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Int J Health Geogr [journal]
- Characteristics of residential areas and transportational walking among frail and non-frail Dutch elderly: does the size of the area matter? [JOURNAL ARTICLE]
- Int J Health Geogr 2014 Mar 4; 13(1):7.
A residential area supportive for walking may facilitate elderly to live longer independently. However, current evidence on area characteristics potentially important for walking among older persons is mixed. This study hypothesized that the importance of area characteristics for transportational walking depends on the size of the area characteristics are measured, and older person's frailty level.The study population consisted of 408 Dutch community-dwelling persons aged 65 years and older participating in the Elderly And their Neighborhood (ELANE) study in 2011-2012. Characteristics (aesthetics, functional features, safety, and destinations) of residential areas surrounding participants' homes ranging from a buffer of 400 meters up to 1600 meters (based on walking path networks) were linked with self-reported transportational walking using linear regression analyses. In addition, interaction effects between frailty level and area characteristics were tested.An increase in functional features (e.g. presence of sidewalks and benches) within a 400 meter buffer, in aesthetics (e.g. absence of litter and graffiti) within 800 and 1200 meter buffers, and an increase of one destination per buffer of 400 and 800 meters were associated with more transportational walking, up to 2.89 minutes per two weeks (CI 1.07-7.32; p < 0.05). No differences were found between frail and non-frail elderly.Better functional and aesthetic features, and more destinations in the residential area of community-dwelling older persons were associated with more transportational walking. The importance of area characteristics for transportational walking differs by area size, but not by frailty level. Neighbourhood improvements may increase transportational walking among older persons, thereby contributing to living longer independently.
- A spatially filtered multilevel model to account for spatial dependency: application to self-rated health status in South Korea. [Journal Article]
- Int J Health Geogr 2014; 13(1):6.
This study aims to suggest an approach that integrates multilevel models and eigenvector spatial filtering methods and apply it to a case study of self-rated health status in South Korea. In many previous health-related studies, multilevel models and single-level spatial regression are used separately. However, the two methods should be used in conjunction because the objectives of both approaches are important in health-related analyses. The multilevel model enables the simultaneous analysis of both individual and neighborhood factors influencing health outcomes. However, the results of conventional multilevel models are potentially misleading when spatial dependency across neighborhoods exists. Spatial dependency in health-related data indicates that health outcomes in nearby neighborhoods are more similar to each other than those in distant neighborhoods. Spatial regression models can address this problem by modeling spatial dependency. This study explores the possibility of integrating a multilevel model and eigenvector spatial filtering, an advanced spatial regression for addressing spatial dependency in datasets.In this spatially filtered multilevel model, eigenvectors function as additional explanatory variables accounting for unexplained spatial dependency within the neighborhood-level error. The specification addresses the inability of conventional multilevel models to account for spatial dependency, and thereby, generates more robust outputs.The findings show that sex, employment status, monthly household income, and perceived levels of stress are significantly associated with self-rated health status. Residents living in neighborhoods with low deprivation and a high doctor-to-resident ratio tend to report higher health status. The spatially filtered multilevel model provides unbiased estimations and improves the explanatory power of the model compared to conventional multilevel models although there are no changes in the signs of parameters and the significance levels between the two models in this case study.The integrated approach proposed in this paper is a useful tool for understanding the geographical distribution of self-rated health status within a multilevel framework. In future research, it would be useful to apply the spatially filtered multilevel model to other datasets in order to clarify the differences between the two models. It is anticipated that this integrated method will also out-perform conventional models when it is used in other contexts.
- How well do modelled routes to school record the environments children are exposed to?: a cross-sectional comparison of GIS-modelled and GPS-measured routes to school. [Journal Article]
- Int J Health Geogr 2014; 13(1):5.
The school journey may make an important contribution to children's physical activity and provide exposure to food and physical activity environments. Typically, Geographic Information Systems (GIS) have been used to model assumed routes to school in studies, but these may differ from those actually chosen. We aimed to identify the characteristics of children and their environments that make the modelled route more or less representative of that actually taken. We compared modelled GIS routes and actual Global Positioning Systems (GPS) measured routes in a free-living sample of children using varying travel modes.Participants were 175 13-14 yr old children taking part in the Sport, Physical activity and Eating behaviour: Environmental Determinants in Young people (SPEEDY) study who wore GPS units for up to 7 days. Actual routes to/from school were extracted from GPS data, and shortest routes between home and school along a road network were modelled in a GIS. Differences between them were assessed according to length, percentage overlap, and food outlet exposure using multilevel regression models.GIS routes underestimated route length by 21.0% overall, ranging from 6.1% among walkers to 23.2% for bus users. Among pedestrians food outlet exposure was overestimated by GIS routes by 25.4%. Certain characteristics of children and their neighbourhoods that improved the concordance between GIS and GPS route length and overlap were identified. Living in a village raised the odds of increased differences in length (odds ratio (OR) 3.36 (1.32-8.58)), while attending a more urban school raised the odds of increased percentage overlap (OR 3.98 (1.49-10.63)). However none were found for food outlet exposure. Journeys home from school increased the difference between GIS and GPS routes in terms of food outlet exposure, and this measure showed considerable within-person variation.GIS modelled routes between home and school were not truly representative of accurate GPS measured exposure to obesogenic environments, particularly for pedestrians. While route length may be fairly well described, especially for urban populations, those living close to school, and those travelling by foot, the additional expense of acquiring GPS data seems important when assessing exposure to route environments.
