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W3-Perry-Social_CapitalHealth_Care_Experiences1.pdf
RESEARCH AND PRACTICE
Social Capital and Health Care Experiences Among Low-Income Individuals | Megan Perry, PhD, Robert L. Williams, MD, MPH, Nina Wallerstein, DrPH, and Howard Waitzkin, MD, PhD
The concept of social capital emerged from work in the social sciences by Putnam and others who defined social capital as what originates from social networks and the reci- procity, trustworthiness, and civic engagement created by these networks.1–7 Epidemiologists have applied these concepts to public health and have worked to illuminate cause-and- effect relationships.3,7,8 At the same time, community interventions and community psy- chology researchers have used similar con- cepts of community capacity, sense of com- munity, and community control to explore how to facilitate health status improvements.
Researchers have found associations be- tween high levels of community social capital and reduced all-cause mortality rates, better self-rated health, and lower levels of college binge drinking.3,9–11 These findings have led to the suggestion that social capital may play a role in mediating the relationship between income inequality and health.3,4,8,12,13
One mechanism by which social capital may influence health, particularly in low- income communities, is its influence on peo- ple’s use of health care services. Residents of a community with high social capital may provide one another with greater instrumen- tal and psychosocial support than do residents of a community with low social capital, or the community’s level of interconnectedness and trust may reduce barriers to care. To date, however, little research has examined the re- lationship of social capital to health service measures such as use of services, participation in care, or satisfaction with services.
In one of the few studies to date, commu- nity social capital independently predicted the level at which patients trusted physicians.14
In another study, conducted among homeless individuals with mental illness in 18 different communities, associations emerged between community social capital and greater service integration, increased access to housing assis- tance, and a higher probability of individuals
Objectives. We examined relationships between social capital and health ser- vice measures among low-income individuals and assessed the psychometric properties of a theory-based measure of social capital.
Methods. We conducted a statewide telephone survey of 1216 low-income New Mexico residents. Respondents reported on barriers to health care access, use of health care services, satisfaction with care, and quality of provider communication and answered questions focusing on social capital.
Results. The social capital measure demonstrated strong psychometric properties. Regression analyses showed that some but not all components of social capital were related to measures of health services; for example, social support was inversely re- lated to barriers to care (odds ratio=0.73; 95% confidence interval=0.59, 0.92).
Conclusions. Social capital is a complex concept, with some elements appearing to be related to individuals’ experiences with health services. More research is needed to refine social capital theory and to clarify the contributions of social cap- ital versus structural factors (e.g., insurance coverage and income) to health care ex- periences. (Am J Public Health. 2008;98:330–336. doi:10.2105/AJPH.2006.086306)
obtaining suitable housing, although an asso- sample of low-income individuals. We then ciation with clinical outcomes did not ap- used these measures to examine the association pear.15 The few studies conducted, however, between social capital and individuals’ health leave unanswered the broader question of care experiences. whether and how social capital is associated with access to, use of, and satisfaction with METHODS health services. Also, these studies have not examined the relative contributions of social We conducted a population-based, state- capital and structural factors such as geo- wide telephone survey of low-income house- graphic and financial conditions, which exert holds throughout New Mexico to examine the important effects on health care access. psychometric properties of our social capital
An additional, methodological difficulty in measures and to assess the relationships be- this field has been the lack of a consistent op- tween these measures and respondents’ re- erationalization of the concept of social capi- ported experiences with respect to (1) barri- tal. Because unique measures of social capital ers to health care access, (2) use of health have been applied in most published studies, services, (3) satisfaction with and ratings of between-study comparisons remain problem- care services, and (4) quality of communica- atic. Also, the wide variation in measurement tion by health care providers. Our study was approaches makes interpretation of results part of a project evaluating the effects of a difficult. Finally, because published studies Medicaid managed care program on New rarely provide results from psychometric test- Mexico’s low-income population.16
ing of their social capital measures, assessing the validity or reliability of these measures Setting and Sampling Strategy has remained difficult. In 2000, New Mexico’s population was ap-
In this study, we sought to address the need proximately 1.8 million.17 About one third of for standard measures of social capital by creat- the population lived in the metropolitan Al- ing theory-based measures and testing their buquerque area, and about half lived in psychometric properties in a large, statewide small towns or rural areas.18 From 1999
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through 2001, 18.8% of the population lived in poverty, the highest rate of any state, and 23.2% of New Mexicans lacked health insurance coverage, again the highest rate in the country.19,20
The sample frame for the survey, con- ducted between January and March 2001, included all New Mexico residents living in households meeting 2 criteria. First, the household had to have an operational tele- phone, and second, the household’s total in- come had to be less than 185% of the fed- eral poverty level (the income level at which New Mexico residents are eligible for Medic- aid benefits).
