Comparison of ten policy options to equitably reduce children's exposure to unhealthy food marketing.

OBJECTIVE: Reducing children's exposure to unhealthy food marketing is crucial to combat childhood obesity. We aimed to estimate the reduction of children's exposure to food marketing under different policy scenarios and assess exposure differences by socio-economic status. DESIGN: Data on children's exposure to unhealthy food marketing were compiled from a previous cross-sectional study in which children (n 168) wore wearable cameras and Global Positioning System (GPS) units for 4 consecutive days. For each exposure, we identified the setting, the marketing medium and food/beverage product category. We analysed the percentage reduction in food marketing exposure for ten policy scenarios and by socio-economic deprivation: (1) no product packaging, (2) no merchandise marketing, (3) no sugary drink marketing, (4) no confectionary marketing in schools, (5) no sugary drink marketing in schools, (6) no marketing in public spaces, (7) no marketing within 400 m of schools, (8) no marketing within 400 m of recreation venues, (9) no marketing within 400 m of bus stops and (10) no marketing within 400 m of major roads. SETTING: Wellington region of New Zealand. PARTICIPANTS: 168 children aged 11-14 years. RESULTS: Exposure to food marketing varied by setting, marketing medium and product category. Among the ten policy scenarios, the largest reductions were for plain packaging (60·3 %), no sugary drink marketing (28·8 %) and no marketing in public spaces (22·2 %). There were no differences by socio-economic deprivation. CONCLUSIONS: The results suggest that plain packaging would result in the greatest decrease in children's exposure to food marketing. However, given that children are regularly exposed to unhealthy food marketing in multiple settings through a range of marketing mediums, comprehensive bans are needed to protect children's health.

Using floating catchment area (FCA) metrics to predict health care utilization patterns.

BACKGROUND: Floating Catchment Area (FCA) metrics provide a comprehensive measure of potential spatial accessibility to health care services and are often used to identify geographic disparities in health care access. An unexplored aspect of FCA metrics is whether they can be useful in predicting where people actually seek care. This research addresses this question by examining the utility of FCA metrics for predicting patient utilization patterns, the flows of patients from their residences to facilities. METHODS: Using more than one million inpatient hospital visits in Michigan, we calculated expected utilization patterns from Zip Codes to hospitals using four FCA metrics and two traditional metrics (simple distance and a Huff model) and compared them to observed utilization patterns. Because all of the accessibility metrics rely on the specification of a distance decay function and its associated parameters, we conducted a sensitivity analysis to evaluate their effects on prediction accuracy. RESULTS: We found that the Three Step FCA (3SFCA) and Modified Two Step FCA (M2SFCA) were the most effective metrics for predicting utilization patterns, correctly predicting the destination hospital for nearly 74% of hospital visits in Michigan. These two metrics were also the least sensitive to changes to the distance decay functions and parameter settings. CONCLUSIONS: Overall, this research demonstrates that FCA metrics can provide reasonable predictions of patient utilization patterns and FCA utilization models could be considered as a substitute when utilization pattern data are unavailable.

Effects of payments for ecosystem services on wildlife habitat recovery.

Conflicts between local people's livelihoods and conservation have led to many unsuccessful conservation efforts and have stimulated debates on policies that might simultaneously promote sustainable management of protected areas and improve the living conditions of local people. Many government-sponsored payments-for-ecosystem-services (PES) schemes have been implemented around the world. However, few empirical assessments of their effectiveness have been conducted, and even fewer assessments have directly measured their effects on ecosystem services. We conducted an empirical and spatially explicit assessment of the conservation effectiveness of one of the world's largest PES programs through the use of a long-term empirical data set, a satellite-based habitat model, and spatial autoregressive analyses on direct measures of change in an ecosystem service (i.e., the provision of wildlife species habitat). Giant panda (Ailuropoda melanoleuca) habitat improved in Wolong Nature Reserve of China after the implementation of the Natural Forest Conservation Program. The improvement was more pronounced in areas monitored by local residents than those monitored by the local government, but only when a higher payment was provided. Our results suggest that the effectiveness of a PES program depends on who receives the payment and on whether the payment provides sufficient incentives. As engagement of local residents has not been incorporated in many conservation strategies elsewhere in China or around the world, our results also suggest that using an incentive-based strategy as a complement to command-and-control, community- and norm-based strategies may help achieve greater conservation effectiveness and provide a potential solution for the park versus people conflict.

