Jurnal1

Percentage of Gestational Diabetes MellitusAttributable to Overweight and ObesityShin Y. Kim, MPH, Lucinda England, MD, MSPH, Hoyt G. Wilson, PhD, Connie Bish, PhD, MPH, Glen A. Satten, PhD, and Patricia Dietz, DrPH

Gestational diabetes mellitus (GDM) is definedas carbohydrate intolerance leading to hyper-glycemia with onset or first recognition duringpregnancy.1 GDM affects 1% to 14% of preg-nancies, depending on the population studiedand the diagnostic tests used.1,2 It has beenassociated with maternal, fetal, and infant com-plications, including infant macrosomia and birthtrauma, infant hypoglycemia, cesarean section,and increased medical costs.37 Although somewomen with diagnosed GDMwill have persistentabnormal glycemia, most women will revert tonormal carbohydrate metabolism after delivery.8

However, women with a history of GDM remainat increased risk of developing type 2 diabetesmellitus in the future.9 GDM and type 2 diabetesshare many common risk factors, includingoverweight and obesity, and GDM is consideredby many to be a precursor of type 2 diabetes.10

In addition, evidence suggests that the in-cidence of GDM increased in the 1990s.11,12

This rise, which was concurrent with the growingprevalence of prepregnancy obesity (a 69.3%increase between 19931994 and 20022003)13 and increases in type 2 diabetes in thegeneral population (a 48.8% increase from1994through 2002),14 was independent of other riskfactors such as maternal age and parity.13 Al-though GDM risk increases substantially withincreasing prepregnancy body mass index (BMI;defined as weight in kilograms divided by heightin meters squared),15 the percentage of GDMspecifically attributable to overweight and obe-sity is currently unknown.

Population-based risk estimates are neededto calculate the percentage of GDM cases thatcould potentially be prevented if all womenwho are overweight or obese had a GDM riskequivalent to that of women of normal weight.We sought to calculate the percentage ofpregnancies affected by GDM and the per-centage of GDM attributable to overweight andobesity as a means of better understanding thepotential effects of weight management onGDM prevalence.

METHODS

We analyzed data from the Pregnancy RiskAssessment Monitoring System (PRAMS), anongoing population-based surveillance systemthat collects information on self-reported ma-ternal characteristics before, during, and afterpregnancy in participating states. Each month,a stratified systematic sample of approximately150 mothers is selected from the birth certifi-cate records of each state. To participate inPRAMS, women must be state residents whohave recently delivered a live-born infant,typically in the preceding 3 or 4 months.

A self-administered,14-page questionnaire ismailed to each eligible mother. If the motherfails to respond, a second or third questionnaireis sent to her. If there is no response to theseadditional mailings, attempts are made to reachthe mother for a telephone interview. Eachmothers self-reported survey data are linkedback to her childs birth certificate record; onlyselected birth certificate variables are includedin the final PRAMS data set. Currently, 37

states, New York City, and the Yankton SiouxTribe in South Dakota participate in PRAMS.

We selected states that had an annualweighted PRAMS response rate of 70% orhigher and had implemented the 2003 revisedUS birth certificate, the latter because thisversion of the birth certificate includes infor-mation on GDM separate from preexistingdiabetes and does not combine the 2 condi-tions. Our study sample included 23904women who were surveyed in 2004, 2005, or2006 in 7 states: Florida (20042005),Nebraska (20052006), New York (excludingNew York City; 20042006), Ohio (2006),South Carolina (20042006), Vermont(20042006), and Washington (20042006). We increased the sampling weights ofrecords from states with fewer years of data sothat the sum of each states weights representedthe same number of years.

Maternal Characteristics

We used birth certificate information toanalyze data on maternal characteristics such

Objectives. We calculated the percentage of gestational diabetes mellitus

(GDM) attributable to overweight and obesity.