To enhance our ability to reach low-income households, we initially selected all zip code areas in New Mexico where 20% or greater of the population lived below the federal pov- erty level (232 of the 453 zip code areas in New Mexico). Telephone prefixes within those zip code areas were determined, and num- bers from those prefixes were dialed ran- domly. When a household was reached, we asked questions about family size and income to determine eligibility; we then interviewed (in either English or Spanish) the individual most knowledgeable about the household’s health care situation.
Sample size calculations showed that, at an alpha level of 0.05 and a power of 0.90, a sample of 1191 was sufficient to demonstrate a difference of 0.10 in the proportion of indi- viduals with a particular characteristic (e.g., private health insurance coverage) in a com- parison of this characteristic between 2 sub- groups of unequal size (e.g., Hispanic and non-Hispanic). A subgroup size ratio of 1.5 was used in these calculations.
Survey Instrument The survey instrument included 70 items
assessing respondents’ health care experiences during the preceding 12 months, their percep- tions of their health status, demographic vari- ables, and selected health risk factors. Many of the items were derived from well-known, standardized instruments (e.g., the Behavioral Risk Factor Surveillance System survey, the Consumer Assessment of Health Plans Sur- vey).21,22 Further survey details are available elsewhere,16 and copies of the survey instru- ment are available from the authors.
In addition, the survey included 12 items designed to assess 3 separate social capital constructs: social support, psychosocial inter- connectedness, and community participation. The 4 items used to examine social support were as follows: (1) If a medical emergency arose in your home, would you be likely to call your neighbors for help? (2) If you needed a ride to the clinic, would you be likely to call a neighbor for a ride? (3) If you needed help filling out medical or social ser- vice forms, would you be likely to ask a neighbor for help? and (4) Within the past year, have you and neighbors helped each other often with small tasks, such as repair work or grocery shopping?
The following 4 items were used to assess psychosocial interconnectedness: (1) Would you say most people in this community can be trusted? (2) Would you say your commu- nity is a good place for kids to grow up? (3) Would you say you expect to live in this community for a long time? and (4) Would you say you regularly stop and talk with peo- ple in your community?
Finally, the 4 items used to assess commu- nity participation were as follows: (1) Would you say you can influence decisions that af- fect your community? (2) Would you say by working together with others in your commu- nity, you can influence decisions that affect your community? (3) Would you say people in your community have connections to peo- ple who can influence what happens in your community? and (4) Would you say if there is a problem in your community, people who live there can get it solved?
Of the 4 items assessing social support, 3 dealt with health care needs. The items de- signed to assess psychosocial interconnected- ness included a question on trust derived from the epidemiological literature and ques- tions on sense of community derived from the literature on community psychology.3,23
We used 2 questions from the public health intervention literature on perceived neigh- borhood control and 2 questions on neigh- borhood participation to measure commu- nity participation.23 There were 4 possible response options (yes, no, don’t know, re- fused) for each item. (Participants were not offered a set of responses; their responses were categorized by the surveyor.)
We used local Spanish speakers to develop a Spanish version of the survey through stan- dard methods of translation and back transla- tion. The interview was conducted in either Spanish or English according to respondents’ preference. After pilot testing involving both in-person interviews and random-digit dialing telephone interviews, we modified the instru- ment to improve item clarity.
Data Analysis We conducted the data analysis in 3
phases. In phase 1, we examined characteris- tics of the sample using univariate analysis. We dichotomized the following key sample demographic variables: age (younger than 65 years vs 65 years or older), ethnicity (His- panic vs non-Hispanic), education (less than high school diploma vs high school diploma or more), insurance coverage status (coverage vs no coverage, Medicaid vs non-Medicaid), and area of residence (rural vs urban). We created the residency variable by dividing the number of rural residents by the total population within each US census zip code tabulation area in New Mexico.24,25 Resi- dents living in a tabulation area in which more than 20% of individuals resided in rural areas were labeled “rural”; all other resi- dents were designated “urban.”
