7/31/2019 1459.full.pdf

1/7

Racial and Ethnic Variation in Access toHealth Care, Provision of Health CareServices, and Ratings of Health Among

Women With Histories of GestationalDiabetes MellitusCATHERINE KIM, MD, MPH1

BRANDY SINCO, MS2

EDITH A. KIEFFER, MPH, PHD2

OBJECTIVE The purpose of this study was to assess racial/ethnic variation in access tohealth care, use of particular health care services, presence of cardiovascular risk factors, andperceptions of health and impairment among women at risk for type 2 diabetes because of theirhistories of gestational diabetes mellitus (hGDM).

RESEARCH DESIGN AND METHODS We performed a cross-sectional study usingthe 20012003 Behavioral Risk Factor Surveillance System, a national population-based, ran-dom sample telephone survey. We assessed access to health care, use of family planning, mea-surement and elevation of cholesterol, elevation of blood pressure, and respondents perceptionsof health and impairment among women aged 1844 years with hGDM (n 4,718). Multivar-iate models adjusted for sociodemographic characteristics, BMI, presence of children in thehousehold, and current smoking.

RESULTS Outcome measures were suboptimal across racial/ethnic groups. Approximatelyone-fifth of the overall population reported no health insurance, cost barriers to physician visits,and no primary care provider. One-quarter had no examination within the past year, and almostone-fifth reported no family planning and elevated cholesterol levels. Latinas were the mostdisadvantaged, with 40% reporting no health insurance and no primary care provider andone-fourth reporting suboptimal perceptions of health. Asian/Pacific Islanders were the mostadvantaged in terms of healthcare access, cholesterol andbloodpressureelevation, andimpaired

physical health. Racial/ethnic differences in health care use and presence of risk factors were notentirely explained by health care access or other covariates.

CONCLUSIONS Significant racial/ethnic variation exists among women with hGDM foraccess to and use of health care, presence of risk factors, and perceptions of health.

Diabetes Care 30:14591465, 2007

Successful translation of diabetes pre-vention trials (13) into clinicalpractice requires that health care

providers monitor health status, provideand reinforce prevention messages, andrefer patients to available prevention pro-

grams (4). For most women with historiesof gestational diabetes mellitus (hGDM),one of the recruitment criteria for the Di-abetes Prevention Program (5), appropri-ate care also includes use of familyplanning (4) and cardiovascular risk fac-

tor assessment, i.e., cholesterol and bloodpressure measurement (6,7). However,women with hGDM may have inadequateaccess to health care outside of preg-nancy; some insurers and Medicaid limitpostdelivery coverage to one visit, atabout 6 weeks postpartum, regardless ofhealth or disease status (8,9). Previousstudies (10) suggested that this lack of ac-cess may be more marked in nonwhiteracial/ethnic groups. In the third NationalHealth and Nutrition Examination Sur-vey, Mexican Americans were signifi-cantly less likely to have health insurancethan non-Hispanic whites (NHWs) or Af-rican Americans, and Mexican Americansand African Americans were less likely tohave private insurance. No studies haveinvestigated racial/ethnic variation in ac-cess to health care nor its association withthe provision of appropriate health careservices to women with hGDM.

In our previous study using the Be-

havioral Risk Factor Surveillance SystemSurvey (BRFSS), a national, population-based survey, we found that women withhGDM more often rated their health asfair or poor than did their unaffectedcounterparts (11). This perception ofhealth may be more sensitive to distinc-tions between racial/ethnic groups thantraditional markers of morbidity andmortality (12), particularly for womenwith hGDM, who have a greater risk thanunaffected women for developing futurechronic disease (13), but who may have

normal glucose after delivery. However,to our knowledge, no studies have inves-tigated racial/ethnic variation in percep-tions of health and impairment amongwomen with hGDM.

In this article, we investigated racial/ethnic variation in access to health care,provision of health care regarding use offamily planning and cholesterol measure-ment, presence of cardiovascular risk fac-tors such as elevated cholesterol andblood pressure, and perceptions of healthand impairment among women withhGDM in the BRFSS. We hypothesized

From the 1Departments of Medicine and Obstetrics and Gynecology, University of Michigan, Ann Arbor,Michigan; and 2School of Social Work, University of Michigan, Ann Arbor, Michigan.

Address correspondence and reprint requests to Catherine Kim, MD, MPH, 300 NIB, Room 7C13, Box0429, Ann Arbor, MI 48109. E-mail: cathkim@umich.edu.

Received for publication 13 December 2006 and accepted in revised form 4 March 2007.Publishedahead of printat http://care.diabetesjournals.orgon 15 March2007. DOI:10.2337/dc06-2523.Abbreviations: A/PI, Asian/Pacific Islander; BRFSS, Behavioral Risk Factor Surveillance System Survey;

hGDM, history of gestational diabetes mellitus; NA/AN, Native American/Native Alaskan; NHW, non-Hispanic white.

