Mapping the urban asthma experience: Using qualitative GIS to understand contextual factors affecting asthma control

Social Science & Medicine 140 (2015) 9e17

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Social Science & Medicine journal homepage: www.elsevier.com/locate/socscimed

Mapping the urban asthma experience: Using qualitative GIS to understand contextual factors affecting asthma control Shimrit Keddem a, b, *, Frances K. Barg c, d, Karen Glanz e, f, Tara Jackson g, Sarah Green f, Maureen George e a

Department of General Internal Medicine, Perelman School of Medicine at the University of Pennsylvania, USA Philadelphia Veterans Affairs Medical Center, USA Mixed Methods Research Lab, Perelman School of Medicine at the University of Pennsylvania, USA d Department of Family Medicine, Perelman School of Medicine at the University of Pennsylvania, USA e University of Pennsylvania School of Nursing, USA f Perelman School of Medicine at the University of Pennsylvania, USA g Cartographic Modeling Lab, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, USA b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 29 August 2014 Received in revised form 11 April 2015 Accepted 29 June 2015 Available online 3 July 2015

Asthma is complex and connected to a number of factors including access to healthcare, crime and violence, and environmental triggers. A mixed method approach was used to examine the experiences of urban people with asthma in controlling their asthma symptoms. The study started with an initial phase of qualitative interviews in West Philadelphia, a primarily poor African American community. Data from qualitative, semi-structured interviews indicated that stress, environmental irritants, and environmental allergens were the most salient triggers of asthma. Based on the interviews, the team identified six neighborhood factors to map including crime, housing vacancy, illegal dumping, tree canopy and parks. These map layers were combined into a final composite map. These combined methodologies contextualized respondents' perceptions in the framework of the actual community and built environment which tells a more complete story about their experience with asthma. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Asthma control Disparities Geographic information systems Semi-structured interviews

1. Introduction Chronic disease is influenced by a confluence of environmental and individual factors. The relationship between neighborhood characteristics and health has been well-documented. It is a complex interplay involving individual-level and neighborhood-level factors, which are sometimes difficult to tease apart (Diez Roux and Mair, 2010; Macintyre, 2003). Geographical variations in disease can be attributed either to the people living in different geographic regions (compositional) or to the physical or social attributes of each geographic region (contextual) (Macintyre, 2003). Neighborhood features not only affect health, but may also contribute to an uneven distribution in health outcomes. While many studies focus on the individual factors that contribute to health behaviors, chronic disease, and outcomes, few focus on contextual elements and the mechanisms by which these

* Corresponding author. CEPACT, Philadelphia VA Medical Center, 4100 Chester Ave, Ste 203, Philadelphia, PA 19104, USA. E-mail address: [email protected] (S. Keddem). http://dx.doi.org/10.1016/j.socscimed.2015.06.039 0277-9536/© 2015 Elsevier Ltd. All rights reserved.

neighborhood characteristics influence health. Using a mixedmethods approach can shed light on the missing links between contextual and compositional contributors to health (O'Campo, 2003). This paper presents a mixed-methods approach to understanding neighborhood influences on asthma control in a lowincome, urban community and describes findings that blend responses to participant interviews with geospatial analyses. 1.1. Looking at asthma In 2010, an estimated 25.7 million Americans were affected by asthma. Asthma disproportionately impacts lower socioeconomic groups and ethnic minorities, specifically Blacks and Native Americans (Akinbami et al., 2012; Gold and Wright, 2005; Oraka et al., 2013; Wright et al., 2008). The prevalence of asthma has increased worldwide, and its burden in the US and globally is expected to continue to rise (Akinbami et al., 2012; Oraka et al., 2013; Wright et al., 2008). Uncontrolled asthma leads to avoidable hospitalization and a lower quality of life (National Asthma Education and Prevention Program, 2007). Asthma has a complex etiology and is connected to a number of