Phase 2 focused on determining the psy- chometric properties of our social capital measures. This phase proceeded in 4 steps: (1) a factor analysis of the 12 social capital survey items examining whether patterns of responses to individual questions were consis- tent with the theorized constructs described earlier (social support, psychosocial intercon- nectedness, and community participation); (2) calculation of Cronbach alpha coefficients and correlation coefficients for the responses within each factor as a measure of internal consistency reliability; (3) correlation analyses among the factors as an assessment of dis- criminant validity; and (4) random sample cross-validation.
The step 1 exploratory factor analysis focused on a matrix of tetrachoric correla- tions between each variable pair.26–30 We performed unweighted least squares analy- ses with oblique rotation at the initial stage, followed by analyses with orthogonal rota- tion for the final model. We retained items
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in the final model if they had factor loadings of at least 0.5 and if they exhibited a differ- ence of at least 0.2 from the primary to the secondary factor. In the final step (step 4), we divided the sample in half on the basis of computer-generated random numbers. We then compared factor analyses of the tetra- choric correlation matrix for the 2 halves of the sample.
Phase 3 assessed the relationships between social capital and measures of health care barriers, use of health care services, satisfac- tion with care, and quality of provider com- munication. We conducted logistic regression analyses controlling for demographic vari- ables such as age, gender, ethnicity, area of residence, education level, and type of insur- ance coverage. We added the number of positive responses for the items from each so- cial capital construct to create new predictor variables representing each construct in the regression. Similarly, we used factor analysis to identify groupings among survey items as- sessing reported health care experiences. We then used the factors resulting from this anal- ysis as outcomes in the regression analyses.
To produce a single variable representing each factor in the regressions, we summed re- sponses to individual items grouped within that factor and used the resulting sum as the variable to represent the factor in the regres- sions. This analysis produced 4 factors with groups of items representing barriers to health care access, use of health care services, satisfaction with care, and quality of provider communication. (Lists of the specific survey items included within each of these 4 factors are available from the authors.) Finally, we conducted comparable logistic regression analyses focusing on subgroups of special in- terest (i.e., subgroups among whom social capital might have a more pronounced influ- ence on health care experiences): Hispanics, respondents residing in rural areas, women, and individuals with chronic illnesses (i.e., dia- betes and hypertension).
RESULTS
Description of the Sample and Univariate Analysis
Of the residents from the 1592 eligible households contacted, 1216 completed the
TABLE 1—Characteristics of Low-Income Survey Respondents: New Mexico, 2001
Sample (n = 1216), %
Older than 65 yearsa 16.3 Female gender 70.9 Married 44.0 Hispanic 49.0 High school graduate 74.7 Employed (either full or part time) 48.4 Health insurance coverage
Private 23.1 Medicare 22.4 Medicaid 9.6 Other 9.9 None 35.0
Rural residence 58.5 Had diabetes 9.5 Had high blood pressure 26.2 Social capital
Likely to call neighbors for help 51.8 in medical emergency
Likely to call neighbor for ride 53.5 to clinic
Likely to ask neighbor for help 33.1 filling out forms
Helped each other with small 47.5 tasks within the past year
Agree most people in community 62.9 can be trusted
Community is good place for 75.6 kids to grow up
Expect to live in community for 72.5 a long time
Regularly stop and talk with 77.2 people in community
Can influence decisions that 47.0 affect your community
Can work with others to 69.6 influence decisions
Believe people have connections 67.4 and can influence what happens
Believe problems that arise in 68.8 community can be solved
a The median age was 43 years.
survey, yielding a response rate of 76.4%. Table 1 shows that most respondents were women and Hispanic; 35% of respondents did not have health insurance coverage. About half of the respondents were em- ployed, and almost 60% resided in rural
areas. Men were more likely than were women to be employed (P < .01) and less likely to reside in a rural area (P = .01) or have Medicaid coverage (P < .01).
Table 1 also shows the percentages of re- spondents providing affirmative responses to the social capital questions. Slightly more than half reported that they would be likely to ask a neighbor for help in a medical emergency or for a ride to a clinic, but fewer than half re- ported that they would ask for help with forms or small tasks. Larger majorities provided affir- mative responses to questions about commu- nity interconnectedness, including trust, plans to continue living in the community, and talk- ing with others. Mixed but generally positive responses emerged for the 4 questions focus- ing on community participation.
