late entry into prenatal care in a rural setting

LATE ENTRY INTO PRENATAL CARE IN A RURAL SETTING

Daniel Chandler, PhD

ABSTRACT

Social support, behavioral risk, and structural or demographicvariables as well as acceptance of pregnancy were tested asdeterminants of late entry into prenatal care in a sample of 176women in a rural county in California. The respondents wereall those over age 18 served by four obstetric practices duringa 4-month period ending in February 2000. One nurse-mid-wifery practice was included. Late entry into prenatal careduring the first trimester occurred in 27.3% of the cases overall.Statistically significant independent variables in bivariate anal-yses were modeled in multivariate logistic regression. Stress,lack of family and friend support, Medicaid enrollment, ageunder 20 or over 34, low acceptance of pregnancy, and lack ofa high school diploma were all predictors of late entry. Lack offamily and friend support modified the effects of stress andMedicaid as payer. Although the determinants of late entrywere remarkably complex in this sample, they have potentialfor public health intervention. J Midwifery Womens Health2002;47:28–34 © 2002 by the American College of Nurse-Midwives.

BACKGROUND

Research has revealed a complex set of demographic,social, and personal factors that influence time of entryinto care (1). However, relatively little time of entryresearch has focused on rural areas (2, 3), leading publichealth officials of Humboldt County in California to seekexplanations specifically relevant to this coastal countyof 127,000 by means of a survey of pregnant women.

Predictive factors to be tested were drawn from recentstudies in both rural and urban areas that cited thefollowing behavioral risks: smoking (4), heavy drinkingor using illicit drugs (2, 5), being in an abusive relation-ship (6), depression (7, 8), and perceived stress (9). Apsychological risk factor, low acceptance of the preg-nancy, has been reported to have no effect on time ofentry (10) and to be associated with early entry (11).

The effects of social support of family, friends, part-ners, and providers appears important, but findings havebeen inconsistent. In some studies, late entry into prena-tal care is more likely when a woman is involved in anextended family or other dense network (12) or whenfamily and friends are transmitting a Native Americanindigenous culture of support (13, 14). In other studies,lack of social support predicted late entry (15, 16). Early

entry appears less likely if a woman does not have arelationship with a current partner and especially if shedoes not communicate freely with her partner about thepregnancy (17–20). Finally, early entry may be lesslikely if women do not perceive providers as part of theirsupport network or if they dislike doctors (1). However,generic social support does not necessarily have the sameeffects as pregnancy-related social support (12).

The experience of local public health nurses suggestedtwo factors regarding the care itself: women’s knowledgeabout early pregnancy genetic testing and where apregnancy test was performed (home, provider’s office,or pregnancy clinic). Other care-related variables ofinterest were whether a woman was currently receivingcare from a gynecologic provider who also providesobstetric care (21); difficulty getting to prenatal care dueto transportation, child care, or difficulty getting off work(22); and the financial burden the pregnancy and childpresented (23).

The objectives of this study were to test in a rural areathose factors that in other studies have been associatedwith late entry into prenatal care and to develop educa-tional and policy interventions based on the study find-ings.

METHODS

Population and Sample

The theoretic population of interest was pregnant womenin Humboldt County and, by extension, pregnant womenin rural communities. Humboldt County is an area on thenorthern California coast with a slowly increasing pop-ulation of 127,000. The annual number of births, how-ever, has declined over the past 10 years from a high of1,800 to 1,444 in 1998. Traditional industries of loggingand fishing have also declined, whereas service jobs haveincreased.

Construction of the sampling frame had two compo-nents. First, the duration of the sampling was limited forpractical reasons from October 21, 1999 through Febru-ary 29, 2000. Second, sampling was conducted in fourlarge obstetric practices serving the four largest popula-tion centers in the county. One of the practices was anurse-midwifery group practice (site A), and anotherprivate practice included both a physician and a nurse-midwife (site B); a third was a community clinic with

Address correspondence to Daniel Chandler, PhD, 436 Old Wagon Road,Trinidad, CA 95570.