Methods. We analyzed 2004 through 2006 data from 7 states using the

Pregnancy Risk Assessment Monitoring System linked to revised 2003 birth

certificate information. We used logistic regression to estimate the magnitude of

the association between prepregnancy body mass index (BMI) and GDM and

calculated the percentage of GDM attributable to overweight and obesity.

Results. GDM prevalence rates by BMI category were as follows: underweight

(1318.4 kg/m2), 0.7%; normal weight (18.524.9 kg/m2), 2.3%; overweight (2529.9

kg/m2), 4.8%; obese (3034.9 kg/m2), 5.5%; and extremely obese (3564.9 kg/m2),

11.5%. Percentages of GDM attributable to overweight, obesity, and extreme

obesity were 15.4% (95% confidence interval [CI]=8.6, 22.2), 9.7% (95% CI=5.2,

14.3), and 21.1% (CI=15.2, 26.9), respectively. The overall population-attributable

fraction was 46.2% (95% CI=36.1, 56.3).

Conclusions. If all overweight and obese women (BMI of 25 kg/m2 or above)

had a GDM risk equal to that of normal-weight women, nearly half of GDM cases

could be prevented. Public health efforts to reduce prepregnancy BMI by

promoting physical activity and healthy eating among women of reproductive

age should be intensified. (Am J Public Health. 2010;100:10471052. doi:10.2105/

AJPH.2009.172890)

RESEARCH AND PRACTICE

June 2010, Vol 100, No. 6 | American Journal of Public Health Kim et al. | Peer Reviewed | Research and Practice | 1047

as age, race/ethnicity, educational level, maritalstatus, parity, smoking status, prepregnancyweight and height, and GDM diagnosis. We didnot obtain information on preexisting diabetesfrom the birth certificates because this infor-mation is not part of the final PRAMS data set.Maternal race/ethnicity was self-reported andcategorized as Hispanic, non-Hispanic White,non-Hispanic Black, or other (Alaska Natives,American Indians, Asian Americans, or indi-viduals of other racial/ethnic backgrounds). InVermont, all women with the exception of non-Hispanic Whites are combined as other be-cause of the small number of women in otherracial/ethnic groups residing in Vermont;thus, we included only information for non-Hispanic White women in our analyses ofVermont data.

Data analyzed from the PRAMS question-naire included prenatal enrollment in the Spe-cial Supplemental Nutrition Program forWomen, Infants, and Children (WIC); Medicaidstatus; smoking status; prepregnancy weightand height; and whether the woman hadpreexisting diabetes. Women who indicated oneither the birth certificate or the PRAMSquestionnaire that they had smoked during thefinal 3 months of their pregnancy were classi-fied as smokers.

When available, we used prepregnancyweight and height data from birth certificates(such data were available in 92% of cases) tocalculate prepregnancy BMI; we used infor-mation from PRAMS if birth certificate datawere missing. Our reason for using birthcertificate data in these calculations was that, inPRAMS, prepregnancy height and weight arecollected typically 3 or 4 months after delivery.We excluded BMI values below the 0.01stpercentile and above the 99.99th percentile(less than13 kg/m2 and more than 64.9 kg/m2,respectively). Thus, on the basis of our exclu-sions and the BMI categories defined by theNational Heart, Lung, and Blood Institute, wedefined underweight as 13 to 18.4 kg/m2,normal weight as 18.5 to 24.9 kg/m2, over-weight as 25 to 29.9 kg/m2, obese as 30 to34.9 kg/m2, and extremely obese as 35 to 64.9kg/m2.

Data Analysis

We excluded the following women from theanalysis: those who reported on PRAMS that

they had preexisting diabetes (these women bydefinition could not develop GDM), those forwhom information about prepregnancy weightor height or information about GDM wasmissing, and those in Vermont who were ofa racial/ethnic group other than non-HispanicWhite. After these exclusions, data on 22767women (95% of the total sample) were avail-able for the final analysis.