Psychometric Properties of Social Capital Measures
A factor analysis of responses to the social capital items identified 3 factors with eigen- values above 1.0 (Table 2); together, these factors explained 69.4% of the total variance in the 12 items. In general, the factor analysis produced a grouping of social capital ques- tions that was consistent with the 3 social capital constructs. Factors 1, 2, and 3 in- cluded 3 of the 4 items measuring social support, psychosocial interconnectedness, and community participation, respectively.
We calculated Cronbach alpha coefficients (indicating the internal consistency of each of the constructs), and these coefficients sup- ported the 3-factor solution of the survey re- sults. Table 2 shows that the alpha coefficient for factor 1 (social support) was highest, at 0.77, but the coefficients for all 3 factors were above 0.60. Table 3 presents correla- tion coefficients among the 12 social capital items. In general, items within each factor showed moderate to high correlations with each other, as opposed to lower correlations with items outside that factor.
With respect to discriminant validity, there was a strong correlation (Spearman coefficient = 0.29; P < .01) between the psychosocial interconnectedness and commu- nity participation factors (Table 3); the social support factor was less strongly correlated (Spearman coefficient = 0.17; P < .01) with each of the other 2 factors. The cross-validation
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TABLE 2—Results of Factor Analysis of Social Capital Survey Items: New Mexico, 2001
Factor 1: Factor 3: Social Factor 2: Community
Item Support Interconnectedness Participation
Likely to call neighbors for help in medical emergency 0.823a 0.088 0.014
Likely to call neighbor for ride to clinic 0.906a 0.098 0.071
Likely to ask neighbor for help filling out forms 0.851a 0.181 0.028
Helped each other with small tasks within the past year 0.469 0.007 0.300
Believe most people in community can be trusted 0.012 0.687a 0.244
Believe community is good place for kids to grow up 0.085 0.877a 0.197
Expect to live in community for a long time 0.207 0.585a 0.147
Regularly stop and talk with people in community 0.273 0.359 0.448
Believe it’s possible to influence decisions that affect your 0.169 0.220 0.828a
community
Believe it’s possible to work with others to influence 0.069 0.162 0.854a
decisions
Believe that people have connections and can influence 0.000 0.221 0.665a
what happens
Believe that problems that arise in community can be solved 0.048 0.491 0.526
Eigenvalue 4.60 1.35 2.37
Variance explained, % 38.3 11.3 19.8
Cronbach α 0.77 0.69 0.62
aDifference between primary and secondary factors was at least 0.2 in addition to factor loading exceeding 0.5; these survey items were retained in subsequent factor analyses (see “Methods” section).
factor analysis yielded a 3-factor solution sim- ilar to that observed in the full sample analy- ses, as well as similar eigenvalues, loadings, and Cronbach alpha coefficients (results are available from the authors).
Relationships Between Social Capital and Health Care Measures
Table 4 presents the results of the logistic regression analyses. Social support was in- versely related to reported barriers to care after control for area of residence, educa- tional level, ethnicity, gender, age, and health insurance coverage. By contrast, social sup- port showed no significant associations with use of health care services, satisfaction with care, or perceived quality of provider commu- nication. A significant relationship emerged between psychosocial interconnectedness and satisfaction but not between psychosocial in- terconnectedness and barriers to health care or use of services. The relationship of psycho- social interconnectedness to perceived quality of provider communication approached but did not reach statistical significance (odds
ratio [OR] = 1.34; 95% confidence interval [CI] = 0.99, 1.81). There were no significant relationships between community participa- tion and any of the health care measures.
The regression analyses also showed the im- portance of several structural and demographic variables as predictors of health care experi- ences. Lack of insurance coverage strongly pre- dicted increased barriers to health care, de- creased use of and satisfaction with care services, and worse perceived quality of com- munication. Male gender was associated with fewer barriers to care and decreased use of and satisfaction with care. Older age (i.e., older then 65 years), presumably as a result of Medicare coverage, was associated with decreased barri- ers, increased use, more satisfaction, and better perceived quality. Rural residence and Hispanic ethnicity were predictors of increased barriers.
As mentioned, we conducted additional re- gression analyses to determine whether the relationships just described differed among selected subgroups (detailed results are avail- able from the authors). Among women and rural residents, the relationships between the
social capital measures and health care expe- riences were similar to those for the sample as a whole; in addition, psychosocial intercon- nectedness was significantly associated with perceived quality of communication. Among Hispanic respondents, the only significant re- lationship was that between psychosocial in- terconnectedness and satisfaction.
