Interrupting the Pathway from Gestational Diabetes Mellitus to Type 2 Diabetes: The Role of Primary Care

Women's Health Issues xxx-xx (2019) 1–9

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Original article

Interrupting the Pathway from Gestational Diabetes Mellitus to Type 2 Diabetes: The Role of Primary Care Lois McCloskey, MPH, DrPH a,*, Emily Quinn, MA a, Omid Ameli, MD, MPH a,b, Timothy Heeren, PhD a, Myrita Craig, MS a, Aviva Lee-Parritz, MD c, Ronald Iverson, MD, MPH c, Brian Jack, MD c, Judith A. Bernstein, RNC, MSN, PhD a a b c

Boston University School of Public Health, Boston, Massachusetts OptumLabs, Boston, Massachusetts Boston University School of Medicine, Boston, Massachusetts

Article history: Received 12 September 2018; Received in revised form 22 July 2019; Accepted 5 August 2019

a b s t r a c t Objective: Our objective was to describe patient-, provider-, and health systems-level factors associated with likelihood of obtaining guideline-recommended follow-up to prevent or mitigate early-onset type 2 diabetes after a birth complicated by gestational diabetes (GDM). Methods: This study presents a retrospective cohort analysis of de-identified demographic and health care system characteristics, and clinical claims data for 12,622 women with GDM who were continuously enrolled in a large, national U.S. health plan from January 31, 2006, to September 30, 2012. Data were obtained from the OptumLabs Data Warehouse. We extracted 1) known predictors of follow-up (age, race, education, comorbidities, GDM severity); 2) novel factors that had potential as predictors (prepregnancy use of preventive measures and primary care, delivery hospital size); and 3) outcome variables (glucose testing within one and 3 years and primary care visit within 3 years after delivery). Results: Asian ethnicity, higher education, GDM severity, and delivery in a larger hospital predicted greater likelihood of post-GDM follow-up. Women with a prepregnancy primary care visit of any type were two to three times more likely to receive postpartum glucose testing and primary care at once year, and 3.5 times more likely to have obtained testing and primary care at 3 years after delivery. Conclusions: A history of use of primary care services before a pregnancy complicated by GDM seems to enhance the likelihood of postdelivery surveillance and preventive care, and thus reduce the risk of undetected early-onset type 2 diabetes. An emphasis on promoting early primary care connections for women in their early reproductive years, in addition to its overall value, is a promising strategy for ensuring follow-up testing and care for women after complicated pregnancies that forewarn risk for later chronic illness. Health systems should focus on models of care that connect primary and reproductive/maternity care before, during, and long after pregnancies occur. Ó 2019 Jacobs Institute of Women's Health. Published by Elsevier Inc.

Approximately 60% of women with gestational diabetes (GDM) will develop type 2 diabetes (T2DM) in the subsequent decade (Kim, Newton, & Knopp, 2002). In a longitudinal, population-based study using national data from OptumLabs

Supported in part by a grant from the National Institutes of Health, United States (NIH RO1 DK107528). * Correspondence to: Lois McCloskey, MPH, DrPH, Associate Professor and Associate Chair, Community Health Sciences Department, Boston University School of Public, Health 801 Massachusetts Ave, Boston, MA 02218. Phone: 617638-5882; fax: 617-638-4483. E-mail address: [email protected] (L. McCloskey).

Data Warehouse (Bernstein et al., 2017), we found that even continuously insured women with GDM have low rates of glucose testing (5.8% postpartum, 21.8% at 1 year, and 51% at 3 years) and even lower rates of contact with primary care for monitoring and preventive measures after delivery (5.7% at 6 months, 13.2% at 1 year, and 40.5% at 3 years), despite their increased risk for T2DM (7.6% with onset within 3 years). Onethird of the 12,622 women studied received no follow-up at all, despite clear evidence that surveillance combined with a program of exercise, nutritional guidance, and in some cases medication can provide effective prevention and/or mitigate progression to of T2DM (Ferrara & Erlich, 2011).

1049-3867/$ - see front matter Ó 2019 Jacobs Institute of Women's Health. Published by Elsevier Inc. https://doi.org/10.1016/j.whi.2019.08.003

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Monitoring blood sugar among women with GDM-affected pregnancies in the year after birth (and beyond) represents a large opportunity for cost savings. The Milken Institute report on the economic burden of chronic disease (Devol, Bedroussian, Klowden, 2007) projects an estimated total expenditure for diabetes care of $79.7 billion in 2023, an increase of 194% over 2003. They suggest that 21.5%, or $118.5 billion, could be saved over two decades through instituting preventive measures. This prediction seems to be on target, given that the prevalence of diabetes increased from 14.1 million (4.93%) in 2003 to 23.3 million (7.4%) in 2015 (CDC Division of Diabetes Translation, 2017). Low rates of follow-up after GDM thus represent a large opportunity for quality improvement and reducing the burden of chronic illness associated with diabetes. A variety of patient-, provider-, and systems-level factors have been investigated as barriers to follow-up. For demographic characteristics, there is a relatively clear picture of effects on follow-up; for example, being Black or Hispanic and having less education decrease the likelihood of postpartum glucose testing (Bennett et al., 2014). For provider type and systems factors the picture is not as clear, except that those who see an endocrinologist or a high-risk maternal-fetal medicine specialist may be more likely to be tested appropriately (Nielson et al., 2014). In one study (McCloskey et al., 2014), women who delivered with Family Medicine clinicians were less likely than those delivered by obstetricians to receive follow-up glucose testing, but overall there is insufficient information about the impact of structural differences to permit conclusions about the role of specialty or institutional type on the likelihood of follow-up care. A lack of preventive care after GDM fuels the epidemic of T2DM, and is a major contributor to chronic disease over the life cycle (Zhu & Zhang, 2016). For this reason, both the American Diabetes Association (2017) and the American College of Obstetricians and Gynecologists (2013, 2016, 2017) have recently reaffirmed guidelines for follow-up after GDM that call for oral glucose tolerance testing (OGTT) within 12 weeks postpartum and retesting at least every 1–3 years, depending on OGTT findings. We follow up here on our initial findings of low follow-up rates using this multisource large data sample to describe available patient-, provider-, and health systems-level factors associated with 1) glucose testing within recommended intervals after delivery, and 2) women’s use of primary care at 1 and 3 years after a GDM-complicated delivery. The purpose of this study was to identify modifiable factors at the systems and individual levels that can inform interventions to improve preventive care for women with GDM. We included in the analysis attributes affecting follow-up that have already been identified in the literature, that is, race/ethnicity and education, along with characteristics reported here for the first time, each with presumed potential to affect follow-up rates: 1) medical status (comorbidities) and pregnancy conditions/complications; extent of insurance coverage; 2) prepregnancy preventive practices, and prior experience with primary care; and 3) structural factors such as the size of the hospital where the delivery occurred, geographic region, and type of obstetric provider. Ours is the first known study to allow for the measurement of women’s care 1 year before, during, and 3 years after a GDM-affected pregnancy among a steadily insured sample. Methods The study involves a retrospective cohort analysis of deidentified claims data from the OptumLabs Data Warehouse,

