APPENDIX G
Consultant Reports

As part of its approach, the committee commissioned analyses from consultants to aid in decision making by providing information not readily available in current literature. Dr. Ellen Nohr from Aarhus University, Denmark, provided analyses from the Danish National Birth Cohort on low and very high categories of gestational weight gain (GWG), as well as data for obese class I, II and III women. Additionally, she provided information on subgroups pregnant women, such as primiparous, short and young women, and smokers (see Part I). Dr. Amy Herring, University of North Carolina, provided data from the 1988 National Maternal and Infant Health Survey (NMIHS) on the association between GWG and pregnancy outcomes by race. She provided additional analyses on the association between GWG and postpartum weight retention by linking the 1988 NMIHS to its 1991 follow-up (see Part II). Dr. Cheryl Stein, Mount Sanai School of Medicine, provided data on adverse outcomes associated with GWG stratified by racial/ethnic group using births data from 1995-2003 in New York City (see Part III). Dr. James Hammitt, Harvard University, conducted a quantitative analysis of risk trade-offs between maternal and child health outcomes associated with GWG (see Part IV).



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appendix G Consultant Reports As part of its approach, the committee commissioned analyses from consultants to aid in decision making by providing information not read- ily available in current literature. Dr. Ellen Nohr from Aarhus University, Denmark, provided analyses from the Danish National Birth Cohort on low and very high categories of gestational weight gain (GWG), as well as data for obese class I, II and III women. Additionally, she provided informa- tion on subgroups pregnant women, such as primiparous, short and young women, and smokers (see Part I). Dr. Amy Herring, University of North Carolina, provided data from the 1988 National Maternal and Infant Health Survey (NMIHS) on the association between GWG and pregnancy outcomes by race. She provided additional analyses on the association be- tween GWG and postpartum weight retention by linking the 1988 NMIHS to its 1991 follow-up (see Part II). Dr. Cheryl Stein, Mount Sanai School of Medicine, provided data on adverse outcomes associated with GWG stratified by racial/ethnic group using births data from 1995-2003 in New York City (see Part III). Dr. James Hammitt, Harvard University, conducted a quantitative analysis of risk trade-offs between maternal and child health outcomes associated with GWG (see Part IV). 0

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0 WEIGHT GAIN DURING PREGNANCY PART I: ANALYSES FROM DR. NOHR COMBINED ASSOCIATIONS OF PREPREGNANCY BODY MASS INDEX AND GESTATIONAL WEIGHT GAIN WITH THE OUTCOME OF PREGNANCY. ANALYSES BASED ON THE DANISH NATIONAL BIRTH COHORT Ellen Aagaard Nohr, PhD Associate Professor of Epidemiology Uniersity of Aarhus, Denmark The combined associations of prepregnancy body mass index (BMI) and gestational weight gain on pregnancy outcomes have until recent years mostly focused on birth weight. Large data collections with detailed infor- mation about maternal characteristics and pregnancy outcomes are now available which makes it possible to investigate these associations in a broader range of maternal and neonatal outcomes while adjusting for important maternal life style factors. Such a study based on the Dan- ish National Birth Cohort (DNBC) (Nohr et al., 2008) was presented to the Committee to Reexamine IOM Pregnancy Weight Guidelines in June 2008 along with a number of analyses that focused on the BMI-specific association between GWG and all outcomes included in the study. These supplementary analyses are presented in the following in the “First DNBC Report.” At the meeting in June, the IOM committee requested new analy- ses for some outcomes where very low and very high categories of GWG as well as obese class I and obese class II + III were included. This work is presented in the “Second DNBC Report.” In August 2008, additional analyses were presented for the IOM committee that provided information in subgroups of women defined by parity, height, smoking and young age. These results are presented in the “Third DNBC Report.” First DNBC Report Study Population The Danish National Birth Cohort (DNBC) is a nationwide study of 100,419 pregnancies among 92,274 women recruited 1996-2002. More detailed descriptions of the study methods and the recruitment were previ- ously published (Olsen et al., 2001; Nohr et al., 2006; Danish National Birth Cohort homepage, available online: http://www.ssi.dk/sw9314.asp [accessed February 2009]). Briefly, data were collected during two tele- phone interviews during pregnancy at approximately 16 and 30 weeks of