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contextual factors including access to healthcare, crime and violence, and environmental triggers (Wright, 2003). Asthma incidence and outcomes vary geographically among countries, cities, communities, and neighborhoods (Carr et al., 1992; Perrin et al., 1989; Wright, 2003). This geographic disparity cannot be solely explained by socioeconomic factors (Akinbami et al., 2012; Crain et al., 1994; Cunningham et al., 1996; Oraka et al., 2013). These inconsistencies have gone largely unexplained and there is a need to better examine the interplay between environmental and sociodemographic factors that contribute to disparities (Wright et al., 1998; Wright et al., 2008). Some research has been conducted to examine spatial trends in asthma prevalence and severity. In a longitudinal mixed-methods study, Wright explored the relationship among a host of neighborhood, environmental and individual characteristics that contribute to prevalence of asthma (Wright et al., 2008). Many studies have examined the spatial relationship between air pollution and prevalence of asthma (English et al., 1999; Maantay et al., 2009; McEntee and Ogneva-Himmelberger, 2008). Others have looked for correlations among spatial distributions of asthma hospitalization and neighborhood characteristics, environmental conditions, and demographic factors (Corburn et al., 2006; Maantay, 2007). Gale's study of asthma, crime, and neighborhood deprivation used census and spatial data to examine asthma trends (Gale et al., 2011). While these studies provide some insight into spatial patterns of asthma, they do not incorporate community and patient perspectives that may explain the mechanisms that lead to spatial differences. A qualitative lens can help to expand our understanding of what it is like to live with asthma from the perspective of the person with asthma. This type of information can support the development of more appropriate and effective interventions. 1.2. GIS and its use in mixed-methods research Geographic Information Systems (GIS) is used in a variety of disciplines for many purposes. Specifically, GIS was incorporated into a number of recent studies to examine contextual factors that can affect health including social capital (Martin, 2002), area deprivation (Gordon-Larsen et al., 2006), healthcare access (Phillips et al., 2000), walkability (Leslie et al., 2007), and violence (Wiebe et al., 2013). Until recently, GIS has been largely known as a positivist tool for storing and analyzing only quantitative data (Kwan and Knigge, 2006). GIS is now being recognized as an inter-disciplinary method valuable in mixed-methods research (Elwood, 2006; Jung and Elwood, 2010). The trend of combining GIS with qualitative methods is growing in a number of areas. Several studies have demonstrated the potential for GIS in a mixed-methods paradigm. In some projects, GIS was used along with interviews or diary entries to collect environmental and ecological information about less accessible and rural areas (Cheong et al., 2012; Hinojosa and Hennermann, 2012). Several studies combined ethnographic methods with GIS technology to better understand the importance of place and its relationship to behavior (Cieri, 2003; Matthews et al., 2005). GIS has been used in combination with qualitative methods in several participatory studies (Dennis et al., 2009; Townley et al., 2009). These studies enable community members to describe their lives through photos and narratives that can be geographically referenced. This study used a mixed-methods, qualitative GIS, approach to answer the following research questions: 1) What are the experiences of urban adults with asthma in controlling their asthma symptoms? and 2) How are these experiences related to where they live?

2. Methods A sequential, mixed-methods design (Creswell et al., 2004; JWPCV et al., 2003) was used to examine the experiences of urban people with asthma in controlling their asthma symptoms. In our design, the qualitative phase was the first phase and informed the quantitative, map-making stage. The qualitative phase involved applying an anthropological techniquedfreelistingd that centers around the idea that shared experiences or cultural values will tend to elicit a common definition of a domain among group members (Fiks et al., 2011; Schrauf and Sanchez, 2008). Freelisting is a semistructured interviewing technique in which each respondent is asked to list all the words they would use to describe a specific construct (for example, list all the things that make your asthma act up). These responses are combined across all participants in the group to identify salient constructs and boundaries of a particular domain for members of the group. In the second phase of the study, data from freelisting were used to inform maps of existing spatial data about participants' neighborhood characteristics, including crime incidents and the presence of vacant housing, trash, and green space. By combining these methodologies, respondents' perceptions were contextualized in the framework of the actual community and built environment to tell a more complete story. It also provided insight into the nature of the variation of perceptions across space. The University of Pennsylvania Institutional Review Board approved the study protocol.