We estimated the prevalence and standarderrors of various demographic characteristicsby state and calculated the prevalence of GDMby category for each characteristic. Using sam-ple-weighted multivariate logistic regression,we estimated the independent contributions ofBMI to GDM risk via aggregated data from the7 states in our study. We assessed potentialconfounding for each demographic character-istic and considered a covariate to be potentialconfounder if its inclusion in the regressionmodels changed the unadjusted odds ratio (OR)by 10% or more. Using the logistic regressionresults, we computed relative risks (RRs) andtheir confidence intervals (CIs) according tomethods described by Flanders and Rhodes.16

Finally, employing methods described byGraubard and Fears,17 we used the logisticregression results to estimate the population-attributable fraction (PAF) and its CI for eachoverweight or obese BMI category and for alloverweight and obese categories combined. Weinterpreted each PAF estimate to be the reduc-tion in disease prevalence that would beexpected to occur if all women in the overweightor obese BMI categories had a GDM risk equiv-alent to that of women in the normal BMIcategory, assuming that the risk for GDM amongthose with a low or normal BMI remainedunchanged.18

We also used locally weighted scatterplotsmoothing (LOESS) to estimate the probabilityof GDM as a continuous function of BMI. In thismethod, the smoothed value of the function ateach data point is computed from a weightedregression fit to neighboring points. Neighbor-ing points that are closer to the point at whichthe smoothing occurs are weighted moreheavily.19,20

In all of the analyses other than those in-volving LOESS, the data were weighted toadjust for survey design, months of data sam-pling for the state, nonresponse, and the extentto which some individuals in the target

population were not included in the sampledpopulation. We used Sudaan version 10.0(Research Triangle Institute, Research TrianglePark, NC) to fit the logistic models and computeORs and their standard errors, S-Plus version7.020 to perform the LOESS analyses, and SASversion 9.1 (SAS Institute, Cary, NC) for all othercomputations.

RESULTS

Demographic characteristics of the PRAMSpopulation in each of the 7 states are describedin Table 1. The overall GDM prevalence was4.0% (SE=0.2), with a range from 3.1%(SE=0.4) in Florida to 5.0% (SE=0.7) in Ohio(Table 1). For all states combined, GDM prev-alence estimates by BMI category were asfollows: underweight, 0.7% (SE=0.3); normalweight, 2.3% (SE=0.3); overweight, 4.8%(SE=0.5); obese, 5.5% (SE=0.7); and ex-tremely obese, 11.5% (SE=1.3; Table 2).

In addition, we found that 0.9% (SE=0.4) ofwomen with gestational diabetes were under-weight, 28.4% (SE=2.8) were of normalweight, 28.5% (SE=2.7) were overweight,16.2% (SE=2.1) were obese, and 26.0%(SE=2.7) were extremely obese. The proba-bility of GDM increased with increasing BMI,although the confidence bands became quitewide when BMIs exceeded 40 kg/m2 (Figure1).There was no clear BMI threshold below whicha doseresponse relationship was not evident.

Because none of the potential confounderschanged ORs by 10% or more, we included inour adjusted model covariates that have beenfound in the literature to be associated withboth the exposure (BMI) and the outcome(GDM). When the normal-weight BMI categorywas used as a reference group, we found thatthe unadjusted RRs of developing GDM were0.3 (95% CI=0.1, 0.7) for underweightwomen, 2.1 (95% CI=1.6, 2.9) for overweightwomen, 2.4 (95% CI=1.7, 3.4) for obesewomen, and 5.0 (95% CI=3.6, 6.9) for ex-tremely obese women. RRs did not changeafter adjustment for maternal age, race/eth-nicity, marital status, and parity (Table 3).