Spatial assessment of attitudes toward tigers in Nepal.

In many regions around the world, wildlife impacts on people (e.g., crop raiding, attacks on people) engender negative attitudes toward wildlife. Negative attitudes predict behaviors that undermine wildlife management and conservation efforts (e.g., by exacerbating retaliatory killing of wildlife). Our study (1) evaluated attitudes of local people toward the globally endangered tiger (Panthera tigris) in Nepal's Chitwan National Park; and (2) modeled and mapped spatial clusters of attitudes toward tigers. Factors characterizing a person's position in society (i.e., socioeconomic and cultural factors) influenced attitudes toward tigers more than past experiences with tigers (e.g., livestock attacks). A spatial cluster of negative attitudes toward tigers was associated with concentrations of people with less formal education, people from marginalized ethnic groups, and tiger attacks on people. Our study provides insights and descriptions of techniques to improve attitudes toward wildlife in Chitwan and many regions around the world with similar conservation challenges.

Regional health care planning: a methodology to cluster facilities using community utilization patterns.

BACKGROUND: Community-based health care planning and regulation necessitates grouping facilities and areal units into regions of similar health care use. Limited research has explored the methodologies used in creating these regions. We offer a new methodology that clusters facilities based on similarities in patient utilization patterns and geographic location. Our case study focused on Hospital Groups in Michigan, the allocation units used for predicting future inpatient hospital bed demand in the state's Bed Need Methodology. The scientific, practical, and political concerns that were considered throughout the formulation and development of the methodology are detailed. METHODS: The clustering methodology employs a 2-step K-means + Ward's clustering algorithm to group hospitals. The final number of clusters is selected using a heuristic that integrates both a statistical-based measure of cluster fit and characteristics of the resulting Hospital Groups. RESULTS: Using recent hospital utilization data, the clustering methodology identified 33 Hospital Groups in Michigan. CONCLUSIONS: Despite being developed within the politically charged climate of Certificate of Need regulation, we have provided an objective, replicable, and sustainable methodology to create Hospital Groups. Because the methodology is built upon theoretically sound principles of clustering analysis and health care service utilization, it is highly transferable across applications and suitable for grouping facilities or areal units.

Measuring geographic access to health care: raster and network-based methods.

BACKGROUND: Inequalities in geographic access to health care result from the configuration of facilities, population distribution, and the transportation infrastructure. In recent accessibility studies, the traditional distance measure (Euclidean) has been replaced with more plausible measures such as travel distance or time. Both network and raster-based methods are often utilized for estimating travel time in a Geographic Information System. Therefore, exploring the differences in the underlying data models and associated methods and their impact on geographic accessibility estimates is warranted. METHODS: We examine the assumptions present in population-based travel time models. Conceptual and practical differences between raster and network data models are reviewed, along with methodological implications for service area estimates. Our case study investigates Limited Access Areas defined by Michigan's Certificate of Need (CON) Program. Geographic accessibility is calculated by identifying the number of people residing more than 30 minutes from an acute care hospital. Both network and raster-based methods are implemented and their results are compared. We also examine sensitivity to changes in travel speed settings and population assignment. RESULTS: In both methods, the areas identified as having limited accessibility were similar in their location, configuration, and shape. However, the number of people identified as having limited accessibility varied substantially between methods. Over all permutations, the raster-based method identified more area and people with limited accessibility. The raster-based method was more sensitive to travel speed settings, while the network-based method was more sensitive to the specific population assignment method employed in Michigan. CONCLUSIONS: Differences between the underlying data models help to explain the variation in results between raster and network-based methods. Considering that the choice of data model/method may substantially alter the outcomes of a geographic accessibility analysis, we advise researchers to use caution in model selection. For policy, we recommend that Michigan adopt the network-based method or reevaluate the travel speed assignment rule in the raster-based method. Additionally, we recommend that the state revisit the population assignment method.