A table elsewhere in this issue shows conventional and Systeme International (SI) units and conversionfactors for many substances.

2007 by the American Diabetes Association.The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby

marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

E p i d e m i o l o g y / H e a l t h S e r v i c e s / P s y c h o s o c i a l R e s e a r c hO R I G I N A L A R T I C L E

DIABETES CARE, VOLUME 30, NUMBER 6, JUNE 2007 1459

7/31/2019 1459.full.pdf

2/7

that minority women, particularly Lati-nas, would report decreased access, de-creased family planning practices andcholesterol screening, and poorer percep-tions of health and degree of impairmentcompared with NHW women. We alsohypothesized that race/ethnicity wouldbe independently associated with access

measures, decreased use of family plan-ning, cholesterol screening, elevated cho-l e s t e r o l a n d b l o o d p r e s s u r e , a n dperceptions of health and degree of im-pairment after adjustment for other de-mographic characteristics.

RESEARCH DESIGN AND

METHODS We used data from the20012003 waves of the BRFSS. Thiscross-sectional telephone survey is con-ducted by the Centers for Disease Control

and Prevention in conjunction with statehealth departments. The survey uses amultistage cluster design based on ran-dom-digit dialing methods of sampling toselect a representative sample from eachstates noninstitutionalized civilian resi-dents aged 18 years. Data collectedfrom each state are pooled to produce na-tionally representative estimates. Thisstudy used responses to a core set of ques-tions asked in all states. Median responserates varied from 77 to 80% over thestudy period (14). A detailed descriptionof the survey methods has been published

previously (11,15). The sample includedwomen aged 18 44 years who answeredthe 20012003 survey question Haveyou ever been told by a doctor that youhave diabetes? Responses included yes,yes but only during pregnancy, no,and dont know or not sure. Partici-pants who first answered yes were fur-ther asked was this only when you werepregnant? Women who responded yesand only during pregnancy were classi-fied as having hGDM, and women whoresponded yes were classified as having

current diabetes. Participants who re-sponded no were classified as not hav-ing hGDM, and women who respondeddont know or not sure were classifiedas such. Thus, the categories of hGDMand diabetes were mutually exclusive. Weexcluded women who reported currentdiabetes from this analysis (n 4,412),and women who replied dont know, notsure, or refused (n 132), for a samplesize of 4,718. Five studies have reportedhigh overall reliability of the BRFSS ques-tion about a diagnosis of diabetes (0.600.86) (15); to our knowledge, no

studies reported the validation of the ges-tational diabetes component.

Main outcome measuresAccess to health care was measured byquestions inquiring about lack of healthinsurance, the presence of cost barriers tophysician visits in the past year, lack of a

primary care provider, location of pri-mary health care facility (no usual place ofcare or primary source of care in emer-gency room or urgent care facility versusdoctors office, outpatient department,public health clinic, or community cen-ter), and lack of a physical examinationwithin the past year. Questions on costbarriers, location of primary care facility,and physical examination were only askedin 2003, 2002, and 2001, respectively.

Family planning questions includedthe question Are you or your partner do-

ing anything now to keep from gettingpregnant? and was asked only in 2002;however, no questions inquired about thedesire for pregnancy. Women were clas-sified as not using family planning if theydid not use birth control. Women couldalso be classified as using birth control,being sterile, or lacking contact with men.Respondents were also asked, Have youe v e r h a d y o u r b l o o d c h o l e s t e r o lchecked? If they answered yes, theywere asked, Have youever been told by adoctor, nurse, or other health care profes-sional that your blood cholesterol is

high? These questions were asked only in2001 and 2003.

Responses to perceptions of healthwere excellent, very good, good, fair, andpoor (16). Impaired physical health wasdefined as the presence of physical healthproblems, including physical illness or in-

jury, that prevented work or recreation on15 days during the past 30 days (17).Impaired mentalhealth was definedas thepresence of mental health problems, in-cluding stress, depression, and problemswith emotions, that prevented work or

recreation on

15 days during the past30 days (17). Impaired mental and phys-ical health questions were asked in 2001and 2003 only. These measures havemoderate to strong retest reliability (18).

AnalysisThe primary independent variable wasself-reported race/ethnicity (NHW, non-Hispanic African American, Hispanic orLatina, Asian/Pacific Islander [A/PI], Na-tive American/Native Alaskan [NA/AN],or other). Covariates included age (years),education level (less than high school,

high school, and greater than highschool), income level, current employ-ment, married or partnered status, pres-ence of children aged 18 years in thehousehold, BMI (measured as weight inkilograms divided by the square of heightin meters), and current smoking. Reliabil-ity coefficients exceeded 0.75 for these

covariates and moderate to high validityexcept where otherwise noted. Validity ofthese measures is high (15); height is gen-erally overestimated by an average of 0.5inches, and in the BRFSS the correlationbetween measured height and self-reported height was 0.92 in women. Sim-ilarly, weight is generally underestimated,and in the BRFSS the correlation betweenmeasured weight and self-reportedweight exceeded 0.90 (15). We collapsededucational level into high school gradu-ate or not and income into $25,000,

$25,000, and unknown income as thepatterns of effects between race/ethnicgroups were similar in these categories.