2.1. Phase I: Patient data collection 2.1.1. Sampling Potential participants were sampled from a previous asthma study in five zip code regions in West Philadelphia (George et al., 2014). These zip codes were targeted because they have the highest rates of asthma in the city of Philadelphia. The sample was stratified to achieve representation from each zip code and from both males and females, yielding a purposive quota sampling approach. Participants were contacted on the phone by a community interviewer and asked to participate in the study. The first 35 participants who agreed to participate and met our quota criteria were recruited (see map for participant distribution across zipcodes).

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2.1.2. Measures All interviews were conducted in-person either at a participant's home or another convenient location. Freelisting responses to 5 questions were elicited during the first 10 min of a longer semi-structured interview lasting approximately 30 min in total. The goal of the interview was to elicit participants' perspectives of how neighborhood level factors affect their control of their asthma and their general health. The semi-structured interview guide was developed collaboratively by the interdisciplinary research team. At the end of the interview, the interviewer also administered a questionnaire to collect information about participants' demographic characteristics (e.g, age, gender, education, address), smoking history, prescribed asthma medication, medication use, recent asthma-related hospitalizations, and self-reported height and weight. Asthma control was calculated using metrics recommended by national asthma guidelines that included two patientreported outcomes: the number of short-acting b-2 agonist (SABA) doses in the prior 7 days and/or the number of nocturnal awakenings due to asthma in the prior 30 days (National Asthma Education and Prevention Program, 2007). 2.2. Analysis 2.2.1. Freelisting analysis Freelisting responses were transcribed, cleaned in Excel, and then imported into Anthropac for analysis. Freelist data were reviewed by the research team to combine synonyms and standardize categories of responses. For example, “smells,” “perfume,” and “deodorant” were all coded as “scents/smells.” This was done for each of the five freelisting questions included in the interview guide. The cleaned and formatted datasets were then imported into Anthropac, a software program designed to analyze freelist data. Anthropac sorts the lists by item frequency and generates a salience index (Smith's S) for each item using the formula S ¼ (S (L2Rj þ 1)/ L)/N where L is the length of each list, Rj is the rank of item J in the list, and N is the number of lists in the sample (Borgatti and Carboni, 2007; Handwerker and Borgatti, 1998). Taking into account both the frequency of the words on the lists as well as their rank order, this measure of saliency indicates whether a listed item is typically used to define the domain among group members. Salience scores were examined overall for each of the five questions. Then the results were stratified by demographic characteristics of the participants in order to determine which results remained salient across groups. 2.2.2. Asthma control analysis The two patient-reported outcomes (number of SABA doses in the prior 7 days and/or the number of nocturnal awakenings due to asthma in the prior 30 days) were used to determine asthma control. Using the two outcomes recommended by national asthma guidelines, asthma control was classified as ‘Wellcontrolled’ (SABAs 2 days per week and/or awakenings 2x per month), ‘Not Well-Controlled’ (SABAs >2 days per week and or awakenings 13x per week) or ‘Very Poorly-Controlled’ (SABAs several times a day and/or awakenings 4x per week). For the purpose of analysis and interpretation, two categories of uncontrolled asthma (‘Not Well-Controlled’ and ‘Very Poorly Controlled’) were collapsed into one category (‘Uncontrolled’) in order to characterize participants as either ‘Controlled’ or ‘Uncontrolled.’ This control variable was later used to split the sample to examine differences in freelisting responses between those with controlled and uncontrolled asthma.

