NC1171: Interactions of individual, family, community, and policy contexts on the mental and physical health of diverse rural low-income families
(Multistate Research Project)
NC1171: Interactions of individual, family, community, and policy contexts on the mental and physical health of diverse rural low-income families
Duration: 10/01/2014 to 09/30/2019
Statement of Issues and Justification
Rural communities and families continue to experience health inequalities when compared to their urban and suburban counterparts. In fact, several measures (deaths related to cancer and hospital admission with pneumonia, as well as rates of obesity) indicate an increase in rural health disparities despite efforts to improve health care quality, access, and affordability among rural areas (AHRQ, 2012). Further, emerging research indicates that rural female mortality rates continue to rise despite an increase in medical care expenditures and public health efforts (Kindig & Cheng, 2013). In order to fully understand and effectively address rural health disparities, research and interventions are needed that examine how various system components (i.e., individual, family, community) and policy interrelate and influence the health and well-being among rural communities and families. This is especially critical given the continued changing socio-demographics of rural America and recent federal health care legislation (i.e., Affordable Care Act). Yet, there is a dearth of data on rural family health, especially data related to the health of diverse rural low-income families and the community and policy contexts they experience that shape their health and well-being.
Residents of rural areas have unique issues and needs, including distinct health concerns. These health concerns are associated with multiple factors, including growing concentrated poverty in rural America, as well as an increase of racial/minority families in rural America who are at greater risk for health disparities. The poverty rate in rural America was 16.5% in 2006-2010 compared to 14.8% in 2000. Hispanics and African Americans are the largest minority groups in rural America (9.3%, 8.2% respectively). Additionally, more than half of the rural population growth over the last decade was due to increase in the Hispanic population (Housing Assistance Council, 2012). While concentrated poverty has historically been associated with the rural South, today it has spread to rural West and Midwest communities, partially due to the recent economic recession and slow recovery. Furthermore, the prevalence of high-poverty counties and the proportion of the poverty population residing in high-poverty counties have been higher in rural areas than in urban areas. In 2006-10, 26.2 % of rural counties were high poverty, and 36.1 % of the rural poverty population resided in high-poverty counties. Among urban counties, 10.3 % were high poverty in 2006-10, and 14.5% of the urban poverty population resided in those counties (Farrigan & Parker, 2012). Additionally, while overall in the U.S., unemployment rates in rural areas are similar to rates in urban areas, employment growth rates over longer periods are higher in urban areas than in rural areas (Kusmin & Hertz, 2010).
Lower household incomes and higher poverty rates in rural communities compared to urban communities are associated with greater levels of adult (McLaren, 2007) and child obesity (Grow, et al., 2010; Eagle, et al., 2012). Additionally, death rates for children and young adults are higher in rural counties, and rural residents have a higher incidence of certain types of heart disease, have more activity limitations due to chronic health problems, and a higher suicide rate. Lack of health care providers, especially dentists and mental health providers, and lower incidence of health insurance coverage, additionally contribute to poorer health outcomes for rural residents (Eberhardt, Ingram, & Makuc, et al., 2001; HHS Rural Task Force, 2002).
Studies reveal that the health and well-being of families living in high-poverty areas is undermined by a lack of access to mainstream social and economic opportunities. For example, children who grow up in high-poverty neighborhoods are less likely to succeed academically, complete high school, or attend college than those who grow up elsewhere. Additionally, rural racial/ethnic minorities commonly reside more often in high poverty communities than their urban counterparts; thus they are more likely to experience many of the problems and limitations associated with urban poverty, even if they are not poor themselves. Among those who are poor, they have fewer options to live elsewhere. Rural immigrants who have less than a high school education or who are female heads of families with children are at high risk. Their urban counterparts are less likely to be at risk as they are less likely to reside in high-poverty counties (Farrigan & Parker, 2012).
To improve health, especially among low-income populations, multiple factors, including social factors (e.g., inadequate housing, unemployment or under employment, low income, low education, inadequate access to healthy food and transportation) need to be addressed. Studies have revealed that social factors play a greater role in determining peoples life expectancy than health care, and have been linked to more frequent emergency room visits, hospitalizations and overall poorer health, all of which place strain on the U.S. health care system (Goldstein & Holmes, 2011).
