This issue is large and comes with a supplement. First, the supplement: The Health Resources and Services Administration (HRSA) and The National Institute on Minority Health and Health Disparities at the National Institutes of Health (NIMHD/NIH) jointly sponsored the supplement collection, titled Advancing Obesity Prevention: Quality Improvements, Emerging Models and Best Practices. The Guest Editors are Nishadi Rajapakse, PhD, MHS; Richard Berzon, DrPH, PA; Sarah Linde, MD; Natasha Coulouris, MPH; Ligia Artiles, MA; Eliza Heppner, MPA. The supplement shines a light on advances sponsored with federal funds in the national trek towards healthy weight.

The articles in the regular issue are arranged into five parts:

PART 1: HOMELESSNESS
PART 2: IMMIGRATION, BORDERS, LANGUAGE
PART 3: PRIMARY CARE AND CANCER
PART 4: INNOVATIVE PROGRAMS
PART 5: HEALTH POLICY AND EPIDEMIOLOGY

Before these five parts, readers will find our regular ACU Column, which this month concerns the important role that community health centers can play in family planning.

PART 1: HOMELESSNESS
The six papers that open this issue directly concern people who are homeless. The first emerges from work conducted by Health Care for the Homeless (in collaboration with Harris County [Houston] officials and the Baylor College of Medicine) to connect people released from jail immediately with behavioral health services as a means of preventing their relapse into homelessness (C. Brown et al.). The second focuses on release from hospitalization and the quality of the care that people who are homeless receive in those circumstances (Greyson et al.). The third paper reports on geriatric syndromes (including falls, cognitive impairment, frailty, major depression, sensory impairment, and urinary incontinence) in homeless adults, identifying as risk factors such characteristics as having less than a high school education, medical comorbidities (diabetes and arthritis), as well as alcohol and drug use problems (R. Brown et al.). The fourth paper is a postdictive validity study of a Vulnerability Index, an instrument used to assess medical vulnerability among people who are homeless (Cronley et al.). Molinari and colleagues report on perceptions of homelessness among veterans, Veterans Administration staff, and providers of housing interventions, concluding that older veterans who are homeless may be more motivated to change than their younger counterparts, due to receiving less social support and facing greater challenges in employment and health. Finally, the systematic review that closes out this section continues the theme begun earlier in the papers about people who are homeless being released from jail and hospitals still in need of care: Doran and colleagues review the literature on respite care—care for homeless patients who are too sick to be on the streets or in a traditional shelter, but not sick enough to warrant inpatient hospitalization

PART 2: IMMIGRATION, BORDERS, LANGUAGE
This part includes 10 research papers concerning populations in the U.S. or on one of its borders who originate from other countries and/or who speak languages other than English. The first asks how physicians who have second language skills decide when to call an interpreter (Andres et al.), and reports on in-depth interviews about this with 25 such physicians in different practice settings. In a related paper, Radwin and colleagues analyze data collected at an urban safety-net hospital oncology unit to examine the relationships among race, language, patient-centered nursing care, and patient outcomes. They find that patients who speak a language other than English perceive nurses’ responsiveness differently from English-speaking patients, and in turn have less trust in nurses. They write: “[M]ore liberal use of interpreter services may be warranted with patients who do not speak English at home. More routine use of interpreters may enhance patient perceptions that the nurse respects the patient, is attending to the patient carefully, and that the nurse is demonstrating due concern for the patient.”

In a cross-cultural study, Lim and colleagues investigate health behavior changes after breast cancer treatment, reporting their qualitative comparisons of Chinese American, Korean American, and Mexican American survivors. On the topic of colorectal cancer screening, Lee and Im investigate the influence of culture on practice among Korean American immigrants. Two papers in this part concern Hmong populations: Fang et al. address cervical cancer screening and Kue and Thorburn address hepatitis B screening and vaccination among Hmong people in the U.S. Renfrew et al. investigate barriers to care for diabetics from Cambodia.

Border communities come into focus in the papers by Jiménez and colleagues (on the close connection between poverty and obesity) and Talavera-Garza and colleagues (on manufacturing workers’ access to and use of health care). Finally, we learn through a qualitative study about perceived mental health needs and priorities of the population of underserved Latinos in Montgomery County, Maryland (Watson et al.).

PART 3: PRIMARY CARE AND CANCER
As is the case for every issue, the heart of this one is in primary care and chronic care for medically underserved populations. The topic is vast.

As these subjects recur so often among the medically underserved, it is no surprise that some of the papers here concern colorectal cancer (Whitaker et al.), breast cancer (Allicock et et al.), vaccination for HPV (Aragones et al. and Perkins et al.), and asthma (Martin et al.). Others take us into new territory: Mieh and colleagues write about home-based caregiving for people living with HIV/AIDS in South Africa, and Bipasha Biswas writes about the health of women and children in the Sundarbans Islands.1

How best to connect primary care with specialty care is a question with a lot of currency right now, as health care reform through the Affordable Care Act comes into being. Two papers here bear on that question: Handy and colleagues identify barriers to specialty care for uninsured patients in East Baltimore, and in a Commentary Mark Hall advocates one way of overcoming just this kind of barrier.

The legal system influences health and can provide opportunities for better care, whether they are exploited or not. Pettignano and colleagues report on seven years’ worth of data from the Health Law Partnership, a collaborative endeavor between Children’s Healthcare of Atlanta, the Atlanta Legal Aid Society, and Georgia State University College of Law. Of 250 cases accepted by the legal team after administering what they refer to as a legal check-up to patients in need, most concerned housing (19.6%), family law (18.0%), disability/SSI (17.2%), education (16.4%), and Medicaid (10.0%). Children in foster care are by definition involved in the legal system: Landers and colleagues compare their preventive visits with those of other children on Medicaid.

At the other pole of the legal system from ensuring the health of children is prison and the health of people who endure it. Harner and Riley report on their qualitative research with incarcerated women on how prison has affected their health.

Finally, this part of the present issue includes a fascinating paper on medical skepticism and the use of complementary and alternative medicine by older, rural African Americans and Whites (Bell et al.). The authors report a high degree of skepticism but little connection to either race/ethnicity or use of complementary therapies.

PART 4: INNOVATIVE PROGRAMS
Innovations in care for underserved populations may motivate both providers and communities in their efforts to improve health. Etienne Phipps and colleagues from Philadelphia report on their program to provide financial incentives for low-income families to buy more fruits and vegetables. Participants received pre-paid coupons to buy fresh produce at the study store during the intervention period. A financial incentive provided by study coupons increased the average weekly purchase of fresh fruit but was less successful with fresh vegetables.

Hepatitis C virus (HCV) infection is a chronic condition requiring specialized provider knowledge. As access to specialists for low-income patients is often impeded, when they have HCV such patients may face severe long-term health problems such as liver failure. Project Extension for Community Healthcare Outcomes (Project ECHOTM)—originally developed at the University of new Mexico—is an innovative program that uses teleconferencing, case-based learning, and disease management to expand access to specialty care for underserved patients. In weekly Project ECHO teleconferences, a faculty team of specialists make presentations, followed by primary care providers presenting individual cases. The Community health Center, Inc., Connecticut’s system of federally qualified health centers, (FQHCs) studied and tailored Porject ECHO to their needs for treating HCV in FQHCs. Kushbu Khatri and colleagues write very informatively about that endeavor here.

Low levels of compliance with U.S. Public Health Service recommendations for supplemental folic acid consumption by pregnant women—especially among Latinas—motivated Lamb and colleagues from the University of Oklahoma College of Pharmacy to hold a multi-faceted promotional event at a YWCA in Oklahoma City, work on which they report here. Angier and colleagues, in an effort to increase the relevance of research to underserved communities, went on a retreat with community health workers so researchers and front-line staff could engage with one another about what health concerns they find most pressing.

PART 5: HEALTH POLICY AND EPIDEMIOLOGY
Health policy and epidemiology generate interleaving results. This section begins with the implementation of a data-collection process advocated by the Institute of Medicine (IOM). Detailed ethnicity data—granular ethnicity—can help identify and target health disparities more precisely, in the IOM’s view. Here, Wilson and colleagues from the Institute for Family Health in New York (a group of 17 community health centers) report on that organization’s ground-breaking efforts to collect granular ethnicity data, and on the application of that information to improving diagnosis of hepatitis B.

Next, two Commentaries address new legislative efforts to improve health. Ramirez and Safford look from two sides at California’s Senate Bill 1413 of 2010, which requires school districts to offer free fresh water at mealtimes. On first glance, the bill seems to promise institutional commitment to fighting childhood obesity. However, it has had the unintended consequences of squeezing the already tight budgets of schools in very low-income areas, especially those with contaminated tap water. The question of how simultaneously to achieve viable public health improvement programs in schools without hampering their central mission of education comes to the forefront. Rodriguez and colleagues address another important issue arising in many states in their Commentary on the merits of licensing mid-level dental providers in the absence of an adequate number of dentists.

This issue concludes with four research papers. Lee and Hicken analyze social risk and racial/ethnic disparities in obesity during the transition to adulthood. Onoye and colleagues in Hawai’i at Mānoa examine emergency room use by patients with mental health disorders (especially PTSD). Harking back to the Commentary on mid-level dental providers, Wallace and MacEntee from the University of British Columbia report on their interviews with dentists (n=4), dental hygienists (n=30), dental clinic staff (n=17), and other health care and social service providers (n=12) about increasing numbers of community dental clinics in low-income areas. They conclude that “unmet dental need of vulnerable people requires political attention and that restricted dentistry for underserved communities is socially unacceptable.” Finally Wright and Ricketts investigate whether the proportion of consumers on FQHC governing boards is associated with their use of federal grant funds to provide uncompensated care, concluding that it is not.

Before closing, I would like to alert readers to a new book. Johns Hopkins University Press (JHUP) has just published our collection of papers on free clinics and student-run clinics, with a preface by Dr. Charles Mouton (Dean of the School of Medicine at Meharry). This collection of over 30 papers seeks to represent the great variety of responses to the needs of people who are uninsured throughout the United States. The title is Free Clinics: Local Responses to Health Care Needs. It is available on Amazon and through JHUP.

Virginia M. Brennan, PhD, MA
Associate Professor, Meharry Medical College
Editor, JHCPU

[1] The Sundarbans Islands lie outside our geographic scope (in West Bengal, India), but we include this paper because of the importance of infant mortality as a theme in JHCPU. We do not anticipate any more such exceptions to our scope (North and Central America, the Caribbean, and sub-Saharan Africa) in the future.

February 2013

Chau-Kuang Chen, EdD
Michelle Bruce, MD, MSPH
Lauren Tyler, BS
Claudine Brown, MSPH
Angelica Garrett, MD
Susan Goggins, MD
Brandy Lewis-Polite, MD
Mirabel L Weriwoh, MD, MSPH
Paul D. Juarez, PhD
Darryl B. Hood, PhD
Tyler Skelton, MS

The authors are affiliated with the Department of Institutional Research at Meharry Medical College (MMC) [C-KC]; Department of Internal Medicine, MMC [MB]; the Department of Biology, Tennessee State University [LT]; the School of Medicine at MMC [AG, SG, B L-P]; the School of Graduate Studies and Research, MMC [CB, MLW]; the Department of Family and Community Medicine, MMC [PDJ, TS]; and the Department of Neuroscience and Pharmacology, NIMHD Health Disparities Research Center of Excellence, Environmental Health Disparities and Medicine, Center for Molecular and Behavioral Neuroscience, MMC [DBH]. Please address correspondence to Darryl B. Hood, PhD, Professor, Department of Neuroscience and Pharmacology, NIMHD Center of Excellence in Health Disparities, Center for Molecular and Behavioral Neuroscience, Meharry Medical College, Nashville, TN 37208; (615) 327-6358; dhood@mmc.edu.