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0 APPENDIX G gestation, and two telephone interviews after birth when the child was ap- proximately 6 and 18 months old. The women included in the cohort were mostly Caucasians as only 4 percent were born outside Scandinavia. This study used information about 60,892 liveborn, full-term singleton (≥ 37 wk of gestation) infants whose mothers had participated in the first pregnancy and the first postpartum interview and provided information about prepregnancy BMI, GWG and postpartum weight retention 6 months after birth. In the following, the data and methods of the study will be shortly presented. A more detailed description has been published (Nohr et al., 2008). Independent Variables The main exposures were prepregnancy BMI and GWG. In the first pregnancy interview, the women reported their prepregnancy weight and height, which was used to calculate their prepregnancy BMI and catego- rize them according to the World’s Health Organization’s definitions as underweight (BMI < 18.5 kg/m2), normal weight (18.5 ≤ BMI < 25 kg/m2), overweight (25 ≤ BMI < 30 kg/m2), and obese (BMI ≥ 30 kg/m2) (WHO, 2000). Gestational weight gain was based on information from the tele- phone interview 6 months after birth. At this time, the woman was asked “How much (in kg) was your total gain in pregnancy?” Her response was divided into four categories: low (< 10 kg) medium (10-15 kg), high (16- 19 kg, and very high (≥ 20 kg). The medium category, which has been as- sociated with minimum infant mortality in other populations (IOM, 1990) was used as reference. From the first pregnancy interview, we also used information about the mother’s age at conception, parity, smoking, alcohol intake and physi- cal exercise during pregnancy, and social status defined by education and occupation. Information about duration of breastfeeding was reported by the women in the first postpartum interview. The categorization of these variables is described in greater detail elsewhere (Nohr et al., 2008). Maternal Outcomes Pregnancy outcomes during late pregnancy included preeclampsia/ eclampsia, chronic/gestational hypertension and gestational diabetes and were identified through linkage to the National Hospital Discharge Reg- ister. Because we suspected some underreporting of gestational diabetes, we added self-reported information about this disease from the pregnancy interviews. Birth complications were also identified in the National Hospital Dis- charge Register and included instrumental deliveries, which in nearly all

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0 WEIGHT GAIN DURING PREGNANCY cases covered vacuum extraction, and planned and emergency cesarean deliveries. The latter type covered cesarean section carried out when the woman was in labor. Postpartum weight retention was calculated as the difference between the woman’s prepregnancy weight and her weight 6 months postpartum as reported in the first postpartum interview. Postpartum weight retention was summarized by two variables defined as postpartum weight loss (loss ≥ 2 kg) and postpartum weight retention (gain of ≥ 5 kg) relative to a woman’s prepregnancy weight. In the same way, postpartum weight reten- tion at 18 months was calculated for those women in the study population who participated in the second postpartum interview, who had not given birth again and who were not pregnant again (39,776 women). Neonatal Outcomes Neonatal outcomes were identified in the National Birth Register and included birth weight, length, gestational age as recorded at birth, and Apgar score after 5 minutes. Birth weight was standardized by gestational age according to the reference curve of Marsal et al. (1996). Standardized birth weight was dicotomized into either a small-for-gestational age (SGA) infant (z-score < 10th percentile) or a large-for-gestational age (LGA) infant (z-score > 90th percentile). Additionally, results for SGA defined as a z-score 4000 gram were presented. To estimate the relative fat tissue of the infant, we calculated ponderal index of the newborn (birth weight in grams divided by the birth length in cm cubed). We defined low ponderal index as values < 10th percentile and high ponderal index as values > 90th percentile. Low Apgar score was defined as a value < 8 after 5 min. Statistical Methods A BMI- and GWG-specific variable was generated by cross-classifying BMI group (four categories) and GWG group (four categories). In multiple logistic regression models, the associations between this variable and preg- nancy outcomes were estimated. This corresponds to the full model with an interaction term between the original BMI and GWG variables. Normal weight women with medium GWG (10-15 kg) were used as reference. These models were adjusted for a number of maternal characteristics and lifestyle factors and for gestational age at birth. In the analyses of birth complications, neonatal complications, and postpartum weight retention, women with preeclampsia and gestational diabetes were excluded (n = 1,787). In the analyses of emergency cesarean deliveries, women with a planned cesarean were excluded, and in the analyses of instrumental de-