28 Journal of Midwifery & Women’s Health • Vol. 47, No. 1, January/February 2002

© 2002 by the American College of Nurse-Midwives 1526-9523/02/$22.00 • PII S1526-9523(01)00214-8Issued by Elsevier Science Inc.

physician providers (site C), and a fourth was an obstetricgroup practice (site D). Together, these four offices haddelivered 62% of the births in the county in the 9 monthsbefore the study. Providers not included in the samplewere smaller obstetric practices and family practitionersas well as a small number of licensed midwives doinghome births. The final sample comprised 176 women: 43from site A, 53 from site B, 17 from site C, and 62 fromsite D.

The average age of the women in the sample was 26(youngest 18; oldest 44). Only 12% did not hold a highschool diploma or its equivalency, whereas 55% had atleast some college. The subjects were predominatelywhite (82%), 7% were Hispanic, and 5% AmericanIndian. Most of the women paid for prenatal care withMedicaid (54%), but 43% of the others had privateinsurance. Twenty-one percent of the women were nottogether with a partner or spouse during the pregnancy.Thirty-six percent of respondents were having their firstbaby, and an equal percentage were having their second;27% reported two or more prior births.

Procedures

During the sampling period, surveys were distributed toall women age 18 and older in their fifth to ninth monthof pregnancy who were receiving care in the fourpractices. A total of 176 completed survey forms werereceived, which represented about 12% of the annualbirths in the county.

The study and survey instrument were granted Insti-tutional Review Board approval by the Committee forthe Protection of Human Subjects at the California StateUniversity, Humboldt, on condition that the survey formsbe anonymous and that persons under 18 not be included.Informed consent was obtained from all participants. Acover letter to respondents explained the study and itssponsorship and stressed that participation was volun-tary. A token gift of soap, wrapped as a gift, was used asan incentive for respondents. At each office, a staffsurvey coordinator was identified and given a smallincentive payment. Written procedures were given toeach coordinator. Graduate students picked up the sur-veys weekly and answered questions from staff whenthey did so. During the course of the survey, there wereno reports of respondents who were uncomfortable orunhappy with the survey, and only one refusal to partic-ipate was reported.

Survey Instrument

Each of the factors discussed previously was representedby at least one question. Pregnancy-specific social sup-port by family and friends and by partners were measuredby psychometrically sound scales developed by Camp-bell (and used by permission), as was acceptance ofpregnancy (24). Disclosing problems with alcohol orother drugs or domestic violence or depression can beuncomfortable, so questions about these domains wereinterspersed with questions about other less sensitive“self-care” issues such as diet. Respondents were firstasked if their provider had asked about or discussed eachof these self-care issues. Each respondent was then askedwhich of these issues she had experienced at the time shelearned she was pregnant.

The dependent variable—late entry into care—wasdefined as occurring if the first prenatal visit took placemore than 13 complete weeks after the start of the lastmenstrual period. Office staff were asked to fill in thedate of the first visit and the due date; the last menstrualperiod was calculated as due date minus 280 days.However, in 22 cases, information to be supplied byoffice staff was missing; thus, we used the respondent’sown estimate of the trimester in which she started care.

Analysis

The analytic method used to determine predictors ofearly entry into care is multiple logistic regression.Factors significant at an alpha of 0.10 in bivariateanalysis were entered into a multiple logistic regressionmodel. Factors were tested for effect modification (inter-action) if theory suggested it, particularly for the rela-tionship of social support and behavioral risk (12).Multicollinearity was assessed by using variance infla-tion factors (25). Akaike’s Information Criterion and theBayesian Information Criterion (26) were used for as-sessing model fit.

RESULTS

Differences Between Sites

Overall, 27.3% of respondents entered care after the firsttrimester compared with a corresponding 72.7% whoentered care early. This percentage differed considerablyby site: 27.9% of the midwifery respondents, 39.6% ofthe combined (midwife plus obstetrician) respondents,11.8% of the community clinic respondents, and 21.0%of the obstetrician respondents entered late (�2 � 7.36;df � 3; P � .06). In addition to differences on thedependent variable of late entry, the percentage learningof their pregnancy through a home test and the percent-age with less than a high school degree were significantlydifferent by site. Because of these site-based differences,

Daniel Chandler received a PhD in sociology from the University ofCalifornia, Berkeley. He has worked as a mental health consultant tothe California Legislature and as Chief of Planning and Evaluation forthe Sacramento County public mental health program. Since 1987, hehas been an independent health policy analyst and consultant.