Abstract: The goal of this study was to analyze a 54-item instrument for assessment of perception of exposure to environmental contaminants within the context of the built environment, or exposome. This exposome was defined in five domains to include 1) home and hobby, 2) school, 3) community, 4) occupation, and 5) exposure history. Interviews were conducted with child-bearing-age minority women at Metro Nashville General Hospital at Meharry Medical College. Data were analyzed utilizing DTReg software for Support Vector Machine (SVM) modeling followed by an SPSS package for a logistic regression model. The target (outcome) variable of interest was respondent’s residence by ZIP code. The results demonstrate that the rank order of important variables with respect to SVM modeling versus traditional logistic regression models is almost identical. This is the first study documenting that SVM analysis has discriminate power for determination of higher-ordered spatial relationships on an environmental exposure history questionnaire.

The socioeconomic circumstances of people and the places where they live work and play strongly influence their health.1 Healthy homes are essential to a healthy community and serve to protect families from exposure to potential environmental contaminants (such as chemicals and allergens) thereby preventing exposure-related phenomena. In contrast, inadequate housing contributes to infectious and chronic disease and injuries and has been shown to affect child development adversely.1 The definition of inadequate housing is related to the basic structure and systems of a housing unit, whereas the definition of unhealthy housing is related to exposure to environmental pollutants and other toxicants.1 The Centers for Disease Control and Prevention (CDC) has defined unhealthy housing as housing with characteristics that might negatively affect the health of its occupants, including rodents, water leaks, peeling paint in homes built before 1978, and absence of a working smoke detector.

The most recent CDC report concludes that efforts to decrease disparities in access to healthy housing will have the immediate effect of decreasing disparities in health status.1 Among the approximately 110 million housing units in the United States, approximately 5.8 million are classified as inadequate, and 23.4 million are considered unhealthy. Inadequate and unhealthy housing disproportionately affect populations that have the fewest resources (e.g., people with low incomes and limited education). Studies reveal that certain ethnic and racial populations (such as African Americans), have a greater likelihood of residing in areas in which there is poor housing and poor air quality primarily defined in terms of fine particles (PM2.5).2 Minority populations are especially vulnerable and susceptible to exposure to PM2.5 as a result of their homes being located in close proximity to the emissions from smokestacks and vehicles (heavy traffic volumes).3,4 Historical studies have shown that vulnerable segments of minority populations, such as pregnant women and young children, are particularly susceptible to the effects of PM2.5 air pollution.5–8 These facts illustrate the need to develop survey instruments for the physician to have at his fingertips when taking initial environmental exposure histories. This is particularly relevant for physicians practicing in indigent care hospitals where many health problems plague urban minority populations with low socioeconomic status.

Recent reports on exposure during pregnancy to components of air pollution have revealed deleterious effects on development of the fetus. High levels of components of photochemical smog, including carbon monoxide (CO), nitrogen dioxide (NO2), and ambient particulate matter (PM2.5) have been associated with very low and low birth weight, preterm birth, and infant mortality.9,10 Particulate matter is material that is suspended in the air in the form of minute solid particles or liquid droplets as an atmospheric pollutant. Such environmental contaminants have also been associated with significant differences in biparietal diameter and head circumference both measured during pregnancy and at birth.11,12 In utero exposure to polycyclic aromatic hydrocarbons (PAHs) during pregnancy has been shown to correlate with impaired cortical function and cognitive developmental delay.13–17

A recent epidemiologic study reported that use of gas appliances and increased NO2 in the home during the first three months of life are associated with decreased cognitive test scores and increased inattention at four years of age.18 In a separate study, Suglia and colleagues estimated lifetime residential exposure to carbon black, a proxy for traffic-related PM, among 8–11 year old children with an associated decline in performance on intelligence and memory tasks as a function of increasing carbon black levels.19 Interestingly, autism spectrum disorder has been associated with estimated regional concentrations of hazardous air pollutants, including arsenic and nickel, and with diesel PM exposure in early childhood.20 Thus, there is an emerging literature suggesting that nearby roadway traffic-related air pollutants, possibly influenced by specific components such as particulate matter or PAHs, adversely affect neurodevelopmental processes.

In the present study conducted out of Metro Nashville General Hospital at Meharry Medical College, we have used support vector machines (SVM) to analyze responses to a 54-item environmental exposure questionnaire towards validation of the instrument for future use in recruiting pregnant women for prospective longitudinal studies. Support vector machines are a relatively new classification or prediction method developed by Cortes and Vapnik21 in the 1990s as a result of the collaboration between the statistical and the machine-learning research communities. Support vector machines attempt to classify datasets by finding a separating boundary, referred to as a hyperplane. The main advantage of the SVM is that it can, with relative ease, overcome the high dimensionality problem, i.e., the problem that arises when there is a large number of input variables relative to the number of available observations.22 Further, because the SVM approach is data-driven and possible without a theoretical framework, it is believed to have important discriminative power for classification, especially with datasets where sample sizes are small.23 The technique has recently been used to improve methodology for detecting diseases in clinical settings.24,25 The SVM analysis of responses from this study will serve as a point of reference for recruitment of a prospective cohort of pregnant African American women living in an inner city environment to quantify the frequency of exposure related adverse pregnancy outcomes. (841 words)

Methods

Demonstration of similarity of demographics between inner-city Atlanta area (Grady Memorial Hospital) and inner-city Nashville area (Metro Nashville General Hospital at Meharry Medical College) as a means to validate our 54-item instrument. All procedures in accordance with administration of the 54-item instrument within the subspecialty clinics at Nashville General Hospital at Meharry Medical College were submitted to the Meharry Medical College Institutional Review Board (IRB). The Institutional Review Board determined that the project was exempt, based on category 45 CFR 46.101.b (2) of the federal regulations concerning the use of survey procedures when the information is recorded in such a manner that subjects cannot be identified. No consent form was needed and the IRB application was approved (IRB number 051115DBH195-11).

Our 54-item environmental exposure instrument is an adaptation of the Agency for Toxic Substances and Disease Registry (ATSDR) environmental exposure history questionnaire (http://www.atsdr.cdc.gov/hec/csem/exphistory/docs/exposure_history.pdf.) and therefore required validation and calibration. As a means of validating our instrument, we sought to determine the similarity of demographic characteristics between inner-city Atlanta, Georgia and inner-city Nashville, Tennessee based on 2000 and 2010 census data. For additional clarity, we also determined the similarity of demographic characteristics between the Grady Memorial Hospital area and the Metro Nashville General Hospital at Meharry area. These data are shown in Table 1 and indicate that the respondent population at Grady Memorial Hospital in inner-city Atlanta and the respondent population at Metro Nashville General Hospital at Meharry are very similar in terms of the Black and/or African American representation. The percent differences in 2012 demographic data for Atlanta compared with Nashville in favor of Atlanta (16.4%) is offset by the robust African American demographic (97.3%) in ZIP code 37208 (where Metro Nashville General Hospital at Meharry is located). The Grady Memorial Hospital area’s African American population represents 76.3%of the total population in the area. There are similar percent differences between the Atlanta Grady Memorial Hospital area and the Metro Nashville General Hospital at Meharry area in educational attainment, sex, and school enrollment. All of these indices proved important as a part of the calibration-validation process regarding our 54-item instrument.

Study subjects. A total of 187 child-bearing-age (18–35) African American or Hispanic women were interviewed when presenting for services at the Obstetrics and Gynecology, Family and Community Medicine, or Pediatrics subspecialty clinics at Metropolitan Nashville General Hospital at Meharry Medical College.

Personal interview. The 54-item environmental exposure questionnaire was administered to consenting eligible child-bearing-age (18–35 years) African American and Hispanic women. The survey was partitioned into five domains including 1) home and hobby, 2) school, 3) community, 4) occupation, and 5) exposure history. Demographic information pertaining to the woman’s age and education level were recorded, as was the ZIP code in which she primarily resided. This 54-item questionnaire was modeled from both the CDC and the ATSDR environmental exposure history questionnaires.26

Exposed vs. control dichotomization of ZIP codes. Based on the proximity of EPA reporting sites with emissions to Meharry Medical College-respondents that resided in ZIP codes 37206, 37207, 37208 and 37209 were assigned to the exposed group. Respondents residing in any other ZIP codes (37210, 36606, 37011, 37013, 37062, 37075, 37076, 37082, 37086, 37087, 37115, 37219, 37138, 37203, 37210, 37211, 37212, 37213, 37214, 37215, 37216, 37217, 37218, 37219, 37220, 37221, 37228, 37235, and
38556) were assigned to the control group.

Overview of the SVM classifier. The SVM algorithm is a supervised machine learning method which has demonstrated its ability to solve complex classification problems, especially in the field of bio informatics.27,28 The SVM model is operated on the principle of structural risk minimization.29 It is designed to minimize true risk of misclassifying examples during the model training. It has its advantage in the practical application for
small sample and generalization because of structural risk minimization.29–31

Unlike many other more popular data analysis tools, the SVM algorithm is data-driven and model-free and, as such, may have much discriminative power for classification in instances where many explanatory variables are used and the sample size is relatively small. Support vector machines establishes sophisticated models by constructing a multidimensional hyperplane optimally discriminating (maximizing the margin separation) between two different classes.32 The algorithm achieves its high discriminative power by using special non-linear functions called kernel functions to transform the original data into a high-dimensional space.33 Justified by Cover’s theorem, any dataset can be separated out if the data dimension grows.

Data analysis. A database was created from the collected responses. Entries with missing data points were excluded from the analysis and key domains of interest were identified. The overall response rate to the 54-item instrument was (94.3%). Our method converted the variable which spoke to the respondent’s neighborhood from a categorical variable (Rural, Suburban and Urban) into a dichotomous variable by combining the urban and suburban fields for the sake of parity. Respondents’ ZIP codes were dichotomized to form an exposure and control group with persons residing in the 37206, 37207, 37208 and 37209 ZIP codes being assigned to the exposed group based on the number of EPA reporting industrial facilities and sites coupled with their proximity to Meharry Medical College. Respondents residing in ZIP codes (37210, 36606, 37011, 03713, 37062, 37075, 37076, 37082, 37086, 37087, 37115, 37219, 37138, 37203, 37210, 37211, 37212, 37213, 37214, 37215, 37216, 37217, 37218, 37219, 37220, 37221, 37228, 37235, and 38556) outside of this area, were assigned to the control group. Data analysis was performed on the dataset using DTReg software for Support Vector Machine (Sherrod, PH., 2008, Digital Tree Regression (DTREG) (Version 4.0) [Software]. Brentwood, Tennessee) and SPSS version 17 for the logistic regression model (Statistical Product and Service Solutions (SPSS) (Version 17) [Software] Armonk, New York).

Six steps were systematically adopted to yield the most suitable SVM and logistic regression models to accurately classify respondents living in distinct ZIP code groups.

Step 1 involved generating a research dataset consisting of one dependent variable and multiple independent variables collected from respondent’s questionnaires.