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 APPENDIX G liveries, all women with cesarean deliveries were excluded. In all adjusted models, Wald’s test with nine degrees of freedom and a significance level of 0.05 (two sided p-value) was used to assess the hypothesis that there was no effect modification by BMI group of the association between GWG and pregnancy outcomes. Because we observed that background risks of most pregnancy out- comes increased with increasing BMI groups in a way that was not well reflected in a multiplicative model, we also used an additive approach to the data. Thus, we used the calculated odds ratios from the above models to compute 16 absolute adjusted risks for each pregnancy outcome according to each category within the BMI- and GWG-specific variable for a woman with a given set of confounder categories: She was primiparous, 25-29 years old, 1.60-1.69 m tall, reported no smoking, no alcohol intake and no exercise during pregnancy, was of high social status and gave birth after 280 days of conception. For postpartum weight retention, she breastfed < 14 weeks. Results Figures G-1 through G-18 (and corresponding tables, G-1 through G- 18) in this report are supplementary to the study by Nohr et al. (2008). The first 17 figures display odds ratios and adjusted absolute risks for different outcomes. In Figure G-18, the absolute risks for four important outcomes are stratified on BMI group and combined to evaluate the “trade-off” be- tween mother and infant according to GWG: • Figures G-1A/G-1B (Tables G-1A/G-1B): Preeclampsia • Figures G-2A/G-2B (Tables G-2A/G-2B): Other hypertensive disorders • Figures G-3A/G-3B (Tables G-3A/G-3B): Gestational diabetes Figures G-4A/G-4B (Tables G-4A/G-4B): SGA infant (< 2.5th • percentile) Figures G-5A/G-5B (Tables G-5A/G-5B): SGA infant (< 10th • percentile) Figures G-6A/G-6B (Tables G-6A/G-6B): LGA infant (> 90th • percentile) Figures G-7A/G-7B (Tables G-7A/G-7B): Birth weight > 4000 g • • Figures G-8A/G-8B (Tables G-8A/G-8B): High ponderal index (> 90th percentile) • Figures G-9A/G-9B (Tables G-9A/G-9B): Low ponderal index (< 10th percentile) • Figures G-10A/G-10B (Tables G-10A/G-10B): Caesarean delivery before labor (planned)

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 WEIGHT GAIN DURING PREGNANCY 100.0 Underweight Normal Weight Overweight Obese 10.0 Odds Ratio 1.0 0.1 Low Medium High Very High Gestational Weight Gain Category FIGURE G-1A Preeclampsia. NOTE: Full model. Odds ratios adjusted for age, parity, height, smoking, alcohol consumption, social status, exercise, gestational age (days). APP G FIGURE 1A PT 1 COLOR TABLE G-1A Preeclampsia, Adjusted Odds Ratios (gestational weight fully editable vectors gain by BMI) Low Moderate High Very High Underweight 0.0 0.6 0.4 1.3 Normal weight 0.7 1.0 1.6 3.3 Overweight 1.7 2.1 3.8 5.4 Obese 3.6 6.1 7.7 11.2

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 APPENDIX G 0.150 Underweight Normal Weight Overweight 0.125 Obese 0.100 Risk 0.075 0.050 0.025 0.000 Low Medium High Very High Gestational Weight Gain Category FIGURE G-1B Preeclampsia. NOTE: Absolute risks derived from odds ratios. Presents risk of a primiparous woman, age 25-29, height 1.60-1.69, nonsmoker, no alcohol consumption, high social status, no exercise, 280 days of gestation. APP G FIGURE 1B PT 1 COLOR TABLE G-1B vectors fully editable Preeclampsia, Adjusted Risks (gestational weight gain by BMI) Low Moderate High Very High Underweight 0.8% 0.5% 0.8% Normal weight 1.0% 1.4% 2.2% 4.4% Overweight 2.3% 2.9% 5.0% 7.0% Obese 4.8% 7.9% 9.7% 13.6%

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 WEIGHT GAIN DURING PREGNANCY 10.0 Odds Ratio 1.0 Underweight Normal Weight Overweight Obese 0.1 Low Medium High Very High Gestational Weight Gain Category FIGURE G-2A Hypertensive disorders. NOTE: Full model. Odds ratios adjusted for age, parity, height, smoking, alcohol consumption, social status, exercise, gestational age (days). APP G FIGURE 2A PT 1 TABLE G-2A Hypertensive Disorders, Adjusted Odds Ratios (gestational COLOR weight gain by BMI) fully editable vectors Low Moderate High Very High Underweight 0.5 0.8 0.8 0.0 Normal weight 0.6 1.0 1.1 1.5 Overweight 1.8 1.6 1.8 2.3 Obese 4.2 3.4 4.3 3.8