The ecological systems perspective (Bronfenbrenner, 1979) provides theoretical framework for this project. The ecological framework organizes the contexts within which families function into a discrete series of nested systems encompassing societal norms and values, institutional structures, interactions between families and other systems, and the family system itself. Collectively referred to as the ecosystem, these systems are interdependent; they exhibit mutual influence. The influence of individual systems may also change over time. For instance, the cultural context (i.e. macrosystem) in which individuals live evolves over time as cultural norms shift and evolve. Likewise, the patterning of environmental events and transitions over the life course (i.e. chronosystem) impacts the developmental course of individuals and systems. Most research on families focuses on only one system level without consideration of interactions among the levels or changes over time, but research generated from NC1171 and NC223/NC1011 has demonstrated that multiple contexts influence individual and family well-being. This project is in a unique position to enable examination across all of the system levels and over time to determine impacts on the physical and mental health outcomes of individuals living in rural low-income families.
Most rural data are collected from communities whose populations are 50,000 or more residents. This proposed project will contribute findings and dissemination products that will advance the understanding of multiple factors that influence the health of low-income families living in small rural communities (UIC of 5 or higher), and contribute to the development of opportunities to address long-standing health challenges in rural communities.
Quantitative and qualitative data related to child and family health in the NC1171 data set are very rich. Quantitative data (e.g., BMI, maternal depression, household food security, child internal and external behaviors, financial stress) collected during wave 1 (N=444) will be complemented with qualitative data (e.g., changes in health, barriers and enablers to health) collected during wave 2 (N=80) to examine individual, family, and community level factors and policies that influence rural family health. The qualitative data captures families' perspectives and contexts and will assist in developing a more complete systemic picture of the interactions and influences on health outcomes over time. An emergent approach to the qualitative data will be used allowing previously unidentified variables of importance to emerge in order to capture potentially new and critical constructs of interest. Additionally, a third wave of quantitative data collected during 2013-2014 will provide opportunity to compare baseline (wave 1) data to data collected over time (wave 2, wave 3) to identify trends and relationships among factors influencing individual and family health.
Furthermore, the 2010 U.S. Census and American Community Survey data will be matched with the NC1171 family level data in order to compare demographics of study families with other families in the study communities. Additionally, participants addresses will be matched with data provided in the USDA food environment atlas (http://www.ers.usda.gov/data-products/food-environment- atlas.aspx#.UhP9bj-Dkyk) in order to identify and analyze relationships, patterns, and trends between factors such as where participants live and the local food environment. Additionally qualitative data collected during wave 2 will provide additional insight into why participants do or do not access food and health resources in the community.
This multi-state research team has been in existence since 1998 (NC223, NC1011, NC1171) and has a history of using their complementary strengths to develop research questions, design, and conduct quantitative and qualitative analyses to produce holistic studies. In addition to members who serve as senior faculty and Extension specialists at their institutions, several team members began their work with the project as graduate students, and now are junior faculty members at different institutions. The team has spanned distance and time by using technologies to communicate and share data files via a SharePoint site, a Web site, conference calls, annual on-site meetings, and Adobe Connect. The projects governance document is annually updated, and a tracking tool is put in place via SharePoint to facilitate collaboration among team members in regard to developing presentations, policy briefs, manuscripts and other products. Team members are positioned to build upon the existing infrastructure and begin a new multi-state project.
Advantages of working as a multi-state effort
This project extends and builds upon a longstanding multi-state, multidisciplinary effort consisting of family scientists, family economists, nutritional scientists, social workers, extension specialists, psychologists, and sociologists. Furthermore, this multi-state, multidisciplinary project seeks to promote a larger scope of understanding of the contextual factors that influence the health and well-being of rural low-income families. The study of many states representing different geographic regions allows for a comparison of contextual factors such as access to health care and food, and emphasis and opportunities for recreation, tobacco use, etc. The study of several states provides a deeper understanding of how these factors act and interact to influence health outcomes. Each state is also characterized by a different population profile. Race/ethnicity, immigration status, and acculturation are all important to health outcomes.
