Rural and Remote Health Journal photo
African section Asian section Australasian section European section Latin American section North American section
current articles

information for authors
status/user profile
about us

Original Research

Receipt of preventive counseling among reproductive-aged women in rural and urban communities

Submitted: 21 September 2010
Revised: 20 November 2010
Published: 28 January 2011

Full text: View a printable version.

Author(s) : McCall-Hosenfeld JS, Weisman CS.

Jennifer McCall-Hosenfeld

Carol Weisman

Citation: McCall-Hosenfeld JS, Weisman CS.  Receipt of preventive counseling among reproductive-aged women in rural and urban communities. Rural and Remote Health (Internet) 2011; 11: 1617. Available: (Accessed 17 October 2017)

[View Author Details]


Introduction: Preventive health interventions often occur less frequently among rural women compared to urban women. Preventive counseling is an important feature of comprehensive preventive healthcare provision, but geographic disparities in the receipt of preventive counseling services have not been fully described. In this study the framework of the behavioral model of healthcare utilization was employed to investigate the association between rurality and receiving preventive counseling. It was hypothesized that demographic differences in rural and urban communities, as well as differential healthcare resources, explain rural–urban healthcare disparities in preventive counseling.
Methods: Data were collected by telephone survey during 2004–2005 for 2002 participants aged 18–45 years in the Central Pennsylvania Women’s Health Study. Measures of preventive counseling were based on US Preventive Services Task Force recommendations as of 2004. Multivariable models assessed the independent contribution of rurality to the receipt of counseling for smoking, alcohol/drug use, birth control, nutrition, weight management, and physical activity. Rurality was assessed using Rural-Urban Communting Area Codes. All models controlled for variables that predispose individuals to use health services (age, race/ethnicity, educational level), variables that enable or impede healthcare access (having a usual healthcare provider, using an obstetrician-gynecologist, poverty, and continuous health insurance coverage) and need-based variables (health behaviors and indicators).
Results: In bivariate analysis, the rural population was older, had lower educational attainment, and was more likely to be White, non-Hispanic. Urban women tended to report seeing an obstetrician-gynecologist more frequently, and engaged more frequently in binge drinking/drug use. Preventive counseling was low among both rural and urban women, and ranged from 12% of the population for alcohol/drug use counseling, to 37% for diet or nutrition counseling. The degree of rurality appeared to impact counseling, with women in small or isolated rural areas significantly less likely than urban women and women in large rural areas to receive counseling related to smoking, alcohol/drug use and birth control. Overall, rural women reported less counseling for alcohol/drug use, smoking, birth control, nutrition and physical activity. In multivariable analysis, rurality was independently associated with lack of preventive counseling for physical activity. However, adjusting for predisposing, enabling and need-based variables fully attenuated the effect of rurality in the remaining models. Younger age, higher educational attainment, and seeing any obstetrician-gynecologist were associated with receipt of counseling in several models.
Conclusions: Most women do not receive recommended preventive counseling. While rural women are less likely than urban women to receive counseling, rurality generally was not independently associated with receipt of counseling once demographics, access to health care, and health behaviors and indicators were controlled. This suggests that both demographic differences between rural and urban communities as well as aspects of healthcare access govern rural–urban healthcare disparities in preventive counseling. These results speak to important targets for reducting urban–rural healthcare disparities in receiving preventive counseling, improving the health literacy of the rural population, educating rural healthcare providers about the need for preventive counseling, and the expansion of access to obstetrician-gynecologists in rural communities.

Key words: counseling, educational status, health services accessibility, healthcare disparities, preventive health services, USA, women.



Prior research has shown that residents of rural areas are less likely than those in urban areas to receive recommended clinical preventive services such as Pap smears, mammograms, and influenza vaccinations1,2. While it is often assumed that rural–urban disparities in healthcare utilization are explained by a lower availability of health services in rural areas, other attributes of rural populations, such as older age, higher poverty, lower educational attainment, and poorer insurance status3,4, could be implicated in rural–urban disparities. To date, most studies of rural–urban healthcare disparities have focused on screening tests or vaccinations and have not incorporated recommended preventive counseling services1,5,6. Studies that have examined rural–urban differences in counseling do not adjust for sociodemographic factors that differentiate rural and urban populations7.