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 APPENDIX G 0.08 Underweight Normal Weight Overweight Obese 0.06 Risk 0.04 0.02 0.00 Low Medium High Very High Gestational Weight Gain Category FIGURE G-2B Hypertensive disorders. NOTE: Absolute risks derived from odds ratios. Presents risk of a primiparous woman, age 25-29, height 1.60-1.69, nonsmoker, no alcohol consumption, high social status, no exercise, 280 days of gestation. TABLE APP GHFIGURE 2BDisorders, Adjusted Risks (gestational weight G-2B ypertensive PT 1 gain by BMI) COLOR fully editable vectors Low Moderate High Very High Underweight 0.6% 0.9% 1.0% Normal weight 0.7% 1.2% 1.3% 1.7% Overweight 2.2% 1.9% 2.2% 2.7% Obese 4.8% 3.9% 4.9% 4.3%

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 WEIGHT GAIN DURING PREGNANCY 100.0 Underweight Normal Weight Overweight Obese Odds Ratio 10.0 1.0 Low Medium High Very High Gestational Weight Gain Category FIGURE G-3A Gestational diabetes. NOTE: Full model. Odds ratios adjusted for age, parity, height, smoking, alcohol consumption, social status, exercise, gestational age (days). APP G FIGURE 3A PT 1 TABLE G-3A Gestational Diabetes, Adjusted Odds Ratios (gestational COLOR weight gainfullyBMI) by editable vectors Low Moderate High Very High Underweight 0.0 1.0 0.0 1.7 Normal weight 3.2 1.0 1.2 1.4 Overweight 7.0 3.2 1.4 3.2 Obese 15.1 7.7 7.5 7.4

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 APPENDIX G 0.08 Underweight Normal Weight Overweight Obese 0.06 Risk 0.04 0.02 0.00 Low Medium High Very High Gestational Weight Gain Category FIGURE G-3B Gestational diabetes. NOTE: Absolute risks derived from odds ratios. Presents risk of a primiparous woman, age 25-29, height 1.60-1.69, nonsmoker, no alcohol consumption, high social status, no exercise, 280 days of gestation. TABLE G-3B FIGURE 3B Diabetes, Adjusted Risks (gestational weight APP G Gestational PT 1 gain byCOLOR BMI) fully editable vectors Low Moderate High Very High Underweight 0.4% 0.6% Normal weight 1.1% 0.4% 0.4% 0.5% Overweight 2.4% 1.1% 0.5% 1.1% Obese 5.0% 2.6% 2.6% 2.5%

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 WEIGHT GAIN DURING PREGNANCY 10.0 *Odds Ratio (95% CI) 5.0 2.0 1.0 0.5 0.3 20 kg: mal ght no l w ht wt t 15 kg: se 20 g:o e e e 20 kg:u rw t t rw t t t t ht er ght h gh de igh de gh gh 0k de gh ve igh gh 9k es 0k es es 0k rm eig rm eig ig e ei ei un wei ei ei ei i e -1 ob -1 ob b ob 15 kg:o we g: we 20 kg: we w w 15 g: erw w 10 kg: g: r er r 0k er al al a -1 und -1 ov -4 ov ov m 9 4 n -1 un 0- -4 or r 10 kg: -1 no no g: g: 10 kg:n 9 4 9 15 g: g: 9k 4k 0- -1 9 4k 0- -4 9 9 0- -1 -4 10 GWG (kg) *adjusted for race/ethnicity; referent=10-14 kg FIGURE G-56 Gestational weigh gain and term large-for-gestational age (LGA) by body mass index (BMI). necessary to consider how to balance increasing risks of some outcomes against decreasing risks of others. To assist this consideration, a quantita- APP G FIGURE 5 PT 3 tive analysis of risk tradeoffs was performed. fully editable vectors Based on discussion with the Committee to Reexamine IOM Pregnancy Weight Guidelines, three outcomes were considered: infant mortality, post- partum weight retention (PPWR), and childhood obesity. These endpoints were selected because they were believed to be quantitatively important and to be reasonably estimable with available data. (In this context, quantitative importance requires that the occurrence of each outcome has significant effects on health and the probability of occurrence varies significantly with GWG.) Other outcomes (e.g., SGA, LGA) were not quantified in part because estimating the effect of these outcomes on health (i.e., ensuing morbidity and mortality) was judged to be too difficult or speculative given available data and resources. The analysis was framed by estimating how the probability of each outcome varies with GWG controlling for pregravid BMI category (using the World Health Organization [WHO] categories: underweight < 18.5, normal 18.5-24.9, overweight 25-29.9, and obese ≥ 30 kg/m2). These es- timates are obtained from observational epidemiological data and assume that the observed associations are causal.