We classified women into obese (30 kg/m2), overweight (2530 kg/m2), nor-mal or underweight (25 kg/m2), andunknown.

We compared women with hGDM byrace/ethnicity in unadjusted analyses us-ing Rao-Scott 2 tests for categorical vari-ables (19) and Students t tests withsurvey-based standard errors for continu-ous variables; the reference group wasNHWs. To examine the association be-

tween thedependent variables of access tohealth care measures and the primary in-dependent variable of race, we created alogistic regression model in which eachaccess to health care measure (lack of in-surance, cost barriers to care, lack of pri-m a r y c a r e p r o v i d e r , a n d l a c k o f performance of physical examinationwithin the past year) was examined in aseparate model. Multivariate models ad-

justed for the covariates listed above. BMIwas included as a covariate becausegreater BMI has been associated with

lower rates of insurance in other BRFSSanalyses, presumably because insurersmay be reluctant to insure those with thismedical risk factor (20).

To examine the association betweenthe outcome measures of lack of use offamily planning, lack of cholesterolscreening, elevated cholesterol, and ele-vated blood pressure and the primary in-dependent variable of race/ethnicity, wecreated logistic regression models foreach of these measures. That is, onemodel had lack of family planning as thedependent variable, another model had

Disparities among women with a history of GDM

1460 DIABETES CARE, VOLUME 30, NUMBER 6, J UNE 2007

7/31/2019 1459.full.pdf

3/7

lack of cholesterol measurement as thedependent variable, a third model had el-evated cholesterol as the dependent vari-able, and a fourth model had familyplanning as the dependent variable. Wehypothesized that these measures wouldbe influenced by access to health care, inthat they required or could be enabled by

physician examination (e.g., blood pres-sure elevation), laboratory test ordering(e.g., cholesterol checked), or prescrip-tion (e.g., family planning usage). There-fore, multivariate models examiningfamily planning, cholesterol screening,and blood pressure elevation also in-cluded lack of insurance and lack of a pri-mary care provider as well as othercovariates (21). Multivariate models withelevated cholesterol as the dependentvariable included only women who re-ported having a cholesterol level measured.

To examine the association betweenthe dependent variables of perceptions ofhealth and impairment and the primaryindependent variable of race/ethnicity,we created logistic regression models sim-ilar to those already described for whichthe outcomes were fair or poor health (yesor no), impaired physical health (yes orno), and impaired mental health (yes orno). These models did not include accessmeasures as covariates, as we did not hy-pothesize that self-rated health and im-pairment were dependent on health care(10).

We evaluated the interactions be-tween age and race/ethnicity for all mod-els. These were not significant andtherefore were not included in the finalmodels. Tests for collinearity betweenthese access measures did not show sig-nificant correlation. In all comparisons,the data were weighted to the age, sex,and racial/ethnic distribution of the non-institutionalized population in the U.S.

All analyses were performed using surveyanalysis procedures in SAS (version 9.1;SAS Institute, Cary, NC) to account for

theweighting andcomplex survey design.

RESULTS Of the 4,718 participantswith hGDM, 58% were NHW, 9% were

African American, 5% were A/PI, 2% wereNA/AN, 25% were Latina, and 1% wereother race/ethnicity. Unadjusted charac-teristics of women with hGDM by racial/ethnic category are reported in Table 1;point estimates and 95% CIs are pre-sented to facilitate comparisons. Com-pared with NHWs with hGDM, African

Americans with hGDM were younger andmore likely to have annual incomes

Table1Unadjustedc

haracteristicsofwomenwithahistoryo

fGDMbyrace/ethnicity

Characteristic

NHW

AfricanAmerican

Hispan

icorLatina

NA/AN

A/PI

Total

n

3,305

389

596

150

203

4,718

Age(years)

33.4(33.133.8)

31.3(30.032.6)

32.9(32.033.7)

31.8(31.232.4)

35.9(35.336.6)

33.2(32.933.5)

Highschoolgraduate(%)

93.5(92.394.8)

92.1(88.795.5)

57.5(50.964.2)

78.9(64.693.2)

96.0(91.3100.0)

84.2(82.186.4)

Income$25,000(%)

71.3(69.073.7)

46.8(37.955.6)

38.1(31.644.5)

57.3(42.971.7)

56.0(47.065.0)

59.4(57.061.9)

Currentlyemployed(%

)

65.2(62.767.7)

60.6(51.270.0)

49.0(42.555.6)

60.8(46.175.5)

40.4(30.550.3)

59.3(56.961.7)