This article extends prior work by addressing receipt of recommended preventive counseling among reproductive-age women in a geographically diverse region. Using the framework of the behavioral model of health services utilization8, rural–urban differences and the factors that might account for them were analyzed. It was hypothesized that receipt of preventive counseling would be less prevalent in rural areas but that these differences would be attenuated by other variables.


Population studied

Baseline data were examined from the Central Pennsylvania Women’s Health Study’s (CePAWHS’s) random digit-dial telephone survey of 2002 women aged 18–45 years, conducted in 2004 and 2005. The design of this study has been described previously9. Briefly, CePAWHS was a population-based survey of reproductive aged women residing in Central Pennsylvania, USA. The target population encompassed 28 counties with oversampling of rural communities. Subjects were excluded for male gender, non-residence in the target region, or not speaking English or Spanish. Communities were prepared for the study by advertisements in the local media. Selected households received a pre-notification letter containing a small ($2) incentive. 

Each selected household was contacted up to 25 times to screen eligible participants. If there was more than one eligible participant per household, a single participant was randomly selected. Consistent with other random digit dial surveys, the response rate (number of complete interviews divided by the number of eligible reporting units in the sample) was 52% and the cooperation rate (proportion of all cases interviewed among all eligible units ever contacted) was 63%10. In comparison with census data, the sample was highly representative of the target population with respect to sociodemographics9

The Pennsylvania State University College of Medicine Institutional Review Board reviewed and approved the study and a Certificate of Confidentiality (CC-HD-04024) was obtained from the National Institutes of Health.

Dependent variables

The US Preventive Services Task Force (USPSTF) evidence-based recommendations for preventive counseling as of 2004 were used to identify dependent variables. Preventive counseling recommendations were selected that had either Level A or B evidence. Level A is good evidence that the service improves important health outcomes and benefits substantially outweigh harms; Level B is at least fair evidence that the service improves important health outcomes and benefits outweigh harms11. The following counseling recommendations were selected for investigation: 

  1. Screen and counsel all adults for tobacco use (level A, 2003).
  2. Screen and counsel all adults for alcohol misuse (level B, 2004).
  3. Provide periodic routine counseling about effective contraception for adults at risk of unintended pregnancy (level B, 1996).
  4. Among obese patients, offer intensive counseling and behavioral interventions to promote sustained weight loss (level B, 2003). 

Measures of receipt of these services were based on the question, 'In the past 12 months, has a doctor or other health professional asked you or talked to you about any of the following things?' The list that followed included smoking, alcohol or drug use, birth control, diet and nutrition, weight management, and physical activity. 

Independent variables

Rurality was assessed using the zip-code based approximation of Rural-Urban Commuting Area (RUCA) codes, a classification taxonomy based on the sizes of cities and towns and daily commuting practices12,13. This taxonomy is particularly useful for studies of health service utilization because the communities into which individuals commute or flow may also be those in which they receive care12. A three-level classification was used that allows for comparison of rural versus urban areas by degree of rurality. In this classification system, urban areas are defined as metropolitan areas with primary commuting flows within an urbanized area of 50 000 individuals or greater; large rural city- or town-focused areas have primary commuting flow within an urban cluster of no more than 10 000 to 49 999 persons; small rural town-focused areas have primary commuting flow within an urban cluster of no more than 9999 persons; isolated small rural towns have primary commuting flow outside of an urban cluster14. Due to small numbers in the isolated rural category in the sample, small and isolated rural towns were combined. For ease of discussion, the three-category classification used in this study will be referred to as: urban, large rural area, and small or isolated rural area.

Independent variables were selected based on the behavioral model of health services utilization, which describes categories of factors that govern health services utilization: (i) variables that predispose individuals to use services; (ii) variables that enable or impede access to care; and (iii) variables that govern the need for health services8

Predisposing factors included age, race/ethnicity, and education. To measure access to healthcare, an enabling factor, respondents were asked whether they have a regular doctor or other healthcare provider. Additionally, because the receipt of preventive services is associated with seeing an obstetrician-gynecologist15 and because counseling content may differ based on the training of the counseling provider16, whether the patient saw an obstetrician-gynecologist for any healthcare was also assessed. Financial access to health care was measured by poverty status and continuity of health insurance coverage in the past 12 months. Poverty status is based on household size and income; because over 10% of respondents did not report household income, they are coded as 'missing poverty' so that they may be compared with other respondents in analyses. Frequency of health services utilization was measured in the survey, but this variable was excluded from the models because it is redundant with the measured outcomes.