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 APPENDIX G For each endpoint, the expected number of quality-adjusted life-years (QALYs) lost over the lifetime of the mother and child was estimated. QALYs are a standard measure of health that combined length of life and quality of health. They are defined as the sum of the time spent in each health state weighted by the health-related quality of life (HRQL) associ- ated with that state. HRQL is a measure of the quality or utility associated with a health state, normalized so that perfect health takes a value of one and a health state equivalent to dead has a value of zero (health states that are viewed as worse than dead may be assigned values smaller than zero). Summing across endpoints (weighted by their probabilities of occurrence) yields an estimate of the total expected number of QALYs lost from these three outcomes. The use of this metric implies that the health impairments of different outcomes, occurring to mothers and children, are appropriately judged by comparing the corresponding expected losses in QALYs. The use of expected QALYs to evaluate health effects within and among individuals is common in health economics and public health, but not without contro- versy (see, e.g., IOM, 2006). The following subsections describe the data used to estimate the prob- abilities and QALYs lost for each outcome. The final section reports the results of summing the estimated health losses across outcomes. Infant Mortality Infant mortality was chosen as an outcome measure that aggregates many of the pathways through which inadequate or excessive GWG may lead to fatal outcomes. Its use is convenient because infant death is clearly more significant than many other birth outcomes and by aggregating across pathways one avoids the necessity of detailed modeling associated with how various outcomes (e.g., SGA) lead to infant fatality. Prevalence Two estimates of the prevalence of infant mortality as a function of GWG and pregravid BMI are available: one by Chen et al. (2008) and a second conducted for the Committee by Amy Herring. Both estimates use data from the 1988 U.S. National Maternal and Infant Health Survey (NMIHS). Within BMI class, Chen et al. (2008) estimate total infant mor- tality prevalence among live births for each of four classes of GWG gain (< 0.15, 0.15-0.29, 0.30-0.45, ≥ 0.45 kg/wk). These were converted to full- term GWG gain by multiplying by 40, yielding the following classes: < 6, 6-12, 12-18, ≥ 18 kg. At the Committee’s request, Herring estimated infant mortality rate excluding congenital defects (that are believed to be unrelated to GWG) and restricting the NMIHS sample to term births. She estimated

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 WEIGHT GAIN DURING PREGNANCY prevalence using the four BMI classes and seven GWG classes (< 0, 0-5, 5-10, 10-15, 15-20, 20-25, ≥ 25 kg). The overall infant mortality rate in the NMIHS data is 1.0/100 live births, substantially larger than the current U.S. value of 0.64/100. To convert to current values, all of the estimates of prevalence were multiplied by 0.64, which assumes a constant proportional improvement in infant mortality rate across BMI/GWG classes. The Chen et al. (2008) and Herring estimates of infant mortality by GWG classes were converted to continuous functions of GWG by fitting polynomial functions to the estimated prevalence for the midpoints of the GWG categories (for open intervals a typical value was assumed). The polynomial functions are saturated, including as many terms as are esti- mable from the categorical estimates (i.e., third order for the Chen et al. estimates, sixth order for the Herring estimates). As a consequence, these polynomial functions exactly reproduce the observations to which they are fit. (These polynomial functions are best viewed as smoothed curves fit to the underlying categorical estimates rather than as statistical models of the relationship between infant mortality and GWG). The categorical estimates are reported in Tables G-48A and G-48B. Note that the Herring analysis shows that infant mortality is lower at the two extreme points than at the adjacent GWG categories (i.e., for the smallest weight gain category among underweight women and for the largest weight gain category among obese TABLE G-48A Infant Mortality (Chen et al., 2008) BMI GWG Rate (kg/wk) GWG (kg in 40 wk) Prevalence (%) < 0.15 <6 Underweight 1.98 (< 18.5) 0.15-0.29 6-12 0.86 0.30-0.45 12-18 0.66 ≥ 0.45 ≥ 18 0.53 < 0.15 <6 Normal 1.28 (18.5-24.9) 0.15-0.29 6-12 0.64 0.30-0.45 12-18 0.50 ≥ 0.45 ≥ 18 0.54 < 0.15 <6 Overweight 0.88 (25.0-29.9) 0.15-0.29 6-12 0.59 0.30-0.45 12-18 0.63 ≥ 0.45 ≥ 18 0.72 < 0.15 <6 Obese 0.87 (≥ 30.0) 0.15-0.29 6-12 0.71 0.30-0.45 12-18 0.79 ≥ 0.45 ≥ 18 1.41 NOTES: BMI = body mass index; GWG = gestational weight gain.