- Mapping the capacities of fixed health facilities to cover people at risk of gambiense human African trypanosomiasis. [Journal Article, Research Support, Non-U.S. Gov't]
- Int J Health Geogr 2014.:4.
The emphasis placed on the activities of mobile teams in the detection of gambiense human African trypanosomiasis (HAT) can at times obscure the major role played by fixed health facilities in HAT control and surveillance. The lack of consistent and detailed data on the coverage of passive case-finding and treatment further constrains our ability to appreciate the full contribution of the health system to the control of HAT.A survey was made of all fixed health facilities that are active in the control and surveillance of gambiense HAT. Information on their diagnostic and treatment capabilities was collected, reviewed and harmonized. Health facilities were geo-referenced. Time-cost distance analysis was conducted to estimate physical accessibility and the potential coverage of the population at-risk of gambiense HAT.Information provided by the National Sleeping Sickness Control Programmes revealed the existence of 632 fixed health facilities that are active in the control and surveillance of gambiense HAT in endemic countries having reported cases or having conducted active screening activities during the period 2000-2012. Different types of diagnosis (clinical, serological, parasitological and disease staging) are available from 622 facilities. Treatment with pentamidine for first-stage disease is provided by 495 health facilities, while for second-stage disease various types of treatment are available in 206 health facilities only. Over 80% of the population at-risk for gambiense HAT lives within 5-hour travel of a fixed health facility offering diagnosis and treatment for the disease.Fixed health facilities have played a crucial role in the diagnosis, treatment and coverage of at-risk-population for gambiense HAT. As the number of reported cases continues to dwindle, their role will become increasingly important for the prospects of disease elimination. Future updates of the database here presented will regularly provide evidence to inform and monitor a rational deployment of control and surveillance efforts. Support to the development and, if successful, the implementation of new control tools (e.g. new diagnostics and new drugs) is crucial, both for strengthening and expanding the existing network of fixed health facilities by improving access to diagnosis and treatment and for securing a sustainable control and surveillance of gambiense HAT.
- Urban sprawl, obesity, and cancer mortality in the United States: cross-sectional analysis and methodological challenges. [Journal Article]
- Int J Health Geogr 2014.:3.
Urban sprawl has the potential to influence cancer mortality via direct and indirect effects on obesity, access to health services, physical activity, transportation choices and other correlates of sprawl and urbanization.This paper presents a cross-sectional analysis of associations between urban sprawl and cancer mortality in urban and suburban counties of the United States. This ecological analysis was designed to examine whether urban sprawl is associated with total and obesity-related cancer mortality and to what extent these associations differed in different regions of the US. A major focus of our analyses was to adequately account for spatial heterogeneity in mortality. Therefore, we fit a series of regression models, stratified by gender, successively testing for the presence of spatial heterogeneity. Our resulting models included county level variables related to race, smoking, obesity, access to health services, insurance status, socioeconomic position, and broad geographic region as well as a measure of urban sprawl and several interactions. Our most complex models also included random effects to account for any county-level spatial autocorrelation that remained unexplained by these variables.Total cancer mortality rates were higher in less sprawling areas and contrary to our initial hypothesis; this was also true of obesity related cancers in six of seven U.S. regions (census divisions) where there were statistically significant associations between the sprawl index and mortality. We also found significant interactions (p < 0.05) between region and urban sprawl for total and obesity related cancer mortality in both sexes. Thus, the association between urban sprawl and cancer mortality differs in different regions of the US.Despite higher levels of obesity in more sprawling counties in the US, mortality from obesity related cancer was not greater in such counties. Identification of disparities in cancer mortality within and between geographic regions is an ongoing public health challenge and an opportunity for further analytical work identifying potential causes of these disparities. Future analyses of urban sprawl and health outcomes should consider exploring regional and international variation in associations between sprawl and health.
- Mapping for maternal and newborn health: the distributions of women of childbearing age, pregnancies and births. [Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't]
- Int J Health Geogr 2014.:2.