Journal of Midwifery & Women’s Health • Vol. 47, No. 1, January/February 2002 29

adjustment was made in all analyses for the fact thatrespondents were independent across sites but may nothave been within sites (27). That is, all analyses accountfor clustering and use robust tests of significance.

Bivariate Analysis

Table 1 summarizes bivariate logistic regression analysesof each of the factors tested for predictive power. Forpresentation, all independent variables were converted toa “yes/no” format, although preliminary analyses usedthe original variables.

As can be seen, the failure to reach statistical signifi-cance may in some instances reflect a small number ofrespondents with a given risk factor, particularly drugusers, heavy drinkers, and persons involved in domesticviolence. (It is also likely that, despite anonymity, theseissues were underreported.)

Multivariate Model with Effect Modifiers

The statistically significant variables (using a thresholdof P � .10) were used to develop a multiple logistic

regression model that also included interactions betweenvariables, or in other terminology, effect modification.The model is shown in Table 2. The variables “lack offull family support regarding pregnancy,” “stress,”“Medicaid as payer,” “lack of full acceptance of thepregnancy,” and “higher risk age categories” remained inthe model, whereas depression and smoking weredropped out. “Lack of high school degree” was includedbecause of its significance in earlier birth certificateanalysis we had conducted. Having at least three priorbirths turned out to be highly collinear with stress,high-risk age, and lack of acceptance of pregnancy and,thus, was dropped. Two significant interactions werefound between lack of full family support and stress andlack of full family support and Medicaid as payer. Themodel shown in Table 2 was evaluated with Akaike’sInformation Criterion and the Bayesian Information Cri-terion (26). Even though some P values appear to be notsignificant, removing these variables reduced the model’sfit to the data.

Figures 1 and 2 illustrate the impact of the effectmodifiers with the other factors in Table 2 held constant.

TABLE 1Bivariate Effect of Each Explanatory Variable in Relationship to Entry After the First Trimester

Risk FactorN

(Overall � 176)*Percent

Entering Late†OddsRatio

ConfidenceInterval

PValues‡

Social supportDid not want a lot of provider support 98 23.5 0.66 0.28–1.53 NSLack full family and friend support 63 39.7 2.55 1.15–5.62 0.02Lack full partner support 37§ 32.4 1.36 0.54–3.43 NS

Behavioral riskSmoked 42 35.7 1.68 1.09–2.59 0.02Stress 58 41.4 2.84 1.56–5.21 0.00Depressed 32 37.5 1.78 1.39–2.39 0.00Experienced a lot of stress 58 41.4 2.85 1.56–5.20 0.00Drank heavily 7 42.9 2.05 0.15–28.8 NSUsed illicit drugs 18 33.3 1.37 0.50–3.76 NSIn abusive relationship 4 0.0 NA� NA NS

Not full acceptance of pregnancy 25 56.0 4.34 1.73–10.86 0.00Care-related factors

Pregnancy test at home not office 97 27.8 1.19 0.79–1.79 NSLack knowledge of genetic testing 29 34.5 1.49 0.74–3.04 NSNot already seeing provider 150 28.0 1.23 0.42–3.62 NS

Structural factorsThree or more prior births 56 37.5 2.1 0.99–4.43 0.03Higher risk age: 18–19 or over 34 35 42.9 2.58 1.75–3.81 0.00Difficulty getting to prenatal care 20 35.0 1.50 0.47–4.78 NSPregnancy or child financial burden 44 36.4 1.81 0.81–4.06 NSMedicaid as payer 96 33.3 1.97 1.20–3.23 0.01No high school diploma 21 42.9 2.25 0.71–7.17 NSNot Caucasian 32 34.4 1.47 0.50–4.33 NS

* N is number of respondents in the sample with this risk factor. Thus, 98 of 176 women (56%) “Did not want a lot of provider support.”