In Step 2, the underlying models were developed by the difficult selection of kernel functions for the SVM. Thus, our only strategy was performed a trial and error process by applying all kernel functions. For SVM classifier, the following four kernel functions were readily available for model construction: linear, radial basis function (RBF), polynomial, and sigmoid.

In Steps 3 and 4, all possible SVM candidate models were trained and tested to achieve minimal classification error by means of adjusting parameters and performing cross-validation. For the SVM classifier, a five-fold cross-validation was implemented to minimize the bias generated by random sampling of the training and testing datasets. The input dataset was divided into five mutually exclusive subsets. One subset was used for testing and the remaining four were used to train the data. The process was repeated five times to ensure that the model was tested in each subset.

In Step 5, the most suitable SVM model was generated and compared with a logistic regression model to determine validity relative to our 54-item instrument. Initial data analysis was carried out by comparing the normalized importance, rank order of independent variables, and classification accuracy on the SVM to those of the logistic regression. The classification accuracy was compared by the measures of 1) sensitivity and 2) specificity, and 3) combined accuracy for each SVM classifier.

Sensitivity was defined as a measure of ability of the model to detect important variables associated with those respondents living in ZIP code’s 37208, 37209 and 37210. Specificity is a measure of the ability of the model to specify and delineate differences by the respondents living in ZIP codes that make up the control group.

In Step 6, the most important variables and related rank orders of variables for the SVM model were generated to facilitate variable selection and explanation. The normalized importance was calculated by dividing the value of the highest relative importance into the value of the other relative importance. The normalized importance provides a hierarchal viewpoint of the ranking of the explanatory variables.

Creation of relational database and Web portal for geographical information systems mapping. We have established a relational database and Web portal , in part, from the EPA databases listed at www.IMNashville.com. This Web portal enables us to 1) build the capacity to process and analyze large secondary databases; 2) conduct inter-disciplinary training; 3) use public participatory GIS; and 4) develop interactive mapping of health disparities and community assets and risk factors associated with environmental exposures. Emissions data from the EPA-databases listed below was converted into a mapping format to visually communicate the location of EPA reporting industrial plants with emissions in ZIP codes as a function of the ZIP codes of respondents to our 54-item instrument that perceived themselves vulnerable to exposure to contaminants.

USEPA databases used in the present study. The EPA reporting sites are defined as any site in Metro Nashville Davidson County that emissions data was available from the following EPA-databases 1) Air Quality Subsystem (AQS), 2) AIRS Facility Subsystem (AFS), 3) AIRS Data and 4) Toxic Release Inventory.

Air Quality Subsystem (AQS). The Air Quality System (AQS) is EPA’s repository of ambient air quality data. AQS stores data from over 10,000 monitors, 5,000 of which are currently active. Specifically, this EPA database contains measurements of ambient concentrations of air pollutants and meteorological data from thousands of monitoring stations operated by EPA, state, and local agencies.

AIRS Facility Subsystem (AFS). The Air Facility System (AFS) contains compliance and enforcement data and permit data for stationary sources of air pollution regulated by EPA, state and local air pollution agencies. The environmental regulatory community uses this information to track the compliance status of point sources with various programs regulated under the Clean Air Act. See the Clean Air Act enforcement page for information on enforcement activities.

Types of data found in in AIRS facility subsystem. A plant is a facility represented by its physical location and defined by property boundaries. Plant-level data include plant name, address, Standard Industrial Classification (SIC), U.S. Census Bureau North American Industry Classification System, and compliance status.

A stack is where emissions are introduced into the atmosphere. Stack-level data include the height and diameter of the stack as well as the temperature, flow rate, and velocity of the gas released into the atmosphere. Stack-level data is used in emission inventory reporting.

A point is a physical piece of equipment or a process that produces emissions. Point-level data include normal operating schedule and the percentage of annual activity occurring each season.

A segment is a component of a point process, such as fuel combustion, that is used in the computation of emissions. Segment-level data in AFS include Source Classification Code (SCC), annual process rate, and fuel parameters. Segment-level data is used in emission inventory reporting. Emission inventory data can be found at the Technology Transfer Network Clearinghouse.

AIRS data provide easy access to summaries of air monitoring data for the current and five prior years, the latest available estimates of air pollutant emissions from major point sources, the overall regulatory compliance status of those sources, and names of contacts in EPA and state/local air pollution agencies. All these data pertain to the criteria pollutants (carbon monoxide, nitrogen dioxide, sulfur dioxide, ozone, particulate matter, lead).

Toxics Release Inventory Program (TRI). The TRI database contains data on disposal or other releases of over 650 toxic chemicals from thousands of U.S. facilities and information about how facilities manage those chemicals through recycling, energy recovery, and treatment. One of TRI’s primary purposes is to inform communities about toxic chemical releases to the environment.

Results

Similarity of demographic characteristics between inner-city Atlanta area (Grady
Memorial Hospital) and inner-city Nashville area (Metro Nashville General Hospital at Meharry Medical College). A version of our 54-item instrument was administered previously to inner-city populations at Grady Memorial Hospital in Atlanta, Georgia. As a means of validating the instrument that was developed by our group, we sought to determine the similarity of demographics between inner-city Atlanta and inner-city
Nashville based on 2000 and 2010 census data. For additional clarity, we also determined the similarity of demographics between the Grady Memorial Hospital area and the Metro Nashville General Hospital at Meharry area, as discussed in the Methods section and as demonstrated on Table 1. All of the endpoints and indices contributed to the validation -process of our 54-item instrument.

SVM analysis. Shown in Table 2 are the dependent or outcome variable as residential ZIP code areas that were regrouped into a binary measure of one or two. The respondent population was split between ZIP codes for the respondents living in (37206, 37207, 37208, 37209) and other ZIP codes for respondents living in (37210, 36606, 37011, 03713, 37062, 37075, 37076, 37082, 37086, 37087, 37115, 37219, 37138, 37203,
37210, 37211, 37212, 37213, 37214, 37215, 37216, 37217, 37218, 37219, 37220, 37221, 37228, 37235, and 38556). The five domains of independent variables (community, home and hobby, school, occupation, and environmental exposure history) included Q6 (polluted lake/stream: Community); Q17A (in your home: Home and Hobby); Q17C (on your lawn or garden: Home and Hobby); Q22 (school neighborhood [urban or rural]); Q24A (carpeted classrooms: School); Q24E (windows that open: School); Q24F (moldy smell: School); Q25G (flood, water leaks: School); and Q26G (odorous cleaning products: School).

Approximately one-tenth (9%) of the survey respondents lived near a polluted lake/stream community (Q6). In terms of using pesticides, more than half (54%) of the survey respondents used them in their home (Q17A) while more than one-tenth (14%) used them on their lawn or garden (Q17C). More than half (53%) of the survey respondents indicated that their children‘s school was located in a rural area (Q22). While inspecting their children’s schools, the survey respondents revealed that more than half of the schools had carpet (51%) (Q24A), windows that opened (55%) (Q24E), and a moldy smell (61%) (Q24F). Furthermore, nearly one-tenth (8%) of the survey respondents reported flood and/or water leaks in their children’s school (Q25G), and more than three quarters (76%) of the survey respondents indicated that the schools used odorous cleaning products (Q26G).

Table 3 presents major findings using SVM and logistic regression modeling approaches with the latter approach being the traditional model used to measure a binary outcome. The backward procedure is one of the procedures used to select significant risk factors to form the logistic regression model. The model fitting the statistic, pseudo R-squared value was used to measure the success of this model by explaining the variations in the data. The Nagelkerke R squared value (0.32) was significantly different from zero, indicating that 32% of the variations in the binary outcome variable (residential ZIP codes) was accounted for by the risk factors. Using the SVM, the independent variables were assigned a normalized importance and given a rank order based on this normalized value. According to the sigmoid function of SVM, the variables by level of importance to the outcome were the following; Q17A (in your home), Q17C (on your lawn or garden), Q25G (flood, water leaks), Q6 (polluted lake/stream), Q24F (moldy smell), Q26G (odorous cleaning products), Q25E (child’s school—new flooring or furniture), Q22 (school neighborhood [urban or rural]), and Q24A (carpeting in classrooms). The Wald test of Logistic regression was used at different levels of p values, 0.001, 0.01 or 0.05, to determine the significant independent variables associated with the binary outcome (residential ZIP codes). The regression coefficients for the following explanatory variables, Q17A, Q17C, Q25G, Q6, Q24F, Q26G, Q24A were found to be significantly different from zero at given p values level mentioned above and using the Wald test.

Table 4 represents a summary of the measures of accuracy used to evaluate both of the data analysis techniques used. Sixty-one percent (61%) of respondents perceived little to no exposure within the 5-domains. Sixty-nine percent (69%) of respondents perceived that they were susceptible to environmental exposures within the five domains. The SVM method outranked the logistic regression analysis with respect to sensitivity, but not specificity. In terms of combined accuracy, both SVM and logistic regression models were comparable (69% vs. 70%), which in some instances may be considered low; however, an important measure of accuracy is the sensitivity criterion, the so-called power of the test. Model sensitivity determines if the tool used in the study was accurate in measuring the outcome variable. If more survey respondents who were exposed to the risk factors did not have the disease, then the overall study power would be weakened. The SVM was found to be a more sensitive model (69%) than the logistic regression model (63%), meaning that SVM has a greater ability to identify important variables related to the binary outcome (residential ZIP codes). On the contrary, logistic regression was found to have a greater ability to specify differences between the populations of interest (women residing in the ZIP codes; 37206, 37207, 37208, 37209) and the control group used (women residing in other ZIP code areas).

Mapping of EPA-Reporting Industrial Sites Located in the North Nashville ZIP Code 37208. Figure 1 shows the EPA-Toxic Release Inventory (TRI) and smokestack emission sites located in the North Nashville ZIP code 37208. The TRI sites are shown in squares and smokestack emission sites in circles. Mapping of EPA-reporting industrial sites located in the North Nashville ZIP code 37208 allows for visualization of the SVM analysis finding of identifying the important variable related to the binary outcome as residential ZIP code from respondents of the 54-item instrument.

Discussion

In this study we validated our 54-item instrument with SVM analysis. The validation of this questionnaire was set in an inner city area of Nashville, Tennessee (North Nashville) which is composed largely of an African American population. North Nashville is known to suffer from increased adverse health outcomes compared with whites in the same area of Davidson County.35 The respondent population at Grady Memorial Hospital in inner-city Atlanta and the respondent population at Metro Nashville General Hospital at Meharry were shown to be very similar in terms of various demographic characteristics including race, educational attainment, sex, and school enrollment. This 54-item instrument will be used in the very near future to recruit a large cohort of pregnant African American women into a longitudinal study designed to quantify perturbations in sensory function in their infants prior to 24 months. The primary study site, Metro Nashville General Hospital on the Campus of Meharry Medical College operates as the community’s public hospital to provide access to care for all the city’s residents—whether uninsured, underinsured or insured. As a result of the validation of our instrument, our hospital will continue to serve as the primary recruitment site for translational research projects under the Meharry-Vanderbilt Alliance umbrella. It will be an overarching hypothesis in the future that women living in ZIP codes 37208 and 37209 will have a higher perceived risk of exposure to environmental toxicants as compared to women living in the surrounding suburban areas.