Marriedorpartnered(%

)

76.9(74.779.0)

44.5(35.853.3)

82.3(78.386.3)

63.1(48.178.1)

67.0(59.874.1)

74.2(72.276.2)

Childrenaged18yea

rsinhouse(%)

86.7(85.088.4)

85.9(77.094.9)

96.3(94.598.1)

75.8(59.392.3)

91.3(85.896.8)

88.9(87.590.4)

BMI(kg/m

2)

26.7(26.427.0)

30.4(28.732.1)

28.5(27.629.4)

29.1(28.729.4)

25.3(24.426.1)

27.4(27.127.8)

Currentsmoker(%)

30.3(27.832.7)

19.7(12.726.7)

10.5(7.013.9)

27.8(16.838.8)

11.7(4.718.7)

23.3(21.425.2)

Nohealthinsurance(%

)

14.7(12.816.7)

19.2(10.228.3)

38.1(31.744.4)

24.1(10.537.7)

6.1(1.311.0)

20.7(18.522.9)

Didnotseeadoctorbe

causeofcostduring

pastyear(%)*

17.2(13.520.8)

23.4(12.933.9)

27.3(16.038.7)

18.6(4.432.9)

6.4(0.012.9)

19.6(15.823.4)

Noprimaryprovider(%

)

14.7(12.816.7)

18.2(9.027.3)

37.3(30.843.8)

22.4(10.134.6)

34.4(26.742.2)

21.7(19.624.0)

Primaryhealthcarefacilityisurgentcareor

emergencyfacility(%

)

13.9(10.517.3)

30.5(14.246.9)

20.5(11.429.6)

20.0(0.839.1)

14.5(031.6)

17.1(13.420.9)

Physicalexamination

1yearago(%)

25.7(21.330.1)

19.0(9.228.8)

33.4(22.444.4)

34.7(0.768.8)

3.1(0.07.9)

26.5(22.330.7)

Dataaremeansorpercentages(95%CI).Boldfontindicatessignificantdiff

erencesfromNHWs.*Denominatorincludes2003respondentsonly.Denominatorincludes2002respondentsonly.Denominatorincludes

2000respondentsonly.

Kim, Sinco, and Kieffer

DIABETES CARE, VOLUME 30, NUMBER 6, JUNE 2007 1461

7/31/2019 1459.full.pdf

4/7

$25,000 and greater than average BMI.They were less likely to be married orpartnered and to smoke. They were sim-ilarly likely to be high school graduates,employed, and to have children 18years of age in the household. Compared

with NHWs with hGDM, Latinas withhGDM were more likely to be poor, to beheavier, and to have children 18 yearsof age in the household. They were lesslikely to be high school graduates, to becurrently employed, and to smoke. Com-pared with NHWs with hGDM, NA/ANswith hGDM were younger and were morelikely to be heavier. However, in contrastwith other racial/ethnic groups, A/PIswith hGDM were older and wealthier andhad a lower BMI than NHWs with hGDM.

A/PIs were less likely to be employed and

were less often smokers than NHWs withhGDM.Table 1 also illustrates unadjusted ac-

cess to health care measures by race/ethnicity with point estimates and 95%CIs to facilitate comparisons betweenNHW racial/ethnic groups. Comparedwith NHWs, African Americans were sim-ilarly likely to report access, although CIswere wide. Compared with NHWs, Lati-nas were more likely to report lack ofhealth insurance and lack of a primarycare provider. Compared with NHWs,NA/ANs were also similarly likely to re-

port access measures, although as withAfrican Americans, CIs were wide. A/PIshad more access to health care thanNHWs; they were less likely to report lackof health insurance or recent examinationand cost barriers, although they were more

likely to lack a primary care provider.Table 1 also shows unadjusted use of

services and presence of risk factors byrace/ethnicity with point estimates and95% CIs to facilitate comparisons be-tween minority groups. Compared withNHWs, African Americans reported sim-ilar rates of family planning, cholesterolmeasurement and elevation, and bloodpressure elevation. Compared withNHWs, Latinas actually reported usingfamily planning more often and also re-ported lack of cholesterol measurement

more often. Compared with NHWs, NA/ANs reported statistically similar use ofservices and presence of risk factors, al-though CIs were wide. As with accessmeasures and other demographic charac-teristics, A/PIs had a more favorable pro-file than NHWs, reporting cholesterolelevation and blood pressure elevationless often. Latinas reported poor or fairperceptions of health more often thanNHWs. A/PIs reported impaired physicalhealth less often than NHWs as well asmost women from other racial/ethnicgroups (Table 1).