Exploring Persistent Racial and Ethnic Representation Disparity in U.S. Geography Doctoral Programs: The Disciplinary Underrepresentation Gap.

Underrepresentation among U.S. citizen racial and ethnic minorities in geography has a long history, one perpetuated through-and readily measurable by-its doctoral degree-granting record. This article examines the history of efforts to redress underrepresentation since the 1960s, explores modern underrepresentation, measures the degree of its persistence in the discipline and within individual departments, and identifies drivers that exacerbate the racial and ethnic representation disparity among U.S. citizens in geography doctoral programs. To quantify the degree to which the discipline is underperforming demographically, we contrasted the rate of domestic underrepresented minority doctoral degree conferrals with those of White doctoral recipients in geography, the social sciences, and the entire academy over a twenty-three-year period from 1997 through 2019. During that span, geography consistently trailed the social sciences and the academy: This underrepresentation gap has widened in the past decade. Four drivers were identified: (1) lack of dedicated funding for underrepresented minority doctoral students, (2) minimal prior exposure to academic and professional geography, (3) passive recruitment strategies, and (4) pervasive Whiteness of departments. We conclude with a call to action for geographers to meet the moral imperative of racial and ethnic representational equity by becoming agents of measurable change.

The Advancing Geography Through Diversity Program: A Framework to Address the Persistent Underrepresentation of Domestic Racial and Ethnic Minorities in U.S. Geography Doctoral Programs.

Domestic racial and ethnic minorities have been persistently underrepresented in U.S. geography doctoral programs. Efforts to improve diversity have taken many forms over the years, but most have been short-lived with limited success. In this article, we introduce the Advancing Geography Through Diversity Program (AGTDP), a four-pronged cohort-based model that systematically and sustainably increases the presence of African Americans, Hispanic Americans, and Native Americans in geography doctoral programs. The program is currently in its fourth year in the Department of Geography, Environment, and Spatial Sciences at Michigan State University. Context for the development of the framework is provided, followed by a detailed discussion of each pillar of the program: recruitment, support, engagement, and retention. We evaluate the current state of the program along with lessons learned for successful implementation. To date, the program has effectively increased the representation across all three underrepresented groups within the department's doctoral program. We believe the AGTDP can serve as a model for more widespread deployment to other geography departments.

The distribution of harmful product marketing in public outdoor spaces and the effectiveness of marketing bans.

Children's exposure to the marketing of harmful products in public outdoor spaces may influence their consumption of those products and affect health into adulthood. This study aimed to: i) examine the spatial distribution of children's exposure to three types of marketing-related 'harms' (alcohol, unhealthy food, and gambling) in outdoor spaces in the Wellington region, New Zealand/Aotearoa; ii) compare differences in the distribution of harms by socioeconomic deprivation; and iii) estimate the effectiveness of different policies that ban such marketing. Data were from 122 children aged 11-13y who wore wearable cameras and GPS devices for four consecutive days from July 2014 to June 2015. Images were analysed to identify harmful product marketing exposures in public outdoor spaces. Eight policy scenarios were examined to identify the effectiveness of marketing bans, for all children and by socioeconomic deprivation. Children's ratio of harmful marketing was higher for children from high deprivation households and was also found to cluster, with hots spots observed around city centers. The effectiveness of marketing bans depended on the target setting and ban area, with banning 400 m around bus stops leading to the largest reduction. Effectiveness varied also by type of harm and socioeconomic deprivation. For example, banning alcohol marketing in residential areas was estimated to have a larger effect on exposure reduction for children from high deprivation households. Our findings suggest that alcohol, unhealthy food and gambling marketing often cluster outdoors and that targeted bans of such marketing would likely improve child health and, for some banning scenarios, promote equity.

