul-de-sacs, and park space. The associationbetween some of these features and youths’ activity levels were in theopposite direction to what has previously been reported in adults andyounger children.

Journal ArticleDOI
TL;DR: An open-source simple agent-basedWalkable catchment tool that can be used by researchers, urban designers, planners, and policy makers to test scenarios for improving neighborhood walkable catchments and has the capacity to influence planning and public health advocacy and practice.
Abstract: Background Pedestrian-friendly neighborhoods with proximal destinations and services encourage walking and decrease car dependence, thereby contributing to more active and healthier communities. Proximity to key destinations and services is an important aspect of the urban design decision making process, particularly in areas adopting a transit-oriented development (TOD) approach to urban planning, whereby densification occurs within walking distance of transit nodes. Modeling destination access within neighborhoods has been limited to circular catchment buffers or more sophisticated network-buffers generated using geoprocessing routines within geographical information systems (GIS). Both circular and network-buffer catchment methods are problematic. Circular catchment models do not account for street networks, thus do not allow exploratory ‘what-if’ scenario modeling; and network-buffering functionality typically exists within proprietary GIS software, which can be costly and requires a high level of expertise to operate.

Journal ArticleDOI
TL;DR: In this article, the authors describe the construction and validation of two objective walkability indexes for Sydney, Australia, which describe the capacity of the built environment to support walking for various purposes.
Abstract: Background Walkability describes the capacity of the built environment to support walking for various purposes. This paper describes the construction and validation of two objective walkability indexes for Sydney, Australia.

Journal ArticleDOI
TL;DR: The method used to generate and field locate random homes for surveys and water sampling was an effective means of selecting random households in a rural environment lacking geolocation infrastructure.
Abstract: A remote sensing technique was developed which combines a Geographic Information System (GIS); Google Earth, and Microsoft Excel to identify home locations for a random sample of households in rural Haiti. The method was used to select homes for ethnographic and water quality research in a region of rural Haiti located within 9 km of a local hospital and source of health education in Deschapelles, Haiti. The technique does not require access to governmental records or ground based surveys to collect household location data and can be performed in a rapid, cost-effective manner. The random selection of households and the location of these households during field surveys were accomplished using GIS, Google Earth, Microsoft Excel, and handheld Garmin GPSmap 76CSx GPS units. Homes were identified and mapped in Google Earth, exported to ArcMap 10.0, and a random list of homes was generated using Microsoft Excel which was then loaded onto handheld GPS units for field location. The development and use of a remote sensing method was essential to the selection and location of random households. A total of 537 homes initially were mapped and a randomized subset of 96 was identified as potential survey locations. Over 96% of the homes mapped using Google Earth imagery were correctly identified as occupied dwellings. Only 3.6% of the occupants of mapped homes visited declined to be interviewed. 16.4% of the homes visited were not occupied at the time of the visit due to work away from the home or market days. A total of 55 households were located using this method during the 10 days of fieldwork in May and June of 2012. The method used to generate and field locate random homes for surveys and water sampling was an effective means of selecting random households in a rural environment lacking geolocation infrastructure. The success rate for locating households using a handheld GPS was excellent and only rarely was local knowledge required to identify and locate households. This method provides an important technique that can be applied to other developing countries where a randomized study design is needed but infrastructure is lacking to implement more traditional participant selection methods.

Journal ArticleDOI
TL;DR: This study provides proof of concept for the use of the SenseCam to capture built environment data in real time that may be related to active transportation as well as investigating the utility of wearable cameras to objectively audit and quantify environmental features along work-related walking and cycling routes.
Abstract: Background: Active transport can contribute to physical activity accumulation and improved health in adults. The built environment is an established associate of active transport behaviours; however, assessment of environmental features encountered during journeys remains challenging. The purpose of this study was to examine the utility of wearable cameras to objectively audit and quantify environmental features along work-related walking and cycling routes. Methods: A convenience sample of employed adults was recruited in New Zealand, in June 2011. Participants wore a SenseCam for all journeys over three weekdays and completed travel diaries and demographic questionnaires. SenseCam images for work-related active transport journeys were coded for presence of environmental features hypothesised to be related to active transport. Differences in presence of features by transport mode and in participant-reported and SenseCam-derived journey duration were determined using two-sample tests of proportion and an independent samples t-test, respectively. Results: Fifteen adults participated in the study, yielding 1749 SenseCam images from 30 work-related active transport journeys for coding. Significant differences in presence of features were found between walking and cycling journeys. Almost a quarter of images were uncodeable due to being too dark to determine features. There was a non-significant tendency for respondents to under-report their journey duration. Conclusion: This study provides proof of concept for the use of the SenseCam to capture built environment data in real time that may be related to active transportation. Further work is required to test and refine coding methodologies across a range of settings, travel behaviours, and demographic groups.