documenting services provided to privately and publicly insured enrollees in a large U.S. health plan. This longitudinal health information represents a diverse mixture of ages, ethnicities, and geographical regions across the United States. The health plan provides comprehensive insurance coverage for physician, hospital, laboratory, and prescription drug services. Comprehensive de-identification processes are in place and regularly monitored by OptumLabs. This study was determined to be not human research by the institutional review board. Sample Selection As reported in our initial study (Bernstein et al., 2017), we started with all unique women with continuous enrollment for 1 year before pregnancy to 3 years after the delivery of a livebirth between January 31, 2006, and September 30, 2012, using the International Classification of Diseases, 9th revision (ICD-9) (N ¼ 280,933). We then identified the first GDM-affected livebirth in the system (either one inpatient or two outpatient ICD-9 codes of 648.8 first appearing in the third trimester of pregnancy) as the index delivery. This gave us a starting sample of 23,181 women with GDM without prior history of T2DM. In accordance with prespecified criteria, wee next excluded 10,599 women who lacked validated demographic data and/or were missing information about service providers and delivery institutions, or had a subsequent delivery within the 3-year followup period. Women who were excluded did not differ significantly on outcomes (glucose testing rates and transition to primary care) from those who were entered into analysis. The final data sample consisted of 12,622 women, followed between January 1, 2005, and September 30, 2015, who had the complete data set for demographic characteristics, medical conditions, and institutional factors necessary for an analysis of predictors. This 10-year period began after guidelines for post-GDM care were issued by both the College of Obstetricians and Gynecologists and the American Diabetes Association, and it allowed us to follow women for the required 5 years. Variable Construction Demographic characteristics include age, race/ethnicity, educational attainment, type of insurance coverage, net worth of assets, and geographic region. More comprehensive insurance refers to the extent of copay as a rating factor, a category provided to us in the dataset. Coexisting conditions include a modified Charlson Comorbidity Index that excluded diabetes (Quan et al., 2011), and documentation of substance use disorder or mental health diagnoses (Bernstein et al., 2015). We characterized GDM severity broadly as two types: controlled with diet and exercise versus requiring medication along with diet and exercise to reduce glucose levels. Prevention behavior indicators include 1) a flu shot before conception, 2) any visit with a primary care clinician in the preconception year; and 3) early versus late initiation of prenatal care (National Committee for Quality Assurance, 2009). Conditions associated with pregnancy and delivery include gestational hypertension, preeclampsia/ eclampsia, excess prenatal weight gain, prescription of oral hypoglycemic agents during pregnancy, substance use and mental health diagnosis in pregnancy, preterm birth, Caesarean section, obstructed labor, hemorrhage, and traumatic delivery (Sutton et al., 2014). Postpartum conditions includes postpartum depression in the year after delivery and postpartum visit. Health

L. McCloskey et al. / Women's Health Issues xxx-xx (2019) 1–9

care system variables, derived by OptumLabs from the American Hospital Association data provided to them, include characteristics of hospital where delivery occurred and type of provider attending the delivery. The delivery date is calculated in months to permit the monitoring of any external trends in outcomes that may have occurred over the study time period. Comprehensiveness of insurance was measured only as the extent to which insurance covered the cost of care; we did not have information about type of insurance (i.e., Medicaid or Medicare Advantage vs. private). Outcome variables include 1) glucose testing (OGTT within the period of 56–84 days [8–12 weeks]) after delivery, as recommended during the study period; OGTT, fasting blood glucose, or glycolated hemoglobin test within 1 year after delivery and within 3 years after delivery; and the number and type of glucose tests during the three time periods (8–12 weeks, 1 year, 3 years after delivery); and 2) any visit to an internal medicine or family medicine clinician within 1 year after delivery and by 3 years after delivery. Although guidelines recommend OGTT testing in the immediate postpartum period (8–12 weeks) and/or by 6 months after delivery, Lawrence, Black, Hsu, Chen, and Sacks (2010) found that extending the testing period to 6 months postpartum did not add much to the proportion of women with postpartum testing (4%). For this reason, we decided against inclusion of 6-month data as an outcome measure for OGTT. Analytic Measures Analytic measures test the hypothesis that both a pattern of preventive service use before pregnancy and the structural characteristics of the health system in which care is delivered may positively influence the odds of glucose testing and connection to primary care in the 1- and 3-year period after delivery, even after accounting for sociodemographic and clinical profiles. Our initial approach was to include all of the independent variables shown in at least one prior study to be associated with GDM or with receipt of follow-up care (Battarbee & Yee, 2018) along with the novel variables for which there was a rationale for barriers to follow-up of other conditions, and select those that demonstrated a statistically significant association with outcome variables for inclusion in multivariable models. We then used logistic regression (Cox, 1958) to assess the odds of our key outcomes following deliverydby 12 weeks, 1 year, and 3 years after delivery. The Hosmer-Lemeshow test (1982) was used to measure goodness of fit. Variables that did not demonstrate association with the specified outcomes in bivariate analysis were not included in the regression models described in Tables 2 through 5. We also created a three-level composite outcome to capture the type of surveillance received and whether a primary care visit occurred at 1 year and at 3 years after delivery: surveillance and care gap (no glucose testing and no primary care visit); suboptimal surveillance or care (either glucose test or primary care visit but not both); and optimal surveillance and care (both glucose testing and primary care visit). We then conducted bivariable and multivariable testing of the aggregated model, using generalized logistic regression analysis for a multinomial outcome (Menard, 2002) with surveillance and care gap as the reference point. Again, independent variables were only entered into regressions if they demonstrated association with outcomes at a p level of less than .05 on bivariate analyses. Because data included deliveries over a 10-year period, we added a date variable to serve as a time referent, allowing us to assess the impact