Comparison of these diverse populations from several states will enhance our understanding of how these factors influence health in rural low-income families. A multi-state approach will also provide a valuable opportunity to study the effect of policy on families' health. Policy varies both between and within states; this variation is essential to the study of interactions between policy and other contexts for health. No one state can capture the diversity of a national sample; a multi-state approach provides a cost-effective alternative to a national rural sample by capturing variation in factors that influence the health of rural families. Additionally, the use of a common protocol for both the quantitative and qualitative components allows the development of a rich multi-state dataset.
This fruition of this understanding lies in the analyses of the substantial data set collected during NC1171, and the subsequent dissemination of findings. It is expected that this project will create a unique lens for understanding rural poverty, culture, and ultimately the social determinants of health and well-being. Thus, a deeper understanding will inform policy, pedagogy, and practice. Expected Impacts
This project is uniquely positioned to contribute to and enrich the body of knowledge regarding the health of low-income families in rural America. Through a multidisciplinary approach, a framework focused on improving the health of rural families will center on the strengths and challenges rural families face in obtaining optimal physical and mental health. It is anticipated that this framework will allow researchers, extension and other educators, and community stakeholders to further develop research agendas, as well as create products (e.g., curricula, programs, policy briefs) that recognize the unique context and needs of rural America and would specifically address current challenges and barriers low-income rural families face in achieving optimal health. Dissemination products will focus on multiple factors that influence health outcomes, including social factors (e.g., inadequate housing, unemployment or under employment, low income, low education, inadequate access to healthy food and transportation).
Involving project members representing a variety of disciplines and position responsibilities (e.g., teaching, extension/outreach, research) in developing and implementing the dissemination plan will expand the capacity of the land-grant system to educate and train graduate students; enrich the curricula of courses in sociology, economics, human development and family studies, nutrition and health; inform Cooperative Extension programming; and further extend expertise of the land grant system to support prosperity in rural America. This project will lead to impacts at multiple levels (i.e., family, community, state, national).
Related, Current and Previous Work
Previously work (NC223/1011) allowed us to examine the well-being of rural families post welfare reform legislation. Through analyses of the data, health surfaced as a critical aspect of rural families lives that needed to explored in more depth. Thus, NC1171 was created to allow us to collect specific health-related data of rural families. At this time, the NC1171 dataset includes one wave of complete quantitative data (baseline data), and one wave of qualitative data has just been added. We are currently in the process of collecting a second wave of quantitative data that will be available for analysis by end of summer 2014. Additionally, the Core Health Message project gathered additional data from rural low-income mothers and stakeholders in rural communities. During the NC1171 project time period, our work across the three projects during the NC1171 time period (See Appendix A) have resulted in 23 peer reviewed publications, in which 12 are peer reviewed journal articles, and 11 peer reviewed book chapters. In addition, this group has produced an edited peer reviewed book entitled, Rural Families and Work. Our reach has broad impact, as a total of 43 peer reviewed conference proceedings and presentations have been taken place at both the national and international levels. Furthermore, 10 dissertations and thesis were produced. Five awards have recognized team members (three members were students) for their work. We secured $600,000 for multi-state and $41,500 for individual states to support research and outreach pertaining to NC1171 (see Appendix A).
In this renewal proposal we will continue the successful trajectory of the past projects (NC223, NC1011, NC1171) as we intend to further analyze NC1171 data in-depth to understand the interrelationships among of family, community and policy level factors that influence the health and well-being of rural low-income families. Specifically, we intend to further analyze wave 1 data, begin to analyze wave 2 data, and then examine changes in families over time. Additionally, we plan to explore the implications of community context (e.g., accessibility of health resources in the community) and the Affordability Care Act on the health of families.