Among respondents with diabetes, no significant relationships emerged between the social capital measures and health care experiences. However, there were significant relationships between psychosocial intercon- nectedness and satisfaction and between social support and perceived quality of communica- tion among respondents with hypertension.
DISCUSSION
Social Capital and Health Care Experiences
Our results provide evidence of relation- ships between social capital and health care experiences among low-income indi- viduals. Social support inversely predicted barriers to health care, whereas psychoso- cial interconnectedness emerged as a sig- nificant predictor of satisfaction with care. At the same time, community participation showed no association with the health care measures, and none of the social capital measures predicted use of care services or perceived quality of communication by providers. Findings among subgroups var- ied somewhat but generally conformed to those from the overall sample.
By contrast, several structural and demo- graphic variables were as strong as or stronger than the social capital measures in terms of predicting health care experiences. For instance, lack of insurance coverage was a strongly adverse predictor of all of the de- pendent variables.
These findings show a complex association between social capital and health care experi- ences. As a theoretical construct, social capital is perhaps best understood as a composite of community attributes, some of which may re- late to health care experiences and some of which may not. As a predictor of health care experiences, the broad concept of social capi- tal may prove less important than these spe- cific community attributes.
February 2008, Vol 98, No. 2 | American Journal of Public Health Perry et al. | Peer Reviewed | Research and Practice | 333
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This study is among the few to provide data assessing the psychometric properties of social capital measures. We developed and applied a short, easy-to-use questionnaire adaptable in other health service research. Our factor analysis produced 3 factors con- sisting of items derived from earlier research on social capital and community interven- tions. The items composing these factors bridged 2 previously unconnected litera- tures, incorporating items from the “sense of community” construct of community psy- chology, as well as items from the “trust” construct of epidemiological research on so- cial capital.
Limitations Our data were derived from only 1 state
and from only low-income individuals, and thus different associations between social capital and health care experiences may emerge in other settings. Although we based our measures of social capital on previously theorized components of the construct, these measures may have failed to capture ele- ments of social capital that have important relationships with health care experiences. Recent theories have posited that “linking” social capital—that is, developing trusting re- lationships across community groups of dif- ferent status and power—may have a positive effect on access to care, partly through in- creased trust in providers on the part of pa- tients.7,31 Our design did not permit assess- ment of this newly theorized component of social capital.
Furthermore, our statewide sample may have obscured important variability in the relationship of social capital to health care experiences occurring at local levels. If com- munity advocacy, for example, led to estab- lishment of a health clinic, community partic- ipation might show a correlation with access to care in that community. Finally, a portion of the low-income population in New Mexico does not have telephone service and may have experiences different from those of our respondents. However, we conducted a house-to-house survey among 100 low- income households and found response pat- terns nearly identical to those observed in our telephone sample (data available from the authors).
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334 | Research and Practice | Peer Reviewed | Perry et al. American Journal of Public Health | February 2008, Vol 98, No. 2
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TABLE 4—Health Care Measures Predicted by Social Capital Measures and Key Predictor Variables: New Mexico, 2001
Perceived Quality Satisfaction of Provider
Barriers to Care, Use Of Services, With Care, Communication, OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)
Social capital measure
Social support 0.73** (0.59, 0.92) 1.02 (0.83, 1.26) 1.05 (0.85, 1.29) 1.15 (0.93, 1.42)
Interconnectedness 0.87 (0.64, 1.19) 1.01 (0.74, 1.36) 1.77** (1.32, 2.39) 1.34 (0.99, 1.81)
Community participation 0.86 (0.65, 1.15) 0.90 (0.68, 1.18) 1.04 (0.83, 1.30) 1.00 (0.76, 1.32)
Predictor variable
Rural resident 1.30* (1.03, 1.63) 1.12 (0.90, 1.39) 0.88 (0.71, 1.09) 0.89 (0.72, 1.11)
High school graduate 1.23 (0.94, 1.60) 0.93 (0.72, 1.20) 1.00 (0.78, 1.28) 1.01 (0.78, 1.31)
Hispanic ethnicity 1.31* (1.04, 1.64) 1.15 (0.93, 1.43) 0.97 (0.79, 1.20) 1.00 (0.81, 1.25)
Age older than 65 y 0.52** (0.37, 0.72) 1.40* (1.04, 1.89) 2.20** (1.64, 2.97) 2.01** (1.47, 2.74)
Male gender 0.74* (0.58, 0.95) 0.60** (0.48, 0.76) 0.61** (0.49, 0.77) 0.83 (0.66, 1.05)
Uninsured 2.40** (1.89, 3.06) 0.28* (0.22, 0.36) 0.75* (0.60, 0.95) 0.73** (0.58, 0.92)
Note. OR = odds ratio; CI = confidence interval. Cell entries are point estimates of the ORs of the relationships between social capital measures and key predictor variables in the rows and outcome measures in the columns after controlling for the other predictor variables in the rows. *P < .05; **P < .01.