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2.3. Phase II: Spatial data collection 2.3.1. Spatial data collection and analysis A spatial database consisting of Philadelphia neighborhood characteristics was compiled using a mixture of administrative, census, and survey data. Address level crime incident and sanitation collection data were obtained from the city's police and streets departments. Data on vacant properties was obtained from the US Postal Service. Location and boundaries of parks were obtained from the City of Philadelphia's Department of Parks and Recreation. Data on tree canopy originates from the City of Philadelphia and was obtained through the Pennsylvania geospatial data clearinghouse. 2.3.2. Combining freelisting and spatial data Freelisting results were reviewed by the interdisciplinary team at several points during data collection. At each meeting, the team discussed emerging themes and how these themes might be informed by available sources of spatial data. Six neighborhood characteristics were selected from the spatial database based on the final salient topics that surfaced in the freelisting analysis. Topics were also chosen if they were variably present across groups. Based on the freelisting results, the team chose to map six neighborhood factors: vacant properties, illegal dumping, parks, tree canopy, aggravated assaults, and theft. Vacant property and illegal dumping were selected as proxy measures of neighborhood irritants. Parks and tree canopy were chosen to represent the pollen and environmental allergens mentioned by participants. To integrate participants' mention of stress as well as lack of physical activity, aggravated assault and theft were mapped as a proxy of neighborhood safety and walkability. Proxies were selected based primarily on freelisting results in the context of evidence from the literature. With regards to environmental allergens, studies have shown that living in close proximity to parks and denser tree canopy is associated with higher prevalence of allergic sensitization and asthma flares (Dadvand et al., 2014; Lovasi et al., 2013). Housing quality, urban crowding, and zoning violations have also been linked with poor asthma control (Bryant-Stephens, 2009; Rosenfeld et al., 2010). Lastly, asthma has also been connected to violence and crime in urban settings (Sternthal et al., 2010; Wright, 2006). GIS was used to analyze and visually represent the influence and intensity of each of these neighborhood characteristics on asthma control. First, descriptive displays of the spatial distribution of each characteristic were generated in ArcGIS (Fig. 1). In preparation for creating the composite map, GIS was used to calculate and display the density of theft, assault, illegal dumping, and housing vacancy. This was done in GIS using the kernel density function which fits a smooth surface radiating from each incident point (Fig. 2.) The surface value is highest at the location of the incident and decreases with increasing distance from that location. To account for areas where there were more or less people, each of the point locations was linked to data on the number of people living in the census block group containing it. This information on the number of people at each point was incorporated into the kernel density estimation hereby providing a weighted visualization of density. The resulting map is a raster map layer, consisting of a continuous surface represented as a grid of cells. Raster maps provide a more refined way to see how the neighborhood characteristics vary from location to location and can be manipulated and combined to produce new maps. To standardize the layers, cell values in each raster layer were sorted based on the distribution of values across all cells in that

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Fig. 1. Selected neighborhood criteria variables in West Philadelphia.

layer and grouped into ordinal classes from low to high. Each class of cells was assigned a risk value, ranging from 0 (no influence or risk to asthma control) to 10 (high influence or risk). The six variables were then combined into one map by summing all of the layers, with each layer weighted by the salience scores from the freelisting results. Table 2 shows summarizes the weights used. Therefore, factors related to neighborhood vacancy and abandonment (dumping, vacant properties) were weighted most heavily in the raster model, while crime (theft, aggravated assaults) were weighted less so. The resulting composite map is a visual representation of the areas across West Philadelphia that pose the most or least influence on asthma control (Fig. 3.)

3. Results 3.1. Freelisting sample description The sample of interview participants consisted of a total of 35 people with asthma living in an urban setting. A majority of the sample were female (71%) and African American (89%). Sixty-three percent of the sample were obese and 71% had uncontrolled asthma. 4. Phase I: Freelisting results Freelisting responses were analyzed by question. The responses

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Fig. 2. Kernal density.

were then stratified by demographics, body mass index (BMI), and asthma control to examine similarities and differences between and among groups. Table 1 shows the overall results by question with lists sorted in descending order by salience (Smith's S). The most salient response to the question “List of all the things you can think of that make your asthma act up” (Asthma Act Up) was “dirt/dust.” This was followed by “weather”, “animals”, and “pollen”. Other salient items to what makes one's asthma act up included “foods”, “emotions”, and “scents/smells”. The topmost item in response to “List of all the things you can think of that make it hard to take care of your asthma” (Hard to Care For) was “not having medicine.” “Stress” and “weather” were the next most salient terms for what makes it hard to care for one's asthma. In response to the question, “List all of the things you can think about that make it difficult to keep yourself healthy” (Difficult to Keep Healthy), respondent listed “emotions”, “making the right food choices”, and “not active” most frequently. “Smoking” was listed as the fourth most salient response as something that makes it difficult to stay healthy. When asked to “list all of the things you do to keep yourself healthy” (Keep Healthy), participants' most salient responses included “right diet” and “physical activity”. Lastly, the most salient response to the question “List all of the things that help control your asthma” (To Control Asthma) was “medicine”. When each list was analyzed by participant characteristics, four overarching themes emerged: the challenges of stress and emotions; dirt and dust as triggers; green space as an irritant; and the