Comparative infection modeling and control of COVID-19 transmission patterns in China, South Korea, Italy and Iran.

The COVID-19 has become a pandemic. The timing and nature of the COVID-19 pandemic response and control varied among the regions and from one country to the other, and their role in affecting the spread of the disease has been debated. The focus of this work is on the early phase of the disease when control measures can be most effective. We proposed a modified susceptible-exposed-infected-removed model (SEIR) model based on temporal moving windows to quantify COVID-19 transmission patterns and compare the temporal progress of disease spread in six representative regions worldwide: three Chinese regions (Zhejiang, Guangdong and Xinjiang) vs. three countries (South Korea, Italy and Iran). It was found that in the early phase of COVID-19 spread the disease follows a certain empirical law that is common in all regions considered. Simulations of the imposition of strong social distancing measures were used to evaluate the impact that these measures might have had on the duration and severity of COVID-19 outbreaks in the three countries. Measure-dependent transmission rates followed a modified normal distribution (empirical law) in the three Chinese regions. These rates responded quickly to the launch of the 1st-level Response to Major Public Health Emergency in each region, peaking after 1-2 days, reaching their inflection points after 10-19 days, and dropping to zero after 11-18 days since the 1st-level response was launched. By March 29th, the mortality rates were 0.08% (Zhejiang), 0.54% (Guangdong) and 3.95% (Xinjiang). Subsequent modeling simulations were based on the working assumption that similar infection transmission control measures were taken in South Korea as in Zhejiang on February 25th, in Italy as in Guangdong on February 25th, and in Iran as in Xinjiang on March 8th. The results showed that by June 15th the accumulated infection cases could have been reduced by 32.49% (South Korea), 98.16% (Italy) and 85.73% (Iran). The surface air temperature showed stronger association with transmission rate of COVID-19 than surface relative humidity. On the basis of these findings, disease control measures were shown to be particularly effective in flattening and shrinking the COVID-10 case curve, which could effectively reduce the severity of the disease and mitigate medical burden. The proposed empirical law and the SEIR-temporal moving window model can also be used to study infectious disease outbreaks worldwide.

Effects of freshwater blue spaces may be beneficial for mental health: A first, ecological study in the North American Great Lakes region.

Research linking green space and mental health abounds. It also appears that oceanic blue spaces may be salutogenic, benefitting mental health through their expansive viewscapes, and possibly auditory and olfactory stimuli. Yet, it is unknown whether the same is true for freshwater bodies. In this ecological study, we explored associations between hospitalizations for anxiety/mood disorder in Michigan (>30,000) and proximity to the North American Great Lakes. As a sensitivity analysis, we examined associations for 15 different inland lake sizes. Results showed small, protective effects for distance to Great Lake (ß = 0.06, p<0.001) and percentage of inland lakes (ß = -0.04, p = 0.004). Unexpectedly, shorter distance to nearest inland lake was associated with higher anxiety/mood disorder hospitalizations. The protective effects of percentage area covered by inland lakes was observed for all lake sizes. These initial findings provide a foundation for future individual-level research with finer measurement of health outcomes and blue space exposure.

Interactive spatial scale effects on species distribution modeling: The case of the giant panda.

Research has shown that varying spatial scale through the selection of the total extent of investigation and the grain size of environmental predictor variables has effects on species distribution model (SDM) results and accuracy, but there has been minimal investigation into the interactive effects of extent and grain. To do this, we used a consistently sampled range-wide dataset of giant panda occurrence across southwest China and modeled their habitat and distribution at 4 extents and 7 grain sizes. We found that increasing grain size reduced model accuracy at the smallest extent, but that increasing extent negated this effect. Increasing extent also generally increased model accuracy, but the models built at the second-largest (mountain range) extent were more accurate than those built at the largest, geographic range-wide extent. When predicting habitat suitability in the smallest nested extents (50 km2), we found that the models built at the next-largest extent (500 km2) were more accurate than the smallest-extent models but that further increases in extent resulted in large decreases in accuracy. Overall, this study highlights the impacts of the selection of spatial scale when evaluating species' habitat and distributions, and we suggest more explicit investigations of scale effects in future modeling efforts.