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of historic trends in clinical practice and health care delivery patterns over time. Results Sample characteristics are presented in Table 1. The women in this cohort were of reproductive age and diverse in race/ ethnicity, but with lower representation of African Americans than in the nation as a whole, and higher levels of education and net worth than national averages. They were primarily from Central and Southern regions of the United States. They demonstrated a range of pregnancy and delivery complications often associated with GDM, and delivered in mid- to large-size hospitals, primarily with obstetricians. In the year before conception, 17% had a primary care visit for any reason, and only 23% received a flu shot. Predictors of OGTT by 12 Weeks Characteristics that predict follow-up with a postpartum OGTT are presented in Table 2. Demographic factors Asian women were almost twice as likely as White women to receive an OGTT (odds ratio [OR], 1.95; 95% confidence interval [CI], 1.53–2.48); Black and Hispanic women received OGTT at similar rates as White women. Higher educational level was positively associated with testing (OR, 1.34; 95% CI, 1.07–1.68), but more comprehensive insurance, which should represent greater access, was negatively associated with receipt of an OGTT (OR, 0.73; 95% CI, 0.58–0.92). Prevention behaviors A flu shot (OR, 1.43; 95% CI, 1.16–1.77) and/or a primary care visit (OR, 1.27; 95% CI, 1.01–1.60) in the year before pregnancy had a moderately positive influence on the receipt of an OGTT by 12 weeks postpartum. Clinical factors Women who received hypoglycemic medication during pregnancy were almost twice as likely to obtain an OGTT by 12 weeks (OR, 1.89; 95% CI, 1.54–2.32). Preexisting or concurrent medical conditions were not associated with OGTT once other factors were taken into account. Health care delivery factors The size of the hospital in which the delivery took place (reflected in the number of admissions) and the timing of delivery were not significantly related to an OGTT by 12 weeks. Predictors of Glucose Testing by 1 Year Predictors of glucose testing within 1 year are presented in Table 3. Sociodemographic characteristics Women’s odds of being tested increased with older age (OR, 1.17; 95% CI, 1.07–1.28), Asian race (OR, 1.51; 95% CI, 1.34–1.72), Hispanic ethnicity (OR, 1.32; 95% CI, 1.16–1.50), and higher educational level (OR, 1.21; 95% CI, 1.09–1.33), but not with Black race or more comprehensive insurance (OR, 0.85; 95% CI, 0.76– 0.94).

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Table 1 Demographic, Health, and Health Care Characteristics of Insured Women with GDM (N ¼ 12,622) Variable Demographics Age (y) Race/ethnicity Asian African American Hispanic White Education Less than 12th grade High school diploma Some college or degree Insurance coverage Comprehensive Limited (EPO) Net worth of assets (missing ¼ 333) <$25k $25,000–$149,000 $150,000–$249,000 $250,000-$499,000 $500,000 Geographic region (missing ¼ 3) New England Mid Atlantic East North Central West North Central South Atlantic East South Central West South Central Mountain Pacific Coexisting conditions Chronic illness: Charlson Index Any SUD before delivery Any non-SUD mental health disorder Polycystic ovary syndrome Pregnancy conditions Preterm birth Cesarean delivery Gestational hypertension Preeclampsia or eclampsia Excess gestational weight gain Obstructed labor Third trimester (late) initiation of prenatal care Postpartum hemorrhage GDM therapy: medication required Postpartum depression (incident case) OB clinician type Nurse-midwife Obstetrician Family practitioner Other Health care system Endocrinology visit in pregnancy Nutritionist visit in pregnancy Type Government Not for profit, church operated Not for profit, secular For profit, partnership For profit, corporation Hospital size 1–49 beds 50–199 beds 200–399 beds 400 beds

Mean  SD or %

95% CI

33.3  4.8

33.3–33.4

12.3 7.4 12.9 67.4

11.7–12.9 6.9–7.9 12.4–13.5 66.6–68.2

2.1 30.9 67.0

1.9–2.4 30.1–31.7 66.2–67.8

80.9 19.1

80.2–81.6 18.4–19.8

29.3 34.3 14.6 15.2 6.5

28.5–30.1 33.5–35.2 13.9–15.2 14.6–15.9 6.1–7.0

3.3 7.5 14.1 13.1 25.9 2.9 15.4 7.9 10.0

3.0–3.7 7.0–8.0 13.5–14.7 12.5–13.7 25.2–26.7 2.5–3.1 14.7–16.0 7.5–8.4 9.5–10.5

0.20  0.59 1.0 9.3

0.19–0.22 0.9–1.2 8.8–9.9

6.6

6.1–7.0

9.4 13.2 8.2 7.4 2.7 3.9 9.4

8.9–9.9 12.6–13.8 7.7–8.7 6.9–7.8 2.4–3.0 3.5–4.2 8.9–9.9

0.2 20.8 1.2

0.1–0.3 20.1–21.5 1.0–1.4

0.8 93.7 2.2 3.3

0.7–1.0 93.3–94.1 1.9–2.5 3.0–3.6

19.9 3.3

19.2–20.6 3.0–3.7

6.6 14.0 63.4 4.4 11.5 1.4 19.3 36.5 42.7

6.2–7.0 13.4–14.6 32.6–34.3 4.1–4.8 11.0–12.1 1.2–1.6 18.7–20.0 35.7–37.4 41.9–43.6

Abbreviations: CI, confidence interval; EPO, exclusive provider organization; GDM, gestational diabetes mellitus; SD, standard deviation; SUD, substance use disorder.