Journal ArticleDOI
TL;DR: Results suggest POI is a viable alternative to council data, particularly in terms of PPVs, which remain robust across urban/rural and SES divides, and sensitivities showed an overall ‘moderate’ sensitivity, although this varied significantly by outlet type.
Abstract: Interest in the role of food environments in shaping food consumption behaviours has grown in recent years. However, commonly used secondary food environment data sources have not yet been fully evaluated for completeness and systematic biases. This paper assessed the accuracy of UK Points of Interest (POI) data, compared to local council food outlet data for the county of Cambridgeshire. Percentage agreement, positive predictive values (PPVs) and sensitivities were calculated for all food outlets across the study area, by outlet type, and across urban/rural/SES divisions. Percentage agreement by outlet type (29.7-63.5%) differed significantly to overall percentage agreement (49%), differed significantly in rural areas (43%) compared to urban (52.8%), and by SES quintiles. POI data had an overall PPV of 74.9%, differing significantly for Convenience Stores (57.9%), Specialist Stores (68.3%), and Restaurants (82.6%). POI showed an overall ‘moderate’ sensitivity, although this varied significantly by outlet type. Whilst sensitivies by urban/rural/SES divides varied significantly from urban and least deprived reference categories, values remained ‘moderate’. Results suggest POI is a viable alternative to council data, particularly in terms of PPVs, which remain robust across urban/rural and SES divides. Most variation in completeness was by outlet type; lowest levels were for Convenience Stores, which are commonly cited as ‘obesogenic’.

Journal ArticleDOI
TL;DR: A ubiquitous spatial data collection approach using a spatial video that can be used in any environment to improve local area health analysis and intervention and its simplicity should also be used to encourage local participatory collaborations.
Abstract: Fine-scale and longitudinal geospatial analysis of health risks in challenging urban areas is often limited by the lack of other spatial layers even if case data are available. Underlying population counts, residential context, and associated causative factors such as standing water or trash locations are often missing unless collected through logistically difficult, and often expensive, surveys. The lack of spatial context also hinders the interpretation of results and designing intervention strategies structured around analytical insights. This paper offers a ubiquitous spatial data collection approach using a spatial video that can be used to improve analysis and involve participatory collaborations. A case study will be used to illustrate this approach with three health risks mapped at the street scale for a coastal community in Haiti. Spatial video was used to collect street and building scale information, including standing water, trash accumulation, presence of dogs, cohort specific population characteristics, and other cultural phenomena. These data were digitized into Google Earth and then coded and analyzed in a GIS using kernel density and spatial filtering approaches. The concentrations of these risks around area schools which are sometimes sources of diarrheal disease infection because of the high concentration of children and variable sanitary practices will show the utility of the method. In addition schools offer potential locations for cholera education interventions. Previously unavailable fine scale health risk data vary in concentration across the town, with some schools being proximate to greater concentrations of the mapped risks. The spatial video is also used to validate coded data and location specific risks within these “hotspots”. Spatial video is a tool that can be used in any environment to improve local area health analysis and intervention. The process is rapid and can be repeated in study sites through time to track spatio-temporal dynamics of the communities. Its simplicity should also be used to encourage local participatory collaborations.

Journal ArticleDOI
TL;DR: This study proved that tracking technology an effective technique for obtaining data for micro-scale influenza transmission research and revealed micro- scale transmission hotspots on a university campus and provided insights for local control and prevention strategies.
Abstract: Infectious diseases pose increasing threats to public health with increasing population density and more and more sophisticated social networks. While efforts continue in studying the large scale dissemination of contagious diseases, individual-based activity and behaviour study benefits not only disease transmission modelling but also the control, containment, and prevention decision making at the local scale. The potential for using tracking technologies to capture detailed space-time trajectories and model individual behaviour is increasing rapidly, as technological advances enable the manufacture of small, lightweight, highly sensitive, and affordable receivers and the routine use of location-aware devices has become widespread (e.g., smart cellular phones). The use of low-cost tracking devices in medical research has also been proved effective by more and more studies. This study describes the use of tracking devices to collect data of space-time trajectories and the spatiotemporal processing of such data to facilitate micro-scale flu transmission study. We also reports preliminary findings on activity patterns related to chances of influenza infection in a pilot study. Specifically, this study employed A-GPS tracking devices to collect data on a university campus. Spatiotemporal processing was conducted for data cleaning and segmentation. Processed data was validated with traditional activity diaries. The A-GPS data set was then used for visual explorations including density surface visualization and connection analysis to examine space-time activity patterns in relation to chances of influenza infection. When compared to diary data, the segmented tracking data demonstrated to be an effective alternative and showed greater accuracies in time as well as the details of routes taken by participants. A comparison of space-time activity patterns between participants who caught seasonal influenza and those who did not revealed interesting patterns. This study proved that tracking technology an effective technique for obtaining data for micro-scale influenza transmission research. The findings revealed micro-scale transmission hotspots on a university campus and provided insights for local control and prevention strategies.