† These percentages can be compared to the 27.3 in the overall sample. Thus, of those who did not receive full family and friend support, 37.9% enteredlate versus 27.3% overall.

‡ NS (nonsignificant) was a P value � .10; NA � not applicable.

§ Includes 11 women with no partner.

� If the variable predicts success of failure perfectly, odds ratios are not applicable.


Figure 1 shows the interaction of stress and lack offamily and friend support. Stress is a powerful determi-nant that is associated with late entry even when thewoman has “full support from family or friends” (mean-ing all nine items on this scale are positive). Amongstressed women, family and friend support was mini-mally predictive of late entry. For unstressed women,however, full support by family and friends is veryimportant. Only 10% of the unstressed women who had

full family and friend support entered care after the firsttrimester versus 49% of the unstressed women who didnot have full support.

In Figure 2, the interaction between full family andfriend support and Medicaid as a payer is shown (withother factors in Table 2 held constant). Medicaid as payerrepresents two different types of risks. It is both a proxyfor social class and an indication that the woman mayhave been uninsured when she learned she was pregnant(and might have had a delay due to arranging Medicaidpayment for prenatal care). Having Medicaid as payer isassociated with somewhat more late entry if the womanis not supported fully by family and friends. (Womenwith family and friend support and Medicaid as payerenter late 27% of the time; without family and friendsupport it is 37%.) However, the lack of family andfriend support is far more critical for those not usingMedicaid. In this group, 50% of the women lacking fullfamily and friend support enter care late versus 10% forthose with full family and friend support. The presence orlack of family and friend support makes a very substan-tial difference for those with HMO, private insurance, orself-pay, although the small number (N � 18) in thiscategory suggests some caution.

Prevention and Attributable Risk

Preventing late entry into care requires a method ofdetermining which risk factors are most important (aswell as which may be feasibly modified). Although the

TABLE 2Multiple Logistic Regression Model IncludingInteractions

Odds Ratios

Stress reported 5.16 (2.36)*Medicaid as payer 3.33 (5.79)†Lack full family and friend support 9.05 (1.63)Lack full family and friend support* stress 0.17 (1.09)Lack full family and friend support* medicaid

as payer0.18 (5.11)†

Teen or over 34 1.95 (5.19)†Not full acceptance of pregnancy 2.34 (1.63)‡No high school degree 1.46 (0.53)No. of observations§ 173

Note: Robust z-statistics in parentheses.

* Significant at 5%.

† Significant at 1%.

‡ Significant at 10%.

§ Differs from 176 because of missing values on some variables.

FIGURE 1.Late entry to care: full family and friend support is protective forwomen not reporting stress.

FIGURE 2.Late entry to care: family and friend support has a moderate effecton those with medicaid but is critical for those with other payers.


odds ratios in Table 2 give an idea of the magnitude ofeffect for each variable, they do not easily convert intochanges in the likelihood of entering late with or withouta given factor—termed the discrete change in the prob-ability (26). The striped bars in Figure 3 show thediscrete change in the probability for the variables inTable 2, omitting the interactions but controlling for allother variables. Lack of full acceptance of the pregnancyis the most potent of the risk factors, followed by stressand high-risk age.

Although discrete change of probability allows us tounderstand relative risk for each factor, it does not take intoaccount the size of the group having the risk factor.Epidemiologists frequently use the concept of the popula-tion attributable risk (or fraction) as a method of quantifyingthe amount of a health outcome due to a particular riskfactor, thus focusing intervention priorities. In this case,“late entry into prenatal care” is the health outcome we wantto understand. Population attributable risk increases bothwith the strength of an association (which is measuredwith discrete change in probability) and with how com-mon or prevalent the risk factor is. The populationattributable risk compares the rate of late entry for theentire population (including those with a risk factor) with

that of those who do not have the risk factor. Thus, it tellsus what proportion of all late entry would be eliminatedif the number having the risk factor were reduced to zero(no one had the risk factor). Moreover, if an assumptionof causality can be made, how much late entry ispreventable can be extrapolated. Comparing the attribut-able risk for different factors permits judgment of thepotential overall impact of intervention.