The overall adjusted PAF due to overweightand obesity was 46.2% (95% CI=36.1, 56.3;Table 2). Adjusted percentages of GDM in-dividually attributable to overweight, obesity,and extreme obesity were 15.4% (95%


1048 | Research and Practice | Peer Reviewed | Kim et al. American Journal of Public Health | June 2010, Vol 100, No. 6

TABLE 1Sample-Weighted Demographic Characteristics: Pregnancy Risk Assessment Monitoring System, 7 US States, 20042006

Maternal Characteristic

Total

(20042006),

% (SE)

Florida

(20042005),

% (SE)

Nebraska

(20052006),

% (SE)

New Yorka

(20042006),

% (SE)

Ohio

(2006),

% (SE)

South Carolina

(20042006),

% (SE)

Vermont

(20042006),

% (SE)

Washington

(20042006),

% (SE)

Age, y

< 20 10.4 (0.3) 11.0 (0.1) 7.4 (0.5) 7.5 (0.7) 12.0 (1.2) 13.3 (0.9) 7.1 (0.5) 9.0 (0.6)

2034 74.7 (0.5) 74.9 (0.8) 80.2 (0.8) 70.7 (1.1) 75.5 (1.5) 76.2 (1.1) 75.7 (0.8) 76.2 (0.9)

35 14.9 (0.4) 14.1 (0.8) 12.4 (0.7) 21.7 (1.0) 12.5 (1.1) 10.5 (0.8) 17.1 (0.7) 14.8 (0.7)Education, y

< 12 18.2 (0.5) 20.4 (0.8) 14.9 (0.6) 16.2 (1.0) 15.7 (1.3) 23.0 (1.2) 9.3 (0.6) 17.4 (0.7)

12 27.9 (0.6) 32.4 (1.0) 21.1 (0.9) 23.1 (1.0) 28.4 (1.6) 26.0 (1.2) 32.1 (0.9) 24.5 (0.9)

> 12 53.9 (0.6) 47.2 (1.1) 64.1 (0.9) 60.7 (1.2) 55.9 (1.7) 50.9 (1.3) 58.6 (0.9) 58.1 (1.0)

Race/ethnicityb

Hispanic 15.6 (0.4) 27.1 (1.0) 14.0 (0.1) 13.1 (0.9) 3.3 (0.7) 7.4 (0.7) NA 17.5 (0.1)

Non-Hispanic White 63.9 (0.5) 48.2 (1.0) 75.5 (0.2) 74.5 (1.1) 77.3 (1.0) 58.3 (1.3) 100.0 64.9 (0.4)

Non-Hispanic Black 15.2 (0.2) 20.1 (0.5) 5.5 (0.1) 7.9 (0.7) 14.8 (0.2) 31.9 (1.3) NA 3.2 (0.1)

Other 5.3 (0.3) 4.7 (0.5) 4.9 (0.2) 4.5 (0.5) 4.6 (0.8) 2.4 (0.4) NA 14.4 (0.4)

Married

Yes 61.9 (0.6) 58.2 (1.0) 71.1 (0.9) 67.0 (1.2) 60.1 (1.7) 56.9 (1.3) 68.8 (0.9) 69.6 (0.9)

No 38.1 (0.6) 41.8 (1.0) 28.9 (0.9) 33.0 (1.2) 39.9 (1.7) 43.1 (1.3) 31.2 (0.9) 30.4 (0.9)

Medicaid recipient

Yes 45.9 (0.6) 51.2 (1.1) 40.9 (1.0) 35.5 (1.2) 41.7 (1.7) 58.1 (1.3) 42.1 (0.9) 49.2 (1.0)

No 54.1 (0.6) 48.8 (1.1) 59.1 (1.0) 64.5 (1.2) 58.3 (1.7) 41.9 (1.3) 57.9 (0.9) 50.8 (1.0)

WIC recipient

Yes 55.7 (0.6) 52.1 (1.1) 63.3 (0.9) 62.4 (1.2) 58.1 (1.7) 45.0 (1.3) 59.2 (0.9) 55.9 (1.0)