Preventive behaviors Both a primary care visit and a flu shot in the year before pregnancy (OR, 1.41; 95% CI, 1.27–1.57 and OR, 1.17; 95% CI, 1.06–1.29 respectively) slightly increased the odds of testing within 1 year. Clinical factors Most notably, women whose GDM required hypoglycemic medication were twice as likely as their counterparts to receive glucose testing within 1 year after delivery (OR, 2.13; 95% CI, 1.93–2.35). One other clinical factordpolycystic ovarian syndromedalso had a positive, although more moderate, effect on the odds of glucose testing (OR, 1.43; 95% CI, 1.22–1.68). Preexisting chronic medical conditions were not related to any glucose testing in the postdelivery year. Health care delivery factors Delivery in a larger hospital had a modest effect on women’s odds of receiving any glucose testing by 1 year after delivery (OR, 1.26; 95% CI, 1.12–1.42), but date of delivery did not change the odds.

Predictors of a Primary Care Visit Within 1 Year after Delivery Factors that predict a primary care visit within 1 year after delivery are presented in Table 4. Sociodemographic characteristics Being Asian (OR, 1.48; 95% CI, 1.28–1.72), age 35 or older (OR, 1.26; 95% CI, 1.13–1.40), and having some college education (OR, 1.14; 95% CI, 1.02–1.29) all exerted a modest effect on the likelihood of a primary care visit in the postdelivery year. Other characteristics did not predict a primary care visit by 1 year. Preventive behaviors A primary care visit in the year before pregnancy was the most powerful predictor, more than doubling the odds of connection to primary care within the year after delivery (OR, 2.67; 95% CI, 2.38–3.00). Neither a postpartum OGTT within 12 weeks nor a preconception flu shot had any impact, but early entry into prenatal care was moderately associated (OR, 1.31; 95% CI, 1.11–1.55). Clinical factors Women with excess gestational weight gain were significantly less likely to visit a primary care provider in the postdelivery year (OR, 0.62; 95% CI, 0.42–0.91), whereas women with a preexisting mental health diagnosis were more likely to connect with primary care (OR, 1.30; 95% CI, 1.10–1.54). GDM severity was not predictive. Health care delivery factors Women who delivered in larger hospitals were more likely to have a primary care visit (OR, 1.33; 95% CI, 1.15–1.54). The date of delivery did not change the odds.

Analyses of Aggregate Outcomes: Level of Surveillance and Care We aggregated our measures of surveillance and follow-up care in assessing the impact of sociodemographic, preventive, clinical, and health care delivery factors on the receipt of recommended follow-up after a GDM-affected pregnancy at 1 year and then at 3 years after delivery.

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Table 2 Predictors of OGTT by 12 Weeks Postpartum and Any Glucose Test by 1 Year (n ¼ 12,662) Variable

Postpartum OGTT by 12 Weeks

Age, 35þ vs. <35 years Race Asian vs. white Black vs. White Hispanic vs. White Some college/degree vs. less More comprehensive insurance Chronic medical conditions* (Charlson Index) Polycystic ovarian syndrome* Preconception: flu shot Primary care visit within 3 years GDM therapy: medication required Hospital admissions/y >10,001 Delivery date relative to January 1, 2006

Any Glucose Testing by Year 1

Point Estimate (aOR)

95% CI

p Value

Point Estimate (aOR)

95% CI

p Value

1.12

0.92–1.35

.26

1.17

1.07–1.28

.00

1.95 1.01 1.06 1.34 0.73 – – 1.43 1.27 1.89 1.29 1.01

1.53–2.48 0.68–1.51 0.78–1.44 1.07–1.68 0.58–0.92 – – 1.16–1.77 1.01–1.60 1.54–2.32 0.98–1.69 1.00–1.01

.00 .95 .71 .01 .01 – – .00 .04 .00 .07 .08

1.51 1.07 1.32 1.21 0.85 1.12 1.43 1.17 1.41 2.13 1.26 1.01

1.34–1.72 0.91–1.27 1.16–1.50 1.09–1.33 0.76–0.94 1.00–1.26 1.22–1.68 1.06–1.29 1.27–1.57 1.93–2.35 1.12–1.42 1.00–1.01

.00 .41 .00 .00 .00 .06 .00 .00 .00 .00 .00 .00

Abbreviations: aOR, adjusted odds ratio; CI, confidence interval; GDM, gestational diabetes mellitus; OGTT, oral glucose tolerance test. Factors reported in Table 1 but not entered into Tables 2–5 regression models (e.g., net assets, insurance coverage, geographic region, type of provider, specific pregnancy/delivery conditions, type of hospital) were excluded because of lack of statistical significance with specified outcomes in bivariate analyses. * Not entered into the regression model because of the lack of a significant association in the bivariate model with documentation of a postpartum glucose tolerance test.