challenges and importance of physical activity. 4.1. Freelisting by asthma control There were many similarities between responses of participants with controlled and uncontrolled asthma. When asked to list the things that make it hard to take care of one's asthma, only those with uncontrolled asthma mentioned “stress and emotions”. Respondents with uncontrolled asthma were also the only ones to mention “happiness” and “faith” as a way of controlling their asthma. Those with controlled and uncontrolled asthma listed “emotions” as a factor that makes it difficult to keep healthy. 4.2. Freelisting by BMI There were many common responses across the three BMI categories (underweight/normal, overweight, and obese). In response to what makes their asthma act up, “dirt/dust” and “weather” were in the top 4 most salient responses across all three BMI categories. In all three BMI categories, “emotions” were indicated as something that makes it difficult to keep healthy. Emotions were at the top of the list for overweight and obese participants, but at the very bottom of the list for the underweight/normal participants. In describing what makes it hard to take care of their asthma, all three BMI groups indicated “stress”. While “stress” was listed in the top four salient responses for the overweight and obese participants, it appears twelfth for underweight/normal respondents.

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Table 1 Freelisting responses for all participants, sorted by salience score. Asthma Act up

S

Hard to care for

S

Difficult to keep healthy

S

Keep healthy

S

Dirt/dust Weather Animals Pollen Foods

0.344 0.235 0.221 0.207 0.174

Not having medicine Stress Weather Physical ailments Dirt/dust

0.378 0.163 0.162 0.132 0.115

Emotions Making the right food choices Not active Smoking Dirt

0.289 0.243 0.221 0.167 0.112

Right diet Physical activity Take medicine Drink water Sleep/rest

0.53 0.444 0.293 0.183 0.089

Emotions Scents/smells

0.114 Not taking medicine 0.11 Environmental triggers 0.085 0.085 0.066 0.06

Temperatures Physical activity

0.163 Not following directions 0.162 Not enough physical exercise 0.129 Environmental triggers 0.125 Forgetting medicine 0.116 Not using medicine 0.112 Too much physical exercise 0.099 Right diet 0.092 Animals

Allergies Colds

0.088 Smells 0.079 Chores/daily life

0.05 Physical exertion 0.048 Weight

0.054 Avoid animals 0.053 Avoid environmental triggers 0.04 No smoking 0.039 Avoid dirt/dust

Smoke/burning Environment Car exhaust Laying on back in bed Cleaning products Shortness of breath

0.076 0.045 0.041 0.019

0.042 0.034 0.029 0.028

0.034 0.029 0.029 0.02

Perfume Grass Chemicals Smoking

Not taking medicine Lack of sleep Not having medicine Hypertension Traveling

Job Money Grass Talking

0.017 Mental health 0.017 Barbecue/charcoal 0.01

Get too bad

Scents Alcohol Weather/temperature Mental discipline

0.055 Animals 0.053 Poor sleep/fatigue

Obligations/work Being in public places Shortness of breath Back pain, spasm

0.024 Mental health/depression 0.015 Not keeping doctors' appointments 0.015 Cold/fever

To control asthma

S 0.671 0.201 0.179 0.176 0.126

0.095 Doctor 0.083 Faith

Medicine Medical care Family Diet Watch the people I'm around 0.081 Plenty of water 0.068 Exercise/physical activity

0.076 0.073 0.07 0.069

0.06 0.06 0.058 0.056

0.093 0.085 0.084 0.076

Avoid triggers Keep house and yard clean Don't overexert Hobbies

Avoid triggers Reduce stress Not overexerting Avoid animals

0.116 0.108

0.054 Clean air 0.052 Avoid scents

0.069 0.053

0.046 Happiness 0.044 Environment/ surroundings 0.04 Cleanliness 0.039 Schedule 0.037 Take care of general health 0.036 Friends