No 44.3 (0.6) 47.9 (1.1) 36.7 (0.9) 37.6 (1.2) 41.9 (1.7) 55.0 (1.3) 40.8 (0.9) 44.1 (1.0)

Parity

0 42.1 (0.6) 43.3 (1.1) 36.7 (1.0) 41.6 (1.2) 41.0 (1.7) 42.4 (1.3) 44.8 (0.9) 42.9 (1.0)

12 47.9 (0.6) 46.8 (1.1) 50.8 (1.1) 49.1 (1.2) 47.3 (1.8) 50.3 (1.3) 48.2 (0.9) 47.0 (1.1)

> 2 10.0 (0.4) 9.8 (0.7) 12.5 (0.7) 9.3 (0.7) 11.7 (1.1) 7.3 (0.7) 6.9 (0.5) 10.1 (0.6)

Smoking status

Smokerc 13.4 (0.4) 9.1 (0.7) 15.8 (0.8) 15.5 (0.9) 17.4 (1.3) 15.6 (1.0) 18.3 (0.7) 11.5 (0.7)

Nonsmoker 86.6 (0.4) 90.9 (0.7) 84.2 (0.8) 84.5 (0.9) 82.6 (1.3) 84.4 (1.0) 81.7 (0.7) 88.5 (0.7)

BMI category, kg/m2

Underweight (1318.4) 4.8 (0.3) 6.1 (0.5) 4.2 (0.4) 3.3 (0.4) 5.1 (0.8) 4.7 (0.6) 3.2 (0.3) 3.0 (0.3)

Normal weight (18.524.9) 50.3 (0.6) 53.8 (1.1) 50.3 (1.1) 50.4 (1.2) 48.0 (1.7) 44.1 (1.3) 50.5 (0.9) 49.0 (1.0)

Overweight (2529.9) 23.8 (0.5) 22.1 (0.9) 24.5 (0.9) 24.3 (1.0) 24.2 (1.5) 25.3 (1.2) 24.1 (0.8) 26.4 (0.9)

Obese (3034.9) 11.9 (0.4) 10.5 (0.7) 13.0 (0.7) 12.3 (0.8) 12.3 (1.2) 14.7 (0.9) 12.2 (0.6) 12.0 (0.7)

Extremely obese (3564.9) 9.2 (0.4) 7.6 (0.6) 7.9 (0.6) 9.7 (0.7) 10.4 (1.0) 11.2 (0.8) 10.1 (0.6) 9.5 (0.6)

GDM

Yes 4.0 (0.2) 3.1 (0.4) 4.0 (0.4) 3.8 (0.4) 5.0 (0.7) 4.9 (0.6) 3.4 (0.3) 4.8 (0.4)

No 96.0 (0.2) 96.9 (0.4) 96.0 (0.4) 96.2 (0.4) 95.0 (0.7) 95.1 (0.6) 96.6 (0.3) 95.2 (0.4)

Note. BMI = body mass index; GDM = gestational diabetes mellitus; NA = not applicable; WIC = Special Supplemental Nutrition Program for Women, Infants, and Children. The total sample size wasN = 22767; for Florida, n = 4053; for Nebraska, n = 3411; for New York, n = 2744; for Ohio, n = 1497; for South Carolina, n = 3848; for Vermont, n = 3097; for Washington, n = 4117. Overall and statesample sizes are unweighted.aExcludes New York City.bOnly non-Hispanic White women were included in analyses of Vermont data.cDefined as smoking during the final 3 months of pregnancy (self-reported in PRAMS) or during the third trimester (reported on birth certificates).



CI=8.6, 22.2), 9.7% (95% CI=5.2, 14.3), and21.1% (95% CI=15.2, 26.9), respectively.

DISCUSSION

Our results show an increased risk of GDMassociated with increasing BMI. The overallPAF due to overweight and obesity was 46.2%.In other words, if all women with BMIs of 25 orabove had a GDM risk equal to that of womenin the normal BMI category, nearly half ofGDM cases potentially could be prevented. Inaddition, we found that more than 70% of allwomen with GDM had a BMI of 25 or higher,whereas approximately a quarter had a normalBMI.