Optimal Surveillance and Care (Relative to No Surveillance or Care) at 1 Year After Delivery Sociodemographic factors Asian race doubled the likelihood of optimal surveillance and care (OR, 2.12; 95% CI, 1.72–2.61). Other sociodemographic factors, such as older age (OR, 1.41; 95% CI, 1.20–1.65), Hispanic ethnicity (OR, 1.64; 95% CI, 1.31–2.06), and higher educational level (OR, 1.31; 95% CI, 1.10–1.58) also had a strong although smaller positive impact; Black race was not associated with the receipt of optimal surveillance and care at 1 year. (White women served as the reference group.) Preventive behaviors Again, we found a primary care visit in the year before pregnancy to be the most powerful predictor of optimal

Table 3 Predictors of a Primary Care Visit by 1 Year after Delivery (n ¼ 12,662) Variable

Point Estimate (aOR)

95% CI

p Value

Age 35þ vs. <35 y Race Asian vs. White Black vs. White Hispanic vs. White Some college/degree vs. less Chronic medical condition Mental health diagnosis Preconception: flu shot Primary care visit Prenatal care First trimester vs. second Third trimester vs. second Excess gestational weight gain GDM therapy: medication required Hospital admissions/y 10,001 vs. <10,001 Postpartum glucose tolerance test Delivery date relative to January 1, 2006

1.26

1.13–1.40

.00

1.48 1.12 1.10 1.14 1.15 1.30 1.11 2.67

1.28–1.72 0.91–1.37 0.94–1.29 1.02–1.29 1.00–1.32 1.10–1.54 0.98–1.25 2.38–3.00

.00 .28 .23 .03 .06 .00 .09 .00

1.31 1.02 0.62 1.04 1.33

1.11–1.55 0.80–1.30 0.42–0.91 0.92–1.18 1.15–1.54

.00 .87 .01 .55 .00

1.26 1.01

0.98–1.62 1.00–1.01

.07 .00

Abbreviations: aOR, adjusted odds ratio; CI, confidence interval; GDM, gestational diabetes mellitus.

surveillance and care (OR, 2.74; 95% CI, 2.30–3.25), almost tripling the likelihood of receiving a glucose test and a primary care visit by 1 year after delivery, as recommended. Early entry into prenatal care was not associated with receiving optimal surveillance and care in the year after birth. Clinical factors We did not include medical conditions, mental health diagnoses, and pregnancy conditions in the analysis because of their lack of association in bivariate analyses. However, having had a visit with an endocrinologist, which is often associated with severity of GDM, increased a woman’s likelihood of receiving optimal surveillance and care, over and above the measure of GDM severity based on receipt of hypoglycemic medication (OR, 1.52; 95% CI, 1.26–1.82). Health care delivery factors Larger hospital size increased the odds of receiving optimal surveillance and care (OR, 1.46; 95% CI, 1.16–1.84), as did month of delivery (OR, 1.4601; 95% CI, 1.00–1.801). In our analysis of suboptimal surveillance and care as the aggregate outcome measure, our results were similar, yet most variables were less powerful. Optimal Surveillance and Care at 3 Years After Delivery Our extended analysis of follow-up care out to 3 years after delivery yielded similar findings (Table 5). Many factors increased the likelihood of optimal care, but the strongest predictor by far was a primary care visit in the year before pregnancy, which doubled the odds of receiving some surveillance or care (OR, 1.98; 95% CI, 1.74–2.25), and tripled the odds of receiving optimal surveillance and care (OR, 3.51; 95% CI, 3.07– 4.02). Discussion GDM is a risk marker for T2DM and, as such, sheds light on the limited ability of our existing health care system to respond to warning signs across time to catalyze prevention of chronic illness. Our study, premised on the health, economic, and social

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Table 4 Predictors of Optimal or Suboptimal Postdelivery Surveillance and Care at 1 Year (vs. No Surveillance or Care; n ¼ 12,662) Variable*

Point Estimate (aOR)

95% CI

p Value

Point Estimate (aOR)

95% CI

p Value

Age 35þ vs. <35 y Race Asian vs. White Black vs. White Hispanic vs. White Some college/degree vs. less Chronic medical condition Mental health diagnosis* Polycystic ovarian syndrome Preconception: flu shot Primary care visit Prenatal care First trimester vs. second Third trimester vs. second Nutritionist visit in pregnancy GDM therapy: medication required Endocrinologist visit Preeclampsia/eclampsia Hospital admissions/y, 10,001 More comprehensive insurance Month of delivery relative to January 1, 2006

1.10

1.01–1.20

.03

1.41

1.20–1.65

.00

1.31 1.00 1.11 1.12 1.15 1.14 1.25 1.13 1.82

1.15–1.49 0.85–1.19 0.97–1.26 1.02–1.23 1.03–1.30 0.99–1.32 1.06–1.47 1.02–1.25 1.64–2.03

.00 .95 .12 .02 .02 .06 .01 .02 .00

2.12 1.35 1.64 1.31 1.08 1.26 1.04 1.20 2.74

1.72–2.61 1.00–1.82 1.31–2.06 1.10–1.58 0.87–1.35 0.97–1.63 0.76–1.43 1.00–1.43 2.30–3.25

.00 .05 .00 .00 .47 .08 .79 .05 .00

1.18 1.04 1.15 1.62 1.48 1.26 1.23 0.81 1.00

1.03–1.34 0.87–1.25 0.92–1.44 1.46–1.79 1.34–1.64 1.08–1.46 1.10–1.38 0.73–0.89 1.00–1.01

.02 .67 .21 .00 .00 .00 .00 .00 .00

1.21 0.93 1.33 1.79 1.52 0.90 1.46 0.84 1.01

0.95–1.54 0.65–1.33 0.92–1.92 1.50–2.14 1.26–1.82 0.66–1.25 1.16–1.84 0.69–1.02 1.00–1.01

.13 .68 .13 .00 .00 .54 .00 .08 .00

Abbreviations: aOR, adjusted odds ratio; CI, confidence interval; GDM, gestational diabetes mellitus. * Excess gestational weight gain and postpartum visit were not entered into the regression model because of the lack of a significant association in the bivariate model with the level of postdelivery surveillance and care at year 1.