Criticisms and Applications of Social Capital
Research on social capital has come under criticism as a result of concern that attention to psychosocial risk factors may obscure the contributions to poor health of larger structural conditions such as ma- terial deprivation, inequitable policies, un- equal distribution of infrastructure, and unequal distributions of toxic environmen- tal exposures.13,32 Social capital empha- sizes social relationships and does not in- clude other community dimensions, such as marginalization, power conflicts, eco- nomic underdevelopment, or history of successful community organizing to attract
32 resources. Although research on social capital does
not negate the importance of these structural factors, some critics have expressed fears that the increasing interest in social capital of the World Bank and other international financial institutions may focus interventions on building trust rather than addressing broader ecological conditions.7,32,33 Our findings dem- onstrate the importance of both structural conditions (e.g., lack of insurance coverage) and social capital as predictors of health care experiences.
The concept of social capital also has been criticized for lack of precision in characteriz- ing social support mechanisms. For instance, most concepts and studies of social capital do not involve determination of which support networks may prove to be health enhancing and which may lead to damaging effects (such as drug trafficking gangs).34 Further studies of social capital could help to clarify how the value orientations of differing networks relate to health care and health outcomes.
These criticisms highlight the importance of avoiding oversimplification when analyz- ing the complexity of community dynamics. Our data suggest that social capital func- tions not as a unitary attribute but, rather, as a composite of attributes that have vary- ing associations with health service mea- sures. A more complete understanding of community dynamics and their relation- ships to health and health care will require additional research on these different at- tributes of social capital.
To date, most of the discussion about appli- cation of social capital to health care has taken place at the macropolicy level, with debate about the potential role of major inter- national funders in establishing programs ad- vancing social capital in communities. There
has been less discussion at the micropolicy level about how local planners might use so- cial capital concepts to improve health. With an enhanced understanding of these con- cepts, local planners might, for example, promote community organizing to expand social support and reduce barriers to care. Before such applications can be recom- mended, however, interventional research (as opposed to our observational study) must confirm their effectiveness.
Conclusions We produced evidence that 2 components
of social capital—social support and psychoso- cial interconnectedness—are related to certain health care experiences. Our findings provide further support for the thesis that community dynamics influence health. Because our brief measures of social capital constructs showed favorable psychometric properties, health planners may find them useful in conducting research on social capital relationships and health measures.
We recommend additional research to con- firm our findings in other populations, to test additional social capital measures, and to vali- date further the measurement of social capital in other groups. Finally, future research should clarify the relative importance of and potential interaction between social capital and structural factors as predictors of health care experiences.
About the Authors Megan Perry is with the Department of Anthropology, East Carolina University, Greenville, NC. Robert L. Williams and Nina Wallerstein are with the Department of Family and Community Medicine, University of New Mexico, Al- buquerque. Howard Waitzkin is with the Department of Family and Community Medicine and the Department of Sociology, University of New Mexico, Albuquerque.
Requests for reprints should be sent to Robert L. Williams, MD, MPH, Department of Family and Community Medi- cine, MSC09 5040, 1 University of New Mexico, Albu- querque, NM 87131 (e-mail: [email protected]).
This article was accepted April 29, 2007.
Contributors M. Perry conducted the data analysis and was responsi- ble for the initial writing. R. L. Williams originated the study, supervised the data collection, assisted with anal- ysis and interpretation, and led the writing. N. Waller- stein co-originated the study and assisted with analysis and interpretation. H. Waitzkin originated and directed the parent study of Medicaid managed care.
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Acknowledgments This study was supported in part by the Agency for Healthcare Research and Quality (grant R01 HS09703) and by the Dedicated Health Research Funds (grant C-2220-RAC) of the University of New Mexico School of Medicine.
We acknowledge John Bock for his assistance in data collection.
Human Participant Protection This study was approved by the human research review committee of the University of New Mexico Health Sci- ences Center. Respondents provided verbal informed consent to participate at the time of the survey.
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