As a multi-state project, efforts of individual states are multiplied. A majority of the participating universities receive support from their Agricultural Experiment Stations. Each partnering state/university seeks additional outside funding for the multi-state research/Extension project efforts, many times in collaboration with partner states. By working as a multi-state project, all funding received by individual states help support the entire project’s outcomes and success. During the first year of the project, a subgroup will be formed to identify specific tasks of the project in which to seek external funding from private (e.g., foundations such as Robert Woods Johnson) and government entities (e.g., USDA, HHS, NIH). Some states (e.g., TN, IA, CA) receive in-kind support from Cooperative Extension to help support the work of the project. Additionally, project team members will elicit in-kind support from other colleagues on their campus to help carry out aspects of the project that are of interest to them (e.g., IA- faculty member to help guide development of historical data set).
To conduct extensive higher order analyses in all waves of quantitative and qualitative data to further explore factors that create barriers or enhance the physical and mental health of diverse rural low-income families.
To understand the impact of the Affordable Care Act (ACA) on rural low-income families.
To disseminate findings, based on the proposed analyses, in order to further the empirical knowledge base and increase understanding among family serving professionals and policy makers regarding factors that contribute to or create barriers to the physical and mental health of diverse rural low-income families.
MethodsThe primary goal of the proposed project is the analysis and interpretation of the primary data already collected along with easily obtainable existing, secondary data and dissemination of the findings. As ecological theory specifies, families and communities are not seen as separate entities but rather nested systems, i.e., within the context of their own family system as well as the community system. Triangulating personal and family factors with place-based factors, such as local resource, opportunities, and conditions will yield a clearer picture of the interdependent factors that lead to family well-being in rural communities. In addition, the utilization of existing parallel datasets collected in previous versions of this project (NC223 and 1011) allows valuable collaborative reanalysis that has proven by others to be beneficial to understanding health outcomes (Anstey et al., 2007). All quantitative data analyses will be performed using SPSS 20 (IBM Corp., 2011) or STATA (StataCorp, 2013), or the appropriate statistical software [e.g. Mplus (Muthen & Muthen, 1998-2011), Amos (Arbuckle, 2006)]. Qualitative analyses will be performed to gain an in-depth understanding of the experiences of low-income families in context of rural communities. All qualitative analyses will be performed using MAXQDA 11 (Verbi Software, 2013). Procedures for Objective 1 Our previous work established that the following variables, which represent different layers of the ecological framework, significantly contribute to health outcomes of rural, low-income families: public assistance policy (TANF, SNAP, housing assistance, supplementary security income, etc.), community characteristics (local food banks, access to health care system, public transportation etc.), informal social support (family support), family and individual characteristics (social and demographic characteristics such as race/ethnicity, education, age, etc.). We will extend these findings by applying the ecological theory framework to examine interrelationships among these variables and family functioning and health outcomes, as well as differences among subgroups (e.g., race/ethnicity; geographic location; food security level; extent of depressive symptomology). We will also identify change and stability over time in key predictors of rural health and well-being, including. For all of the above, health will be operationalized broadly including physical, dental, mental, and behavioral health as well as nutrition, physical activity level, and access to healthcare. Family well-being is operationalized as constructs that contribute to family functioning, including: life satisfaction, degree of deprivation, parenting attitudes, relationship quality, transportation, and financial stress. Initial hypotheses testing will be performed using simple tests to examine and identify the interrelationships and differences among variables and subgroups. For example, we hypothesize that access to health care (availability, affordability, acceptability, etc.) in a local community has significant positive associations with the reported health status of family members. We also expect that this association will be mediated by informal social support that a family receives and individual characteristics (e.g. health literacy). It is further hypothesized that race/ethnicity and native language will moderate the association such that ethnic minority families and those with an ESL head of household will be less likely to effectively utilize the available health care. Appropriate analyses, such as multivariate analysis (multiple linear and logistic regression including repeated measures) will be utilized to model the combined effects of the hypothesized variables on target health outcomes such as overall family health status, physical functioning, obesity, depression, stress level, and child behaviors. Based on these findings, path analysis and structural equation modeling will be performed to identify the direction of associations between key variables. In order to examine changes in system levels and their associations with health outcomes over time, we will leverage