immediate postpartum period and at 1 and 3 years after delivery. We analyzed data for outcomes at 6 months, but found no significant difference from postpartum results. Because there were so much data to report, we wanted to simplify the story to bring out the highlights, so we do not report the 6-month analysis here. Further, the periods of clinical significance covered in the guidelines include the immediate postpartum period and 1 year after delivery. Our analyses yielded confirmation of the previously reported associations of demographic characteristics with follow-up rates. Our study results reinforce the positive influence of being Asian, 35 years or older, or having higher education attainment on postpartum GDM follow-up (Eggleston et al., 2016; Ferrara, Peng, & Kim, 2009; Tovar, Chasan-Taber, Eggleston, & Oken, 2011), and establish that the pattern is sustained at 3 years after delivery. As

benefits of continuous preventive care across the life course (Halfon, Larson, Lu, Tullis, & Russ, 2013), examined the likelihood that women with a GDM-affected pregnancy would receive recommended follow-up glucose tolerance testing and transition to primary care in the postpartum period and at 1 and 3 years after delivery. The OptumLabs DataWarehouse, a large private insurance sample of continuously insured women, allowed us to expand on prior research by adding preconception use of preventive health services and attributes of the health care system to previously well-studied clinical and demographic characteristics that predict follow-up care after GDM. We also extended the traditionally measured time period of postdelivery surveillance to 3 years. We examined predictors of glucose testing and connection to primary care jointly (optimal, suboptimal, or none) during the

Table 5 Predictors of Level of Postdelivery Surveillance and Care at 3 Years (n ¼ 12,662) Variable

Suboptimal Surveillance or Care

Optimal Surveillance and Care

Point Estimate (aOR)

95% CI

p Value

Point Estimate (aOR)

95% CI

p Value

Age 35þ vs. <35 y Race Asian vs. White Black vs. White Hispanic vs. White Some college/degree vs. less Chronic condition (Charlson index) Polycystic ovarian syndrome Preconception: flu shot Primary care visit Prenatal care: first trimester vs. second Third trimester vs. second Nutritionist visit in pregnancy GDM therapy: medication required Endocrinology visit in pregnancy Hospital admissions/y, 10,001 More comprehensive insurance Delivery month relative to January 1, 2006

0.90

0.83–0.99

.02

0.96

0.87–1.07

.48

1.42 0.92 1.31 1.24 1.06 1.66 1.19 1.98 1.15 0.90 1.15 1.36 1.29 1.04 0.95 1.00

1.23–1.64 0.79–1.08 1.15–1.48 1.13–1.35 0.94–1.20 1.38–2.00 1.08–1.32 1.74–2.25 1.02–1.30 0.76–1.08 0.89–1.48 1.22–1.52 1.16–1.45 0.94–1.16 0.86–1.06 1.00–1.00

.00 .33 .00 .00 .32 .00 .00 .00 .03 .26 .28 .00 .00 .42 .36 .02

2.05 1.16 1.52 1.42 1.13 1.74 1.24 3.51 1.29 0.90 1.59 1.70 1.50 1.18 0.78 1.01

1.76–2.40 0.96–1.40 1.31–1.77 1.27–1.58 0.98–1.29 1.41–2.14 1.10–1.39 3.07–4.02 1.11–1.50 0.73–1.12 1.21–2.08 1.51–1.92 1.32–1.71 1.04–1.35 0.69–0.89 1.01–1.01

.00 .11 .00 .00 .09 .00 .00 .00 .00 .35 .00 .00 .00 .01 .00 .00

Abbreviations: aOR, adjusted odds ratio; CI, confidence interval; GDM, gestational diabetes mellitus.

L. McCloskey et al. / Women's Health Issues xxx-xx (2019) 1–9

expected, we found several clinical conditions also to be consistently associated with follow-up outcomes. GDM severity almost doubles women’s odds of receiving an OGTT by 12 weeks and any test by 1 year, or receiving optimal surveillance and care by one and 3 years after delivery. Our health system and policyrelated measures yielded modest results. Women who delivered in a larger hospital (>10,000 admissions per year) were somewhat more likely to receive glucose testing and a primary care visit by 1 year and by 3 years after delivery. Surprisingly, the extent of insurance coverage seemed to be inversely related to follow-up rates for glucose testing and primary care contact. Unfortunately, the database lacked the context that might have facilitated interpretation, and the relationship between comprehensiveness of coverage and use of preventive measures remains a point to be explored in the future. The significance of one factor consistently surpassed all others in our models: women who had a primary care visit in the year before pregnancy were two to three times more likely to be tested and to have a primary care visit in the immediate postpartum period, at 1 year, and at 3 years after delivery. Women who had attended a preconception primary care visit were 3.5 times more likely to receive optimal surveillance and care by 3 years after delivery, as guidelines recommend. This increased odds ratio stands out against the much smaller increases in odds for known factors such as age, race/ethnicity, and education, and the 20% increase associated with receipt of a preconception flu shot, another more specific measure of preventive care. In a prior study, Lawrence, et al. (2010) analyzed predictors of glucose testing rates at 6 months postpartum in a sample of 11,825 women who delivered in 1999 through 2006. He found that only 50% received any testing within 6 months and only one-half of those tests occurred in the recommended time interval after delivery. Battarbee & Yee (2018) reported a completion rate or 59% for postpartum glucose testing in a sample drawn from the same time period as our study. We included in this study the major predictors of testing that these studies and others identified, but expanded our focus of interest to include transition to primary care where ideally continued glucose monitoring and preventive counseling takes place. Our finding adds weight to and extends the findings of Bennett et al. (2014), which also identified the importance of a preconception primary care visit in determining the likelihood of a primary care visit 1 year after delivery for women with medically complicated pregnanciesdin both commercially and Medicaid insured women. (They did not limit their sample to those with continuous coverage and did not measure care at 3 years after delivery.) It is important to note, however, only a small proportion of women in our study and in the study conducted by Bennett et al. (2014) had any contact with primary care before conception (17.2% and 40.0%, respectively). Together these findings point to the weak link between obstetric care and primary care among both private and publicly insured women, and to the importance of such a link if women with complicated pregnancies are to receive the follow-up care recommended for chronic illnesses to be prevented. Access to primary health care at critical points in a woman’s life cycle has received widespread attention in recent years as a matter of quality in health care (McDonough, 2003). Research in the field of follow-up care for women with GDM, and for other pregnancy complications with implications for long-term chronic illness, has focused on the gaps in a single care transition from obstetrics to comprehensive primary care. Martinez, Charlotte, and Yee (2017), for example, point to the absence of