The health and survival of women and their new-born babies in low income countries has been a key priority in public health since the 1990s. However, basic planning data, such as numbers of pregnancies and births, remain difficult to obtain and information is also lacking on geographic access to key services, such as facilities with skilled health workers. For maternal and newborn health and survival, planning for safer births and healthier newborns could be improved by more accurate estimations of the distributions of women of childbearing age. Moreover, subnational estimates of projected future numbers of pregnancies are needed for more effective strategies on human resources and infrastructure, while there is a need to link information on pregnancies to better information on health facilities in districts and regions so that coverage of services can be assessed.This paper outlines demographic mapping methods based on freely available data for the production of high resolution datasets depicting estimates of numbers of people, women of childbearing age, live births and pregnancies, and distribution of comprehensive EmONC facilities in four large high burden countries: Afghanistan, Bangladesh, Ethiopia and Tanzania. Satellite derived maps of settlements and land cover were constructed and used to redistribute areal census counts to produce detailed maps of the distributions of women of childbearing age. Household survey data, UN statistics and other sources on growth rates, age specific fertility rates, live births, stillbirths and abortions were then integrated to convert the population distribution datasets to gridded estimates of births and pregnancies.These estimates, which can be produced for current, past or future years based on standard demographic projections, can provide the basis for strategic intelligence, planning services, and provide denominators for subnational indicators to track progress. The datasets produced are part of national midwifery workforce assessments conducted in collaboration with the respective Ministries of Health and the United Nations Population Fund (UNFPA) to identify disparities between population needs, health infrastructure and workforce supply. The datasets are available to the respective Ministries as part of the UNFPA programme to inform midwifery workforce planning and also publicly available through the WorldPop population mapping project.
- Mapping amyotrophic lateral sclerosis lake risk factors across northern New England. [Journal Article, Research Support, N.I.H., Extramural, Research Support, U.S. Gov't, Non-P.H.S.]
- Int J Health Geogr 2014.:1.
Amyotrophic lateral sclerosis (ALS) is a progressive, fatal neurodegenerative disease with a lifetime risk of developing as 1 in 700. Despite many recent discoveries about the genetics of ALS, the etiology of sporadic ALS remains largely unknown with gene-environment interaction suspected as a driver. Water quality and the toxin beta methyl-amino-alanine produced by cyanobacteria are suspected environmental triggers. Our objective was to develop an eco-epidemiological modeling approach to characterize the spatial relationships between areas of higher than expected ALS incidence and lake water quality risk factors derived from satellite remote sensing as a surrogate marker of exposure.Our eco-epidemiological modeling approach began with implementing a spatial clustering analysis that was informed by local indicators of spatial autocorrelation to identify locations of normalized excess ALS counts at the census tract level across northern New England. Next, water quality data for all lakes over 6 hectares (n = 4,453) were generated using Landsat TM band ratio regression techniques calibrated with in situ lake sampling. Derived lake water quality risk maps included chlorophyll-a (Chl-a), Secchi depth (SD), and total nitrogen (TN). Finally, a spatially-aware logistic regression modeling approach was executed characterizing relationships between the derived lake water quality metrics and ALS hot spots.Several distinct ALS hot spots were identified across the region. Remotely sensed lake water quality indicators were successfully derived; adjusted R² values ranged between 0.62-0.88 for these indicators based on out-of-sample validation. Map products derived from these indicators represent the first wall-to-wall metrics of lake water quality across the region. Logistic regression modeling of ALS case membership in localized hot spots across the region, i.e., census tracts with higher than expected ALS counts, showed the following: increasing average SD within a radius of 30 km corresponds with a decrease in the odds of belonging to an ALS hot spot by 59%; increasing average TN within a radius of 30 km and average Chl-a concentration within a radius of 10 km correspond with increased odds of belonging to an ALS hot spot by 167% and 4%, respectively.The strengths of satellite remote sensing information can help overcome traditional field limitations and spatiotemporal data gaps to provide the public health community valuable exposure data. Geographic scale needs to be diligently considered when evaluating relationships among ecological processes, risk factors, and human health outcomes. Broadly, we found that poorer lake water quality was significantly associated with increased odds of belonging to an ALS cluster in the region. These findings support the hypothesis that sporadic ALS (sALS) can, in part, be triggered by environmental water-quality indicators and lake conditions that promote harmful algal blooms.
- An objective index of walkability for research and planning in the Sydney Metropolitan Region of New South Wales, Australia: an ecological study. [Journal Article]
- Int J Health Geogr 2013.:61.