Need for preventive counseling is assumed because only services recommended by the USPSTF were identified for the target population. However, because the need for counseling is greater among individuals engaging in unhealthy behaviors, also included were measures of smoking status and whether or not the woman binge drinks (defined as 5 or more drinks on one occasion in the past month) or has used any illicit drugs in the past month. Obesity, which is specific indicator for need of counseling as defined by the USPSTF, was measured as a calculated body mass index (BMI) of greater than 30. Participant self-reported height and weight was used to calculate BMI.

Statistical analyses

Chi-square tests were used to perform bivariate comparisons of urban–rural differences on all study variables. Receipt of each counseling service was modeled using multiple logistic regression. All candidate variables were assessed for multicollinearity; no variables were excluded on this basis. All predisposing, enabling and need factors defined above were included as covariates in the models. Unhealthy behaviors and health indicators are included in appropriate models. All statistical analyses were performed using SAS v9.2 (SAS Institute; Cary, NC, USA).


Sixty-one percent of the sample was classified as urban, 22% as residing in large rural areas, and 16% as residing in small or isolated rural areas. Associations between study variables and rurality are shown (Table 1). The rural population was older, had lower educational attainment, and was more likely to be White, non-Hispanic. Although rural and urban women were equally likely to identify a regular healthcare provider, urban women tended to report seeing an obstetrician-gynecologist more frequently. The only other statistically significant difference in independent variables was for binge drinking or drug use: urban women were more likely than rural women to engage in these behaviors.

Overall counseling rates ranged from 12% of the population for alcohol/drug use counseling, to 37% for diet or nutrition counseling. Women in small or isolated rural areas were significantly less likely than urban women and women in large rural areas to receive counseling related to smoking, alcohol/drug use and birth control. Rural women were also less likely to receive nutrition and physical activity counseling compared with urban women.

The results of the multivariable analyses are shown (Table 2). After controlling for demographics, healthcare access, and health behaviors and indicators, no independent association was found of rurality with counseling for smoking, alcohol/drug use, or birth control. However, residence in a large rural area significantly decreased the odds of receiving physical activity counseling compared with urban women. In multivariable models, younger age, higher educational attainment, and seeing any obstetrician-gynecologist were associated with increased odds of receiving several preventive counseling services. For smoking counseling, the need for counseling (indicated by current smoking status) increased the odds of receiving counseling nine-fold. Similarly, obesity (which indicated need for counseling on diet or nutrition, weight management, and exercise or physical activity) substantially increased the odds of receiving these counseling interventions.

Table 1: Bivariate comparisons by degree of rurality - Central Pennsylvania Women’s Health Study, 2004–2005

Table 2: Predictors of Receiving Preventive Counseling - Central Pennsylvania Women’s Health Study, 2004–2005


The overall rates of receiving preventive counseling services were low for both urban and rural women, and rural women were less likely than urban women to report receiving five out of six preventive counseling services. In multivariable analysis, women residing in large rural areas were less likely than urban women to receive exercise counseling. However, rurality was not independently associated with receipt of preventive counseling for tobacco, alcohol/drug use, birth control, nutrition, or weight management. Other predictors of receiving counseling included predisposing factors (younger age, higher educational attainment), enabling factors (having continuous health insurance coverage and seeing an obstetrician-gynecologist), and need for counseling (smoking status, obesity). Thus these variables largely account for the effect of rural residence on receipt of counseling.

The finding that higher educational attainment was associated with increased odds of preventive counseling is consistent with prior research on receipt of preventive services5,17 and counseling15. This finding may be explained in part by greater health literacy18 or help-seeking among more highly educated women. Provider perceptions that preventive counseling is less relevant for some groups of women (eg older women) also may account for the findings. Due to pregnancy-related risks, all women of reproductive age should receive counseling related to tobacco use, alcohol/drug use, birth control, nutrition, and physical activity19. The findings suggest that providers may see these as issues only for younger women or women who engage in adverse health behaviors.