Support vector machines have been widely used in various areas, such as recognition, reliability evaluation, bioinformatics and medicines, for survival time classification and assessment of the severity of many acute and chronic diseases.36 However, to our knowledge, this study is the first attempt to investigate perceived exposure to environmental contaminants by administration of a 54-item instrument in a population of child-bearing-age African American and Hispanic women via SVM. The novelty of our study was demonstrating the discriminative power of this SVM approach as compared with that of commonly used logistic regression models.

In association with other studies being conducted in our center, creation of environmental relational databases will serve as a means to promote civic engagement in vulnerable communities and neighborhoods in close proximity to Meharry Medical College. The database described here enables us to begin to analyze the relationships between physical, built, and social environments relative to the health status of persons living in targeted neighborhoods or communities. The long-term goal is to use the data to develop and implement targeted interventions to improve health conditions on a community level. Our Web portal (www.IMNashville.com) allowed us to bring together data on health outcomes with data on environmental conditions, assets (salutogenic features), and risk factors (pathogenic features). As indicated earlier, our approach builds on the ecological environmental justice framework that explores the role that salutogenic
(health-promoting) and pathogenic (health-restricting) built and social features play in driving health and health disparities in differentially burdened communities.37

For the purposes of such relational databases, the natural environment includes air, food, soil, water, and vegetation.38 Environmental hazards refer to the effects of the environment on a person’s health and the effects of human activity on the health of the environment. Such hazards can be toxicants in a physical space that can harm health. Natural environmental datasets in our relational database include: 1) Day and night land surface temperature (LST) data from NASA’s Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite sensor (1-km spatial resolution); 2) Daily spatial surfaces of ambient fine particulate matter (PM2.5) generated by algorithms developed by scientists at the Universities Space Research Association (USRA) and NASA/Marshall Space Flight Center and using MODIS satellite data and EPA ground observations39 (10-km resolution); 3) Daily maximum/minimum air temperature and daily maximum heat index from the North American Land Data Assimilation System (NLDAS) forcing data (12-km resolution); and 4) Finer resolution (30-m and 60-m) datasets of Landsat-derived land cover/land use and LST for 1992 and 2006 for a focused study in Nashville/Davidson County.

The built environment has been found to play a major role in directing individual physical activity, and Nashville has made big strides in improving policies and regulations related to building and site design to improve the built environment for pedestrians and cyclists, including passage of (1) specific plan zoning; (2) revised subdivision regulations that have introduced a so-called walkable subdivision option for developers; and (3) a community-character manual that will guide future land-use planning.40 In the future, our relational database will allow for the analysis of relationships between changing characteristics of the built environment and occurrence of health disparities. Establishment of this relational database will allow us to build longitudinal datasets to track changes in environmental conditions and effects on health disparities both retrospectively and prospectively.

Our study however, is not without limitations. The primary limitation of this study is its relatively small sample size but this fact did not impact the ability to achieve statistical significance with regard to the determination of higher ordered relationships to responses on the 54-item instrument. The second limitation is that not all of the women who participated were pregnant at the time of interview. We are aware of the implications of this fact, and remain cautious as a result. However, given the demographic profile of the patient base at Metro Nashville General Hospital at Meharry Medical College, it is highly likely that the percent of unintended births will track with that observed in the United States (37%).41 Many studies on unintended childbearing including a comprehensive review by the Institute of Medicine42 and a recent white paper reviewing more than 60 additional studies on this topic,43 have also shown that births that were unintended by the mother are at elevated risk of adverse social, economic, and health outcomes for the mother and the child. Unintended births are associated with delayed prenatal care, smoking during pregnancy, not breastfeeding the baby, poorer health during childhood, and poorer outcomes for the mother and the mother-child relationship.44 Further, longer-term negative consequences for these children have been found by some longitudinal studies of unintended pregnancies that track the children into adulthood.45

Thirdly, our cross-sectional design does not allow any inference to be drawn with respect to the causal relationships among independent variables. Finally, our data is based on a 15-minute interview in an indigent care facility that is located in a known socioeconomically impoverished ZIP code. This fact may have contributed to the perception of exposure to pollutants as has been observed in Berkson’s bias.46 Connecting environmental problems with health disparities can be difficult due to limitations in the way health data are collected and made available.47 Many health databases and tables provide data only at the county level. Without a geo-coded reference at a sub-county level, these data are limited as to how they can be related to environmental hazards or exposures that typically are features of a small geographic area. In addition, the health data often tells us very little about how a person may have come in contact with an environmental hazard.

Conclusion. To our knowledge, this is the first study using SVM analysis determination of higher-ordered spatial relationships on an environmental exposure history questionnaire. Previous to this one, the study that most closely resembled this one used SVM to predict adherence to medication in heart failure patients. In that study, data about medication adherence was collected from patients at a university hospital through self-reported questionnaire. The data included 11 variables of 76 patients with heart failure. Mathematical simulations were conducted in order to develop a SVM model for the identification of variables that would best predict medication adherence. Using the sigmoid function via SVM analysis,47 the rank order of variables by level of importance to the outcome (of perceived exposure to environmental contaminants) were Q17A (in your home), Q17C (on your lawn or garden), Q25G (flood, water leaks), Q6 (polluted lake/stream), Q24F (moldy smell), Q26G (odorous cleaning products), Q25E (child’s school—new flooring or furniture), Q22 (school neighborhood—urban or rural), and Q24A (carpeting classrooms). The Wald test of logistic regression was used to determine the significance (at different levels of p values: .001, .01, or .05) of independent variables associated with the binary outcome (residential ZIP codes). The regression coefficients for Q17A, Q17C, Q25G, Q6, Q24F, Q26G, Q24A were found to be significantly different from zero at given p value level. Our Nagelkerke R squared value (0.32) was significantly different from zero, indicating that 32% of the variations in the binary outcome variable (residential ZIP codes) were accounted for by the risk factors indicated in the respective questions. In conclusion, SVM analysis was shown to have discriminate power for determination of higher-ordered spatial relationships on the 54-item instrument within the five domains studied in the target ZIP code.

Acknowledgments

This work was supported, in part, by NIH grants S11ES014156-06 and 1R56ES017448- 01A1 to DBH and 3P20MD000516-07S1 to PDJ and DBH. Also, critical to the conduct of these studies were grants from the Simons Foundation Autism Research Initiative, Research Centers in Minority Institutions (RCMI) G12RRO3032 and S06GM08037, Nuclear Regulatory Commission Grant NRC-27-10-515 as well as Meharry Medical College -Vanderbilt University Alliance for Research Training in Neuroscience Grant
(T32MH065782).

Notes

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21. Cortes C, Vapnik V. Support-vector networks. Boston, MA: Kluwer Academic Publishers/ Machine Learning, 1995; (20):273–97. Available at: http://image.diku.dk /imagecanon/material/cortes_vapnik95.pdf.
22. Verplancke T, Van Looy S, et al. Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies. BMC Med Inform Decis Mak. 2008 Dec 5; 8:56.
23. Yu W, Liu T, Valdez R, et al. Application of support vector machine modeling for prediction of common diseases: the case of diabetes and prediabetes. BMC Med Inform Decis Mak. 2010 Mar 22;10:16.
24. Maglogiannis I, Loukis E, Zafiropoulos E, et al. Support vectors machine-based identification of heart valve diseases using heart sounds. Comput Methods Programs Biomed. 2009 Jul;95(1): 47–61.
25. Thurston RC, Matthews KA, Hernandez J, et al. Improving the performance of physiologic hot flash measures with support vector machines. Psychophysiology. 2009 Mar; 46(2): 285–92.
26. Agency for Toxic Substances and Disease Registry (ATSDR). Case studies in environmental medicine: taking an exposure history. Washington, DC: Agency for Toxic Substances and Disease Registry, 1992 Oct. Available at: http://www.atsdr.cdc.gov /hec/csem/exphistory/docs/exposure_history.pdf.
27. Rice SB, Nenadic G, Stapley BJ. Mining protein function from text using term-based support vector machines. BMC Bioinformatics. 2005; 6(Suppl 1):S22.
28. Ng KL, Mishra SK. De Novo SVM Classification of Precursor microRNAs from genomic pseudo hairpins using global and instrinsic folding measures. Bioinformatics. 2007 Jun 1;23(11):1321–30.
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30. Wan V, Campbell WM. Support vector machines for speaker verification and identification. Presented at: IEEE International Workshop on Neural Networks for Signal Processing. Sydney, Australia, 2000.
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35. Blaha MJ, Kusz KL, Drake W, et al. Hypertension prevalence awareness, treatment and control in North Nashville. Tenn Med. 2006 Apr; 99(4):35–7.
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23.4, November 2012

Nicholas I. Sikic, MD, MPH
Nancy Erbstein, PhD
Kearnan Welch, MS
Ethan Grundberg, MS
Elizabeth Miller, MD, PhD

Abstract: Introduction. This study examined the acceptability and feasibility of Fresh Producers, a student-run fruit and vegetable distribution program at three urban high schools located in low-income neighborhoods, and its potential impact on the nutrition and professional development of participating students. Methods. Thirteen focus groups conducted with 72 students explored the program’s impact on their dietary habits and professional skill development, and discussed program challenges. Responses were coded for common themes by multiple investigators. Results. Participants reported increased fruit and vegetable consumption, and improved interpersonal, team-building, and organizational skills. Challenges included integration into the school schedule and environment and limited faculty support for business activities. Conclusion. This program is acceptable and feasible for secondary school students in a variety of school settings. Students reported positive changes in professional skills and nutrition. Training and support for students and faculty, including strategies to improve program integration into the school context, could increase participation.

Fruit and vegetable consumption among American children and adolescents remains far below recommended levels. Nutritional surveys have found that only about 10% of adolescents meet the daily recommendations for fruit and vegetable intake, and that the largest source of vegetables that children and adolescents consume are fried potatoes.1–3 These surveys also show that the least affluent children and adolescents eat the fewest fruits and vegetables and consume the most fast food and soda.2,4,5 Low-income youths often live in food deserts, areas without supermarkets or access to healthy foods such as fresh fruits and vegetables.6–8

Improving fruit and vegetable consumption among children and adolescents could have an important public health impact. Increased intake of fruits and vegetables has been associated with improved symptoms of respiratory illness in childhood,9–11 decreased markers of heart disease in adolescence,12 and lower rates of chronic diseases later in adult life.13–15 Although the relationship between obesity and produce consumption in children and adolescents has not been established, initial studies have shown that a higher intake of and preference for fruits and vegetables is associated with healthier weights in children.16–18

As the central institution in children’s lives, schools have become a natural proving ground for interventions aimed at increasing fresh produce intake.19,20 These interventions have increasingly focused on non-didactic, experiential strategies, such as garden projects and cooking classes.21,22 Many of these school-based interventions have been shown to improve student knowledge and attitudes about fruits and vegetables and increase their intake of fresh produce.23–28 Because most of these interventions have been implemented in elementary schools, few have involved secondary school students, raising questions about effective and sustainable strategies for improving fresh produce consumption among adolescents.

Fresh Producers. Fresh Producers is a non-profit school-based program that provides high school students with an entrepreneurial experience and empowers them to become the source of convenient, affordable fresh fruits and vegetables in their communities. Based in South Sacramento, a multi-ethnic urban area that was recently identified as a major food desert,29 the program has the dual objectives of increasing fruit and vegetable consumption in communities with low access to fresh produce, while providing participants with experience running their own business. Students function as co-owners would, managing various functions of distribution, product marketing, and point of purchase sales. They take weekly orders for fresh fruits and vegetables throughout the school year, and then receive seasonal, fresh produce through a wholesaler, which they repackage and arrange for customer pick-up. The opportunity to earn income for themselves or their school is an additional incentive to participate in the program. Since its inception in 2007, Fresh Producers has delivered over 125,000 servings of fresh produce in the greater Sacramento area.

