Matern Child Health J DOI 10.1007/s10995-015-1769-z
The Effects of Prepregnancy Body Mass Index and Gestational Weight Gain on Fetal Macrosomia Among American Indian/ Alaska Native Women Karilynn Rockhill1,2 • Haley Dorfman1,3 • Meghna Srinath1,4 • Carol Hogue1,5
Ó Springer Science+Business Media New York 2015
Abstract Objectives The American Indian/Alaska Native (AI/AN) population is a high-risk group across many health indicators, including fetal macrosomia. We aimed to investigate the effects of prepregnancy body mass index (BMI) and gestational weight gain (GWG) on macrosomia and explore possible racial and geographical variations among AI/AN women. Methods This retrospective cohort study was conducted from the Pregnancy Risk Assessment Monitoring System in eight states (2004–2011) among live, singleton, term births to AI/AN women 20 years or older. Prevalence of macrosomia (birth weight C 4000 g) by select characteristics were estimated; differences were assessed with Chisquares. Multivariable logistic regression was conducted to calculate adjusted odds ratios (aOR) for effects on macrosomia of BMI and GWG (enumerating the pounds women deviated from the Institute of Medicine guidelines for GWG) controlling for other factors in the total sample and stratified by race and state of residence. Results The prevalence of macrosomia was 14 %, ranging from 8 to 21 % (Utah–Alaska). Among AI/AN women, 30 % were obese prepregnancy and 50 % had excess GWG. Significant independent effects were found for macrosomia of prepregnancy overweight (aOR 1.27; 95 % Confidence Interval 1.01–1.59), obesity (aOR 1.63; & Karilynn Rockhill [email protected] 1
Emory University, Atlanta, GA, USA
2
1514 Sheridan Road, Apt. 4310, Atlanta, GA 30324, USA
3
789 Hammond Drive #321, Atlanta, GA 30328, USA
4
611 East 11th Street, Apt. 4A, New York, NY 10009, USA
5
1513 Clifton Road NW, Atlanta, GA 30322, USA
1.29–2.07), and excess GWG (aOR 1.16; 1.13–1.20 per five pounds gained beyond appropriate). Adjusted estimates varied between race and state. Conclusions Prepregnancy BMI and GWG are independent factors for macrosomia among AI/AN women. Future research should prioritize development, testing, and implementation of weight management programs, which account for variations among AI/AN women, both before and during pregnancy for BMI regulation and GWG control. Keywords Macrosomia Body mass index Gestational weight gain American Indian/Alaska Native PRAMS
Significance American Indians/Alaska Natives (AI/AN) are a growing population experiencing some of the greatest health disparities. Fetal macrosomia has great risks to the mother and child. High body mass index (BMI) and excess gestational weight gain (GWG) are well-established risk factors for macrosomia in other populations. Data show high obesity rates among AI/ AN women, but population-based estimates on GWG are missing. This study aimed to provide surveillance estimates of BMI and GWG, and assess the contributions on macrosomia among AI/AN women; stratifying to explore possible racial and regional variations. We utilized a new method using incremental weight measurements to assess GWG.