After multivariable adjustment, Afri-can Americans were more likely thanNHWs to report that their primary loca-tion of health care consisted of an emer-gency room or urgent care facility andpoor perceptions of health(Table 2). After

multivariable adjustment, Latinas werestill more likely than NHWs to report lackof health insurance and a primary careprovider and poor perceptions of health.However, the associations between His-panic ethnicity and lack of cholesterolmeasurement and family planning nolonger persisted. After multivariate ad-

justment, NA/ANs were more likely thanNHWs to report lack of family planning.In contrast, A/PIs were less likely thanNHWs to lack health insurance, a recentphysical examination, blood pressure el-

evation, or impaired physical health andalmost less likely to have elevations incholesterol. As in unadjusted analyses,

A/PIs were still more likely to lack a pri-mary care provider.

Table 3 illustrates the associations be-tween the dependent variables and co-variates other than race. Access measureswere associated with greater income. Alack of health insurance and a lack of arecent physical examination were also as-sociated with a lack of high school educa-tion. Current smokers were more likely toreport cost barriers to physician care.

Table 2Adjusted associations between racial/ethnic groups and access to health care measures

Characteristic African American Hispanic or Latina NA/AN A/PI

n 389 596 150 203Access measures*

No health insurance 0.9 (0.51.5) 1.8 (1.32.5) 1.3 (0.72.6) 0.3 (0.10.7)Did not see a doctor because of cost during past year 1.1 (0.42.6) 1.5 (0.83.0) 1.2 (0.53.0) 0.3 (0.11.1)

No primary health care provider 0.9 (0.51.5) 2.3 (1.63.3) 1.4 (0.63.4) 2.4 (1.63.6)Primary health care facility is emergency or urgent

care facility2.4 (1.15.0) 1.1 (0.52.1) 1.5 (0.45.5) 1.3 (0.35.7)

Physical examination 1 year ago 0.6 (0.31.2) 1.3 (0.72.4) 1.4 (0.36.3) 0.1 (

7/31/2019 1459.full.pdf

5/7

Women who had children at home wereless likely to use urgent care facilities fortheir primary source of care. Women withchildren at home were more likely to re-port use of family planning. Youngerwomen with lower education and in-comes were less likely to have their cho-lesterol measured. Of the women who

had their cholesterol measured, older,overweight and obese, and uninsuredwomen were more likely to report eleva-tions. Current smoking and obesity alsopredicted greater prevalence of hyperten-sion. Greater age, lower income, currentsmoking, and obesity were associatedwith poorer perceptions of health andgreater impairment.

CONCLUSIONS In a nationallyrepresentative population-based surveyof women with hGDM, we found that sig-

nificant lack of access, lack of recom-mended services, and poor perception ofhealth exist across all racial/ethnicgroups. In this population at high risk forfuture diabetes and pregnancies affectedby glucose intolerance, approximatelyone-fifth reported a lack of insurance, costbarriers to physician visits, and lack of aprimary care provider. More than one-fourth reported lack of a physical exami-nation in the past year, which in turn is amissed opportunity for glucose screeningand reinforcement of family planning,diet and exercise, and other preventive

measures. Almost one-fifth of women atrisk for pregnancy did not use familyplanning. In this population at increasedrisk for cardiovascular risk factors, ap-proximately one-fifth reported choles-terol elevation. Finally, in a relativelyyoung population aged slightly 30years, almost 15% reported poor percep-tion of health.

We also found that significant racial/ethnic variation existed in measures of ac-cess to health care, use of servicesincluding family planning and cholesterol

measurement, cholesterol and bloodpressure elevation, and perception ofhealth and degree of impairment. Suchvariation persisted after multivariate ad-

justment. With the exception of A/PIs,minority women reported less favorableprofiles than NHW women. Hispanicwomen, the racial/ethnic group with thehighest prevalence of hGDM, reportedthe greatest access barriers, more fre-quently reporting lack of health insuranceand lack of a primary care provider. A/PIsreported a more favorable profile thanwomen from other racial/ethnic groups,

Table3Adjustedassociationsbetweencovariatesotherthanrace/ethnicity

Characteristic

Age(years

18)

Highschool

education*

Highschool

education*

Income$25,000

Incomeunknow

n

Employed

Married/partnered

Nohealthinsurance

1.0(1.01.0)

1.8(1.22.8)

0.6(0.40.8)

0.3(0.20.4)

0.7(0.51.1)

0.7(0.50.9)

1.0(0.71.4)

Didnotseeadoctorbe

causeofcostduring

pastyear

1.0(1.01.0)

0.9(0.41.9)

0.7(0.41.3)

0.4(0.20.8)

0.5(0.21.1)

1.2(0.72.0)

0.9(0.51.6)

Noprimaryhealthcare

provider

1.0(1.01.0)

1.3(0.82.0)

0.9(0.61.2)

0.4(0.30.6)

0.8(0.51.2)

0.8(0.61.0)

0.8(0.61.1)

Primaryhealthcarefacilityisemergencyor

urgentcarefacility

1.0(1.01.0)

1.6(0.73.7)

0.7(0.41.4)

0.5(0.20.9)

0.8(0.41.7)