parallel data from previous iterations of the project that share key variables of interest collected at different points in time. To do this, we will combine data across multi-state projects (NC223-1011-1171) to examine cohort effects of key shared variables (e.g., financial security, food security, federal program assistance participation, employment, health insurance status, social support, transportation access, housing conditions) that have been found to be associated with health (e.g., depression, functional health, chronic health conditions, general physical and dental health ratings). Our hypotheses address both mean differences between cohorts, as well as potential differences between cohorts in the associations among constructs in our model. Specifically, we hypothesize that due to the economic downturn that occurred during the period of data collection across these samples, employment, food security, and financial security will be significantly lower for more recent samples. We also hypothesize that the association between key variables and health outcomes will not change across cohorts. In essence, we aim to both test for replication of findings between the datasets as well as test for overall cohort effects across the combined sample. Both of these approaches will significantly add to our understanding of factors associated with health in rural, low-income families. As noted by Conger et al. (1995), replication allows the testing of associations based on shared theoretical bases across samples to substantiate common relations among the variables. The use of latent variable structural equation modeling allows some deviation in measurement of specific indicators that have strongly shared theoretical underpinnings. To test our hypotheses, we will first test for sample equivalence across the 3 cohorts by comparing them to census data for the counties in which they were collected. If samples are not significantly different from the census estimates we will assume they each represent approximately equivalent samples of rural, low-income mothers. Second, we will test for mean differences in key predictor variables across the cohorts (e.g. financial security, food security, federal program assistance participation, employment, health insurance status, social support, transportation access, housing conditions). Results will inform whether or not these key predictors of rural family health are different at different points in historical time. Finally, we will utilize multiple group analysis to test whether the pattern of associations between key variables (e.g. public assistance usage, social support, depression, and physical health) is replicated across the cohorts. To do this, we will split the combined sample into 3 groups (NC223, 1011, 1171) and generate individual SEM models. We will then equate the model estimates between the groups to examining changes in the fit indices when the models are combined. Results will explain whether there is meaningful heterogeneity in the associations between the groups. To understand subtle nuances of family experience underlying the resulting associations, grounded theory analysis will be used for the qualitative data of family health and well-being. For example, role of social support in family health management will be compared and contrasted between Latino families and non-Latino families; explanations for differences in health status will be examined in relation to community resources; and patterns of technology use to search for health information will be identified within the context of geographic locations. Furthermore, we will utilize data from the Core Health Messages Project (Mammen, Braun, & Sano, 2010-2012; 2011-2013), a NC1171 related project funded through the NIFA Rural Health and Safety Education Competitive Grants Program (Grants 2010-46100-21791, 2011-46100-31135). The goal of this project was to improve the health outcomes of rural, low-income families with children by developing culturally appropriate health messages (Core Health Messages; CHMs) to rural residents, and identifying a most effective delivery method of the CHMs. In addition to data from forums, focus groups, and individual interviews, the CHM project interviewed community stakeholders from NC1171 counties in order to capture community context and understand their perspectives on rural low-income families experiences reflected in the NC1171 data. We will utilize this data to provide further understanding and context to the analyses, in addition to using the preferred delivery methods for health information in our dissemination plan. Procedures for Objective 2 In addition to data that were collected during wave 1 and wave 2 of NC1171, data were collected from families and community service providers as part of the Core Health Messages Project (Mammen, Braun, & Sano, 2010-2012; 2011-2013; for project details see http://ruralfamiliesspeak.org/). These data provide opportunity to assess changes in individual and family health and well-being in rural communities before and after the full implementation of the ACA. For example, we will be able to examine changes related to public and private insurance coverage and access to health service providers, and the effects of these kinds of changes on individual health and family well-being, as well as practices of community service providers. ACA provisions mandate that health care providers produce and make readily available information to support consumer health care provider decision-making. NC1171 is well-positioned to gain an understanding of rural families’ awareness of information sources, their information-seeking behavior, and to highlight likely relationships between these factors and the health status of rural families. This particular opportunity is time sensitive in order to capture the knowledge, behavior, and stories of