Seeing an obstetrician-gynecologist was independently predictive of increased counseling, and this may be highly relevant to women in rural areas where obstetrician-gynecologists are less available. Differential access to services provided by obstetrician-gynecologists in rural areas have prompted calls for increasing access to obstetrician-gynecologists as an important tool in reducing urban–rural healthcare disparities20.

Comprehensive weight management counseling was less frequent for subjects in large rural areas than for urban women, independent of seeing an obstetrician-gynecologist, but the reasons for this cannot be discerned from this study. Further study of the predictors of counseling, including contextual factors describing women’s communities, is needed to understand why some rural women do not receive preventive counseling.

Study limitations and strengths

This study has several limitations. First, data are self-report and thus may be subject to inaccurate recall of receipt of counseling. However, because patient counseling is not recorded in a standardized way, chart review cannot be used reliably to assess counseling behaviors; thus, self-report is the standard methodology for assessing preventive health counseling21,22. Moreover, although such factors as age, educational level, socioeconomic status, and social desirability could affect the reporting of preventing counseling23, to the authors’ knowledge these forms of recall bias are not known to differentially affect urban and rural populations.

Inaccurate recall of covariates may affect some of the effect estimates. For example, it is possible that participants’ self-reported weights and heights were inaccurate, and that these inaccuracies would underestimate BMI by a small amount, resulting in a misclassification of true BMI categories24. However, self-reported height and weight have been found to accurately represent BMI abstracted from medical records for reproductive-age women25. Thus, it is unlikely that BMI misclassification affected the findings.

Another potential limitation of this analysis is that USPSTF guidelines are not specific about the frequency of recommended periodic counseling services. Participants were asked about counseling within the past year, but annual counseling is not necessarily recommended. Controlling for adverse health behaviors enabled us to partially address the timeliness of counseling. Additionally, participants reported that they had discussed these topics with their doctor in the past year, but discussing a topic with a doctor does not mean that appropriate counseling occurred; the content of the counseling was not measured in this study. Finally, although the sample is highly representative of the target population, the findings may not be representative of other regions.

An important strength of this work is its focus on rurality and the exploration of covariates that might explain rural–urban differences in counseling. These covariates have not been explicitly addressed in prior research. An additional strength of this work is its focus on clinical preventive counseling services that are evidence-based and recommended by the USPSTF.


Because rurality was generally not independently associated with receipt of counseling in multivariable models, the work indicates that other predisposing, enabling, and need factors account for counseling deficits in rural areas. Thus, public health efforts to reduce urban–rural healthcare disparities in preventive counseling should focus on increasing the health literacy of the reproductive-age rural population, educating providers about the need for preventive counseling, and expanding access to obstetrician-gynecologists in rural communities.


Dr McCall-Hosenfeld was funded by the NIH Office of Research on Women’s Health’s Building Interdisciplinary Research Careers in Women's Health career development award, 5 K12 HD05582-03. The Central Pennsylvania Women’s Health Study was funded, in part, by grant number 4100020719 from the Pennsylvania Department of Health. The Pennsylvania Department of Health specifically disclaims responsibility for any analyses, interpretations or conclusions. The funders did not have any role in the study design, collection, analysis or interpretation of the data or in the writing of the report. The views expressed herein are those of the authors and do not necessarily reflect the opinions of the funders.


1Casey MM, Thiede Call K, Klingner JM. Are rural residents less likely to obtain recommended preventive healthcare services? American Journal of Preventive Medicine 2001; 21(3): 182-188.

2Larson S, Correa-de-Araujo R. Preventive health examinations: a comparison along the rural-urban continuum. Women’s Health Issues 2006; 16(2): 80-88.

3Eberhardt MS, Pamuk ER. The importance of place of residence: examining health in rural and nonrural areas. American Journal of Public Health 2004; 94(10): 1682-1686.

4Harris R, Leininger L. Preventive care in rural primary care practice. Cancer 1993; 72(3 Suppl): 1113-1118.

