Read the article titled: “Social Capital and Health Care Experiences among Low-Income Individuals” (see attached). Critique the study’s sampling design by answering the attached questions (make sure not to answer with a yes/no; elaborate on each answer by describing what the authors did). Please use the attached word document template to add your answers to. Please adhere

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Assignment 3 Rubric & Don't use plagiarized sources. Get Your Custom Essay on Week 3 assignment CI Just from $13/Page Order Essay Answers – Perry
Question	Answers	Possible Points
1 .	Is the study population identified and described? Are eligibility criteria specified?									–	–
Study Population										5
Eligibility criteria
2 .	Dependent variable			6
3.	Independent variable
4 .	What approach do the authors use to establish content validity? Content validity is the “degree to which an instrument has an appropriate sample of items for the construct being measured and adequately covers the construct domain.” To ensure a “content-valid” instrument, researchers should start with a conceptualization of the construct. This can be based on: · First-hand knowledge · Literature review · Expert consultations · Preliminary qualitative studies
5.	What type of sampling plan was used? Would an alternative sampling plan have been preferable? Was the sampling plan one that could be expected to yield a representative sample?
Sampling plan.
Alternative sampling plan				2
Sampling plan yielding representative sample	2
6.	If sampling was stratified, was a useful stratification variable selected? If a consecutive sample was used, was the time period long enough to address seasonal or temporal variation?
Stratification		1
Consecutive sampling
7.	If cluster sampling was utilized, what were the clusters and how was sampling done within clusters?
8.	How were people recruited into the sample? Does the method suggest potential biases?
Recruitment		4
Potential biases
9.	Did some factor other than the sampling plan affect the representativeness of the sample?
10.	Are possible sample biases or weaknesses identified by the researchers themselves?
11.	Are key characteristics of the sample described (e. g., mean age, percent female)? If yes, please provide detailed information on sample characteristics.	12
12.	Was the sample size justified on the basis of a power analysis or other rationale?
13.	What approach do the authors use to check for internal reliability?
14.	What approach do the authors use to establish interrater reliability?
15.	What approach do the authors use to establish test-retest reliability?
16.	What approach do the authors use to establish discriminant validity? Discriminant validity is “the ability to differentiate a construct from other similar constructs.”
17.	Does the sample support inferences about external validity? To whom can the study results reasonably be generalized?
External validity
Generalizability

Source: Nursing Research: Generating and Assessing Evidence for Nursing Practice, 9th edition, p. 289.

 RESEARCH AND PRACTICE 

Social Capital and Health Care Experience

s

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

330 | Research and Practice | Peer Reviewed | Perry et al. American Journal of Public Health | February 2008, Vol 98, No. 2

https://areas.18

https://million.17

https://population.16

https://interval=0.59

https://ratio=0.73

https://physicians.14

 RESEARCH AND PRACTICE 

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

February 2008, Vol 98, No. 2 | American Journal of Public Health Perry et al. | Peer Reviewed | Research and Practice | 331

https://participation.23

 RESEARCH AND PRACTICE 

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, 200

1

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

332 | Research and Practice | Peer Reviewed | Perry et al. American Journal of Public Health | February 2008, Vol 98, No. 2

https://coefficient=0.17

https://coefficient=0.29

 RESEARCH AND PRACTICE 

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).

 RESEARCH AND PRACTICE 

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: rlwilliams@salud.unm.edu).

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.

February 2008, Vol 98, No. 2 | American Journal of Public Health Perry et al. | Peer Reviewed | Research and Practice | 335

mailto:rlwilliams@salud.unm.edu

https://gangs).34

 RESEARCH AND PRACTICE 

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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