High maternal BMIs have been consistentlyassociated with an increased risk of GDM in theliterature.15,21 In a meta-analysis estimating themagnitude of GDM risk among women with highprepregnancy BMIs, Chu et al. found that GDMrisk increases substantially with increasing pre-pregnancy BMI.15 Their results showed that,relative to women of normal weight, the

unadjusted ORs for developing GDM were 2.14(95% CI=1.82, 2.53) among overweightwomen, 3.56 (95% CI=3.05, 4.21) amongobese women, and 8.56 (95% CI=5.07, 16.04)among extremely obese women.14 Moreover,similar to our findings, a doseresponse rela-tionship between increasing BMI and type 2diabetes has been described in the generalpopulation, even within the normal BMI cate-gory.2224

Although women with GDM are at increasedrisk for type 2 diabetes,9 evidence stronglysuggests that type 2 diabetes is preventable inthis population. Several randomized trials havedemonstrated that weight loss and increasedphysical activity reduce the risk of type 2 di-abetes in individuals at high risk, includingwomen with a history of GDM.2527 Similarly,evidence suggests that GDM risk is reduced inwomen who engage in high levels of physicalactivity28 and consume high-fiber diets.29

Therefore, to the extent that prepregnancyoverweight and obesity cause GDM, reducingprepregnancy weight in these women should

TABLE 2Sample-Weighted

Gestational Diabetes Mellitus (GDM)

Prevalence, by Demographic

Characteristics: Pregnancy Risk

Assessment Monitoring System, 7 US

States, 20042006

Maternal Characteristic

GDM Prevalence,

% (SE)

Overall 4.0 (0.2)

Age, y

< 20 1.0 (0.3)

2034 3.6 (0.3)

35 8.4 (0.9)Education, y

< 12 2.8 (0.4)

12 3.9 (0.5)

> 12 4.5 (0.3)

Race/ethnicitya

Hispanic 4.4 (0.6)

Non-Hispanic White 4.0 (0.3)

Non-Hispanic Black 3.6 (0.4)

Other 5.4 (1.0)

Married

Yes 4.6 (0.3)

No 3.1 (0.3)

Medicaid recipient

Yes 3.4 (0.3)

No 4.6 (0.4)

WIC recipient

Yes 4.3 (0.4)

No 3.7 (0.3)

Parity

0 3.0 (0.3)

12 4.8 (0.4)

> 2 5.0 (0.9)

Smoking status

Smokerb 3.7 (0.6)

Nonsmoker 4.1 (0.3)

BMI category, kg/m2

Underweight (1318.4) 0.7 (0.3)

Normal weight (18.524.9) 2.3 (0.3)

Overweight (2529.9) 4.8 (0.5)

Obese (3034.9) 5.5 (0.7)

Extremely obese (3564.9) 11.5 (1.4)

Note. BMI = body mass index; WIC = Special Supple-mental Nutrition Program for Women, Infants, andChildren.aOnly non-Hispanic White women were included inanalyses of Vermont data.bDefined as smoking during the final 3 months ofpregnancy (self-reported in PRAMS) or during the thirdtrimester (reported on birth certificates).

Note. BMI is defined as weight in kilograms divided by height in meters squared.

FIGURE 1Unweighted probability of gestational diabetes mellitus (GDM), by mothers

prepregnancy body mass index (BMI): Pregnancy Risk Assessment Monitoring System, 7 US

states, 20042006.



reduce diabetes-related adverse pregnancy out-comes. Sustaining this weight loss beyond preg-nancy should reduce womens future risk fortype 2 diabetes.30

Limitations

To our knowledge, our study provides thefirst population-based estimates of the contri-bution of overweight and obesity to GDM.However, the study involves some limitations.Prepregnancy weight is self-reported in PRAMSand is likely to be self-reported on birthcertificates; estimates of obesity prevalencebased on self-reported weight tend to be lowerthan those based on measured data.31 There-fore, we may have underestimated the preva-lence of prepregnancy overweight and obesity,which could have resulted in an underestimationof the contribution of overweight and obesity tothe PAF assuming that the BMI misclassificationwas nondifferential.