Recent methods use logistic regression models togenerate population attributable risks that take into ac-count confounding by other variables (28). For example,“stress” has an attributable risk of 26% when consideredalone. When one adjusts for the effects of covariates, itdrops to 18%. The solid bars of Figure 3 show theadjusted population attributable risk expressed as a per-centage for each of the variables in Table 2 (without theinteractions). Stress has the greatest attributable risk(18%), with lack of full support by family and friendsand Medicaid enrollment each accounting for about 15%of late entry. It is noteworthy that taking into account thesize of the population affected changes the overallranking of the risk factors compared to the discretechange in probability. The total population attributablerisk for all the variables combined is 53.8%. That is, if

FIGURE 3.Late entry to care: discrete change in probability and population attributable risk for risk factors in Table 2. Discrete change in probabilityshows the increase in probability of entering late if a woman has the risk factor, with all other factors held at their mean. Attributable riskof the population shows the percentage by which late overall would be reduced if a given risk factor were not present, with all other factorsheld at their mean. Attributable risk takes into account the size of the group having the risk as well as the relative risk of late entry.


the entire population had a rate of late entry equal tothose not receiving Medicaid, not stressed, not lackingfamily and friend support, not in a high-risk age category,not lacking in acceptance of the pregnancy, and nothaving less than a high school education, then the amountof late entry would be reduced by more than one-half.

DISCUSSION

Limitations of the Study

The generalizability of results is limited by our samplingstrategy of selecting women already receiving care atlarge obstetric practices. Some women entering care verylate would have been missed by our procedures as werewomen using licensed home birth midwives and othertypes of care (family practice physicians and UnitedIndian Health Service providers). Results also are un-likely to generalize to rural counties with very differentpopulations (eg, counties having many migrant workersor those having a scarcity of obstetric providers).

All of the information collected was by self-report.Problems with self-report of behavioral risk factors suchas drinking and use of illicit drugs are well known.Because the survey was intended to be short enough to beanswered in a waiting room, the only multiple item scaleswere for family and friend support, partner support, andacceptance of pregnancy. It would have been preferableto have multi-item measures for other risk factors such asdepression, although single-item questions can beequally valid (29).

In particular, we have little idea what was in respon-dents’ minds when they said that they had “felt a greatdeal of stress” at the time they found out they werepregnant. An exploratory logistic regression analysisshows that the only study variables significantly relatedto stress are factors associated with hardship: Medicaidas payer, more than two prior births, financial hardship ofa pregnancy, and difficulties in getting to prenatal care.Thus, the question on “stress” may have served as alightning rod for all of these other measures of difficulty.Whatever its underlying components, survey results dotell us that 83% of the participants who felt stressreported that their provider had discussed it with themduring prenatal care. Further research should attempt todetermine the components of self-reported stress.

Factors Affecting Late Entry into Care

Six factors were important when both how widespreadthe factor is and the strength of its relationship with earlyentry were taken into account (attributable risk): stress,Medicaid as a payer, being a teen or over age 34, lack ofacceptance of the pregnancy, and not having a highschool degree. The findings for social support, behavioral

risk, and acceptance of pregnancy confirm the necessityof measuring social and psychological variables goingbeyond those available in birth certificate data. At thesame time, structural and demographic factors, such aseducation, age, and payer, also play an important role indetermining time of entry into care.

Results in this survey differ in a number of respectsfrom findings in other studies. It is not clear to whatextent this reflects the rural nature of the populationbeing sampled. The failure of a number of the variablesto reveal strong associations with late prenatal care entryis somewhat surprising. Concrete barriers to care—financial problems caused by the pregnancy, transporta-tion, and difficulty getting off work—were not associatedwith late entry even though other surveys have identifiedthem as important (22, 23). The very broad access toMedicaid in California may contribute to this difference.

Other variables also behaved surprisingly. Depression,for example, has been associated elsewhere with earlyentry into care (8). Here, it was significantly linked tolate entry. In addition, women who reported being in anabusive situation were more likely to enter early ratherthan late (which we had expected), and women alreadyseeing an obgyn provider for gynecologic care wereslightly more likely to have their first prenatal visit afterthe first trimester.