The Fresh Producers program was first developed in 2007 at a charter high school, where it was part of a multidisciplinary curriculum designed to teach 11th and 12th grade students about nutrition and provide them with the skills to assess and improve the health of their community. The curriculum was taught across different subjects with the involvement of multiple classes and teachers. Fresh Producers facilitated student activities in the community, which included selling fresh fruit and vegetable boxes in the surrounding neighborhood.

The program became focused solely on the student-run sales of fruit and vegetable boxes, without the multidisciplinary curriculum, when it expanded to two new schools in 2009: a continuation school for students at risk of not graduating from high school, where it was run as a stand-alone extracurricular activity, and a traditional four-year high school, where it became a project for students in the school’s business academy. At both schools, the program involved 11th and 12th grade students. A percentage of the proceeds from fruit and vegetable sales were returned to the two schools. At the traditional high school, this revenue funded academic and extracurricular activities, and at the continuation high school participating students received the proceeds in the form of gift cards.

This study was designed as an initial evaluation of the acceptability, feasibility, and potential impact of Fresh Producers among students in the three high schools described above, focusing on three specific areas of inquiry: 1) impact of the program on the nutrition and dietary choices of participants; 2) the professional skills development reflected on by participants; and 3) challenges with program implementation and feedback for improvement.

Methods

Participants. Thirteen focus groups were conducted with a total of 72 students at the three participating high schools: eight focus groups with 54 students at the charter school, three focus groups with 10 students at the traditional high school, and two focus groups with eight students at the continuation school. All three high schools are located in low-income neighborhoods in South Sacramento, with student bodies that are predominantly African American and Latino. More than 50% of students at each school are eligible based on family income for free or reduced price meals from the National School Lunch Program. All focus group participants were in either 11th or 12th grade. The focus groups were conducted in the spring of 2009 at the charter school and the spring of 2010 at the traditional and continuation schools.

Students returned signed parental consent forms, and completed a youth assent form. The written consent forms were reviewed aloud in detail, confidentiality was discussed, and questions were answered about how the information would be used. The study design was reviewed and approved by the University of California at Davis Institutional Review Board.

Data collection. Focus groups were semi-structured, pursuing questions in three areas: (1) the program’s impact on participants’ dietary choices, including any changes in fruit and vegetable consumption; (2) participants’ development of organizational, communication, and team-building skills; and (3) participants’ suggestions for improving program implementation. Each focus group included between two to 12 students and lasted 30 to 60 minutes. They were conducted by research team members, and took place on the campuses of the three participating schools. A research team member took notes during the focus groups at the charter school; at the traditional and continuation high schools focus groups were documented via notes and audio-recordings.

Data analysis. The authors developed a list of provisional themes and sub-themes related to the focus group questions. Audio-recordings from the traditional and continuation high schools were transcribed, and each transcript was then reviewed and coded for these themes by three members of the evaluation team. Notes taken at the charter school were also reviewed and coded by three members of the evaluation team. Using the method of thematic analysis described by Ryan and Bernard,30,31 initially the three coders created an a priori list of codes related to key themes of interest such as community engagement, business skills, fresh produce consumption, and challenges with program implementation. Additional codes were added as these emerged in the review of each transcript, discussed among the team, and the range of responses confirmed through an iterative process of multiple reviews of the transcripts. Differences in coding were discussed until consensus was achieved.

Results

Participant responses largely focused on the three areas of interest (dietary impact, professional development, and program challenges) but one additional major theme arose from the discussions: the financial incentive to take part in Fresh Producers. This theme emerged only at the continuation high school, where students were paid with gift cards for their work with the program, and was a key feature of the program reported
by all of the focus group participants at that school.

Promotion of a healthy diet. At both the charter school and traditional high school, the majority of students in the focus groups reported an increase in fruit and vegetable intake because of their involvement with Fresh Producers. They also reported developing knowledge about healthy foods and nutrition. Students specifically mentioned consuming more fruits and vegetables and less soda and junk food, as well as increasing their levels of exercise.

• My diet has improved. I know more about calories and vitamins now.
• I saw different types of produce. I didn’t realize that there is more than one type of cucumber.
• I eat fewer sweets now, and more produce.

At the continuation high school, however, only one of the eight students in the focus
groups reported eating more fruits and vegetables after participating in Fresh Producers.
The other students reported that their dietary and exercise habits had not changed.

• I don’t like fruit.
• I already eat fruits and vegetables.

Professional skill development. The majority of students at all three high schools felt that they had gained valuable work experience from Fresh Producers. Being part of a team and working in a sales environment were identified as important factors, and students reported improved interpersonal, time management, and team building skills. Students at each school felt that the experience they had gained would be valuable for
them in future jobs.

• It’s taught me social skills, like how to really get out there and speak and know how to come up to a person and present yourself.
• You get a good mindset from doing this. You need to know what you’re doing . . . you need to know how to talk to [customers].
• I have a better understanding of the business world now.

Several students also noted that the experience changed their attitudes and behaviors toward school, describing how these shifted over time.

• Fresh Producers made me more involved with school. It started off as a lot of work,
  but then it became fun. It will be good for my application and for a business major.
• I can’t act like I used to [in school], I had to grow and evolve.

However, this was not a broadly shared outcome.

Financial incentive. The financial incentive of working with Fresh Producers was not mentioned as a key motivator by students at the charter high school, where no proceeds were returned to the site, or the traditional high school, where the money from fruit and vegetable sales were spent on classroom activities. At the continuation high school, where students were paid for their work with gift cards, earning an income was a major motivation for all of the students in the focus groups.

• I wanted to make some quick money.
• [Fresh Producers] made me want to wake up and get $10.
• They said go to Fresh Producers, come here at 8 o’clock ’cause you’re getting some money.

Acceptability, feasibility, and implementation challenges. Overall, students were positive about program implementation. They found the program itself to be appropriate for a school setting and logistically viable. Students at all three schools reported that the system for taking orders and providing deliveries ran smoothly, with no significant breakdowns. Deliveries were on time and contained the correct number of orders, and customer feedback was generally positive about the content and delivery of the fresh produce parcels. The majority of students felt that the program was a good “real-world” supplement to their classroom learning, and that the faculty and staff involved with Fresh Producers emphasized the educational experience as well as the business components.

However, students also identified program challenges. At the charter high school, students reported that some of the participating teachers seemed unfamiliar with the goals of Fresh Producers and with the integrated curriculum that was intended to build associated academic skills and knowledge. The students felt that this led to a lack of faculty support for program activities at the school.

At the traditional high school, where Fresh Producers was a project for students involved in the school’s business academy, students struggled to find potential customers. They also felt that the produce could have been marketed in a more student-friendly manner.

• It was hard to get people to buy. Students aren’t interested.
• It was hard to establish connections, [students] weren’t listening.
• We should sell individual fruits so you can grab and go.
• There should be smaller bags of produce.

At the continuation high school, the main challenge identified by students was the lack of an organized system for their financial reimbursement. It was difficult for them to determine exactly how much money they had earned and what they were owed, and they said that they did not receive gift cards at regular intervals. Every student in the focus groups identified this as the major challenge with the program.

• The pay wasn’t right, though. Everybody was unhappy with the pay.
• [My teacher] tried to say it was a job that was going to help us get money but all we get is $10 every, what, two or three weeks?
• You want the money to be fair.

Despite the problems with their payments, all of the students at the continuation school emphasized that they would still be willing to work with the program in the future, with the hope that a smoother payment process could be devised.

Discussion

The Fresh Producers program was found to be both acceptable and feasible to students at three different high schools, with self-reported improvement in participants’ nutrition and professional skills. The program was designed to engage high school students both as a community health activity and as an entrepreneurial experience. Focus group discussions highlight that this dual interface did affect at least some participants’ dietary habits and professional development. However, student attitudes about the program, and what they felt they learned from the experience, varied greatly at the different program sites.

The varied experiences and opinions of students at each school highlight the program’s flexibility and its ability to engage students on different levels. The program can succeed as either a classroom or extracurricular activity, and in both a traditional high school as well as in schools with alternative curricula. In these diverse settings, the program was found to encourage the professional development of students, promote healthier
eating, and encourage students to think explicitly about the health of their community.

Fresh Producers had a positive effect on the nutrition of students at the charter high school, where it was part of a multidisciplinary health and nutrition curriculum, a result that has been seen with similar curricula that have included school gardens.22–27 At the traditional high school, where the program was a classroom activity for business academy students who received no associated health education, students also reported increased consumption of fruits and vegetables and decreasing junk food and soda intake. At the continuation high school, however, there was little or no change reported in the diet of students after their experience with Fresh Producers.

This difference in nutritional impact may be a result of several factors. Student involvement may have been self-selected based on the program design at each school, with financially motivated students choosing to work with the program at the continuation school where income was available, and more health-conscious students opting to participate at the other schools. The only site that supplied a nutrition curriculum to accompany Fresh Producers was the charter school. Teachers at the other two schools were free to emphasize the parts of the program that they felt were important. Despite the lack of structured health education, however, students at the traditional high school reported improved dietary habits, suggesting that a didactic component may not be necessary to improve nutrition.

Professional training was also an important part of the program, as the majority of students at all three schools reported gaining real-world job skills, including interpersonal and organizational skills and an improved ability to work as part of a team. Improvement in these types of job skills, as well as increased self-confidence and improved attitudes about school and future employment, have been seen in youth taking part in other entrepreneurial programs.32–34

The challenges reported at each site differed, reflecting differences in how the programs were organized at each school. At the charter school, faculty involvement in an ambitious integrated curriculum was variable and inconsistent. At the traditional high school, marketing fresh produce to other students was difficult to organize. At the continuation high school, the financial part of the program was the great motivator but also the greatest challenge, as students said they were not compensated adequately or in an organized manner.

Clearly, improved coordination of the program could overcome some of these challenges, possibly by providing more professional development and resources for participating teachers, and on-site support to teachers and students, perhaps in the form of a youth coordinator. Partnerships with local organizations such as 4-H clubs or community health agencies could provide additional expertise and resources. Strengthening program support could help students have a greater impact on their school and community, including working with school cafeterias and community partners to create youth-friendly campaigns that promote fruit and vegetable consumption.

Limitations of this study include the qualitative nature of the data collection. While the thematic analysis of the focus group discussions revealed a self-reported increase in fruit and vegetable consumption at two of the high schools involved, further research is needed to confirm this trend. Further study would involve pre- and post-participation surveys including 24-hour food recalls, and the measurement of the body mass index of participants to assess any impact the intervention has on obesity. Comparison with a control group would strengthen the findings.

Another limitation was the significant differences in the implementation of the program at each site. Complementary health education, incentives to participate, and levels of teacher involvement differed greatly between the schools, making it difficult to compare the impact of the program across the three sites. In addition, while this article assesses program acceptability and feasibility from the vantage point of participating students, further analysis should also attend to teacher and administrator experiences and examine the program’s impact on the general student body. Despite these limitations, the focus groups demonstrated that high school students in a variety of settings believed that the Fresh Producers program was acceptable and feasible in a low-income urban area with limited access to fresh fruits and vegetables. Our findings also suggest that operating a business selling fruits and vegetables can have an impact similar to other hands-on nutrition education, such as working in a school garden. Just as school gardens teach students how to grow and sustain plants, the students working with Fresh Producers learned how a business runs and how fruits and vegetables fit into their school environment. They saw how other foods competed with their own product, and learned how to market fresh produce to customers, especially their peers.