Introduction American Indians and Alaska Natives (AI/ANs) comprised 1.7 % of the US population in 2010 and is a diverse group consisting of 566 federally recognized tribes [22, 27]. The
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Matern Child Health J
federal Indian Health Service (IHS), created in 1955, delivers health services in direct IHS, tribally operated, and urban clinics [22]. While the health status of AI/ANs has improved, racial health disparities still persist today [3, 15]. These health disparities are commonly explained by inadequate education, high rates of poverty, high unemployment rates, poor living conditions, and other barriers to receiving health services [5, 11, 13]. Federal relocation and terminations policies of the 1950’s along with opportunities in urban areas, created a residential shift away from reservations. By 2010, 20 % of AI/ANs lived inside AI/AN designated areas [5, 11, 27]. This residential shift leaves many with reduced access to tribal healthcare services since only 1 % of the IHS budget is allocated to healthcare off reservations [3, 5, 11]. The maternal AI/AN population is characterized as a high-risk group for poor pregnancy outcomes across many health indicators and risk factors, including higher prevalence of obesity during pregnancy, inadequate prenatal care, and younger age during pregnancy [3, 7, 11]. Fetal macrosomia, infants with excessive intrauterine growth, comes with many risks to the mother and child. Common short-term maternal concerns include longer first and second stages of labor, instrumental vaginal delivery, emergency cesarean sections, perineal trauma, and postpartum hemorrhage [4, 14, 17]. Delivery complications include shoulder dystocia, brachial plexus injuries, admissions to the neonatal unit, and infant death [4, 14, 17]. Long-term, macrosomic babies have higher risks of diabetes, overweight/obesity during childhood, asthma, and persistent plexus injuries [8, 9, 18]. In 2011, AI/AN women had two percentage points more macrosomic births than the national average [21]. Macrosomia is diagnosed retrospectively after delivery; however, predicting fetal weight is clinically useful to prepare for potential obstetric risks and determine the course of action for the delivery [10, 28]. Addressing possible risk factors before and during pregnancy to reduce the likelihood of inordinate fetal growth is important. High prepregnancy BMI and excess GWG are both well-established risk factors for macrosomia in other racial populations [2, 25, 30]. Surveillance data have shown high obesity prevalence among AI/AN women of reproductive age, but population-based estimates on GWG are still missing [11]. Moreover, contributions of BMI and GWG on macrosomia among AI/ANs are still lacking. This study utilized a new method to assess weight gain by using incremental weight gain measurements. Recognizing the need for more research and surveillance in this population, the two objectives of this study were: (1) to describe the prevalence and investigate the effects of high prepregnancy BMI and excess GWG on fetal macrosomia among AI/AN women and (2) to estimate the
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associations of BMI and GWG with fetal macrosomia by race and state to explore possible racial and regional variations.
Methods This retrospective cohort study was conducted among women who delivered a live birth between 2004 and 2011. Data were analyzed from the Pregnancy Risk Assessment Monitoring System (PRAMS), a population-based survey conducted annually by the Centers for Disease Control and Prevention in collaboration with state health departments. PRAMS used standardized methodology to select a representative sample of all live births in the state from birth certificates. Women are sent a questionnaire by mail about their attitudes and experiences before, during, and after pregnancy and are followed up by telephone. To minimize non-response bias, PRAMS data include only states that reach certain response rate thresholds, C70 % for 2004–2006 and C65 % from 2007 to 2011, but do not supply race-specific rates. To be included in this analysis, at least 5 % of a state’s live births had to be to AI/AN women: Alaska (2004–2010), Minnesota (2004–2011), Nebraska (2004–2011), New Mexico (2004–2005, 2011), Oklahoma (2004–2011), Oregon (2004–2011), Utah (2004–2011), and Washington (2004–2011) [16]. A more detailed explanation of the PRAMS methodology can be found elsewhere [26]. AI/AN women were defined by birth certificate classification as either single race AI or AN or as mixed race with any identification of AI/AN. We included only women who were 20 years or older who delivered a live, singleton, term (C37 gestational weeks) infant (n = 8663). Women under age 20 follow separate BMI classification schemes and therefore were excluded [6]. Preterm infants were excluded because macrosomia may have other implications for those births. Gestational age was extracted from the birth certificate. Variable Definitions Consistent with other studies, macrosomia was defined as birth weight C4000 g as reported on the birth certificate [2, 30]. Prepregnancy BMI was based on maternal self-report on the PRAMS survey and categorized as underweight (\18.5 kg/m2), normal (18.50–24.99 kg/m2), overweight (25.00–29.99 kg/m2), or obese (C30.00 kg/m2) [29]. BMIs \ 13 kg/m2 or [70 kg/m2 were considered implausible values. GWG was extracted from the birth certificate and both a continuous and categorical variable were created. All women gaining 97 lbs. or more are collapsed by PRAMS. Women were categorized based on their
Matern Child Health J
prepregnancy BMI following the 2009 Institute of Medicine’s (IOM) guidelines for recommended weight gain during pregnancy. The recommended ranges are 28–40 lbs. for underweight women, 25–35 lbs. for normal weight women, 15–25 lbs. for overweight women, and 11–20 lbs. for obese women [12]. GWG was defined as inadequate if gain was below the recommended range, appropriate if gain was within the range, or excess if gain was beyond the range. The continuous variable represented the number of pounds above or below the recommended appropriate range. For this variable women gaining within recommended ranges were coded as having zero pounds over appropriate weight gain. Data on maternal age, education level, marital status, parity, and infant sex were extracted from the birth certificate; federal poverty level and timing of entry into prenatal care were based on the PRAMS questionnaire. A positive diabetes status was defined as gestational or prepregnancy diabetes listed on either source. Women were classified as smokers during pregnancy if they were identified as smokers on either source.