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set protocols, infrastructure, and clarity in provider roles during the brief postpartum period (currently limited to 6–8 weeks), but 40% of women do not even have a postdelivery visit with an obstetrician during this period, and the rate of attendance at a postpartum visit is even lower among populations with limited resources (Bennett et al., 2014; Bryant, Haas, McElrath, & McCormick, 2006). The provisions of the Affordable Care Act allow for women to have one well-woman visit each year and obtain recognized preventive care without copay (Gunja et al., 2017), but the low rates of follow-up that we found in this sample of women with GDM who had insurance coverage indicate a major problem with connection to preventive services. Legislation now in congress proposes extension of the postpartum period across the first year after delivery, with concomitant professional training and warm hand-offs from obstetrics to primary care. This new focus on the longer term health of women would address follow-up after GDM and other pregnancy complications that require preventive monitoring, such as toxemia, hypertension, depression, and opioid dependence. The robustness of our finding about the critical impact of preestablished primary care on the follow-up of GDM after delivery leads us to consider an additional dimension: the value of a life-long pattern of primary care that starts with the transition from pediatrics (age 18–21 years) and provides consistent contact through the reproductive years. We standardize preventive care for children as well-child care that has a schedule of required visits and expectations. Given women’s competing responsibilities and priorities, it may be necessary to standardize and institutionalize a similar pattern of well-woman care and introduce accompanying quality measures that cross the gap between obstetrics and primary care. The association of pregnancy complications with chronic illness (for GDM, hypertension, toxemia, addiction, and depression) is strongly supported by a strong body of scientific evidence (Bernstein et al., 2018; Catov, Countouris, & Hauspurg, 2018; Seely, Tsigas, & RichEdwards, 2015; Schauberger, Borgert, & Bearwald, 2019; Torres et al., 2019). A pattern of regular primary care, if established early, can be the cornerstone of prevention before, between and after pregnancies, with particular significance for women whose pregnancies are complicated and warn of future risk (Shi, 2012). The key aims of the pediatric to adult care transition (Blum, 1995; National Committee for Quality Assurance, 2013) are instructive for all health transitions across the life cycle: “to provide high quality, coordinated, uninterrupted health care that is patientcentered, age and developmentally appropriate, culturally competent, flexible, responsive and comprehensive.” These principles can serve as a useful guide for new efforts to address postpartum gaps in care. Study Limitations The findings of this study are strong and consistent, but care must be taken not to overinterpret them, because this was a correlational analysis, not a study of causation. We did not include all variables to test for possible interaction effects. Further work is needed to examine how and why women are receiving medical visits in the year(s) after a birthdis it for acute illness or preventive care or both? Large population-based data sets provide an important overview of problems, but of necessity lack detail that reflects individual women’s actual health care seeking experiences and encounters with the health care system. The use of claims data to measure receipt of primary care after a

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GDM pregnancy required us to use the code for a routine medical visit as a measure of connection to primary care. As such, we are unable to determine the specific purpose of the visit or if the visit prompted glucose testing and/or GDM-relevant counseling. Nor are we able to determine the amount of diet and exercise counseling that was provided by obstetric providers or in a nutritionist visit; documentation of the visit was all that we had. We are also unable to determine which medical specialty ordered the documented glucose testing that we documented. These factors limited our ability to provide a more granular interpretation of the trajectory of post-GDM follow-up. We were also limited by the lack of information about interpregnancy weight gain, an independent risk factor for development of T2DM (Ehrlich et al., 2011; Russell, Dodds, Armson, Kephart, & Joseph, 2008). Although we did not have body mass index available, we did control for excess gestational weight gain during either pregnancy, and found no significant contribution to T2DM onset within the 3-year follow-up period. Furthermore, the sample was intentionally restricted to continuously insured women. This exclusion gave us power to evaluate key predictors of follow-up, holding major financial barriers constant. However the loss of approximately 80,000 women with GDM (8.2% of the 1,004,376 who were excluded for episodic insurance coverage) may have limited generalizability. In this tradeoff, the study gained internal validity at the cost of external validity. Although this sample does not purport to be representative of all U.S. women, the prevalence of GDM among the continuously insured women we studied was comparable with the national average; comparability studies were similar for key factors known to influence follow-up after GDM; and the clinical profile does not differ markedly from national reports of indicators related to GDM prevalence and pregnancy and delivery complications (on-line supplementary material, Bernstein et al., 2017). Conclusions Diabetes is the sixth leading cause of death among women, who account for half of the 1.5 million new cases of T2DM each year among adults (Centers for Disease Control and Prevention, 2016). We identified strong predictors of future chronic disease that appear as warning signs during women’s early reproductive years. We also found that contact with primary care before pregnancy can reduce the likelihood of loss to follow-up. The effects of prior contact with primary care and use of other preventive measures have not been investigated previously, and should be confirmed and explored. If a consistent pattern of association can be determined, it may suggest a new direction for developing intervention strategies based on comprehensive models of care for women that support connections with primary care across the life course, with a focus on transitions in the first postpartum year. The American College of Obstetricians and Gynecologists, for example, is partnering with other professional organizations, including the American Academy of Family Physicians, the American College of Physicians, the American Academy of Pediatrics, and the National Association of Nurse Practitioners in Women’s Health to develop a plan for well woman care that crosses specialties and transitions in women’s lives (American College of Obstetricians and Gynecologists, 2016). Implications for Practice and/or Policy Our current methods of connection to prevention are simply not working for women with pregnancy complications who are