0.053 0.051 0.05 0.049 0.045 0.045

0.013 Be with family 0.01 Take vitamins

0.031 Physical situation 0.026 Knowledge

0.035 0.028

0.009 Interact with people

0.023 Control temperatures

0.025

Avoid weather triggers Avoid smells Healthy weight Avoid alcohol/drugs

0.009 No water 0.006 Crowds

0.015 0.01

good hygiene Breathing exercises

0.023 Faith 0.017 Going out

0.023 0.022

0.005 Dirty water 0.003 Going to er Heat Makeup Newspaper ink

0.006 0.006 0.004 0.004 0.002

Avoid stress Control asthma General cleanliness Foot care Keep busy Take care of myself

0.016 0.016 0.015 0.015 0.014 0.009

0.016 0.011 0.01 0.006 0.005 0.003

In response to the question of what they do to control their asthma, only the obese and overweight participants mentioned “reduce stress”. 4.3. Freelisting by age In describing what makes their asthma act up, there were some similarities across age groups, specifically,“weather”, “foods”, “animals”, “dirt/dust”, and “emotions” were common triggers across all age groups. “Emotions” and “not active” were common across all ages of what makes it difficult to keep healthy. Lists of what makes asthma hard to care for were quite varied across age groups with the only common response being “stress”. 4.4. Freelisting by zip code In lists of asthma triggers, “dirt/dust” was the topmost salient

Good hygiene Do as much as I can Volunteer work Not thinking about asthma Shoes Phone

response in three of the five zip code regions and in the top six overall. “Emotions” came up as a trigger in all five zip code regions. The only common answer across zip codes in response to “what makes it difficult to keep healthy?” was “not active”. To keep healthy, respondents indicated “physical activity” in the top two most salient responses across all zip codes. 4.5. Freelisting by education Across all educational levels, “emotions” and “dirt/dust” were listed as a trigger. “Emotions” were also listed as barrier to staying healthy and listed in the top three most salient responses across all educational levels. Respondents of all educational backgrounds listed “not active” as a something that makes it difficult to stay healthy. To keep healthy, respondents from all educational groups listed “physical activity”. “Stress” was listed by respondents of all educational levels as something that makes it hard to care for their asthma. 4.6. Freelisting by gender

Table 2 Salience scores used to weight layers. Weight (salience)

Map Layers

Dirt/Dust

0.344

Pollen

0.207

Stress

0.163

Vacancy Illegal Dumping Parks Tree Canopy Theft Assault

Both men and women listed “dirt/dust” and “emotions” as triggers of their asthma. “Emotions” came up at the top the list for women as something that makes difficult to keep healthy while “emotions” were listed last for men in response to this question. Only women mentioned “avoiding stress” as a way to keep healthy. Both males and females listed “physical activity” in their top two most salient responses to the question “what do you do to keep

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Fig. 3. Weighted composite map of perceived risk to Asthama Control.

healthy?” Both men and women listed “reduce stress” as a way to control their asthma. 5. Phase II: GIS results After each map was converted to a standardized, pixilated raster image, the maps were weighted by the salience score and summed resulting in the composite map in the figure below. Greener shades represent regions of low risk of asthma flare-up while yellow and red shades represent regions of high risk. High risk areas for asthma control tend to be clustered in the middle and western section of the map and along the southwestern edge. Low risk areas are grouped along the lower eastern regions of the map. West Philadelphia contains a variety of distinct neighborhoods. Variations in risk areas are shaped, in part, by the different neighborhood features. West Philadelphia can be separated into four quadrants divided by two main corridors, 52nd and Market Streets. The south western quadrant of the map which is largely low risk is made up primarily of university campuses, areas that are more developed and have lower crime rates. The north western portion, also low risk, is a large urban park containing no residences. The 52nd Street corridor, running north-south, is a dividing line. Around and west of this line, neighborhoods are primarily lowincome with higher crime rates and poor housing. 6. Discussion This study has shown how the combination of community perspectives collected using qualitative techniques can inform a contextual analysis of asthma triggers. Freelisting, a semi-