Given a larger sample size, it is probable that some ofthe factors that were unimportant here (such as alcoholand drug use) would show themselves to be statisticallysignificant, as they have in other studies.

Prevention of Late Entry

When poor women learn that they are pregnant, theyneed not only seek care, but they must obtain a payer. InCalifornia, Medi-Cal (Medicaid) is available to pregnantwomen with incomes below 250% of poverty; there isalso an assets waiver. In addition, there is a provision forpresumptive eligibility that can be used while startingprenatal care. Thus, it is disturbing that having Medicaidas a payer still so strongly affects late entry. Prenatal careproviders reviewing the results of this survey proposedintensive efforts to 1) give prenatal Medi-Cal a separatename to increase acceptability and 2) train Medi-Caleligibility workers to understand the importance of earlyinitiation of care and the ways in which their role andactivities can further this goal.

Because pregnancy-specific support from family andfriends was so important—as well as interacting with thetwo other major determinants, stress and Medi-Cal use—a“broadband” approach to prevention may be justified. Intheory, each pregnant woman has many family membersand friends to whom media messages could be directed.And such messages may have a delayed impact, beingrecalled after a family member or friend becomes pregnant.


Public health officials might explore the possibility ofpublic service announcements on radio and television di-rected at present and future family and friends of pregnantwomen. Outreach to other public health projects, such asHealthy Start, which address family systems as part ofhealth care, could also address this issue.

Only 36% of the women responding to the survey werehaving a first birth, which means providers had had aprevious chance to educate the other two thirds. Thewomen having already had at least one baby constituted31 of the 50 women who were late (62%). Thus, prenatalcare itself is an ideal opportunity for encouraging womento enter care early at their next pregnancy; however,more emphasis on education may be needed.

Teenagers were late in seeking prenatal care. Agenciesthat serve teens might be used to get the early prenatalcare message across; in particular, “preconception care”might be integrated into “well-woman care” for allfemales reaching physiologic womanhood. Another in-novative approach is going to where teens “hang out,”such as installing posters in the dressing rooms of trendyteen stores. Teens (and their friends who enroll them)might also be a group to whom incentives for early carecan be effectively targeted.

The study was performed under contract with the Humboldt CountyPublic Health Department using funds provided by the California StateDepartment of Health Services, Maternal Child Health Branch, allo-cation #199912. The author gratefully acknowledges the assistanceand support of Rebecca Stauffer, MD, Ninon McCullough, RN, PHN, ofthe Public Health Department and the assistance of research assistantsJeanne Kirk and Sally Stablein. Sociology graduate students ChristinaBegley, Tracie Carrasco, Farnad Darnell, Laura Estetter, Carlin Finke,Diane Goldsmith, John Haskell, Karla Lusby, Marika Myrick, DanSarabia, Marcia Sterling, and Paula Yoon did much developmentalwork in formulating the design and collecting data. Shawna Thorsen,RN; Annie Howell, RN; Michelle Van Ooy, MD; Ruth Howell, RN; BettyBraver, CNM; Cynthia Ihle, RN; Kate Maguire; Holly Baker; EmilyArents, CNM; Carol Meyer, CNM; and Mary Ann Shah, CNM, suggestedstrategies for public health intervention.

REFERENCES

1. Goldenberg RL, Patterson ET, Freese MP. Maternal demographic,situational and psychosocial factors and their relationship to enrollment inprenatal care: a review of the literature. Women Health 1992;19:133–51.

2. Matti LK, Caspersen VM. Prevalence of drug use among pregnantwomen in a rural area. J Obstet Gynecol Neonatal Nurs 1993;22:510–4.

3. Omar MA, Schiffman RF, Bauer P. Recipient and provider perspec-tives of barriers to rural prenatal care. J Community Health Nurs 1998;15:237–49.

4. Svikis D, Henningfield J, Gazaway P, Huggins G, Sosnow K,Hranicka J, et al. Tobacco use for identifying pregnant women at risk ofsubstance abuse. J Reprod Med 1997;42:299–302.