Motivating youth to think about their food system may also lead to innovative solutions for schools and communities in areas with low access to fresh produce. With an understanding of what appeals to their fellow students, family members, and local community, young entrepreneurs are a valuable resource for developing new strategies to increase fruit and vegetable consumption.

Nicholas Sikic is affiliated with the Pediatric Healthy Lifestyle Center, Department of Pediatrics, Santa Clara Valley Medical Center; Nancy Erbstein with the Department of Human and Community Development at the University of California, Davis (UC-D); Kearnan Welch with the Department of Pediatrics and the UC-D School of Medicine; and Ethan Grundberg with the Agricultural Sustainability Institute at UC-D. Elizabeth Miller is affiliated with the University of Pittsburgh School of Medicine and is Chief of Adolescent Medicine, Associate Professor in Pediatrics, Division of Adolescent Medicine at Children’s Hospital of Pittsburgh. Please address correspondence to Dr. Sikic at the Pediatric Healthy Lifestyle Center; Department of Pediatrics; Santa Clara Valley Medical Center, 751 South Bascom Ave; San Jose, CA 95128; (916) 801-0843; nicholas.sikic@hhs.sccgov.org.

Notes

1. Kimmons J, Gillespie C, Seymour J, et al. Fruit and vegetable intake among adolescents and adults in the United States: percentage meeting individualized recommendations. Medscape J Med. 2009;11(1):26. Epub 2009 Jan 26.

2. Lorson BA, Melgar-Quinonez HR, Taylor CA. Correlates of fruit and vegetable intakes in U.S. children. J Am Diet Assoc. 2009 Mar;109(3):474–8.

3. Sebastian RS, Wilkinson Enns C, Goldman JD. U.S. adolescents and MyPyramid: associations between fast-food consumption and lower likelihood of meeting recommendations. J Am Diet Assoc. 2009 Feb;109(2):226–35.

4. Hastert TA, Babey SH, Diamant AL, et al. More California teens consume soda and fast food each day than five servings of fruits and vegetables. Policy Brief UCLA Cent Health Policy Res. 2005 Sep;(PB2005-8):1–7.

5. Hastert TA, Babey SH, Diamant AL, et al. Low-income adolescents face more barriers to healthy weight. Policy Brief UCLA Cent Health Policy Res. 2008 Dec;(PB2008-4):1–8.

6. Morland K, Filomena S. Disparities in the availability of fruits and vegetables between racially segregated urban neighbourhoods. Public Health Nutr. 2007 Dec;10(12):1481–9. Epub 2007 Jun 21.

7. Hosler AS, Rajulu DT, Fredrick BL, et al. Assessing retail fruit and vegetable availability in urban and rural underserved communities. Prev Chronic Dis. 2008 Oct;5(4):A123. Epub 2008 Sep 15.

8. Powell LM, Slater S, Mirtcheva D, et al. Food store availability and neighborhood characteristics in the United States. Prev Med. 2007 Mar;44(3):189–95. Epub 2006 Sep 25.

9. Antova T, Pattenden S, Nikiforov B, et al. Nutrition and respiratory health in children in six Central and Eastern European countries. Thorax. 2003 Mar;58(3):231–6.

10. Chatzi L, Torrent M, Romieu I, et al. Diet, wheeze, and atopy in school children in Menorca, Spain. Pediatr Allergy Immunol. 2007 Sep;18(6):480–5.

11. Forastiere F, Pistelli R, Sestini P, et al. Consumption of fresh fruit rich in vitamin C and wheezing symptoms in children. SIDRIA Collaborative Group, Italy (Italian Studies on Respiratory Disorders in Children and the Environment). Thorax. 2000 Apr;55(4):283–8.

12. Holt EM, Steffen LM, Moran A, et al. Fruit and vegetable consumption and its relation to markers of inflammation and oxidative stress in adolescents. J Am Diet Assoc. 2009 Mar;109(3):414–21.

13. Joshipura KJ, Hu FB, Manson JE, et al. The effect of fruit and vegetable intake on risk for coronary heart disease. Ann Intern Med. 2001 Jun 19;134(12):1106–14.

14. Maynard M, Gunnell D, Emmett P, et al. Fruit, vegetables, and antioxidants in childhood and risk of adult cancer: the Boyd Orr cohort. J Epidemiol Community Health. 2003 Mar;57(3):218–25.

15. Joshipura KJ, Ascherio A, Manson JE, et al. Fruit and vegetable intake in relation to risk of ischemic stroke. JAMA. 1999 Oct 6;282(13):1233–9.

16. Roseman MG, Yeung WK, Nickelsen J. Examination of weight status and dietary behaviors of middle school students in Kentucky. J Am Diet Assoc. 2007 Jul;107(7):1139–45.

17. Lakkakula AP, Zanovec M, Silverman L, et al. Black children with high preferences for fruits and vegetables are at less risk of being at risk of overweight or overweight. J Am Diet Assoc. 2008 Jul;108(11):1912–5.

18. Baric IC, Cvjetic S, Satalic Z. Dietary intakes among Croatian schoolchildren and adolescents. Nutr Health. 2001;15(2):127–38.

19. French SA, Wechsler H. School-based research and initiatives: fruit and vegetable environment, policy, and pricing workshop. Prev Med. 2004 Sep;39 Suppl 2:S101–7.

20. Jaime PC, Lock K. Do school based food and nutrition policies improve diet and reduce obesity? Prev Med. 2009 Jan;48(1):45–53. Epub 2008 Nov 5.

21. Knai C, Pomerleau J, Lock K, et al. Getting children to eat more fruit and vegetables: a systematic review. Prev Med. 2006 Feb;42(2):85–95. Epub 2005 Dec 20.

22. Robinson-O’Brien R, Story M, Heim S. Impact of garden-based youth nutrition intervention programs: a review. J Am Diet Assoc. 2009 Feb;109(2):273–80.

23. Heim S, Stang J, Ireland M. A garden pilot project enhances fruit and vegetable consumption among children. J Am Diet Assoc. 2009 Jul;109(7):1220–6.

24. McAleese JD, Rankin LL. Garden-based nutrition education affects fruit and vegetable consumption in sixth-grade adolescents. J Am Diet Assoc. 2007 Apr;107(4):662–5.

25. Morgan PJ, Warren JM, Lubans DR, et al. The impact of nutrition education with and without a school garden on knowledge, vegetable intake and preferences and quality of school life among primary-school students. Public Health Nutr. 2010 Nov; 13(11):1931–40. Epub 2010 May 5.

26. Morris JL, Koumjian KL, Briggs M, et al. Nutrition to grow on: a garden-enhanced nutrition education curriculum for upper-elementary schoolchildren. J Nutr Educ Behav. 2002 May–Jun;34(3):175–6.

27. Parmer SM, Salisbury-Glennon J, Shannon D, et al. School gardens: an experiential learning approach for a nutrition education program to increase fruit and vegetable knowledge, preference, and consumption among second-grade students. J Nutr Educ Behav. 2009 May–Jun;41(3):212–7.

28. Quinn LJ, Horacek TM, Castle J. The impact of Cookshoptm on the dietary habits and attitudes of fifth graders. Topics in Clin Nutr. 2003 Feb–Mar;18(1):42–8.

29. Defanti D, Kohaya T, Tadlock T, et al. Food desert/food imbalance study. Sacramento, CA: County of Sacramento, Planning and Community Development Department, 2010.

30. Patton MQ. Qualitative evaluation and research methods (2nd ed.). Newbury Park, CA: Sage Publications, 1990.

31. Ryan GW, Bernard HR. Data management and analysis methods. In: Denzin NK, Lincoln YS, eds. Handbook of qualitative research (2nd ed.). Thousand Oaks, CA: Sage Publications, 2000.

32. Bronte-Tinkew J, Redd A. Logic models and outcomes for youth entrepreneurship programs. Washington, DC: DC Children and Youth Investment Trust Corporation, 2001.

33. DeBerg CL, Thornton K. Entrepreneurship education for at-risk youth: a successful model for university/business partnerships. In: DeBerg CL, Thornton K, eds. Annual Conference of the United States Association for Small Business and Entrepreneurship. San Diego, CA: George Mason University, 1999.

34. Hanham AC, Loveridge S, Richardson B. A national school-based entrepreneurship program offers promise. J Comm Dev Soc. 1999;30(2):115–30.

23.4, November 2012

Gayenell S. Magwood, PhD, RN
Jeannette O. Andrews, PhD, APRN, BC, FAAN
Jane Zapka, ScD
Melissa J. Cox, MPH
Susan Newman, PhD, RN, CRRN
Gail W. Stuart, PhD, APRN, BC, FAAN

Scholars committed to advancing knowledge development and improving health care
outcomes through practice and research must work in the complex social, political,
and cultural context of health care delivery. Evolving priorities include a renewed
emphasis on prevention and management of chronic illness; elimination of health
disparities; a call for reciprocal translational research models from the bench to the
bedside and community settings and vice versa; and development of novel approaches
for knowledge dissemination. The development and institutionalization of best practices
for community based participatory research (CBPR) are essential. Research agendas
and innovations promoted by both the National Institutes of Health (NIH) Roadmap
initiative1 and NIH commitment to Clinical and Translational Science Award (CTSA)2,3
development increasingly require transdisciplinary and community alliances. Actualizing
and sustaining authentic partnerships pose challenges at multiple levels (i.e.,
individuals, partnership dyads, organizations, and policy).
Previous reports have introduced models and lessons learned.4–7 Building on this work, this Report from the Field describes the evolution of a Center for Community
Health Partnerships (CCHP) at the Medical University of South Carolina (MUSC). The report traces developments and evolving structures and processes in this Center’s journey to institutionalize commitment to community-academic partnerships aimed at ultimately improving the health of our citizens and reducing disparities.

Evolution of the Center. Individual innovators at MUSC and local community organizations began working together decades ago to improve the health of community constituencies. Early on in this process, College of Nursing faculty and individuals from community health centers, schools, faith-based organizations, and other community organizations began collaborating to address health needs in underserved populations.
South Carolina (SC), like other Southeastern U.S. states, has alarming rates of diabetes, obesity, hypertension, cardiovascular disease, infant mortality, and uninsurance.8,9 Notably, racial and ethnic minorities in SC are disproportionately affected by these conditions and experience worse health outcomes and differences in health care quality.8,10

Typically, these early collaborations evolved from individuals in the academic and community settings who had a prior history of working together, perceived each other as credible partners, and shared mutual interests. Community members contributed expertise on the contexts of the health issues and potential real-world solutions, while academic members contributed expertise on evidence-based approaches and evaluation strategies. Over time, these community-academic partnerships became more formalized with memorandum of understandings (MOUs) as funding was received for targeted projects. Many of these initiatives formed formal and informal advisory boards and/or coalitions.11 Key characteristics of some of these initiatives are summarized in Box 1.