Systematic review of the use of Google Street View in health research: Major themes, strengths, weaknesses and possibilities for future research.

We systematically reviewed the current use of Google Street View (GSV) in health research and characterized major themes, strengths and weaknesses in order to highlight possibilities for future research. Of 54 qualifying studies, we found that most used GSV to assess the neighborhood built environment, followed by health policy compliance, study site selection, and disaster preparedness. Most studies were conducted in urban areas of North America, Europe, or New Zealand, with few studies from South America or Asia and none from Africa or rural areas. Health behaviors and outcomes of interest in these studies included injury, alcohol and tobacco use, physical activity and mental health. Major strengths of using GSV imagery included low cost, ease of use, and time saved. Identified weaknesses were image resolution and spatial and temporal availability, largely in developing regions of the world. Despite important limitations, GSV is a promising tool for automated environmental assessment for health research. Currently untapped areas of health research using GSV include identification of sources of air, soil or water pollution, park design and usage, amenity design and longitudinal research on neighborhood conditions.

Evaluating Michigan's community hospital access: spatial methods for decision support.

BACKGROUND: Community hospital placement is dictated by a diverse set of geographical factors and historical contingency. In the summer of 2004, a multi-organizational committee headed by the State of Michigan's Department of Community Health approached the authors of this paper with questions about how spatial analyses might be employed to develop a revised community hospital approval procedure. Three objectives were set. First, the committee needed visualizations of both the spatial pattern of Michigan's population and its 139 community hospitals. Second, the committee required a clear, defensible assessment methodology to quantify access to existing hospitals statewide, taking into account factors such as distance to nearest hospital and road network density to estimate travel time. Third, the committee wanted to contrast the spatial distribution of existing community hospitals with a theoretical configuration that best met statewide demand. This paper presents our efforts to first describe the distribution of Michigan's current community hospital pattern and its people, and second, develop two models, access-based and demand-based, to identify areas with inadequate access to existing hospitals. RESULTS: Using the product from the access-based model and contiguity and population criteria, two areas were identified as being "under-served." The lower area, located north/northeast of Detroit, contained the greater total land area and population of the two areas. The upper area was centered north of Grand Rapids. A demand-based model was applied to evaluate the existing facility arrangement by allocating daily bed demand in each ZIP code to the closest facility. We found 1,887 beds per day were demanded by ZIP centroids more than 16.1 kilometers from the nearest existing hospital. This represented 12.7% of the average statewide daily bed demand. If a 32.3 kilometer radius was employed, unmet demand dropped to 160 beds per day (1.1%). CONCLUSION: Both modeling approaches enable policymakers to identify under-served areas. Ultimately this paper is concerned with the intersection of spatial analysis and policymaking. Using the best scientific practice to identify locations of under-served populations based on many factors provides policymakers with a powerful tool for making good decisions.

Habitat Use and Selection by Giant Pandas.

Animals make choices about where to spend their time in complex and dynamic landscapes, choices that reveal information about their biology that in turn can be used to guide their conservation. Using GPS collars, we conducted a novel individual-based analysis of habitat use and selection by the elusive and endangered giant pandas (Ailuropoda melanoleuca). We constructed spatial autoregressive resource utilization functions (RUF) to model the relationship between the pandas' utilization distributions and various habitat characteristics over a continuous space across seasons. Results reveal several new insights, including use of a broader range of habitat characteristics than previously understood for the species, particularly steep slopes and non-forest areas. We also used compositional analysis to analyze habitat selection (use with respect to availability of habitat types) at two selection levels. Pandas selected against low terrain position and against the highest clumped forest at the at-home range level, but no significant factors were identified at the within-home range level. Our results have implications for modeling and managing the habitat of this endangered species by illustrating how individual pandas relate to habitat and make choices that differ from assumptions made in broad scale models. Our study also highlights the value of using a spatial autoregressive RUF approach on animal species for which a complete picture of individual-level habitat use and selection across space is otherwise lacking.

