International Journal of Health Geographics - Latest Articles

The proportion of youths' physical inactivity attributable to neighbourhood built environment features

Rachel Laxer — 2013-06-18T00:00:00Z

ObjectiveWe investigated the independent association between several neighbourhood built environment features and physical inactivity within a national sample of Canadian youth, and estimated the proportion of inactivity within the population that was attributable to these built environment features. Methods: This was a cross-sectional study of 6626 youth aged 11--15 years from 272 schools across Canada. Participants resided within 1 km of their school. Walkability, outdoor play areas (parks, wooded areas, yards at home, cul-de-sacs on roads), recreation facilities, and aesthetics were measured objectively within each school neighbourhood using geographic information systems. Physical inactivity (<5 days/week of 60 minutes of moderate-to-vigorous physical activity) was assessed by questionnaire. Multilevel logistic regression analyses, which controlled for several covariates, examined relationships between built environment features and physical inactivity. Results: The final regression model indicated that, by comparison to youth living in the least walkable neighbourhoods, the risks for physical inactivity were 28-44% higher for youth living in neighbourhoods in the remaining three walkability quartiles. By comparison to youth living in neighbourhoods with the highest density of cul-de-sacs, risks for physical inactivity were 28-32% higher for youth living in neighbourhoods in the lowest two quartiles. By comparison to youth living in neighbourhoods with the least amount of park space, risks for physical inactivity were 28-37% higher for youth living in the neighbourhoods with a moderate to high (quartiles 2 and 3) park space. Population attributable risk estimates suggested that 23% of physical inactivity within the population was attributable to living in walkable neighbourhoods, 16% was attributable to living in neighbourhoods with a low density of cul-de-sacs, and 15% was attributable to living in neighbourhoods with a moderate to high amount of park space. Conclusions: 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. The association between some of these features and youths' activity levels were in the opposite direction to what has previously been reported in adults and younger children.

The road most travelled: the geographic distribution of road traffic injuries in England

Rebecca Steinbach — 2013-06-05T00:00:00Z

Background: Both road safety campaigns and epidemiological research into social differences in road traffic injury risk often assume that road traffic injuries occur close to home. While previous work has examined distance from home to site of collision for child pedestrians in local areas, less is known about the geographic distribution of road traffic injuries from other modes. This study explores the distribution of the distance between home residence and collision site (crash distance) by mode of transport, geographic area, and social characteristics in England. Methods: Using 10 years of road casualty data collected by the police, we examined the distribution of crash distance by age, sex, injury severity, area deprivation, urban/rural status, year, day of week, and, in London only, ethnic group. Results: 54% of pedestrians, 39% of cyclists, 17% of powered two-wheeler riders and 16% of car occupants were injured within 1 km of home. 82% of pedestrians, 83% of cyclists, 54% of powered two-wheeler and 53% of car occupants were injured within 5 km of home. We found some social and geographic differences in crash distance: for all transport modes injuries tended to occur closer to home in more deprived or urban areas; younger and older pedestrians and cyclists were also injured closer to home. Crash distance appears to have increased over time for pedestrian, cyclist and car occupant injuries, but has decreased over time for powered two-wheeler injuries. Conclusions: Injuries from all travel modes tend to occur quite close to home, supporting assumptions made in epidemiological and road safety education literature. However, the trend for increasing crash distance and the social differences identified may have methodological implications for future epidemiological studies on social differences in injury risk.

Modeling the spatial distribution of Chagas disease vectors using environmental variables and people´s knowledge

Jaime Hernández — 2013-05-31T00:00:00Z

Background: Chagas disease is caused by the protozoan Trypanosoma cruzi, which is transmitted to mammal hosts by triatomine insect vectors. The goal of this study was to model the spatial distribution of triatomine species in an endemic area. Methods: Vector’s locations were obtained with a rural householders’ survey. This information was combined with environmental data obtained from remote sensors, land use maps and topographic SRTM data, using the machine learning algorithm Random Forests to model species distribution. We analysed the combination of variables on three scales: 10 km, 5 km and 2.5 km cell size grids. Results: The best estimation, explaining 46.2% of the triatomines spatial distribution, was obtained for 5 km of spatial resolution. Presence probability distribution increases from central Chile towards the north, tending to cover the central-coastal region and avoiding areas of the Andes range. Conclusions: The methodology presented here was useful to model the distribution of triatomines in an endemic area; it is best explained using 5 km of spatial resolution, and their presence increases in the northern part of the study area. This study’s methodology can be replicated in other countries with Chagas disease or other vectorial transmitted diseases, and be used to locate high risk areas and to optimize resource allocation, for prevention and control of vectorial diseases.

Mapping HIV clustering: a strategy for identifying populations at high risk of HIV infection in sub-Saharan Africa

Diego Cuadros — 2013-05-22T00:00:00Z

Background: The geographical structure of an epidemic is ultimately a consequence of the drivers of the epidemic and the population susceptible to the infection. The ‘know your epidemic’ concept recognizes this geographical feature as a key element for identifying populations at higher risk of HIV infection where prevention interventions should be targeted. In an effort to clarify specific drivers of HIV transmission and identify priority populations for HIV prevention interventions, we conducted a comprehensive mapping of the spatial distribution of HIV infection across sub-Saharan Africa (SSA). Methods: The main source of data for our study was the Demographic and Health Survey conducted in 20 countries from SSA. We identified and compared spatial clusters with high and low numbers of HIV infections in each country using Kulldorff spatial scan test. The test locates areas with higher and lower numbers of HIV infections than expected under spatial randomness. For each identified cluster, a likelihood ratio test was computed. A P-value was determined through Monte Carlo simulations to evaluate the statistical significance of each cluster. Results: Our results suggest stark geographic variations in HIV transmission patterns within and across countries of SSA. About 14% of the population in SSA is located in areas of intense HIV epidemics. Meanwhile, another 16% of the population is located in areas of low HIV prevalence, where some behavioral or biological protective factors appear to have slowed HIV transmission. Conclusions: Our study provides direct evidence for strong geographic clustering of HIV infection across SSA. This striking pattern of heterogeneity at the micro-geographical scale might reflect the fact that most HIV epidemics in the general population in SSA are not far from their epidemic threshold. Our findings identify 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.