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.Walkability indexes using residential density, intersection density, land use mix, with and without retail floor area ratio were calculated for 5,858 Sydney Census Collection Districts in a geographical information system. Associations between variables were evaluated using Spearman's rho (ρ). Internal consistency and factor structure of indexes were estimated with Cronbach's alpha and principal components analysis; convergent and predictive validity were measured using weighted kappa (κw) and by comparison with reported walking to work at the 2006 Australian Census using logistic regression. Spatial variation in walkability was assessed using choropleth maps and Moran's I.A three-attribute abridged Sydney Walkability Index comprising residential density, intersection density and land use mix was constructed for all Sydney as retail floor area was only available for 5.3% of Census Collection Districts. A four-attribute full index including retail floor area ratio was calculated for 263 Census Collection Districts in the Sydney Central Business District. Abridged and full walkability index scores for these 263 areas were strongly correlated (ρ=0.93) and there was good agreement between walkability quartiles (κw=0.73). Internal consistency ranged from 0.60 to 0.71, and all index variables loaded highly on a single factor. The percentage of employed persons who walked to work increased with increasing walkability: 3.0% in low income-low walkability areas versus 7.9% in low income-high walkability areas; and 2.1% in high income-low walkability areas versus 11% in high income-high walkability areas. The adjusted odds of walking to work were 1.05 (0.96-1.15), 1.58 (1.45-1.71) and 3.02 (2.76-3.30) times higher in medium, high and very high compared to low walkability areas. Associations were similar for full and abridged indexes.The abridged Sydney Walkability Index has predictive validity for utilitarian walking, will inform urban planning in Sydney, and will be used as an objective measure of neighbourhood walkability in a large population cohort. Abridged walkability indexes may be useful in settings where retail floor area data are unavailable.
- Self-organizing maps as an approach to exploring spatiotemporal diffusion patterns. [Journal Article]
- Int J Health Geogr 2013.:60.
Self-organizing maps (SOMs) have now been applied for a number of years to identify patterns in large datasets; yet, their application in the spatiotemporal domain has been lagging. Here, we demonstrate how spatialtemporal disease diffusion patterns can be analysed using SOMs and Sammon's projection.SOMs were applied to identify synchrony between spatial locations, to group epidemic waves based on similarity of diffusion pattern and to construct sequence of maps of synoptic states. The Sammon's projection was used to created diffusion trajectories from the SOM output. These methods were demonstrated with a dataset that reports Measles outbreaks that took place in Iceland in the period 1946-1970. The dataset reports the number of Measles cases per month in 50 medical districts.Both stable and incidental synchronisation between medical districts were identified as well as two distinct groups of epidemic waves, a uniformly structured fast developing group and a multiform slow developing group. Diffusion trajectories for the fast developing group indicate a typical diffusion pattern from Reykjavik to the northern and eastern parts of the island. For the other group, diffusion trajectories are heterogeneous, deviating from the Reykjavik pattern.This study demonstrates the applicability of SOMs (combined with Sammon's Projection and GIS) in spatiotemporal diffusion analyses. It shows how to visualise diffusion patterns to identify (dis)similarity between individual waves and between individual waves and an overall time-series performing integrated analysis of synchrony and diffusion trajectories.
- People living in hilly residential areas in metropolitan Perth have less diabetes: spurious association or important environmental determinant? [Journal Article, Research Support, Non-U.S. Gov't]
- Int J Health Geogr 2013.:59.
Variations in 'slope' (how steep or flat the ground is) may be good for health. As walking up hills is a physiologically vigorous physical activity and can contribute to weight control, greater neighbourhood slopes may provide a protective barrier to weight gain, and help prevent Type 2 diabetes onset. We explored whether living in 'hilly' neighbourhoods was associated with diabetes prevalence among the Australian adult population.Participants (≥25 years; n = 11,406) who completed the Western Australian Health and Wellbeing Surveillance System Survey (2003-2009) were asked whether or not they had medically-diagnosed diabetes. Geographic Information Systems (GIS) software was used to calculate a neighbourhood mean slope score, and other built environment measures at 1600 m around each participant's home. Logistic regression models were used to predict the odds of self-reported diabetes after progressive adjustment for individual measures (i.e., age, sex), socioeconomic status (i.e., education, income), built environment, destinations, nutrition, and amount of walking.After full adjustment, the odds of self-reported diabetes was 0.72 (95% CI 0.55-0.95) and 0.52 (95% CI 0.39-0.69) for adults living in neighbourhoods with moderate and higher levels of slope, respectively, compared with adults living in neighbourhoods with the lowest levels of slope. The odds of having diabetes was 13% lower (odds ratio 0.87; 95% CI 0.80-0.94) for each increase of one percent in mean slope.Living in a hilly neighbourhood may be protective of diabetes onset or this finding is spurious. Nevertheless, the results are promising and have implications for future research and the practice of flattening land in new housing developments.