Donald J. Bachman

Association between Obesity during Pregnancy and Increased Use of Health Care

BACKGROUND In the United States, obesity during pregnancy is common and increases obstetrical risks. An estimate of the increase in use of health care services associated with obesity during pregnancy is needed. METHODS We used electronic data systems of a large U.S. group-practice health maintenance organization to identify 13,442 pregnancies among women 18 years of age or older at the time of conception that resulted in live births or stillbirths. The study period was between January 1, 2000, and December 31, 2004. We assessed associations between measures of use of health care services and body-mass index (BMI, defined as the weight in kilograms divided by the square of the height in meters) before pregnancy or in early pregnancy. The women were categorized as underweight (BMI <18.5), normal (BMI 18.5 to 24.9), overweight (BMI 25.0 to 29.9), obese (BMI 30.0 to 34.9), very obese (BMI 35.0 to 39.9), or extremely obese (BMI > or =40.0). The primary outcome was the mean length of hospital stay for delivery. RESULTS After adjustment for age, race or ethnic group, level of education, and parity, the mean (+/-SE) length of hospital stay for delivery was significantly (P<0.05) greater among women who were overweight (3.7+/-0.1 days), obese (4.0+/-0.1 days), very obese (4.1+/-0.1 days), and extremely obese (4.4+/-0.1 days) than among women with normal BMI (3.6+/-0.1 days). A higher-than-normal BMI was associated with significantly more prenatal fetal tests, obstetrical ultrasonographic examinations, medications dispensed from the outpatient pharmacy, telephone calls to the department of obstetrics and gynecology, and prenatal visits with physicians. A higher-than-normal BMI was also associated with significantly fewer prenatal visits with nurse practitioners and physician assistants. Most of the increase in length of stay associated with higher BMI was related to increased rates of cesarean delivery and obesity-related high-risk conditions. CONCLUSIONS Obesity during pregnancy is associated with increased use of health care services.

Excessive Gestational Weight Gain and Postpartum Weight Retention Among Obese Women

OBJECTIVE: To evaluate the incremental effect of weight gain above that recommended for term pregnancy (15 pounds) on postpartum weight retention at 1 year among obese women. METHODS: In a retrospective cohort study, we identified 1,656 singleton gestations resulting in live births among obese women (body mass index at or above 30 kg/m2) between January 2000 and December 2005 in Kaiser Permanente Northwest. Pregnancy weight change (last available predelivery weight minus weight at pregnancy onset) was categorized as less than 0, 0–15, greater than 15 to 25, greater than 25 to 35, and greater than 35 pounds. Postpartum weight change (weight at 1 year postpartum minus weight at pregnancy onset) was defined as less than 0, 0–10, and greater than 10 pounds. RESULTS: Total gestational weight gain was –33.2 (weight loss) to +98.0 pounds (weight gain). Nearly three fourths gained greater than 15 pounds, and they were younger and weighed less at baseline than women who gained 15 pounds or less. Pregnancy-related weight change showed a significant relationship with postpartum weight change. For each pound gained during pregnancy, there was a 0.4-pound increase above baseline weight at 1 year postpartum. In adjusted logistic regression models, the risk of a postpartum weight greater than 10 pounds over baseline was twofold higher for women gaining greater than 15 to 25 pounds compared with women gaining 0–15 pounds (odds ratio [OR] 2.18, 95% confidence interval [CI] 1.54–3.10), fourfold higher for women gaining greater than 25 to 35 pounds (OR 3.91, 95% CI 2.75–5.56), and almost eightfold higher for women gaining greater than 35 pounds (OR 7.66, 95% CI 5.36–10.97). CONCLUSION: Incremental increases in gestational weight gain beyond the current recommendation for obese women substantially increase the risk of weight retention at 1 year postpartum. LEVEL OF EVIDENCE: II

Using risk-adjustment models to identify high-cost risks.

Background. We examine the ability of various publicly available risk models to identify high-cost individuals and enrollee groups using multi-HMO administrative data. Methods. Five risk-adjustment models (the Global Risk-Adjustment Model [GRAM], Diagnostic Cost Groups [DCGs], Adjusted Clinical Groups [ACGs], RxRisk, and Prior-expense) were estimated on a multi-HMO administrative data set of 1.5 million individual-level observations for 1995–1996. Models produced distributions of individual-level annual expense forecasts for comparison to actual values. Prespecified “high-cost” thresholds were set within each distribution. The area under the receiver operating characteristic curve (AUC) for “high-cost” prevalences of 1% and 0.5% was calculated, as was the proportion of “high-cost” dollars correctly identified. Results are based on a separate 106,000-observation validation dataset. Main Results. For “high-cost” prevalence targets of 1% and 0.5%, ACGs, DCGs, GRAM, and Prior-expense are very comparable in overall discrimination (AUCs, 0.83–0.86). Given a 0.5% prevalence target and a 0.5% prediction threshold, DCGs, GRAM, and Prior-expense captured

Postpartum screening for diabetes after a gestational diabetes mellitus-affected pregnancy.