In addition, because PRAMS collects dataonly on women who have delivered a live-borninfant, our analysis did not include womenwhose pregnancies ended in a miscarriage, fetaldeath, or stillbirth. However, GDM typicallydevelops in the late second or early thirdtrimester of pregnancy, and only a small pro-portion of women (6.3 per 1000 women)experience fetal loss after 20 weeks. Therefore,our estimates of GDM prevalence should nothave been substantially affected by the re-striction of our analysis to live births.

Our data also may not be generalizable tostates not included in our analyses. For exam-ple, GDM prevalence has been shown to varyby racial/ethnic group. Nineteen percent of allUS live births were represented in our analyses,and non-Hispanic White women were over-represented in our study (64% of the mothersincluded in our analysis were non-HispanicWhite, as compared with 55% in the total USpopulation of mothers delivering a live-borninfant).32 Moreover, prepregnancy obesity variesby state, ranging from13.9% to 25.1% in 26PRAMS states that represented 47% of all livebirths in the United States during 20042005.33 This variation, in part, may explain thedifferences in the prevalence of GDM acrossstates. Therefore, a national estimate of thecontribution of prepregnancy weight to GDMrisk may be different from our estimate.

Furthermore, although the association be-tween BMI and GDM risk appears to varyaccording to race/ethnicity, we were not ableto calculate PAFs for specific racial/ethnicgroups as a result of our small poststratificationsample sizes. Large databases are needed toconduct more in-depth analyses of BMI andGDM in specific racial/ethnic groups. In addi-tion, our analysis did not account for potentialconfounders such as physical activity, diet, andgenetics because PRAMS does not collect in-formation on these indicators.

Finally, the quality of the GDM variable onthe revised 2003 birth certificate has not been

validated in the published literature. However,studies in states that have amended their 1989birth certificate to differentiate between pre-existing diabetes and GDM have consistentlyshown specificities above 98% and sensitivitiesranging from 46% to 83% in identifyingGDM.34 With the implementation of the revised2003 birth certificate and as certifiers havebecome more familiar with the separate cate-gories, accuracy of GDM identification mostlikely has improved.35

Conclusions

A large percentage of GDM cases are po-tentially attributable to overweight and obesityand could be avoided by preventing theseconditions. Data such as ours can help publichealth officials estimate the potential effects ofprevention interventions on GDM prevalencerates. Lifestyle interventions designed to re-duce BMIs have the potential to lower GDMrisk. Therefore, public health efforts to promoterecommended levels of physical activity andhealthy eating habits among women of repro-ductive age should be intensified. j

About the AuthorsThe authors are with the Division of Reproductive Health,National Center for Chronic Disease Prevention and HealthPromotion, Centers for Disease Control and Prevention,Atlanta, GA.

Correspondence should be sent to Shin Y. Kim, MPH,4770 Buford Hwy NE, MS K-23, Atlanta, GA (e-mail:[email protected]). Reprints can be ordered at http://www.ajph.org by clicking the Reprints/Eprints link.

This article was accepted October 7, 2009.Note. The findings and conclusions in this article are

those of the authors and do not necessarily represent theofficial position of the Centers for Disease Control andPrevention.

ContributorsS. Y. Kim and L. England originated the study. S. Y. Kimanalyzed the data, led the writing, and supervised allaspects of study implementation. L. England, C. Bish,G.A. Satten, and P. Dietz synthesized the analysis. H.G.Wilson analyzed the data and synthesized the analysis.All of the authors helped conceptualize ideas, interpretfindings, and review drafts of the article.