Journal ArticleDOI
TL;DR: This study compared youth-identified neighborhood boundaries to census-defined neighborhood boundaries, and determined how the amount of time spent and moderate-to-vigorous physical activity (MVPA) levels compared within both boundary types.
Abstract: Background: Numerous definitions of neighborhood exist, yet few studies have considered youth’s perceptions of neighborhood boundaries. This study compared youth-identified neighborhood (YIN) boundaries to census-defined neighborhood (CDN) boundaries, and determined how the amount of time spent and moderate-to-vigorous physical activity (MVPA) levels compared within both boundary types. Methods: Adolescents aged 11–14 years were asked to identify their neighborhood boundaries using a map. Objective location and physical activity data collected using Global Positioning System (GPS) devices and accelerometers were used to calculate the amount of time spent and MVPA within youth-identified and census-defined neighborhood boundaries. Paired bivariate analyses compared mean area (meters squared), percent of total time, daily MVPA (minutes), time density (minutes/m 2 ) and MVPA density (minutes/m 2 ) for both boundary types. Results: Youth-identified neighborhoods (1,821,705 m 2 ) and census-defined neighborhoods (1,277,181 m 2 )w ere not significantly different in area, p=0.30. However, subjects spent more time in youth-identified neighborhoods (80.3%) than census-defined neighborhoods (58.4%), p<0.0001, and engaged in more daily MVPA within youth-identified neighborhoods (14.7 minutes) than census-defined neighborhoods (9.5 minutes), p < 0.0001. After adjusting for boundary area, MVPA density (minutes of MVPA per squared meter of area) remained significantly greater for youth-identified neighborhoods (2.4 × 10 -4 minutes/m 2 ) than census-defined neighborhoods (1.4 × 10 -4 minutes/m 2 ), p =0.02. Conclusions: Adolescents perceive their neighborhoods to be similar in size to census-defined neighborhoods. However, youth-identified neighborhoods better capture the locations in which adolescents spend time and engage in physical activity. Asking adolescents to identify their neighborhood boundaries is a feasible and valuable method for identifying the spaces that adolescents are exposed to and use to be physically active.

Journal ArticleDOI
TL;DR: Testing for effects of SA and CO is necessary before incorporating these covariates into algorithms building a climate envelope, and removal ofSA and CO by a harmonic regression seems most promising because it retains both biological and statistical meaning.
Abstract: Modelling the environmental niche and spatial distribution of pathogen-transmitting arthropods involves various quality and methodological concerns related to using climate data to capture the environmental niche. This study tested the potential of MODIS remotely sensed and interpolated gridded covariates to estimate the climate niche of the medically important ticks Ixodes ricinus and Hyalomma marginatum. We also assessed model inflation resulting from spatial autocorrelation (SA) and collinearity (CO) of covariates used as time series of data (monthly values of variables), principal components analysis (PCA), and a discrete Fourier transformation. Performance of the models was measured using area under the curve (AUC), autocorrelation by Moran’s I, and collinearity by the variance inflation factor (VIF). The covariate spatial resolution slightly affected the final AUC. Consistently, models for H. marginatum performed better than models for I. ricinus, likely because of a species-derived rather than covariate effect because the former occupies a more limited niche. Monthly series of interpolated climate always better captured the climate niche of the ticks, but the SA was around 2 times higher and the maximum VIF between covariates around 30 times higher in interpolated than in MODIS-derived covariates. Interpolated or remotely sensed monthly series of covariates always had higher SA and CO than their transformations by PCA or Fourier. Regarding the effects of background point selection on AUC, we found that selection based on a set of rules for the distance to the core distribution and the heterogeneity of the landscape influenced model outcomes. The best selection relied on a random selection of points as close as possible to the target organism area of distribution, but effects are variable according to the species modelled. Testing for effects of SA and CO is necessary before incorporating these covariates into algorithms building a climate envelope. Results support a higher SA and CO in an interpolated climate dataset than in remotely sensed covariates. Satellite-derived information has fewer drawbacks compared to interpolated climate for modelling tick relationships with environmental niche. Removal of SA and CO by a harmonic regression seems most promising because it retains both biological and statistical meaning.