963,000 (approximately 3%) more “high-cost” sample dollars than other models. DCGs captured the most “high-cost” dollars among enrollees with asthma, diabetes, and depression; predictive performance among demographic groups (Medicaid members, members over 64, and children under 13) varied across models. Conclusions. Risk models can efficiently identify enrollees who are likely to generate future high costs and who could benefit from case management. The dollar value of improved prediction performance of the most accurate risk models should be meaningful to decision-makers and encourage their broader use for identifying high costs.

Newborn Size Among Obese Women With Weight Gain Outside the 2009 Institute of Medicine Recommendation

OBJECTIVE: To estimate trends in postpartum glucose testing in a cohort of women with gestational diabetes mellitus (GDM). METHODS: A validated computerized algorithm using Kaiser Permanente Northwest automated data systems identified 36,251 live births or stillbirths from 1999 through 2006. The annual percentage of pregnancies complicated by gestational diabetes with clinician orders for and completion of a fasting plasma glucose (FPG) test within 3 months of delivery was calculated. Logistic regression with generalized estimating equations was used to test for statistically significant trends. RESULTS: The percentages of pregnancies affected by GDM increased from 2.9% in 1999 to 3.6% in 2006 (P<.01). Clinician orders for postpartum tests increased from 15.9% in 1999 to 79.3% in 2004 (P<.01), and then remained stable through 2006. Completed FPG tests increased from 9.0% in 1999 to 57.8% in 2004 (P<.01), and then remained stable through 2006. No oral glucose tolerance tests were ordered. From 2004 to 2006, the practice site where women received care was the factor most strongly associated with the clinician order, but it was not predictive of test completion. Among women with clinician orders, those who were Asian or Hispanic or who attended the 6-week postpartum examination were more likely to complete the test than their counterparts. CONCLUSION: Postpartum glucose testing in women with GDM-affected pregnancies increased over time. However, even in recent years, 42% of women with GDM-affected pregnancies failed to have a postpartum FPG test, and no test was ordered for 21% of GDM-affected pregnancies. LEVEL OF EVIDENCE: II

A system-based intervention to improve postpartum diabetes screening among women with gestational diabetes

OBJECTIVE: To estimate risk of delivering macrosomic, large-for-gestational-age and small-for-gestational-age neonates in obese women with gestational weight gain outside the 2009 Institute of Medicine recommendation (11–20 pounds). METHODS: In a retrospective cohort study, we evaluated 2,080 obese women (body mass index 30 or higher) with singleton pregnancies that resulted in term live births within one health maintenance organization between 2000 and 2005; women with diabetes or hypertensive disorders were excluded. Gestational weight gain was categorized as less than 0, 0 to less than 11, 11–20 (referent), greater than 20–30, greater than 30–40, and greater than 40 pounds and as above, below, or within Institute of Medicine recommendations. We conducted multivariable logistic regression to estimate the odds of large for gestational age and small for gestational age (birth weights greater than the 90th percentile and less than the 10th percentile for gestational age, respectively) and macrosomia (greater than 4,500 g) adjusting for potential confounders. RESULTS: Eighteen percent gained below, 25% within, and 57% above Institute of Medicine recommendations. Prevalence of macrosomia, large for gestational age, and small for gestational age were 4.3%, 19.8%, and 4.3%, respectively. Compared with weight gain of 11–20 pounds, weight gain above recommendations did not significantly decrease small-for-gestational-age risk but was associated with increased odds of macrosomia (adjusted odds ratio [OR] 3.36; 95% confidence interval [CI] 1.74–6.51; 6.0% compared with 2.1%) and large for gestational age (adjusted OR 1.80; 95% CI 1.36–2.38; 23.8% compared with 16.6%). Weight gain below recommendations was associated with increased odds of small for gestational age (adjusted OR 3.94; 95% CI 2.04–7.61; 8.8% compared with 2.7%) and decreased odds of large for gestational age (adjusted OR 0.56; 95% CI 0.37–0.84; 11.2% compared with 16.6%). CONCLUSION: Regarding small for gestational age and large for gestational age, there is no benefit of weight gain above Institute of Medicine recommendations. Weight gain below recommendations decreases large for gestational age but increases small-for-gestational-age risk. LEVEL OF EVIDENCE: II

Maternal Morbidity Rates in a Managed Care Population

OBJECTIVE We sought to determine whether our process improvement program led to increased postpartum diabetes screening rates among women with gestational diabetes mellitus (GDM). STUDY DESIGN In early 2009, we conducted obstetrics department staff education sessions, revised GDM patient care protocols, and developed an electronic system to trigger reminder calls to patients who had not completed diabetes mellitus screening by 3 months postpartum. We then evaluated the rates of postpartum glucose test order entry and completion for women with GDM delivering from July 2009 through June 2010 (n = 179) and July 2007 through June 2008 (n = 200). RESULTS After the programs implementation, the proportion of women receiving an order for a postpartum glucose test within 3 months of delivery increased from 77.5-88.8% (P = .004), and test completion increased from 59.5-71.5% (hazard ratio, 1.37; 95% confidence interval, 1.07-1.75). CONCLUSION Rates of postpartum diabetes testing can be improved with system changes and reminders.