structured interviewing technique, was used to examine challenges to staying healthy and controlling asthma among urban, mostly minority, people with asthma. The information garnered in these interviews was then used to generate a raster layer map of risk to asthma control in the respondents' neighborhood. This work shows how semi-structured interviewing and geo-spatial analyses can provide a new perspective of challenges faced by people with asthma in controlling their disease in an urban setting. In a city, these challenges can be multi-faceted. These challenges may not be just environmental, but may also relate to neighborhood factors that have psychosocial implications. Qualitative methods have long been used in social science disciplines such as sociology and anthropology. Such methods often employ naturalistic approaches including watching, joining in, talking, and reading (Pope and Mays, 1995). The goal of qualitative inquiry is to develop theories to understand the experiences of people in their everyday lives. Because qualitative methods are inductive, they are more suitable for understanding people's behaviors, perceptions, and interactions. In mixed methods studies, qualitative methods can come before or after quantitative work. Qualitative methods complement quantitative approaches, and this study demonstrates how qualitative methods can inform a GIS analysis. The strength of this study comes from the ability to obtain realtime information ‘on the ground’ from affected stakeholders. GIS is a tool that provides the researcher with the ability to bring to life any type of spatial knowledge or experience (Elwood, 2006). This has powerful implications for policy since it provides a knowledge base rooted in the experiences of local communities (Kwan and Knigge, 2006).

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Our finding that participants perceive stress to be a primary trigger of their asthma is consistent with previous findings that psychological stress is linked to asthma. (Wright et al., 1998; Wright et al., 2005) In addition, other community level factors that cause stress have also been linked to asthma. Sternthal et al. (2010) found that community violence was correlated with asthma risk when controlling for individual and neighborhood-level factors. We chose to map crime and safety related factors (aggravated assault, theft) as proxies of psychosocial stress. Future studies may want to focus on the mechanisms of this relationship. For example, how does perceived safety in the neighborhood influence psychological well-being and asthma control? Much of our sample (63%) was obese, and thus their expressed challenges regarding physical activity are not surprising. The link between obesity and the built environment has been well researched. Obesity has been linked to neighborhood issues of safety, walkability, and access to recreational facilities (GordonLarsen et al., 2006; Sallis and Glanz, 2006). Because obesity has been associated with asthma and has been shown to make it more difficult to control (Ford, 2005), it may create a vicious cycle for people with asthma whereby they encounter challenges to physical activity which make it harder to manage their weight and therefore exacerbates their asthma. Obesity also increases the risk for developing asthma (Ali and Ulrik, 2013). The main limitations of this study include the small sample size and the reliance on administrative data to generate the composite map. Because the initial phase of the study is qualitative and not meant to be generalizable, the sample size was intentionally kept small. With a larger, random sample, it would have been possible to carry out more sophisticated spatial analyses to look for clustering in participants' responses. To generate the composite map, we were limited to available sources of data at certain geographies. Because there is no sufficient source of data on stress or lack of physical activity in the population, this study was limited to using proxies such as crime to account for these elements. However, this study utilized a robust set of GIS tools including raster layers and map algebra to create one, amalgamated visual display of the “landscape” encountered by people trying to control their asthma in this neighborhood. Next, we plan to analyze the in-depth interviews conducted following the freelisting items to examine differences among respondents based on the location of their home on the “risk to asthma control” map shown above. In this study, it has been demonstrated how two methods can be combined to provide a picture of how spatial neighborhood factors might contribute to asthma control. In this instance, the map that was generated was driven by the responses of community members with asthma providing the ‘emic’ or insider perspective. These tools have important implications for community-based research. They allow the community members and researchers to collaborate and contribute to a body of knowledge that can ultimately affect change and improve public health. Recent interventions have successfully utilized trained lay people from the community called “Community Health Workers” to improve hospital readmission rates (Kangovi et al., 2014). Geographic information such as those presented in this paper could position community health workers from these neighborhoods to target interventions to those most in need and improve asthma control.

Acknowledgments The authors thank Dr. Amy Hillier and Dr. Dana Tomlin, PhD (University of Pennsylvania School of Design) for their technical contributions to the methods of this study.

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