5. Funkhouser AW, Butz AM, Feng TI, McCaul ME, Rosenstein BJ.

Prenatal care and drug use in pregnant women. Drug Alcohol Depend1993;33:1–9.

6. Dietz PM, Gazmararian JA, Goodwin MM, Bruce FC, Johnson CH,Rochat RW. Delayed entry into prenatal care: effect of physical violence.Obstet Gynecol 1997;90:221–4.

7. McCaw-Binns A, La Grenade J, Ashley D. Under-users of antenatalcare: a comparison of non-attenders and late attenders for antenatal care,with early attenders. Soc Sci Med 1995;40:1003–12.

8. Pascoe JM, Milburn M, Haynes K. Correlates of first trimester carein a public health prenatal clinic. Fam Med 1990;22:25–8.

9. Sable MR, Wilkinson DS. The role of perceived stress on prenatalcare utilization: implications for social work practice. Health Social Work1999;24:138–46.

10. Altfeld S, Handler A, Burton D, Berman L. Wantedness of preg-nancy and prenatal health behaviors. Women Health 1997;26:29–43.

11. Kost K, Landry DJ, Darroch JE. Predicting maternal behaviorsduring pregnancy: does intention status matter? Fam Plann Perspect 1998;30:79–88.

12. St. Clair PA, Smeriglio VL, Alexander CS, Celentano DD. Socialnetwork structure and prenatal care utilization. Med Care 1989;27:823–32.

13. Long CR, Curry MA. Living in two worlds: Native Americanwomen and prenatal care. Health Care For Women Int 1998;19:205–15.

14. Sokoloski EH. Canadian First Nations women’s beliefs about preg-nancy and prenatal care. Can J Nurs Res 1995;27:89–100.

15. Giblin PT, Poland ML, Ager JW. Effects of social supports onattitudes, health behaviors and obtaining prenatal care. J CommunityHealth 1990;15:357–68.

16. St. John C, Winston TJ. The effect of social support on prenatalcare. J Appl Behav Sci 1989;25:79–98.

17. Casper LM, Hogan DP. Family networks in prenatal and postnatalhealth. Soc Biol 1990;37:84–101.

18. Moss NE, Hensleigh PA. Initiation of prenatal care by adolescents:associations with social support, stress, and Hispanic ethnicity. J Perinatol1990;10:170–4.

19. Schaffer MA, Lia-Hoagberg B. Effects of social support on prenatalcare and health behaviors of low-income women. J Obstet Gynecol Neo-natal Nurs 1997;26:433–40.

20. Stephens CJ. Perception of pregnancy and social support as predic-tors of alcohol consumption during pregnancy. Alcoholism Clin Exp Res1985;9:344–8.

21. Enderlein MC, Stephenson PA, Holt VL, Hickok D. Health statusand timing of onset of prenatal care: is there an association among low-income women? Birth 1994;21:71–6.

22. Leatherman J, Blackburn D, Davidhizar R. How postpartum womenexplain their lack of obtaining adequate prenatal care. J Adv Nurs 1990;15:256–67.

23. Rogers C, Schiff M. Early versus late prenatal care in New Mexico:barriers and motivators. Birth 1996;23:26–30.

24. Campbell JD, Mitchell P, Stanford JB, Ewigman BG. Validating amodel developed to predict prenatal care utilization. J Fam Pract 1995;41:457–64.

25. Chatterjee S, Hadi A, Price B. Regression analysis by example. NewYork: John Wiley & Sons, 2000.

26. Long JS. Regression models for categorical and limited dependentvariables. Thousand Oaks (CA): SAGE Publications, 1997.

27. Stata C. Obtaining robust variance estimates. In: Stata user’s guide:Release 6. College Station (TX): Stata Press, 1999.

28. Brady AR. Adjusted population attributable fractions from logisticregression. Stata Technical Bull 1998;42:8–12.

29. Chochinov H, Wilson K, Enns M, Lander S. “Are you depressed?”Screening for depression in the terminally ill. Am J Psychiatry1997;154:674–76.


late entry into prenatal care in a rural setting

Documents