1.2(0.72.1)

1.2(0.62.3)

Physicalexamination

1yearago

1.0(1.01.0)

2.2(1.14.6)

0.6(0.30.9)

1.1(0.61.9)

1.1(0.52.6)

1.1(0.71.7)

0.6(0.41.0)

Notusingfamilyplanning

1.0(1.01.0)

0.8(0.31.9)

1.0(0.61.5)

1.3(0.82.4)

1.8(0.93.6)

1.2(0.81.8)

1.2(0.72.1)

Cholesterolneverchecked

0.9(0.90.9)

1.6(0.92.7)

0.7(0.50.9)

0.6(0.40.9)

1.0(0.61.7)

0.8(0.61.1)

1.2(0.91.7)

Cholesterolelevated

1.1(1.11.1)

1.4(0.73.2)

1.3(0.72.3)

0.9(0.61.5)

0.8(0.41.7)

1.3(0.91.9)

1.2(0.81.9)

Bloodpressureelevated

1.0(1.01.1)

0.8(0.41.6)

1.0(0.61.5)

0.8(0.51.3)

1.3(0.62.7)

1.2(0.81.8)

1.2(0.71.8)

Perceptionofhealthfai

r/poor

1.1(1.11.1)

1.5(0.92.4)

0.7(0.51.0)

0.3(0.20.5)

0.5(0.30.8)

0.8(0.51.1)

0.8(0.61.2)

Impairedphysicalhealth

1.1(1.11.1)

1.0(0.52.2)

0.9(0.51.5)

0.5(0.30.9)

1.0(0.42.2)

0.7(0.51.1)

0.8(0.51.5)

Impairedmentalhealth

1.0(1.01.0)

1.0(0.61.9)

1.2(0.81.9)

0.7(0.51.1)

0.7(0.41.3)

0.7(0.51.0)

0.8(0.51.1)

Valuesareoddsratios(95%CI).Multivariatemodelsaredescribedinth

elegendtoTable2.Boldtextindicatessignificantassociationswithdependentvariable.*Highschooleducationisthereferencecategory.

Income$25,000isthe

referencecategory.

Kim, Sinco, and Kieffer

DIABETES CARE, VOLUME 30, NUMBER 6, JUNE 2007 1463

7/31/2019 1459.full.pdf

6/7

more frequently reporting health insur-ance and a recent physical examinationand less frequently reporting cardiovas-cular risk factor elevation and impairedphysical health. Associations between Af-rican-American race/ethnicity andNA/AN race/ethnicity with access, use ofservices, presence of risk factors, and per-

ception of health and impairment wereless marked, but African-Americanwomen still were more likely to report theemergency room or urgent care facility astheir primary health care facility. NA/ANwomen still reported lack of use of familyplanning more frequently than NHWs, al-though this lack may have been due todesire for pregnancy. Of note, the wideCIs for African Americans and NA/ANssuggest that significant variation existswithin these particular racial/ethnicgroups, indicating that finer characteriza-

tions may be needed.The associations we observed be-tween race/ethnicity and access measuresand race/ethnicity and other covariatessuch as income have been observed inother populations besides women withhGDM. Among individuals with diabetesin the BRFSS, Gary et al. (22) found thatminority populations were more likely tolack insurance and to report cost barriersto visiting a physician and having a recentexamination, even after adjustment forage, sex, income, and education. Of note,the population with diabetes had better

access to health care than women withhGDM in our current report, with onlyabout one-fifth of Hispanics with diabetesreporting lack of insurance as opposed tothe 38% of Latinas with hGDM in ourreport. Nelson et al. (23) also found thatuninsured individuals with diabetes weremore likely to be African American or His-panic and to report low incomes and,consequently, to be less likely to have haddiabetes-specific care measures. Of note,the more favorable profiles for A/PIs over-all have been noted, but significant varia-

tion within this broad racial/ethniccategory is seen; Southeast Asian groups(24) and Pacific Islanders (25) tend to doworse than NHWs as well as A/PIs overall.It is possible that such variations contrib-ute to the higher odds of no primary careprovider that we found among A/PIs.

After adjustment for lack of insuranceand lack of a primary provider, along withother covariates, Latinas no longer re-ported lower rates of cholesterol measure-ment than NHWs. A/PIs reported morefavorable blood pressure and cholesterolprofiles than NHWs, an association that

has been reported previously (25). How-ever, there were no associations betweenaccess measures and actual cardiovascu-lar risk factor levels. It is possible that thenumber of women with cardiovascularrisk factor abnormalities was too smallamong women with hGDM or that accessin and of itself does not guarantee a better

risk factor profile (26). Conflicting rec-ommendations (7,27) for cholesterolscreening in women aged 45 years mayhave affected associations between accessand risk factor levels, although the direc-tion of the bias is difficult to know; clini-cal care guidelines recommend that adultswith diabetes receive cholesterol screen-ing and do not distinguish between GDMand other types of diabetes.