at high risk of developing chronic illness within the next decade after delivery. This study’s finding of the association of early primary care contact with better rates of postdelivery follow-up offers the possibility of a new front-end systems approach to prevent diabetes and, by extension, other chronic illnesses among women. The back-end measures we use currently to connect women with prevention after GDM have depended on physicians following guidelines and patients doing their best to address barriers and challenges. The follow-up rate found in this continuously insured sample shows that the back-end measures are simply not working well enough, and it is time for innovation and new directions. Current legislative efforts to extend the postpartum period to 1 year to allow for transition to wellwoman care are promising, but will need to be accompanied by enhancements to primary care capacity, extensions to financial coverage, and introduction of postpartum-specific quality measures to ensure effectiveness. Acknowledgments All authors had full access to the data in the study and take responsibility for the integrity of the data and the accuracy of data analysis. References American Diabetes Association. (2017). Standards of medical care in diabetes: Management of diabetes in pregnancy. Diabetes Care, 40(Suppl), S114–S119. American College of Obstetrics and Gynecology. (2013). Practice bulletin no. 137 gestational diabetes. Obstetrics and Gynecology, 22, 406–416. American College of Obstetricians and Gynecologists. (2016). Leading health experts partner to ensure that women receive the preventive care they need. Available: www.acog.org/About-ACOG/News-Room/News-Releases/2016/LeadingHealth-Experts-Partner-to-Ensure-that-Women-Receive-the-Preventive-CareThey-Need?IsMobileSet¼false. Accessed: May 15, 2019. American College of Obstetricians and Gynecologists (2017). Gestational diabetes mellitus. Obstetrics and Gynecology, 130, e17–e31. Battarbee, A. N., & Yee, L. M. (2018). Barriers to postpartum follow-up and glucose tolerance testing in women with gestational diabetes mellitus. American Journal of Perinatology, 35, 354–360. Bennett, W. L., Chang, H. Y., Levine, D. M., Wang, L., Neale, D., Werner, E. F., & Clark, J. M. (2014). Utilization of primary and obstetric care after medically complicated pregnancies: An analysis of medical claims data. Journal of General Internal Medicine, 29, 636–645. Bernstein, J., Belanoff, C., Cabra, H. J., Babakhanlou-Chase, H., Derrington, T., Diop, H., . Kotelchuck, M. (2015). Refining measurement of substance use disorders among women of child bearing age using hospital records: The development of the explicit-mention substance abuse need for treatment in women (EMSANT-W) Algorithm. Maternal Child Health Journal, 19, 2168–2178. Bernstein, J., Quinn, E., Ameli, O., Craig, M., Heeren, T., Lee-Parritz, A., . McCloskey, L. (2017). Follow-up after gestational diabetes: A fixable gap in women’s preventive health care. BMJ Diabetes Research and Care, 5, e000445. Bernstein, J., Quinn, E., Ameli, O., Craig, M., Heeren, T., Lee-Parritz, A., . McCloskey, L. (2018). Onset of T2DM after Gestational Diabetes: What the prevention paradox tells us about risk. Preventive Medicine, 113, 1–6. Blum, R. W. (1995). Transition to adult health care: Setting the stage. Journal of Adolescent Health, 17, 3–5. Bryant, A. S., Haas, J. S., McElrath, T. F., & McCormick, M. C. (2006). Predictors of compliance with the postpartum visit among women living in healthy start project areas. Maternal Child Health Journal, 10, 511–516. Catov, J. M., Countouris, M., & Hauspurg, A. (2018). Hypertensive disorders of pregnancy and CVD prediction: Accounting for risk accrual during the reproductive years. Journal of the American College of Cardiology, 72, 1264–1266. Centers for Disease Control and Prevention. (2016). Leading causes of death and numbers of deaths, by sex, race, and Hispanic origin: United States. Available: www.cdc.gov/nchs/data/hus/hus16.pdf#019l. Accessed: September 6, 2018. Cox, D. R. (1958). The regression analysis of binary sequences (with discussion). Journal of the Royal Statistics Society of Britain, 20, 215–242. CDC Division of Diabetes Translation. (2017). United States Diabetes Surveillance System: Long-term trends in diabetes. Available: www.cdc.gov/diabetes/ data. Accessed: May 14, 2019.

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Author Descriptions Lois McCloskey, MPH, DrPH, Associate Professor and Associate Chair, Community Health Sciences, directs the Center for Excellence in Maternal and Child Health at Boston University. She focuses on disconnects between women’s lives and health care systems meant to serve their lifecourse needs. Emily Quinn, MA, is an analyst and research manager at the Boston University Biostatistics and Epidemiology Data Analytics Center. Omid Ameli, MD, MPH, is a health services researcher at Boston University and a Fellow at OptumLabs Data Warehouse. He is currently working on applications of big data analytics for improving patient and population health. Timothy Heeren, PhD, is a Professor of Biostatistics at Boston University School of Public Health. Dr. Heeren’s research interests are in applied biostatistics, observational studies, behavioral trials, regression models and complex survey design. Myrita Craig, MS, served as Project Manager at the time the research was conducted and is currently a doctoral student at the Boston University School of Public Health. Ronald Iverson, MD, MPH, is Vice Chair of Obstetrics, Director of Labor and Delivery; Assistant Professor of Obstetrics and Gynecology, Boston University School of Medicine. Aviva Lee-Parritz, MD, is a maternal-fetal medicine specialist and Chair of the Department of Obstetrics and Gynecology, Boston Medical Center, Boston University School of Medicine. Her field is management of pregestational/gestational diabetes and its impact on maternal and neonatal outcomes. Brian Jack, MD, is Professor and past Chair of the Department of Family Medicine at Boston University School of Medicine. His work focuses on development of patient and clinician toolkits and safety measures to improve patient care. Judith A. Bernstein, RNC, MSN, PhD, is a Professor of Community Health Sciences, Boston University School of Public Health and Professor of Emergency Medicine, School of Medicine. Her current research activities identify and address gaps in prevention for women and adolescents.