Strategic Plan to Build Skill and a Critical Mass
Over time, a critical mass of researchers, clinicians, and community partners, who were dedicated to innovative community-academic partnership models, formed and the need for a concerted systems level approach to partnerships became evident. As part of an explicit Strategic plan, the College of Nursing’s Dean and faculty began work to establish a formal center, the Center for Community Health Partnerships. Infrastructure development included expert consultation with other NIH funded centers; ongoing dialogue with community partners; and a series of capacity-building workshops on research methods and grant-writing. The College of Nursing committed to the hiring of an experienced health services research methodologist and a biostatistician.

A core group met weekly to adapt a research framework building on the lessons learned via early community collaborations. Consensus was reached to integrate common theoretical underpinnings and evidence-based practice.12–15 An ecologic perspective was adopted to guide the CCHP16–18 (see Figure 1) The Center for Community Health Partnership Model (see Figure 1) integrates and adapts the Chronic Care Model19,20 within a social ecological perspective. This perspective reflects the understanding that activity must function within and between many systems levels. CCHP activity reflects interaction of institutional environment (i.e., structures, policies, personnel), along with the community resource systems are vital for the impact on policy and, ultimately, sustainable health outcomes. Details of the CCHP Model are described elsewhere.15,21

Promoting Infrastructure and Institutionalization
During the evolution of an organized CCHP, the core group generated mission and vision statements guided by its model. The College of Nursing provided financial resources for hiring a director, program coordinator, and administrative assistant. In view of the University’s interest in community partnerships and the rich tradition of community-engaged activity in the College of Nursing, the CCHP received formal recognition as a University Center in 2008. Subsequent CTSA funding in 2009 further enhanced the University’s position for broad impact by engaging in activities with the community addressing local and state health issues. The CTSA’s are exemplars of a growing commitment by the NIH to support community engagement’s role in translational research. The collaborative benefits facilitate the development of transdisciplinary and community research teams with unique and complimentary perspectives that create, implement, and translate effective culturally sensitive primary and secondary prevention and treatment interventions in community settings.

Figure 2 presents the fundamental structure, processes and outcomes of the CCHP. As noted, the goal is to activate a community of informed learners committed to the transformation and improvement of health outcomes for disparate communities. The processes targeted to achieve these goals include: 1) strengthening the capacity for existing and potential academic-community partnerships by developing systems-level communication exchange and dissemination mechanisms, identification and sharing of resources, and CBPR training and education; 2) facilitating partnerships that ask the appropriate questions and reach the appropriate people by coordinating and linking partners and resources, and providing partnership training, education, and support; 3) stimulating the discovery, translation, and dissemination of research in community settings with formal mentoring, training programs, interest group formation, and technical assistance; and 4) establishing mechanisms that sustain the progress of community-based initiatives with the institutionalization of processes and products.

To address the goals and enable the processes needed, the CCHP established a structure to organize activity. The structure of the CCHP consists of two advisory boards and five cores (administrative, partnership, mentoring and consultation, research and evaluation, and dissemination). Boxes 2 and 3 describe key roles and responsibilities of the two advisory boards and five cores. Additional information about the CCHP organizational structure and operations can be found at https://sctr.musc.edu/index.php/ce-about-us/organization.

Implications and challenges in moving the CCHP forward. In moving forward and sustaining momentum, the CCHP faced several challenges. A major hurdle involves the implementation of institutional policies that are reflective of community equity in our projects, such as Institutional Review Board (IRB) approval processes for community co-investigators, processes for obtaining community consent prior to implementation of community projects, and equitable funding allocation to our community partners.

As a recipient of the inaugural NIH Partners in Health Award in 2008, the CCHP established internal supportive resources and processes to provide technical support to community partners who receive (or plan to receive) federal funding directly (i.e., build skills for obtaining federal-wide assurances, and negotiating facilities and administrative costs). This seemingly bureaucratic, but critical process required patient discussion and creative strategies between partners.

Other challenges include the facilitation of transdisciplinary buy-in and support, especially in a health sciences university where the majority of research is conducted at the bench or the clinical setting, as compared with the community. Due to tenure and promotion requirements and timelines, some academic colleagues, although interested in CBPR approaches, are concerned about the time commitment required to build community relationships and to develop processes to promote equity in research. Another challenge is to maintain the balance of community interests and needs with feasibility of CCHP resources and faculty time. Community members frequently request assistance from faculty with program evaluation, grant writing, and technical support for existing projects. Faculty often serve multiple roles in research, practice, and education, and lack time to “add on” new unfunded projects that may or may not enhance their academic trajectory and require considerable personal time. Currently, we do have several faculty who support these community requests as volunteer work during evening and weekend hours, yet the capacity to continue and/or expand this
support is limited.

Although we have demonstrated improvement in linking partners together for better coordination and leveraging of their resources, we remain challenged with collecting and maintaining inventories of academic-community partnerships across the campus and community. A goal is to have a systematic database that is generated either by new IRB applications or grant funding that identifies academic-community partnerships.
Community partners work together to collect a community inventory, yet multiple complexities of ownership and scarce resources among local community organizations are all ongoing challenges.

An exciting opportunity to meet several of these challenges is to further refine the integration of the CCHP objectives to align with the CTSA award funded in 2009. The CTSA Community Engagement Core provides resources that will assist with CCHP activities, yet further expands our objectives and expectations. We have obtained initial success, as the CTSA funded our Community Engaged Scholars (CES) Program. The CES aligns objectives of the CCHP and CTSA Community Engagement Core with the mutual goal to increase the capacity of academic-community partnerships to conduct research. The CES is a 12-month fellowship for academic-community teams, with each team having at least one community partner and one academic partner. During the application process, the team develops a MOU and brief proposal describing a priority health issue and their proposed approach to address the issue. Members of the community advisory board (CAB) and scientific advisory board (SAB) review the applications. Six teams were selected during the first year, including inter-professional academic faculty from five of our six colleges and members from nine community organizations. The teams are provided didactic training on CBPR (monthly), mentorship (academic and/or community), and pilot funds for a formative CBPR project. The planned competencies of the teams at the end of one year are to: 1) understand the concepts and components of CBPR; 2) apply CBPR principles in the conduct of research; 3) incorporate CBPR principles and approaches in grant proposals; 4) communicate with audiences in both academic and community settings about CBPR principles and components; and 5) implement a pilot CBPR initiative. A task committee that includes academic and community members as faculty and mentors has developed the CES program. Institutional support has been received with this new innovative program as evidenced by CTSA funding in year one, and additional intramural funds for the next four years.

The immediate challenge now is sustaining and growing the CCHP during a period of significant dwindling resources, tight federal research funding, and increasing pressures on community organizations and leaders. It is clear that the CCHP must continue to be a critical element of the university’s strategic plan into the next decade. Faculty have been recruited and hired based upon their interest and expertise in community health. Student projects and programs are focused on supporting one of the many community-based programs. Clinical practice activities have developed models of care that reflect the work of the CCHP. Transdisciplinary collaborations that build on existing community-based research and have the potential to increase the overall health impact will be explored and sought. Just as academic health centers support the fundamental resources needed for basic and clinical research (e.g. labs, space, supportive structures such as Clinical Trials Office), infrastructure support from the larger university is also needed for community partnerships.

In summary, the Center for Community Health Partnerships has been a proactive initiative from the College of Nursing to address new paradigms and priorities in health care. It has served to focus faculty research and practice initiatives and to provide institutional direction for strategic planning with the goal of impacting the health of our region. As a university designated Center, the CCHP reflects a transdisciplinary approach to research that is consistent with national directions. We will continue to evolve, creating unique opportunities to expand upon existing community alliances and capacity. The potential is high given a key component of MUSC’s strategic plan, that is, to “promote community-campus partnerships in the public and private sector to reduce health disparities through education, research, and practice.”

Acknowledgments
The authors express their sincere gratitude for the collaboration and contributions of the following people. For contributions to this manuscript we acknowledge the assistance of Janet A. Grossman, PhD, APRN, BC, FAAN. We recognize the groundbreaking work of our community partners, Carolyn M. Jenkins, DrPH, APRN-BC-ADM, FAAN, Marilyn Laken, PhD, RN, Deborah Williamson, DHA, CNM, Brenda Nickerson, MSN, RN, faculty, and advocates in building community partnerships. It is impossible to name all of our community partners, however we do want to acknowledge our sustained leaders including Mrs. Florene Linnen (Georgetown Core Group); Mrs. Virginia Thomas (Charleston Diabetes Coalition); Mrs. Stacey Crawford Jarriel, Alisha Simmons, Juanita Brunson, Tammy McCottry Brown, Christina Hurman (Sister to Sister Project), and Gwen Gillenwater (disAbility Resource Center). Gratitude and admiration are extended to all of our community partners for their efforts. This project was supported by the South Carolina Clinical and Translational Research Institute, Medical University of South Carolina’s CTSA, NIH/NCRR Grant Number UL1RR029882. The contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH or NCRR.

Gayenell Magwood is Associate Professor in the College of Nursing at the Medical University of South Carolina (MUSC). Jeannette Andrews is Professor and Director of the Center for Community Health Partnerships at MUSC. Jane Zapka is Research Professor in the College of Medicine and College of Nursing at MUSC. Melissa Cox is Program Director, Center for Community Health Partnerships. Susan Newman is Assistant Professor in the College of Nursing at MUSC. Gail Stuart is Distinguished Professor and Dean in the College of Nursing at MUSC. Please address correspondence to Gayenell S. Magwood, PhD, RN, Associate Professor; 99 Jonathan Lucas, MSC 160; Charleston, SC 29425-1600; (843) 792-0685; magwoodg@musc.edu.

Notes
1. National Institutes of Health. About the NIH roadmap. Bethesda, MD: National Institutes of Health, 2001. Available at: http://commonfund.nih.gov/aboutroadmap.aspx.

2. Zerhouni EA. Translational and clinical science—time for a new vision. N Engl J Med. 2005 Oct;353(15):1621–3.

3. National Center for Advancing Translation Sciences (NCATS). About the CTSA program. Bethesda, MD: National Institutes of Health, 2011. Available at: http://www.ncats.nih.gov/research/cts/ctsa/about/about.html.

4. National Institutes of Health, CTSA Community Engagement Key Function Committee Task Force. Principles of community engagement (2nd ed.). Bethesda, MD: National Institutes of Health, 2011.
5. Boutin-Foster C, Phillips E, Palermo AG, et al. The role of community-academic partnerships: implications for medical education, research, and patient care. Prog Community Health Partnersh. 2008 Spring;2(1):55–60.

6. May M, Law J. CBPR as community health intervention: institutionalizing CBPR within community based organizations. Prog Community Health Partnersh. 2008 Summer;2(2):145–55.

7. Andrews JO, Newman, SD, Meadows O, et al. Partnership readiness for communitybased participatory research. Health Educ Res. 2012 Aug;27(4):555–71. Epub 2010 Sep 13.
8. United Health Foundation. America’s health rankings: a call to action for individuals and their communities (2011 ed.). Minnetonka, MN: United Health Foundation, 2011.

9. Agency for Health Care Research and Quality. Highlights from the national health quality and disparities report. Rockville, MD: Agency for Healthcare Research and Quality, 2010. Available at: http://www.ahrq.gov/qual/qrdr10.htm.

10. Smedley DB, Stith AY, Nelson AR, eds. Unequal treatment: confronting racial and ethnic disparities in health care. Washington, DC: The National Academies Press, 2003.

11. Newman SD, Andrews JO, Magwood GS, et al. Community advisory boards in community-based participatory research: a synthesis of best processes. Prev Chronic Dis. 2011 May;8(3):A70.