Geographical variation of unmet medical needs in Italy: a multivariate logistic regression analysis

Marina Cavalieri — 2013-05-12T00:00:00Z

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.

Associations of neighborhood characteristics with active park use: an observational study in two cities in the USA and Belgium

Delfien Van Dyck — 2013-05-07T00:00:00Z

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. Methods: Data were collected in 20 parks in Ghent, Belgium and San Diego, USA. Two trained observers systematically coded park characteristics using the Environmental Assessment of Public Recreation Spaces (EAPRS) tool, and park user characteristics using the System for Observing Play and recreation in Communities (SOPARC) tool. Multilevel multiple regression models were conducted in MLwiN 2.25. Results: In San Diego parks, activity levels of park visitors and number of vigorously active visitors were higher than in Ghent, while the number of visitors walking and the overall number of park visitors were lower. Neighborhood walkability was positively associated with the overall number of visitors, the number of visitors walking, number of sedentary visitors and mean activity levels of visitors. Neighborhood income was positively associated with the overall number of visitors, but negatively with the number of visitors being vigorously active. Conclusions: Neighborhood characteristics are important to explain park use. Neighborhood walkability-related attributes should be taken into account when promoting the use of existing parks or creating new parks. Because no strong differences were found between parks in high- and low-income neighborhoods, 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.

A binary-based approach for detecting irregularly shaped clusters

Tai-Chi Wang — 2013-05-06T00:00:00Z

Background There are many applications for spatial cluster detection and more detectionmethods have been proposed in recent years. Most cluster detection methods areefficient in detecting circular (or circular-like) clusters, but the methods whichcan detect irregular-shaped clusters usually require a lot of computing time.Methods e propose a new spatial detection algorithm for lattice data.The proposed method can be separated into two stages:the first stage determines the significant cells with unusual occurrences (i.e., individual clustering)by applying the Choynowski's test,and the second stage determines if there are clusters based on the information of the first stageby a binomial approximate method.We first use computer simulation to evaluate the performance of the proposed methodand compare it with the scan statistics.Furthermore, we take the Taiwan Cancer data in 2000to illustrate the detection results of the scan statistics and the proposed method.Results The simulation results support using the proposed methodwhen the population sizes are large and the study regions are irregular.However, in general, the scan statistics still have better power in detecting clusters,especially when the population sizes are not large.For the analysis of cancer data,the scan statistics tend to spot more clusters,and the clusters' shapes are close to circular (or elliptic).On the other hand, the proposed methods only find one clusterand cannot detect small-sized clusters.Conclusions In brief, the proposed methods can detect both circular and non-circular clusters wellwhen the significant cells are correctly detected by the Choynowski's method.In addition,the binomial-based method can handle the problem of multiple testing andsave the computing time.On the other hand,both the circular and elliptical scan statistics have good power in detecting clusters,but tend to detect more clusters and have lower accuracy in detectingnon-circular clusters.

Defining neighborhood boundaries in studies of spatial dependence in child behavior problems

Margaret Caughy — 2013-05-03T00:00:00Z

Background: The purpose of this study was to extend the analysis of neighborhood effects on child behavioral outcomes in two ways: (1) by examining the geographic extent of the relationship between child behavior and neighborhood physical conditions independent of standard administrative boundaries such as census tracts or block groups and (2) by examining the relationship and geographic extent of geographic peers’ behavior and individual child behavior. Methods: The study neighborhood was a low income, ethnic minority neighborhood of approximately 20,000 residents in a large city in the southwestern United States. Observational data were collected for 11,552 parcels and 1,778 face blocks in the neighborhood over a five week period. Data on child behavior problems were collected from the parents of 261 school-age children (81% African American, 14% Latino) living in the neighborhood. Spatial analysis methods were used to examine the spatial dependence of child behavior problems in relation to physical conditions in the neighborhood for areas surrounding the child’s home ranging from a radius of 50 meters to a radius of 1000 meters. Likewise, the spatial dependence of child behavior problems in relation to the behavior problems of neighborhood peers was examined for areas ranging from a radius 255 meters to a radius of 600 meters around the child’s home. Finally, we examined the joint influence of neighborhood physical conditions and geographic peers. Results: Poor conditions of the physical environment of the neighborhood were related to more behavioral problems, and the geographic extent of the physical environment that mattered was an area with a radius between 400 and 800 meters surrounding the child’s home. In addition, the average level of behavior problems of neighborhood peers within 255 meters of the child’s home was also positively associated with child behavior problems. Furthermore, these effects were independent of one another. Conclusions: These findings demonstrate that using flexible geographies in the study of neighborhood effects can provide important insights into spatial influences on health outcomes. With regards to child behavioral outcomes, specifically, these findings support the importance of addressing the physical and social environment when planning community-level interventions to reduce child behavior problems.

A case-referent study: light at night and breast cancer risk in Georgia

Sarah Bauer — 2013-04-17T00:00:00Z

Background: 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. Methods: 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. Results: 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]). Conclusions: 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.

Relative residential property value as a socioeconomic status indicator for health research

Neil Coffee — 2013-04-15T00:00:00Z

Background: 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. Methods: 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. Results: 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. Conclusions: 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.