Previous analyses have found that mi-nority women were less likely to engage infamily planning than NHWs, although

use in NA/ANs was not commented upon(28). In our report, NA/AN women re-ported lack of family planning more fre-q u e n t l y t h a n N H W w o m e n a f t e radjustment for access and other covari-ates, suggesting that some otherwise un-defined characteristic associated withNA/AN race/ethnicity placed women atrisk. Of note, before adjustment, Latinasactually reported greater use of birth con-trol in our report, although this findingdid not persist after adjustment for otherfactors. The question about family plan-ning was available only in 2002, when

interviews in Spanish were not an option.So, the results for Latinas may be biasedby misinterpretation of the question or byomission of Spanish-speaking women.

We found that the association be-tween poorer self-rated health and Latinaethnicity persisted after multivariate ad-

justment, as did the association betweenA/PI ethnicity and better physical func-tioning. Such associations have been doc-umented among women with (22) andwithout diabetes (29,30), appear to be in-dependent of other risk factors for lower

self-rated health such as income and age,and are potentially mediated by othermarkers of socioeconomic position andsocial stressors (3032). Again, signifi-cant variations in health status existwithin the A/PI group, with subgroups,particularly Pacific Islanders and South-east Asians, reporting poorer perceptionof health (24,25).

We conclude that among womenwith hGDM, a population at high-risk forglucose intolerance and for pregnancies athigh risk for glucose intolerance, access tohealth care and provision of health care

are suboptimal. Latinas, one of the racial/ethnic groups at highest risk for glucoseintolerance, have the greatest restrictionsin access, care provided, and perceptionof health and impairment. In addition, al-though access may be a necessary condi-tion for care, it may not be adequate toimprove care. These findings have impli-

cations for the implementation of effec-tive diabetes prevention strategies as wellas other care. Future research shouldcharacterize better the quality of carewomen with hGDM receive and the asso-ciation with outcomes. Given the rate ofobesity, blood pressure and cholesterolelevations, and future risk of diabetes inthis population, along with the increasingrates of these disordersin thegeneral pop-ulation, improved provision of postpar-tum services could potentially reducefuture cardiovascular morbidity.

Acknowledgments This study was sup-ported by National Institute of Diabetes andDigestive and Kidney Diseases GrantsK23DK071552 (to C.K.), R18DK06234404(to E.K.), and U50/CCU52218902 (to B.S.).

References1. Knowler WC, Barrett-Connor E, Fowler

SE, Hamman RF, Lachin JM, Walker EA,NathanDM: Reductionin theincidence oftype 2 diabetes with lifestyle interventionor metformin.NEnglJMed 346:393403,

20022. Tuomilehto J, Lindstrom J, Eriksson JG,Valle TT, Hamalainen H, Ilanne-ParikkaP, Keinanen-Kiukaanniemi S, Laakso M,Louheranta A, Rastas M, Salminen V,Uusitupa M: Prevention of type 2 diabetesmellitus by changes in lifestyle amongsubjects with impaired glucose tolerance.N Engl J Med 344:13431350, 2001

3. Pan XR, Li GW, Hu YH, Wang JX, YangWY, An ZX, Hu ZX, Lin J, Xiao JZ, CaoHB, Liu PA, Jiang XG, Jiang YY, Wang JP,Zheng H, Zhang H, Bennett PH, HowardBV:Effects of diet and exercise in prevent-ing NIDDM in people with impaired glu-

cose tolerance. Diabetes Care 20:537544, 1997

4. American Diabetes Association: Gesta-tional diabetes mellitus. Diabetes Care 21:S5S19, 1998

5. Diabetes Prevention Program ResearchGroup: Strategies to identify adults athigh-risk for type 2 diabetes: the DiabetesPrevention Program. Diabetes Care 28:138144, 2005

6. Third Report of the Expert Panel on Detec-tion, Evaluation, and Treatment of HighBloodCholesterol in Adults (Adult TreatmentPanel III): 10-Year Risk Calculator. Be-thesda, MD, National Heart, Lung, and

Disparities among women with a history of GDM

1464 DIABETES CARE, VOLUME 30, NUMBER 6, J UNE 2007

7/31/2019 1459.full.pdf

7/7

Blood Institute, 20017. U.S. Preventive Services Task Force:

Screening adults for lipid disorders: rec-ommendations and rationale. Am J PrevMed 20:7376, 2001

8. Jones M, Cason C, Bond M: Access to pre-ventive health care: is method of paymenta barrier for immigrant Hispanic women?Womens Health Issues 12:129137, 2002

9. Williams MA, Emanuel I, Kimpo C,Leisenring WM, Hale CB: A population-based cohort study of the relation be-tween maternal birthweight and risk ofgestational diabetes mellitus in four racial/ethnic groups. Paediatr Perinat Epidemiol13:452465, 1999