5Zhang P, Tao G, Irwin KL. Utilization of preventive medical services in the United States: a comparison between rural and urban populations. Journal of Rural Health 2000; 16(4): 349-356.

6Edwards JB, Tudiver F. Women's preventive screening in rural health clinics. Women’s Health Issues 2008; 18(3): 155-166.

7Probst JC, Moore CG, Baxley EG, Lammie JJ. Rural-urban differences in visits to primary care physicians. Family Medicine 2002; 34(8): 609-615.

8Andersen RM. Revisiting the behavioral model and access to medical care: does it matter? Journal of Health and Social Behavior 1995; 36(1): 1-10.

9Weisman CS, Hillemeier MM, Chase GA, Dyer AM, Baker SA, Feinberg M et al. Preconceptional health: risks of adverse pregnancy outcomes by reproductive life stage in the Central Pennsylvania Women's Health Study (CePAWHS). Women’s Health Issues 2006; 16(4): 216-224.

10American Association for Public Opinion Research. Standard definitions: final disposition of case codes and outcome rates for surveys, 3rd Edition. Lenexa, KS: AAPOR, 2004.

11US Preventive Services Task Force. USPSTF Recommendations. (Online) no date. Available: (Accessed 15 August 2010).

12Hall SA, Kaufman JS, Ricketts TC. Defining urban and rural areas in US epidemiologic studies. Journal of Urban Health 2006; 83(2): 162-175.

13Hart LG, Larson EH, Lishner DM. Rural definitions for health policy and research. American Journal of Public Health 2005; 95(7): 1149-1155.

14WWAMI Rural Health Research Center. RUCA Data code definitions, v2.0. (Online) no date. Available: (Accessed 5 August 2010).

15Henderson JT, Weisman CS, Grason H. Are two doctors better than one? Women's physician use and appropriate care. Women’s Health Issues 2002; 12(3): 138-149.

16Park ER, Wolfe TJ, Gokhale M, Winikoff JP, Rigotti NA. Perceived preparedness to provide preventive counseling: reports of graduating primary care residents at academic health centers. Journal of General Internal Medicine 2005; 20(5): 386-391.

17Carrasquillo O, Lantigua RA, Shea S. Preventive services among Medicare beneficiaries with supplemental coverage versus HMO enrollees, Medicaid recipients, and elders with no additional coverage. Medical Care 2001; 39(6): 616-626.

18Scott TL, Gazmararian JA, Williams MV, Baker DW. Health literacy and preventive health care use among Medicare enrollees in a managed care organization. Medical Care 2002; 40(5): 395-404.

19Johnson K, Posner SF, Biermann J Cordero JF, Attash HK, Parker CS et al. Recommendations to improve preconception health and health care–United States. Morbidity and Mortality Weekly Report Recommendations and Reports 2006; 55(RR-6): 1-23.

20ACOG Committee. ACOG Committee Opinion No. 429: health disparities for rural women. Obstetrics and Gynecology 2009; 113(3): 762-765.

21DePue JD, Goldstein MG, Redding CA, Velicer WF, Sun X, Fava JL et al. Cancer prevention in primary care: predictors of patient counseling across four risk behaviors over 24 months. Preventive Medicine 2008; 46(3): 252-259.

22Armour BS, Campbell VA, Crews JE, Malarcher A, Maurice E, Richard RA. State-level prevalence of cigarette smoking and treatment advice, by disability status, United States, 2005. Preventing Chronic Disease 2007; (4)4. Available: (Accessed 18 November 2010).

23Coughlin SS. Recall bias in epidemiologic studies. Journal of Clinical Epidemiology 1990 43(1): 87-91.

24Nawaz H, Chan W, Abdulrahman M, Larson D, Katz DL. Self-reported weight and height – implications for obesity research. American Journal of Preventive Medicine 2001; 20(4): 294-298.

25Huber LRB. Validity of self-reported height and weight in women of reproductive age. Maternal and Child Health Journal 2007; 11(2): 137-144.

© Jennifer McCall-Hosenfeld, Carol Weisman 2011 A licence to publish this material has been given to James Cook University,

This article has been viewed 5937 times since January 28, 2011.Article No. 1617