families and communities during the initial implementation of the ACA. Case studies with a purposeful selection of families and community stakeholders across geographic regions will be conducted to understand potential impacts of the ACA on rural low-income families. Case study interviews will be used as pilot data to inform dissemination of information. An important attribute of NC1171 is that its participating states include those with different approaches to the health insurance marketplace; both state-based and federally-facilitated marketplaces. Of equal interest is that NC1171 includes states that chose to expand Medicaid or state-based programs as well as states that chose not to. Thus, we will be able to investigate a diversity of cases under the new healthcare legislation. Two lines of follow-up questioning will be conducted with a small sampling of participants. One line will examine the ability to access medical care and if there are differences with individuals with private insurance versus individuals with government provided plans. We will also look at who is insured and who is not insured, comparing individuals residing in states that enacted Medicaid expansion and individuals residing in states that did not enact Medicaid expansion. Data collected during wave 1 data informs us of which families were insured, uninsured and which families received Medicaid. Sharing the experiences of families residing in rural communities during and after the ACA implementation will inform policy makers and local community leaders and family service providers of the impact of the recent healthcare legislation. Procedures for Objective 3 This multi-state, multidisciplinary team has a longstanding history of dissemination and outreach, evidenced by the extensive contributions from NC223, NC1011, and NC1171. The momentum will continue. The unique understanding of rural families and communities that this multi-state, multi-disciplinary project provides will be a beacon of knowledge, both directly to families, and indirectly through educators, professionals, policymakers, and employers as the nation shifts into new policies and procedures related to healthcare reform. This project will continue to carry the voice of rural families to policy makers, so that policy makers will be aware of the context of rural communities as it interfaces with policy. Project members will collaborate to address research questions and prepare manuscripts for submission to peer-reviewed journals, prepare peer reviewed conference proposals, develop policy and research briefs, lessons plans for use in college classes, as well as educational materials for policy makers, program administrators, and consumers.
Measurement of Progress and Results
- Comprehensive data set centered on the health and well-being of rural low-income families created from two waves of qualitative and quantitative data from the NC1171 project
- Comprehensive historical data set of compatible health and well-being variables from parallel populations collected at different points in time (NC223/1011/1171)
- Case studies of purposively selected rural low-income families and community stakeholders during the implementation of the Affordable Care Act (ACA)
- Peer-reviewed publications and presentations representing multiple disciplines targeting diverse audiences (practitioners, researchers, policy makers)
- Research and policy briefs, training materials for elected officials, program administrators, students, consumers
Outcomes or Projected Impacts
- Masters and doctoral trained researchers and educators who are adept at using longitudinal, qualitative and quantitative data in order to increase experiences in producing research and outreach outcomes that focus on health promotion in rural communities.
- Improved knowledge and skills of Extension and other education family-serving professionals who are equipped to address, change, and understand the context of rural health and well-being resulting in improved outcomes for rural low-income families.
- Increased understanding of the impact of the ACA on rural, low-income families.
- Funding leveraged to further examine changes that result from the ACA over time in low-income, rural families.
Milestones(2015): Organization and Interaction of Team Members to Facilitate Research Outputs and Outcomes: Annual research team meeting, Conference calls among officers and work groups, Sharing of data and publications via the Share Point site, and State reports. Objective 1: Develop comprehensive historical data set of compatible health and well-being variables from parallel populations collected at different points in time. Provide input into case study protocols. (CA, HI, IA) Objective 2: Purposely select rural, low-income families and stakeholders for case studies. Develop interview protocol, finalize strategies and other procedures for case study interviews. (NC, TN) Objective 3: Assemble welfare policies for each state. Compile multi-state findings and policy points of interest for the similarities and differences. Peer-reviewed publications and presentations representing multiple disciplines targeting diverse audiences (practitioners, researchers, policy makers). (MA, NC, WA)
(2016): Organization and Interaction of Team Members to Facilitate Research Outputs and Outcomes: Annual research team meeting, conference calls among officers and work groups, Sharing of data and publications via the Share Point site, and State reports. Objective 1: Analyze health and well-being of rural low-income families based on two waves of qualitative and quantitative data from the NC1171 project. Examine changes and stabilities of families and communities based on the historical data set created in Year 1. (CA, HI, IA, MA, TN, WA) Objective 2: Collect, code and clean data from selected case study families. Collect, code, and clean data from community stakeholders. (NC, TN) Objective 3: Distribution of selected policy briefs. Peer-reviewed publications and presentations representing multiple disciplines targeting diverse audiences (practitioners, researchers, policy makers). Begin development of educational materials for elected officials, program administrators, students, consumers. (CA, IA, NC, TN)