AcknowledgmentsWe thank Brian Morrow for his technical expertise andconsultation. Data from the Pregnancy Risk AssessmentMonitoring System (PRAMS) included in this study werecollected at the state level by the following state workinggroup collaborators and their staff: Albert Woolbright(Alabama), Kathy Perham-Hester (Alaska), Mary McGehee(Arkansas), Alyson Shupe (Colorado), Charlon Kroelinger(Delaware), Jamie Fairclough (Florida), Carol Hoban

TABLE 3Sample-Weighted Gestational Diabetes Mellitus (GDM) Risk Data:

Pregnancy Risk Assessment Monitoring System, 7 US States, 20042006

RR (95% CI) PAFb (95% CI)

Unadjusted Adjusteda Unadjusted Adjusteda

Underweight (1318.4 kg/m2) 0.32 (0.14, 0.75) 0.38 (0.16, 0.89) . . . . . .

Normal weight (18.524.9 kg/m2; Ref) 1.00 1.00 . . . . . .

Overweight (2529.9 kg/m2) 2.12 (1.55, 2.90) 2.17 (1.58, 2.97) 15.1 (8.3, 21.9) 15.4 (8.6, 22.2)

Obese (3034.9 kg/m2) 2.41 (1.71, 3.41) 2.51 (1.76, 3.56) 9.5 (4.9, 14.0) 9.7 (5.2, 14.3)

Extremely obese (3564.9 kg/m2) 5.02 (3.64, 6.93) 5.03 (3.64, 6.95) 20.8 (15.0, 26.6) 21.1 (15.2, 26.9)

All BMI categories above normal weight . . . . . . 45.4 (35.3, 55.5) 46.2 (36.1, 56.3)

Note. BMI = body mass index; CI = confidence interval; PAF = population-attributable fraction; RR = relative risk. The samplesize used for unadjusted RR was n = 22 767; for adjusted RR, n = 22 200.aAdjusted models included covariates for maternal age, race/ethnicity, marital status, and parity.bWe interpreted each PAF estimate to be the reduction in disease prevalence that would be expected to occur if all women inthe overweight or obese BMI categories had a GDM risk equivalent to that of women in the normal BMI category, assumingthat the risk for GDM among those with a low or normal BMI remained unchanged.



(Georgia), Mark Eshima (Hawaii), Theresa Sandidge(Illinois), Joan Wightkin (Louisiana), Kim Haggan(Maine), Diana Cheng (Maryland), Hafsatou Diop(Massachusetts), Violanda Grigorescu (Michigan), JanJernell (Minnesota), Marilyn Jones (Mississippi), VenkataGarikapaty (Missouri), JoAnn Dotson (Montana),Brenda Coufal (Nebraska), Lakota Kruse (New Jersey),Eirian Coronado (New Mexico), Anne Radigan-Garcia(New York State), Candace Mulready-Ward (New YorkCity), Paul Buescher (North Carolina), Sandra Anseth(North Dakota), Connie Geidenberger (Ohio), AliciaLincoln (Oklahoma), Kenneth Rosenberg (Oregon),Kenneth Huling (Pennsylvania), Sam Viner-Brown(Rhode Island), Mike Smith (South Carolina), ChristineRinki (South Dakota), Kate Sullivan (Texas), David Law(Tennessee), Laurie Baksh (Utah), Peggy Brozicevic(Vermont), Marilyn Wenner (Virginia), Linda Lohdefinck(Washington), Melissa Baker (West Virginia), KatherineKvale (Wisconsin), and Angi Crotsenberg (Wyoming).Data were also collected by the Centers for DiseaseControl and Prevention PRAMS team.

Human Participant ProtectionThe Pregnancy Risk Assessment Monitoring Systemprotocol was approved by the Centers for DiseaseControl and Preventions institutional review board; theanalysis plan was approved in the participating states.Informed consent was obtained from all participants viaeither mail or telephone.

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