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 APPENDIX G TABLE G-48B Infant Mortality (Herring) BMI GWG (kg) Prevalence (%) <0 Underweight 2.60 (< 18.5) 0-4.9 3.12 5-9.9 1.15 10-14.9 0.46 15-19.9 0.44 20-24.9 0.27 ≥ 25 0.61 <0 Normal 1.66 (18.5-24.9) 0-4.9 1.40 5-9.9 0.80 10-14.9 0.45 15-19.9 0.39 20-24.9 0.39 ≥ 25 0.44 <0 Overweight 1.30 (25-29.9) 0-4.9 0.83 5-9.9 0.67 10-14.9 0.56 15-19.9 0.56 20-24.9 0.44 ≥ 25 0.47 <0 Obese 1.15 (≥ 30) 0-4.9 0.93 5-9.9 0.83 10-14.9 0.54 15-19.9 0.65 20-24.9 1.02 ≥ 25 0.50 NOTES: BMI = body mass index; GWG = gestational weight gain. women). These departures from the anticipated J- or U-shaped relationship between GWG and infant mortality seem implausible and may reflect lim- ited data at the extreme points or artifacts of model estimation. QALYs Lost Infant mortality implies the child’s entire lifetime is lost. A value of 80 QALYs is assumed, consistent with current life expectancy at birth. In prin- ciple, one could adjust this figure downward to recognize that not all years of life are lived in perfect health (especially at older ages), but adjustment

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 WEIGHT GAIN DURING PREGNANCY for this factor is viewed as negligible in comparison with other uncertain- ties and approximations in the risk tradeoff calculations. The figure might also be adjusted downward if it is considered appropriate to discount the value of future life years. Postpartum Weight Retention (PPWR) Prealence Prevalence estimates were provided by Ellen Nohr using data from the Danish National Birth Cohort (Nohr et al., 2008). For this analysis, PPWR is defined as retention of at least 5 kg body mass 6 months after birth. Prevalence estimates were provided for four GWG classes (< 10, 10-15, 16- 19, ≥ 20 kg), as reported in Table G-49. Third order polynomial functions were fit to these estimates. QALYs Lost The effects of PPWR on morbidity and mortality are estimated on the assumption that weight retained post-partum is retained for the rest TABLE G-49 Post-Partum Weight Retention (Nohr) BMI GWG (kg) Prevalence (%) < 10 Underweight 7.9 (< 18.5) 10-15 13.1 16-19 27.6 ≥ 20 46.5 < 10 Normal 5.6 (18.5-24.9) 10-15 13.0 16-19 26.1 ≥ 20 49.7 < 10 Overweight 7.2 (25-29.9) 10-15 16.9 16-19 31.1 ≥ 20 53.2 < 10 Obese 5.1 (≥ 30) 10-15 17.5 16-19 33.0 ≥ 20 45.0 NOTES: BMI = body mass index; GWG = gestational weight gain.