Extent of maternal morbidity in a managed care population in georgia.

OBJECTIVE: To identify and estimate prevalence rates of maternal morbidities by pregnancy outcome and selected covariates during the antepartum, intrapartum, and postpartum periods in a defined population of pregnant women. METHODS: We used electronic data systems of a large, vertically integrated, group-model health maintenance organization (HMO) to develop an algorithm that searched International Classification of Diseases, 9th Revision, Clinical Modification, codes for 38 predetermined groups of pregnancy-related complications among women enrollees of this HMO between January 1, 1998, and December 31, 2001. RESULTS: We identified 24,481 pregnancies among 21,011 women. Although prevalence and type of morbidity varied by pregnancy outcome, overall, 50% of women had at least one complication. The most common complications were anemia (9.3%), urinary tract infections (9.0%), mental health conditions (9.0%), hypertensive disorders (8.5%), and pelvic and perineal trauma (7.0%). CONCLUSION: A range of mild-to-severe pregnancy complications were identified using linked inpatient and outpatient databases. The most common complications we found usually do not require hospitalization so would be missed in studies that use only hospitalization data. Our data allowed examination of a broad scope of conditions and severity. These findings increase our understanding of the extent of maternal morbidity. LEVEL OF EVIDENCE: II

PS2-51: Utilization Quality Assurance: Are We Better Yet?

BACKGROUND Although maternal deaths are among the most tragic events related to pregnancy, they are uncommon in the US and, therefore, inadequate indicators of a womans pregnancy-related health. Maternal morbidity has become a more useful measure for surveillance and research. Traditional attempts to monitor maternal morbidity have used hospital discharge data, which include data only on complications that resulted in hospitalisation, underestimating the frequency and scope of complications. METHODS To obtain a more accurate assessment of morbidity, we applied a validated computerised algorithm to identify pregnancies and pregnancy-related complications in a defined population enrolled in a health maintenance organisation in the south-eastern US. We examined the most common morbidities by pregnancy outcome and maternal characteristics. RESULTS We identified 37 741 pregnancies; in half (50.7%), at least one complication occurred. The five most common were urinary tract infections, anaemia, mental health conditions, pelvic and perineal complications, and obstetrical infections. Complications were more likely in women with low socio-economic status (SES), and among non-Hispanic Black women compared with non-Hispanic White women. Multivariable models stratified by race/ethnicity indicated that in pregnancies among non-Hispanic White women, low SES had a modest effect on the odds of having preexisting medical conditions [adjusted odd ratio (AOR) 1.3 [95% confidence interval (CI) 1.2, 1.5]] or having any morbidity (AOR 1.3 [95% CI 1.2, 1.4]). Low SES had little effect on complications among non-Hispanic Black women. CONCLUSION Our findings suggest that comprehensive health insurance coverage may lessen the unfavourable impact of socio-economic disadvantage on the risk of maternal morbidity.

PS2-05: Does Socioeconomic Status Moderate the Association of Race with Morbidities During Pregnancy Episodes?

Background The HMORN Virtual Data Warehouse (VDW) Utilization files are used in almost every VDW research project for a range of purposes including selecting study populations, building disease registries, measuring health status, and evaluating resource use and appropriateness of care. Utilization data, including encounter, diagnosis and procedure data, comes from multiple data sources including legacy data, electronic health records, and claims. Because the data come from many sources and require complicated processes of standardization, the VDW tables can be very complex to build, potentially leading to inconsistencies across sites. Our objective was to assess, document, and improve the overall quality, availability, and completeness of the VDW Utilization data. To understand whether our QA approach was effective, we compared the current data quality to quality assurance data collected in 2009. Methods The HMORN Utilization Work Group, together with KP CESR staff, developed quality assurance programs to build summary tables for participating sites. The Utilization Work Group then combined the summary tables from each site and graphically compared utilization rates, diagnosis capture, and other statistics across HMORN sites to provide a more complete picture of the variability across sites and identify potential outliers that may indicate data quality concerns. Results Overall, we found that VDW Utilization data quality has improved considerably since 2009, as demonstrated by the reduction in variability across sites. In particular, rates of hospitalization, inpatient days, and doctor’s office visits are considerably more consistent across time and sites. Residual differences likely reflect real-world variation in membership composition and standards of care. In addition, we identified areas with persistent variability that indicate a need for further exploration, such as rates of dialysis and out-of-office encounters. Finally, we also found between-site differences in the interpretation of the Utilization specification, such as the designation of principal and primary diagnoses. Conclusions Identification and resolution of data quality problems through frequent use of the data, cross-site quality checks, QA programs that produce traffic light (pass/warning/fail) reports, and sites sharing ETL (Extract, Transform, Load) code have considerably improved the data quality of the VDW utilization files.