Spatial point pattern analysis of human settlements and geographical associations in eastern coastal China - a case study.

Understanding the spatial point pattern of human settlements and their geographical associations are important for understanding the drivers of land use and land cover change and the relationship between environmental and ecological processes on one hand and cultures and lifestyles on the other. In this study, a Geographic Information System (GIS) approach, Ripley's K function and Monte Carlo simulation were used to investigate human settlement point patterns. Remotely sensed tools and regression models were employed to identify the effects of geographical determinants on settlement locations in the Wen-Tai region of eastern coastal China. Results indicated that human settlements displayed regular-random-cluster patterns from small to big scale. Most settlements located on the coastal plain presented either regular or random patterns, while those in hilly areas exhibited a clustered pattern. Moreover, clustered settlements were preferentially located at higher elevations with steeper slopes and south facing aspects than random or regular settlements. Regression showed that influences of topographic factors (elevation, slope and aspect) on settlement locations were stronger across hilly regions. This study demonstrated a new approach to analyzing the spatial patterns of human settlements from a wide geographical prospective. We argue that the spatial point patterns of settlements, in addition to the characteristics of human settlements, such as area, density and shape, should be taken into consideration in the future, and land planners and decision makers should pay more attention to city planning and management. Conceptual and methodological bridges linking settlement patterns to regional and site-specific geographical characteristics will be a key to human settlement studies and planning.

Do more hospital beds lead to higher hospitalization rates? a spatial examination of Roemer's Law.

BACKGROUND: Roemer's Law, a widely cited principle in health care policy, states that hospital beds that are built tend to be used. This simple but powerful expression has been invoked to justify Certificate of Need regulation of hospital beds in an effort to contain health care costs. Despite its influence, a surprisingly small body of empirical evidence supports its content. Furthermore, known geographic factors influencing health services use and the spatial structure of the relationship between hospital bed availability and hospitalization rates have not been sufficiently explored in past examinations of Roemer's Law. We pose the question, "Accounting for space in health care access and use, is there an observable association between the availability of hospital beds and hospital utilization?" METHODS: We employ an ecological research design based upon the Anderson behavioral model of health care utilization. This conceptual model is implemented in an explicitly spatial context. The effect of hospital bed availability on the utilization of hospital services is evaluated, accounting for spatial structure and controlling for other known determinants of hospital utilization. The stability of this relationship is explored by testing across numerous geographic scales of analysis. The case study comprises an entire state system of hospitals and population, evaluating over one million inpatient admissions. RESULTS: We find compelling evidence that a positive, statistically significant relationship exists between hospital bed availability and inpatient hospitalization rates. Additionally, the observed relationship is invariant with changes in the geographic scale of analysis. CONCLUSIONS: This study provides evidence for the effects of Roemer's Law, thus suggesting that variations in hospitalization rates have origins in the availability of hospital beds. This relationship is found to be robust across geographic scales of analysis. These findings suggest continued regulation of hospital bed supply to assist in controlling hospital utilization is justified.

A methodology for projecting hospital bed need: a Michigan case study.

Michigan's Department of Community Health (MDCH) is responsible for managing hospitals through the utilization of a Certificate of Need (CON) Commission. Regulation is achieved by limiting the number of beds a hospital can use for inpatient services. MDCH assigns hospitals to service areas and sub areas by use patterns. Hospital beds are then assigned within these Hospital Service Areas and Facility Sub Areas. The determination of the number of hospital beds a facility subarea is authorized to hold, called bed need, is defined in the Michigan Hospital Standards and published by the CON Commission and MDCH. These standards vaguely define a methodology for calculating hospital bed need for a projection year, five years ahead of the base year (defined as the most recent year for which patient data have been published by the Michigan Hospital Association). MDCH approached the authors and requested a reformulation of the process. Here we present a comprehensive guide and associated code as interpreted from the hospital standards with results from the 2011 projection year. Additionally, we discuss methodologies for other states and compare them to Michigan's Bed Need methodology.