10. Harris MI:Racial and ethnic differences inhealth care access and health outcomesfor adults with type 2 diabetes. DiabetesCare 24:454 459, 2001

11. Kieffer EA, Sinco B, Kim C: Health behav-iors in womenwith a history of gestationaldiabetes mellitus in the Behavioral RiskFactor Surveillance System. Diabetes Care29:17881793, 2006

12. Otchet F, Carey MS, Adam L: Generalhealth and psychological symptom statusin pregnancy and the puerperium: what isnormal? Obstet Gynecol 94:935941,1999

13. Kim C, NewtonK, Knopp RH: Gestationaldiabetes and incidence of type 2 diabetesmellitus: a systematic review. DiabetesCare 25:18621868, 2002

14. Centers for Disease Control and Preven-tion: Behavioral Risk Factor SurveillanceSystem, 2003 Summary Data Quality Re-port. Atlanta, GA, Centers for DiseaseControl and Prevention, 2005

15. Nelson DE, Holtzman D, Bolen J, Stanw-yck CA, Mack KA: Reliability and validityof measures from the Behavioral Risk Fac-tor Surveillance System. Soz Praventivmed46:S3S42, 2001

16. Ware JE Jr, Kosinski M, Keller SD: A 12-item short-form health survey: construc-tion of scales and preliminary tests ofreliability and validity. Med Care 34:220233, 1996

17. Centers for Disease Control and Preven-tion: The Behavioral Risk Factor Surveil-lance System: Measuring Healthy Days.Atlanta, GA, Centers for Disease Control

and Prevention, 200318. Andresen EM, Catlin TK, Wyrwich KW,

Jackson-Thompson J: Retest reliability ofsurveillance questions on health-relatedquality of life. J Epidemiol CommunityHealth 57:339343, 2003

19. RaoJNK,Scott AJ:The analysis of categor-ical data from complex surveys: chi-squared tests for goodness of fit andindependence in two-way tables. J AmStat Assoc 76:221230, 1981

20. Ayanian JZ, Weissman JS, Schneider EC,Ginsburg JA, Zaslavsky AM: Unmethealth needs of uninsured adults in theUnited States. JAMA 284:20612069,2000

21. Sambamoorthi U, McAlpine DD: Racial,ethnic, socioeconomic, and access dispar-ities in the use of preventive servicesamong women. Prev Med 37:475484,2003

22. GaryTL, Narayan KM,GreggEW, BecklesGL, Saadine JB: Racial/ethnic differencesin the healthcare experience (coverage,utilization, and satisfaction) of US adultswith diabetes. Ethn Dis 13:4754, 2003

23. Nelson KM, ChapkoMK, ReiberG, BoykoEJ: The association between health insur-ance coverage and diabetes care; datafrom the 2000 Behavioral Risk FactorSur-veillance System. Health Serv Res 40:361372, 2005

24. Centers for Disease Control and Preven-tion: Health status of Cambodians andVietnamese-selected communities, United

States,20012002. MMWR Morb MortalWkly Rep 53:760755, 2004

25. Sundaram AA, Ayala C, Greenlund KJ,Keenan NL: Differences in the prevalenceof self-reported risk factors for coronaryheart disease among American women byrace/ethnicity and age: Behavioral RiskFactor Surveillance System, 2001. Am JPrev Med 29:2530, 2005

26. Harris MI: Health care and health statusand outcomes for patients with type 2 di-abetes. Diabetes Care 23:754758, 2000

27. Third report of the National CholesterolEducation Program (NCEP) Expert Panelon Detection Evaluation, and Treatmentof High Blood Cholesterol in Adults(Adult Treatment Panel III): final report.Circulation 106:31433121, 2002

28. ChuangCH, Chase GA,Bensyl DM,Weis-man CS: Contraceptive use by diabeticand obese women. Womens Health Issues15:167173, 2005

29. Zahran HS, Kobau R, Moriarty DG, ZackMM, Holt J, Donehoo R: Health-relatedquality of life surveillanceUnited States,19932002. MMWR Surveill Summ 54:135, 2005

30. Avis NE, Ory M, Matthews KA, SchockenM, Bromberger J, Colvin A: Health-re-lated quality of life in a multiethnic sam-ple of middle-aged women: Study ofWomens Health Across the Nation(SWAN). Med Care 41:12621276, 2003

31. Geronimus AT: Understanding and elim-inating racial inequalities in womenshealth in the United States: the role of theweathering conceptual framework. J AmMed Womens Assoc 56:133136, 2001

32. Krieger N, Smith K, Naishadham D, Hart-man C, Barbaeu EM: Experiences of dis-crimination: validity and reliability of aself-report measure for population healthresearch on racism andhealth. Soc Sci Med61:15761596, 2005

Kim, Sinco, and Kieffer

DIABETES CARE, VOLUME 30, NUMBER 6, JUNE 2007 1465