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 APPENDIX G of a woman’s life and using estimates of how mortality and health-related quality of life vary with BMI. First, average retained weight conditional on retaining at least 5 kg at 6 months post-partum is estimated as 10 kg (based in part on data from committee member Barbara Abrams suggesting that roughly half of women who retain at least 5 kg retain at least 10 kg). The incremental effect on BMI of a 10 kg weight increase is 3.7, calculated using a nominal average height (5 foot 5 inches). Mortality The effect of increased BMI on mortality is calculated using estimates from Peeters et al. (2003) cited by Hu (2008). Using data from the Framingham heart study, they estimated that an average 40 year old female nonsmoker loses 3.3 years of life if overweight and 7.1 years if obese. Using midpoint values of BMI for normal, overweight, and obese (assumed value = 33), a 1 point increment to BMI is associated with about 0.6 life years lost, and so the effect of a 3.7 point BMI increment is estimated as 2.2 years (this is the average of the slopes estimated by comparing overweight and obese with normal weight, 2.1 and 2.3, respectively). This effect is applied only to women with pregravid BMI in the overweight and obese catego- ries. No account is taken of any possible beneficial effect of weight gain on mortality of underweight women. Morbidity Jia and Lubetkin (2005) used data from the U.S. Medical Ex- penditure Panel Survey (MEPS) to estimate how HRQL varies with BMI class. The MEPS includes two measures of individual’s current HRQL ob- tained using the EQ-5D and EQ-VAS. The EQ-5D is a standard instrument used to estimate HRQL based on classification of health into one of three levels (no problem, some problem, severe problem) on each of five dimen- sions or attributes (mobility, self care, usual activities, pain/discomfort, anxiety/depression). The EQ-VAS is an example of a visual analog scale, another common instrument on which respondents mark a point on a visual scale (or report a number on the scale) that they associate with their health state. Jia and Lubetkin (2005) report regression estimates of the partial ef- fect of BMI class on each measure of HRQL, controlling for age, income, race/ethnicity, physical activity, presence of each of several diseases (asthma, hypertension, diabetes, heart disease, stroke, emphysema), and other fac- tors. Compared with normal BMI, the estimated loss in HRQL is 0.013 (EQ-5D) and 0.0052 (VAS) for overweight, 0.033(EQ-5D) and 0.0323 (VAS) for obesity class I, and 0.073 (EQ-5D) and 0.0494 (VAS) for obesity class II (note: class I and II obesity are distinguished by BMI < 30 and ≥ 30). The total effect of higher BMI on HRQL is presumably larger than these estimates because some of the diseases for which Jia and Lubetkin control in their regression models are likely consequences of higher BMI; to adjust for this bias, the partial effects are multiplied by two.

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 WEIGHT GAIN DURING PREGNANCY Assuming these HRQL increments persist for the remainder of the woman’s life (estimated as 50 years) and using midpoint values of BMI within BMI class (assumed value = 37 for obese class II) suggests QALY losses associated with a BMI increment of 5.7 equal 0.9 and 2.0 for over- weight and obese women, respectively (the value for obese women is an average of the values for obese class I and obese class II, 1.7 and 2.3, respectively). Summing the estimates for morbidity and mortality implies that each case of PPWR is associated with expected values of 3.1 and 4.2 QALYs lost for overweight and obese women, respectively. Childhood Obesity Prealence The relative risk of childhood obesity was estimated by committee member Matt Gillman as 1.2 per 5 kg increment in GWG for all maternal BMI groups. This result is based primarily on the Oken et al. (2008) GUTS analysis, supported by results from Wrotniak (2008) and Monteiro (2007). This estimate is for childhood obesity defined as BMI above the 95th per- centile compared with below the 85th percentile for age, observed at ages 9 to 14 years. Prevalence of childhood obesity by maternal pregravid BMI category for the Oken et al. (2008) analysis is 1.9, 5.2, 12.7, and 24.6 percent for underweight, normal, overweight, and obese, respectively. The probability of childhood obesity by GWG conditional on BMI was cal- culated using the estimated relative risk, the prevalence by BMI category, and information on the joint distribution of GWG and BMI from Chen (supplemental material Table G-48B assuming a common ratio of deaths to controls across BMI/GWG classes). (Note that the resulting population prevalence of 7.3 percent exceeds Oken’s reported population prevalence of 6.5 percent.) Third order polynomials were fit to these estimates (reported in Table G-50). QALYs lost Mortality Engeland et al. (2003) analyzed Norwegian data on mortality as a function of adolescent obesity (at ages 14 to 19 years). With average follow-up exceeding 30 years, they estimate that adult mortality rates from about age 30 onward are 80 percent larger for males and 100 percent larger for females whose adolescent BMI exceeded the 95th percentile of a U.S. reference population compared with those whose adolescent BMI was less than the 85th percentile. Adjusting a current U.S. life table to increase age- specific mortality rates by 90 percent for all ages from 30 onward suggests