12. Israel BA, Shulz AJ, Parker EA, et al. Critical issues in developing and following community-based participatory principles. In: Minkler M, Wallerstein N, eds. Community-Based Participatory Research (CBPR) for health. San Francisco, CA: Jossey-Bass, 2003; 56–73.

13. Israel BA, Schulz AJ, Parker EA, et al. Review of community-based research: assessing partnership approaches to improve public health. Annu Rev Public Health. 1998;19:173–202.

14. Israel B, Eng E, Schulz A, et al., eds. Methods in community-based participatory research for health. San Francisco, CA: Jossey-Bass, 2005 Jul; 1–392.

15. Jenkins C, Pope C, Magwood G, et al. Expanding the chronic care framework to improve diabetes management: the REACH case study. Prog Community Health Partnersh. 2010 Spring;4(1):65–79.

16. Bronfenbrenner U. Toward an experimental ecology of human development. Ithaca, NY: Am Psychol. 1977;32:513–30. [Ed: no city needed if this is a journal]

17. Anderson ET, McFarlane J. Community as partner: theory and practice in nursing (4th ed.). Philadelphia, PA: Lippincott, 2006; 169–221.

18. McLaren L, Hawe P. Ecological perspectives in health research. J Epidemiol Community Health. 2005 Jan;59(1):6–14.

19. Wagner EH. Chronic disease management: what will it take to improve care for chronic illness? Eff Clin Pract. 1998 Aug–Sep;1(1):2–4.

20. Barr VJ, Robinson S, Marin-Link B, et al. The expanded Chronic Care Model: an integration of concepts and strategies from population health promotion and the Chronic Care Model. Hosp Q. 2003;7(1):73–82.

21. The Medical University of South Carolina College of Nursing. Center for Community Health Partnerships model. Charleston, SC: Medical University of South Carolina, 2010. Available at: http://www.musc.edu/nursing/departments/researchoffice/cchpartnerships.htm.

For access to the articles listed below, click here.

As we move closer to the U.S. presidential election of 2012, and work in light of the June U.S. Supreme Court decision regarding the Patient Protection and Affordable Care Act (ACA) of 2010, an army of dedicated scholars and clinicians continues its assault on inequities in health, the massive and complex set of imbalances that make this journal necessary and important. In this issue, we present work on four themes (although they are organized only in terms of article type rather than separated into separate sections as in previous issues):

• Theme 1—Cross-Cultural and International Care
• Theme 2—The Safety Net
• Theme 3—Epidemiology, Measurement, and Other Public Health Topics
• Theme 4—Health Policy

THEME 1—CROSS-CULTURAL AND INTERNATIONAL CARE

Six articles fit into the area of cross-cultural and international care. A Report from the Field by Weissman and colleagues describes the two-sided success of free, student-run health initiatives, which provide much needed medical care and teach students about cross-cultural caregiving and systems-based medical practice. In a Commentary, Fernando DeMaio argues for three research priorities on concerning immigrants to Canada: incorporating context into data collected, conducting comparative international studies, and refining the constructions of race and ethnicity to reflect recent developments in social theory.

In another Commentary, Perry Payne calls the question of whether the U.S. Federal government is correct in saying there are mandates to follow National Standards for Culturally and Linguistically Appropriate Services, given that they are often not followed and have even been referred to as voluntary by government officials. Having sketched out the actual state of affairs, Payne goes on to explain why such standards are indeed important, specifically for populations with limited English proficiency. In a related Brief Communication, Shippee and colleagues analyze data from nearly 2,500 Hispanic/Latino, Hmong, and Somali enrollees of public health insurance programs in Minnesota. Unsurprisingly, the Hmong and Somali people experienced much more unmet need for interpreter services than the Latinos.

Chisholm-Staker and colleagues take on the grave issue of human trafficking (or, slavery) in their report on training of emergency room (ER) providers about how to look for and probe patients who may be living as slaves. The ER, the authors convincingly argue, may be one of the few places where care providers and victims of human trafficking come face to face, and thus offers an important opportunity for righting a fundamental wrong.

Njuguna and colleagues from Kenya assessed malaria curative services in Ijara District (in the Northeast of Kenya). They report that all the facilities they studied had the recommended drugs, but only 90% had injectable quinine, and that all facilities lacked rapid diagnostic tests and several other critical components of the malaria-fighting toolkit.

THEME 2—THE SAFETY NET

The core of this issue consists of papers about the so-called safety net, the generally uncoordinated panoply of caregivers, sites of care, and sources of financing to which people who lack private medical resources turn for preventive, maintenance, illness-induced, and catastrophic care. While this journal could hardly function without the term safety net, readers should always read it skeptically: saying there is a safety net counter-factually implies that the net (1) catches everyone who falls ill, and (2) prevents those it does catch from suffering due to their lack of other resources. It does neither. Having said that, however, the safety net does a lot of good in the U.S., and readers of this issue will learn much about its strengths and some of its weaknesses.

Masi’s Commentary on Promise Neighborhoods directs our attention to a the U.S. Department of Education’s program to provide coordinated services for children living in poverty, modeling the program after Geoffrey Canada’s ambitious Harlem Children’s Zone. Masi presents the case based on rapidly accumulating evidence that children experience health benefits when immersed in highly nurturing educational environments. Anderson and Olayiwola’s Commentary concerns patient-centered medical homes and the pressing need to strengthen the federal community health center program (as the ACA proposes to do).

Coordination of primary care and specialty care services is one of the next big challenges that safety-net providers are facing, and in their Brief Communication Lauren Block and colleagues report on The Access Partnership’s record of success in getting uninsured patients to the specialists they needed to see.

A group of empirical papers concern community health centers (CHCs). Ramirez-Zohfeld and colleagues explore the potential—given the limited resources of the mechanisms of CHCs—for outreach to diabetes patients who have fallen out of care; Richard Roetzheim and colleagues study the timeliness of diagnostic evaluation of abnormal cancer screening text results at CHCs; Fernandes and colleagues assess the efficacy of community health workers at a Hawaiian CHC in educating heart patients about their condition so that their clinical outcomes improve over time.

Lopez-Class and colleagues study colorectal cancer screening at seven CHCs and other safety-net clinics in the Washington, D.C. area, providing a transition to the articles in safety-net settings other than CHCs (as some of the clinics in the Lopez-Class study are free clinics). Nuss et al. look at the improvement of colon cancer treatment at safety-net hospitals in terms of a social-ecological model; Sheu et al. report on how student-run free clinics affect the health professions students who work in them; Hwang and colleagues report that the use of free clinics decreases the likelihood that an individual will use and emergency room for primary care, and conclude that this provides strong support for expanding the primary care workforce as the Medicaid population expands.

Further outside the circle of prototypical safety-net settings are Head Start classrooms, churches, and homes, but constructive public health endeavors spring up in those locations, too. Kranz and colleagues compare oral health practices at two types of Head Start programs, providing useful information for others who might want to intervene in the growing problem of early pediatric oral health disorder. Pichon and colleagues study the outcomes of African American faith leaders’ training to deliver a faith-based HIV prevention curriculum to young people and adults in their congregations. Okamoto et al. report on the drug-resistance strategies endorsed by Hawaiian community leaders for rural Hawaiian youth.

THEME 3—EPIDEMIOLOGY, MEASUREMENT, AND OTHER PUBLIC HEALTH TOPICS

Another major theme of this issue are the core public health topics of epidemiology and measurement, as well as four qualitative explorations of the experiences of particular populations in the health system. Juarez and colleagues quantitatively assess the prevalence and heart disease and its risk factors in Hawaii, comparing Asians, Pacific Islanders, and Whites: Native Hawaiians and Filipinos had the highest incidences of hypertension and diabetes; Asians had the highest rates of hyperlipidemia; although Whites had fewer known risk factors than the others, they faced the same overall risk of heart disease. The prevalence curves for the different groups begin to diverge at about age 30. Hanson and Olson report on a longitudinal study over 13 states of food insecurity in low-income rural families with children. Both enduring chronic health conditions and risk for depression predicted lasting food insecurity, while education beyond high school was protective against it. Ziller and colleagues also focus on rural populations, examining the perennial issues of health care access and use, in their case among uninsured residents of rural areas in comparison with uninsured urban dwellers: the rural uninsured are more likely to have a usual source of care and to use it than are their urban counterparts. Wen and colleagues investigated what predicts smoking cessation among low-income pregnant women, finding that factors such as more cigarettes smoked per day, lower education, higher self-efficacy for quitting on one’s own, and more children at home each correlated with a failure to complete cessation programs at different stages of the pregnancy.

Spiers and colleagues remind readers of the persistence of low health literacy as a barrier to optimal health by reporting on a study of over 154 adults eligible for the federal Supplemental Nutrition Assistance Program (the core of which is Food Stamps): health literacy was very low in the population sampled, and the lower it was the more likely the respondent was to eat unhealthy food (such as fried chicken) and not to eat healthy food (such as the peels of fruit). Bauer and colleagues retrospectively examine several years worth of data from a San Francisco medical respite program for homeless patients discharged from the hospital. Over two-thirds of patients stayed the recommended length of time, but those who left early were more likely to be re-admitted within 90 days. Frech and colleagues at the University of Utah studied over 8,000 American Indians and Alaska Natives (AIAN) for risk of osteoporosis and the prevalence of fracture; they report a high prevalence of multiple risk factors for osteoporosis in AIAN and call for attention to the problem. Edwards and colleagues, also at the University of Utah, provide a report on their study of construct validity of the Short-Form 12 (SF-12) Health Survey Instrument and the Mental Component Summary of the SF-12 (MCS-12) in a cohort of AIAN people, which produced positive results.

The four qualitative papers that fit into the core public health theme are Siegel and colleagues’ study of types of dental fear among African American adults in Harlem; Sansgiry et al.’s study of over-the-counter drug purchases by blind consumers; Zaller et al.’s study of the interactions between purchasers and pharmacists in the course of the sale of clean syringes; and Cassady et al.’s study of the H1N1 epidemic among Latino populations whom they deem hard to reach.

THEME 4—HEALTH POLICY

Finally, two papers and in this issue bear directly on financing of health care and thus fall squarely into the health policy section. Fonk and colleagues look at the effect of advance directives on end-of-life costs, perhaps surprisingly finding no relationship once patient health is controlled for. Prather and colleagues at the CDC investigated using microenterprise as a means of simultaneously addressing poverty and HIV by conducting focus groups with young African American women and community leaders in two southern states, and report on their interesting findings here. Finally, the ACU Column in this issue directs all of our attention to the next kind of application that may be on all of our phones: a mobile health app!

CONCLUDING NOTE

In the coming months, readers will have the opportunity to order two new books that we hope will prove valuable: Will Anybody Help? Is a collection of articles concerning the free clinics and student-run clinics in a wide array of localities across the United States, with a preface by Dr. Charles Mouton, Dean of the Meharry Medical College School of Medicine (Virginia M. Brennan, ed.). Obesity Interventions among Underserved U.S. Populations: Evidence and Directions is a collection of peer-reviewed new work sponsored by the Aetna Foundation and edited by Drs. Virginia Brennan, Shiriki Kumanyika, and Ruth Zambrana. Both will be available soon from Johns Hopkins University Press and we hope you will seek them out.

Virginia M. Brennan, PhD, MA
Associate Professor, Meharry Medical College
Editor, JHCPU