Furthermore, we stratified separately by race and state of residence to examine if aggregated data masked any racial or regional variations regarding macrosomia. Race-specific aORs for American Indians (n = 4391), Alaska Natives (n = 1990), and mixed race (n = 1390) women were calculated controlling for the same confounders. The model for Alaska Natives removed state of residence since all Alaska Natives resided in Alaska. We also calculated statestratified prevalence estimates and conducted regression analyses; all models included the same confounders reported above without state of residence. Statistical significance was defined as alpha B0.05. All estimates for strata with inadequate cell sizes (\5) were not displayed. This study was not based on a clinical study and was approved by Emory University’s Institutional Review Board. All analyses were performed using SAS 9.3 and SUDAAN to accommodate the complex survey design of PRAMS (Cary, NC, USA).
Data Analysis
Among all women in the sample, 13.4 % identified as American Indian, Alaska Native, or mixed race with AI/AN. Restricting to live, singleton, term births to women 20 years or older, the sample consisted of 67.9 % of all AI/AN women (Fig. 1). The sample was predominately American Indian women. Most of the sample resided in Oklahoma (39.0 %), followed by Washington (16.5 %) and Alaska (15.4 %) (Table 1). Each of the other states represented less than 10 % of the sample. While all women who identified as Alaska Native resided in Alaska, 2 % in Alaska identified as American Indian. New Mexico was primarily American Indian with only 4 % of women identifying as mixed race. Other states had larger proportions of mixed race women. Mean prepregnancy BMI for the overall sample was overweight at 27.5 kg/m2 [standard deviation (SD): 0.15]. However, the largest proportion of women classified as normal weight (40.1 %), and approximately equal proportions of overweight and obese women (27.5 and 29.6 %, respectively) (Table 1). Almost half the sample gained an excess amount of weight during pregnancy (49.9 %), and only 28.8 % of women gained within the IOM guidelines (Table 1). For the main variables of interest, missing data on birth weight was 0.2 %, prepregnancy BMI was 4.0 %, and GWG was 10.0 % of women. Macrosomia estimates did not differ for BMI and GWG for women missing data. Overall, Alaska Natives had a statistically significantly higher prevalence of macrosomia compared to American Indians and mixed race women (Table 2). The prevalence of macrosomia was also statistically significantly different among the prepregnancy BMI groups and IOM’s GWG categories (Table 2).
The prevalence of macrosomia and 95 % confidence intervals (CI) were calculated for selected demographic and pregnancy characteristics; differences were assessed by Chi-square tests. Using the continuous GWG measure, the mean number of pounds gained above and below appropriate was also calculated for mothers delivering a macrosomic and non-macrosomic infant and stratified by BMI. Pairwise comparisons of the means were calculated for each stratum to see differences in magnitude of GWG using Bonferroni’s method to determine the alpha value (p = 0.006). Multivariable logistic regression was conducted to quantify the association of macrosomia with categorical BMI and continuous GWG adjusted for all possible confounders. Confounders considered were chosen from observed crude associations and potential causal variables from the literature. At first, an interaction term for BMI and GWG was included to assess the potential for effect modification, which was found insignificant. The reported adjusted odds ratios (aOR) examined the independent associations between fetal macrosomia with prepregnancy BMI and GWG (assessed as every five pounds of weight gained above or below the recommended ranges) controlling for: AI/AN race, maternal age, parity, diabetes status, smoking during pregnancy, and state of residence. State of residence was included in the model as a proxy for many regional/state differences within the AI/AN population. Only women with no missing covariate information were included in the regression (n = 7771).