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 APPENDIX G TABLE G-50 Childhood Obesity BMI GWG Rate (kg/wk) GWG (kg in 40 wk) Prevalence (%) < 0.15 <6 Underweight 1.46 (< 18.5) 0.15-0.29 6-12 1.52 0.30-0.45 12-18 1.89 ≥ 0.45 ≥ 18 2.34 < 0.15 <6 Normal 4.06 (18.5-24.9) 0.15-0.29 6-12 4.23 0.30-0.45 12-18 5.21 ≥ 0.45 ≥ 18 6.40 < 0.15 <6 Overweight 10.4 (25-29.9) 0.15-0.29 6-12 10.8 0.30-0.45 12-18 13.2 ≥ 0.45 ≥ 18 15.9 < 0.15 <6 Obese 22.3 (≥ 30) 0.15-0.29 6-12 23.0 0.30-0.45 12-18 27.1 ≥ 0.45 ≥ 18 31.7 about 7 years of life lost (i.e., life expectancy at birth falls from about 77 to 70 years). Hence childhood obesity is estimated to lead to 7 QALYs lost to mortality (implicitly assuming that BMI above the 95th percentile at ages 9 to 14 years persists to ages 14 to 19 years). Morbidity QALYs lost to morbidity are estimated using the results for morbidity associated with PPWR above. Childhood obesity defined as BMI above the 95th percentile is assumed to persist as adult obesity (BMI ≥ 30) and to persist for 70 years. Adjusting the estimated value of 2.0 QALYs lost for morbidity associated with PPWR among obese women for the difference in duration (i.e., multiplying by 70/50) yields 2.8 QALYs. Summing the estimates of mortality and morbidity effects yields an expected value of 9.8 QALYs lost per case of childhood obesity. Results The expected QALYs lost due to infant mortality and the mortality and morbidity consequences of post-partum weight retention and childhood obesity for each maternal BMI category and value of GWG are estimated by multiplying the estimated prevalence of each endpoint by the associated expected value of QALYs lost. Results are summarized in Figure G-57 us-

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0 WEIGHT GAIN DURING PREGNANCY 9 Underweight 8 Normal Weight 7 Overweight 6 Obese 5 4 3 2 1 0 –1 5 10 15 20 25 30 35 –2 GWG (kg) FIGURE G-57 Total expected quality-adjusted life-years (QALYs) lost (Chen et al. [2008] mortality estimates). APP G FIGURE 1 PT 4 ing the Chen et al. (2008) estimates of infant mortality and in Figure G-58 using the Herring estimates. COLOR The conclusions are similar using both sets of infant mortality esti- fully editable vectors mates. For overweight and obese women, the estimated total mortality and morbidity consequences for mother and child of the endpoints included in this analysis are minimized for GWG less than about 10 to 15 kg. For normal and underweight women, estimated mortality and morbidity con- sequences are minimized for GWG greater than about 10 to 15 kg. Within these ranges, estimated total QALY losses are not very sensitive to GWG. In Figure G-58, the prominent departure from a trend for obese women at high GWG, and the less prominent departure from a trend for underweight women at low GWG reflect the surprisingly low estimates of infant mortal- ity prevalence for these categories shown in Table G-48B. As noted above, these departures from the trend toward increasing infant mortality with very low or very high GWG may reflect limited data for these categories or modeling artifacts. Similarly, the trend toward negative QALY losses for high GWG among underweight women shown in Figure G-57 is also likely to reflect limited data and possible model artifacts associated with extrapolation beyond the range of observations. The vertical scale suggests that the expected loss of quality-adjusted

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 APPENDIX G 7 Underweight 6 Normal Weight Overweight 5 Obese 4 3 2 1 0 0 5 10 15 20 25 30 35 GWG (kg) FIGURE G-58 Total expected quality-adjusted life-years (QALYs) lost (Herring infant mortality estimates). life-years per live birth varies from near zero for normal weight and under- weight women who experience adequate gestational weight gain to five or more for overweight and obese women who 2 PT 4 APP G FIGURE experience substantial gesta- tional weight gain. TheseCOLOR values suggest the scale of the public health prob- lem associated with overweight women vectors fully editable and excessive gestational weight gain—the average loss may be on the order of 5 to 10 percent of the total lifetime QALYs experienced per birth. REFERENCES Alexander G. R., J. H. Himes, R. B. Kaufman, J. Mor and M. Kogan. 1996. A United States national reference for fetal growth. Obstetrics and Gynecology 87(2): 163-168. Chen A., S. A. Feresu, C. Fernandez and W. J. Rogan. 2009. Maternal obesity and the risk of infant death in the United States. Epidemiology 20(1): 74-81. Engeland A., T. Bjorge, A. J. Sogaard and A. Tverdal. 2003. Body mass index in adolescence in relation to total mortality: 32-year follow-up of 227,000 Norwegian boys and girls. American Journal of Epidemiology 157(6): 517-523. Gregory M., H. Ulmer, K. P. Pfeiffer, S. Lang and A. M. Strasak. 2008. A set of SAS macros for calculating and displaying adjusted odds ratios (with confidence intervals) for continuous covariates in logistic B-spline regression models. Computer Methods and Programs in Biomedicine 92(1): 109-114. Hu, F, ed. 2008. Obesity Epidemiology. Cary, NC: Oxford.

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