(2017): Organization and Interaction of Team Members to Facilitate Research Outputs and Outcomes: Annual research team meeting, conference calls among officers and work groups, Sharing of data and publications via the Share Point site, and State reports. Objective 1: Analyze results of family & contextual data. Update contextual data for counties so that it coincides with data from families selected for case studies. (HI, KY, WA) Objective2: Collect, code and clean data from selected case study families. Collect, code, and clean data from community stakeholders. Seek additional funding to further examine changes that result from the ACA over time in low-income, rural families. (NC, TN) Objective 3: Write and distribute selected policy briefs. Peer-reviewed publications and presentations representing multiple disciplines targeting diverse audiences (practitioners, researchers, policy makers). Development of educational materials for elected officials, program administrators, students, consumers. (CA, IA, NC, TN)
(2018): Organization and Interaction of Team Members to Facilitate Research Outputs and Outcomes: Annual research team meeting, Conference calls among officers and work groups, Sharing of data and publications via the Share Point site, and State reports. Objective 1: Analyze results of family & contextual data. Incorporate new contextual data into overall data set and analysis. (HI, KY, WA) Objective 2: Sub-coding of interview data. Analyze the impact of the ACA on rural, low-income familiesâ€™ health and well-being. Seek additional funding to further examine changes that result from the ACA over time in low-income, rural families. (NC, TN) Objective 3: Distribution of selected policy briefs. Peer-reviewed publications and presentations representing multiple disciplines targeting diverse audiences (practitioners, researchers, policy makers). Assess effectiveness educational materials for elected official
Projected ParticipationView Appendix E: Participation
Team members of each topical working group (see organization and governance) will disseminate findings appropriate to their respective professional communities, as well as appropriate outreach products to practitioners and policy makers. Potential products include: refereed publications, state, regional, national and international conference presentations, policy and information briefs, web-based trainings and training materials. Team members have extensive experience with these types of products and dissemination methods. A website will be maintained to communicate information about the project to the public, researchers, practitioners and policy makers. A list of publications, presentations, grants will be linked to the website on a regular basis. The website and briefs will allow the project to be disseminated quickly to those interested in the findings. Team members will work with graduate and undergraduate students to complete theses, dissertations, refereed presentations and publications. Thus, students will be trained as researchers and practitioners who understand rural low income families and communities, and complex research projects. Several of the researchers involved in the current project began as graduate students on previous related projects (i.e., NC223, NC1011, NC1171). Actively involving and mentoring students to become strong researchers and practitioners is a key component of the outreach plan.
The organization of the multistate project will be in accordance with the Guidelines for Multistate Research Activities. Administrative guidance will be provided by Dr. Karen Shirer, University of Minnesota.
The initial organization of the team will be led by the core group of states writing the project (WA, CA, TN, IA, TX). This core group will organize the first annual meeting of the potential participants (see Appendix E) at which elections will be held for the executive committee. The executive committee will consist of a chair, vice-chair for data, vice-chair for outreach, and a secretary. When determined necessary by the membership, a vice-chair for funding will be elected. This latter position will be responsible for connecting and managing team resources to secure external funding. Each executive committee member will serve a 2-year term and the members' terms will be staggered in order to maintain continuity of leadership. Working groups will be formed around topics of interest (e.g., child health outcomes, family physical health, family mental health, economic security, training materials. The executive committee will maintain contact with the Administrative Advisor and CSREES representative consistently for the project.
For data collection, analysis and management purposes, each state will be responsible for purchasing equipment (e.g., computer, audio recorder) and software (e.g., SPSS, MAXqda) to carry out project tasks. For the case study interviews, each state conducting case studies will be responsible for transcribing (and translating into English if needed) the interview data. All case study data will be posted on Share Point, a central location (Iowa State University) where the multi-state data set will be compiled and managed. This process of local verification and then central management will ensure a valid and reliable data set accessible to all participants. Share Point is a secure site from which data can be accessed by the individual researchers. Working groups will maintain contact via email, adobe connect and teleconference calls.
Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services (2011). National Healthcare Disparities Report 2011. Accessed August 22, 2013 at http://www.ahrq.gov/research/findings/nhqrdr/nhdr11/nhdr11.pdf
Arbuckle, J. L. (2006). Amos (Version 7.0) [Computer Program]. Chicago: SPSS.
Eberhardt, M.S., Ingram, D.D., Makuc, D.M. et al. (2001). Urban and Rural Health Chartbook. Health, United States, 2001. Hyattsville, Maryland: National Center for Health Statistics.
Eagle, T.F., Sheetz, A., Gurm, R., Woodward, A.C., Kline-Rogers, E., Leibowitz, R., DuRussel-Weston, J., Palma-Davis, L., Aaronson, S., Fitzgerald, C.M., Mitchell, L.R., Rogers, B., Bruenger, P., Skala, K.A., Goldberg, C., Jackson, E.A., Erickson, S.R., & Eagle, K.A. (2012). Understanding childhood obesity in America: Linkages between household income, community resources, and childrens behaviors. Am Heart J., 163:836-843.
Economic Research Service, USDA (September, 2012). Prepared using data from the Annual Social and Economic Supplement (ASEC) to the Current Population Survey (P-60), U.S. Census Bureau. Retrieved September 30, 2012 http://www.ers.usda.gov/topics/rural-economy-population/rural-poverty-well-being/income-nonfarm-earnings.aspx
Economic Research Service, USDA (March, 2011). Prepared using data from the U.S. Census Bureau 2011 Current Population Survey.
Farrigan, T., & Parker, T. (2012). The Concentration of Poverty Is a Growing Rural Problem. Amber Waves. Economic Research Service, United States Department of Agriculture. Accessed: August 20, 2013 at http://www.ers.usda.gov/amber-waves/2012-december/concentration-of-poverty.aspx#.UhQGoD-Dkym
Goldstein, D., & Holmes, J., (December, 2011). Health Care's Blind Side: Overlooked Connection Between Social Needs and Good, Summary of findings from a survey of Americas physicians, Health, Robert Woods Johnson Foundation, Accessed August 20, 2013 at http://www.rwjf.org/content/dam/farm/reports/surveys_and_polls/2011/rwjf71795
Grow, H.M., Cook, A.J., Arterburn, D.E., Saelens, B.E., Drewnowski, A., & Lozano, P. (2010). Child obesity associated with social disadvantage of children's neighborhoods. Soc Sci Med., 71(3): 584-591.
HHS Rural Task Force (2002, July). One Department Serving Rural America: Report to the Secretary. U.S. Department of Health and Human Services.
Housing Assistance Council (HAC) (2012). Rural Research Note: Race & Ethnicity in Rural America. Accessed September 8, 2013 at http://www.ruralhome.org/storage/research_notes/rrn-race-and-ethnicity-web.pdf
IBM Corp. (2011). IBM SPSS Statistics for Windows (Version 20.0) [Computer Program]. Armonk, NY: IBM Corp.
Kindig, D. & Cheng, E. (2013). Even as mortality fell in most US counties, female mortality nonetheless rose in 42.8 percent of counties from 1992 to 2006. Health Affairs,32 (3).
Kusmin, L., & Hertiz, T. (2010). Rural America at a Glance, 2010 Edition. Economic Information Bulletin No. (EIB-68). Economic Research Service, United States Department of Agriculture. Accessed September 8, 2013 at http://www.ers.usda.gov/publications/eib-economic-information-bulletin/eib68.aspx#.UizNlD-Dkyk
McLaren (2007). Socioeconomic Status and Obesity. Epidemiol Rev., 29 (1): 29-48.
Muthén, L. K., & Muthén, B. O. (1998-2011). Mplus User's Guide. Sixth Edition. Los Angeles, CA: Muthén & Muthén.
StataCorp.(2013). Stata Statistical Software (Release 13) [Computer Program]. College Station, TX: StataCorp LP.
VERBI Software.(1989-2013). MAXQDA [Computer Program]. Sozialforschung GmbH, Berlin, Germany.