Kathleen Thiede Call

Are rural residents less likely to obtain recommended preventive healthcare services

BACKGROUND This study examined rural-urban differences in utilization of preventive healthcare services and assessed the impact of rural residence, demographic factors, health insurance status, and health system characteristics on the likelihood of obtaining each service. METHODS National data from the 1997 Behavioral Risk Factor Surveillance System (BRFSS) and the 1999 Area Resource File were used to evaluate the adequacy of preventive services obtained by rural and urban women and men, using three sets of nationally accepted preventive services guidelines from the American Cancer Society, U.S. Preventive Services Task Force, and Healthy People 2010. Logistic regression models were developed to control for the effect of demographic factors, health insurance status, and health system characteristics. RESULTS Rural residents are less likely than urban residents to obtain certain preventive health services and are further behind urban residents in meeting Healthy People 2010 objectives. CONCLUSIONS Efforts to increase rural preventive services utilization need to build on federal, state, and community-based initiatives and to recognize the special challenges that rural areas present.

Barriers to care among american indians in public health care programs

Objective:We sought to examine the extent to which reported barriers to health care services differ between American Indians (AIs) and non-Hispanic Whites (Whites). Methods:A statewide stratified random sample of Minnesota health care program enrollees was surveyed. Responses from AI and White adult enrollees (n = 1281) and parents of child enrollees (n = 572) were analyzed using logistic regression models that account for the complex sample design. Barriers examined include: financial, access, and cultural barriers, confidence/trust in providers, and discrimination. Results:Both AIs and Whites report barriers to health care access. However, a greater proportion of AIs report barriers in most categories. Among adults, AIs are more likely to report racial discrimination, cultural misunderstandings, family/work responsibilities, and transportation difficulties, whereas Whites are more likely to report being unable to see their preferred doctor. A higher proportion of adult enrollees compared with parents of child enrollees report barriers in most categories; however, differences between parents of AIs and White children are more substantial. In addition to racial discrimination and cultural misunderstandings, parents of AI children are more likely than parents of White enrollees to report limited clinic hours, lack of respect for religious beliefs, and mistrust of their childs provider as barriers. Conclusions:Although individuals have enrolled in health care programs and have access to care, barriers to using these services remain. Significant differences between AIs and Whites involve issues of trust, respect, and discrimination. Providers must address barriers experienced by AIs to improve accessibility, acceptability, and quality of care for AI health care consumers.

Increasing response rates in a survey of Medicaid enrollees: the effect of a prepaid monetary incentive and mixed modes (mail and telephone).

Objectives:We sought to evaluate the effect of pairing a mixed-mode mail and telephone methodology with a prepaid

Are lower response rates hazardous to your health survey? An analysis of three state telephone health surveys.

2.00 cash incentive on response rates in a survey of Medicaid enrollees stratified by race and ethnicity. Research Design:Sampling was conducted in 2 stages. The first stage consisted of a simple random sample (SRS) of Medicaid enrollees. In the second stage, American Indian, African American, Latino, Hmong, and Somali enrollees were randomly sampled. A total of 8412 enrollees were assigned randomly to receive a mail survey with no incentive or a

Income Inequality, Race, and Child Well-being: An Aggregate Analysis in the 50 United States*

2.00 bill. Results:The response rate within the SRS after the mail portion was 54% in the incentive group and 45% in the nonincentive group. Response rates increased considerably with telephone follow-ups. The incentive SRS response rate increased to 69%, and the nonincentive response rate increased to 64%. Differences between incentive conditions are more pronounced after the first mailing (P < 0.01); almost all differences remained significant (P < 0.05) after the completion of the mail mode. The inclusion of the

The Importance of Geographic Data Aggregation in Assessing Disparities in American Indian Prenatal Care

2.00 incentive had similar effects on response rates and cost across the different racial and ethnic strata, except for Latino enrollees. Conclusions:A mixed-mode mail and telephone methodology is effective for increasing response rates in a Medicaid population overall and within different racial and ethnic groupings. The effectiveness of this strategy can be enhanced, in terms of response rate and cost, by including a

Barriers to care in an ethnically diverse publicly insured population: Is health care reform enough?

2.00 prepaid incentive.

Facilitating Research Faculty Participation in CBPR: Development of a Model Based on Key Informant Interviews

OBJECTIVE To examine the impact of response rate variation on survey estimates and costs in three health telephone surveys. DATA SOURCE Three telephone surveys of noninstitutionalized adults in Minnesota and Oklahoma conducted from 2003 to 2005. STUDY DESIGN We examine differences in demographics and health measures by number of call attempts made before completion of the survey or whether the household initially refused to participate. We compare the point estimates we actually obtained with those we would have obtained with a less aggressive protocol and subsequent lower response rate. We also simulate what the effective sample sizes would have been if less aggressive protocols were followed. PRINCIPAL FINDINGS Unweighted bivariate analyses reveal many differences between early completers and those requiring more contacts and between those who initially refused to participate and those who did not. However, after making standard poststratification adjustments, no statistically significant differences were observed in the key health variables we examined between the early responders and the estimates derived from the full reporting sample. CONCLUSIONS Our findings demonstrate that for the surveys we examined, larger effective sample sizes (i.e., more statistical power) could have been achieved with the same amount of funding using less aggressive calling protocols. For some studies, money spent on aggressively pursuing high response rates could be better used to increase statistical power and/or to directly examine nonresponse bias.

A Capacity Building Program to Promote CBPR Partnerships between Academic Researchers and Community Members

Interest in income inequality as a predictor of health has exploded since the mid-1990s. Recent analyses suggest, however, that the effect of income inequality on population health is not robust to a control for the racial composition of the population. That observation raises two interpretational questions. First, does income inequality have an independent effect on population health? Second, what does the effect of racial composition on population health mean? We use data from the Urban Institutes Assessing the New Federalism project and the Kids Count Databook to evaluate the aggregate effects of income inequality on diverse measures of child well-being (e.g., infant mortality, high school drop-out rates) in the 50 U.S. states. We replicate the finding that, net of the racial/ethnic composition of the population, the effects of income inequality are not significant. Moreover, the effects of racial composition on child well-being appear to be compositional (i.e., they reflect the less positive outcomes observed among racial/ethnic minorities) rather than contextual (i.e., representing the independent influence of social context). Whereas cross-level effects are still possible, our results cast doubt on the health relevance of these aggregate characteristics of the population.

Health care coverage and access to care: The status of Minnesota's veterans

OBJECTIVES We sought to determine whether aggregate national data for American Indians/Alaska Natives (AIANs) mask geographic variation and substantial subnational disparities in prenatal care utilization. METHODS We used data for US births from 1995 to 1997 and from 2000 to 2002 to examine prenatal care utilization among AIAN and non-Hispanic White mothers. The indicators we studied were late entry into prenatal care and inadequate utilization of prenatal care. We calculated rates and disparities for each indicator at the national, regional, and state levels, and we examined whether estimates for regions and states differed significantly from national estimates. We then estimated state-specific changes in prevalence rates and disparity rates over time. RESULTS Prenatal care utilization varied by region and state for AIANs and non-Hispanic Whites. In the 12 states with the largest AIAN birth populations, disparities varied dramatically. In addition, some states demonstrated substantial reductions in disparities over time, and other states showed significant increases in disparities. CONCLUSIONS Substantive conclusions about AIAN health care disparities should be geographically specific, and conclusions drawn at the national level may be unsuitable for policymaking and intervention at state and local levels. Efforts to accommodate the geographically specific data needs of AIAN health researchers and others interested in state-level comparisons are warranted.