Results
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Matern Child Health J Fig. 1 Flow chart for inclusion/ exclusion criteria for study population: PRAMS, Alaska, Minnesota, Nebraska, New Mexico, Oklahoma, Oregon, Utah, and Washington, 2004–2011
Among women who delivered a macrosomic infant, the mean prepregnancy BMI was statistically higher than among women delivering non-macrosomic infants (29.17 vs. 27.23 kg/m2, respectively; p \ 0.001). Once BMI and the recommended weight gain guidelines were taken into account, no difference was seen in the mean number of pounds gained below the recommended range for women who gained an inadequate amount (Table 3). However, women with excess GWG delivering a macrosomic infant overall gained statistically significantly more weight than women who delivered a non-macrosomic infant (Table 4). No effect modification was found in the total sample between BMI and GWG (interaction term: p = 0.103); therefore the final regression model shows their independent effects for macrosomia. Among the other predictors in the model, the ones which also showed significant associations with macrosomia included a positive diabetes status during pregnancy versus no diabetes (aOR 1.52; 95 % CI 1.22–1.90), multiparity (aOR 1.68; 1.37–2.06), and no smoking versus smoking during pregnancy (aOR 0.55; 0.43–0.70). Compared to the odds for normal weight women, the odds of macrosomia increased in overweight women by 27 % and obese women by 57 %, controlling for GWG and other potential confounders (Table 5). Compared to the odds of women who gained appropriate weight, excess GWG increased the odds of macrosomia; for every five additional pounds gained over the recommended range, the risk for macrosomia increased 16 % (Table 5). Of those women who did gain in excess, AI/AN women gained on average 14.11 lbs. (SD: 0.39) beyond appropriate. A woman representing this average gain would increase her odds of
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macrosomia by 53 % compared to a woman who gained within recommendations adjusting for BMI and other potential confounders (aOR 1.53; 95 % CI 1.40–1.66). When stratified, in all three race categories obesity and excess GWG remained determinants of macrosomia. However, only Alaska Natives showed significant increased odds for macrosomia among overweight women (Table 5). Prevalence of macrosomia ranged from 8.7 % in Utah to 20.9 % in Alaska (Table 6). Utah had a low prevalence of macrosomia and inadequate cell sizes made estimates uninformative. Among the other states, prepregnancy BMI increased the odds of macrosomia among overweight women in Alaska only and among obese women in Alaska, Nebraska, Oklahoma, and Oregon (Table 6). Similar to the aggregate analysis, statistically significant increased odds of macrosomia were found for every five pounds of GWG gained beyond appropriate in all seven states (Table 6).
Discussion Studies have shown AI/ANs have higher birth weights on average compared to other races [3]. The results of this study suggest that higher birth weights among AI/AN women are related to both prepregnancy overweight and obesity and excessive GWG during pregnancy; with no modification of GWG risk by prepregnancy BMI. Women who delivered macrosomic infants had on average higher self-reported prepregnancy BMIs and gained more weight throughout pregnancy. Among AI/AN women, being overweight or obese at conception was significantly associated with
Matern Child Health J Table 1 Sample characteristics among American Indians/Alaska Natives, PRAMS, 8 states, 2004–2011a % (95% CI)
Table 1 continued % (95% CI)b
b
Smoking during pregnancyc,
AI/ANsc
Yes
American Indian
67.84 (65.99–69.65)
Alaska Native
15.15 (14.51–15.82)
Mixed- AI/AN with other
17.00 (15.24–18.92)
No Infant sexd
State of residenced Alaska
15.42 (14.7–16.10)
Minnesota Nebraska
7.57 (6.78–8.44) 3.55 (3.34–3.77)
New Mexico
7.16 (6.54–7.82)
Oklahoma
39.00 (36.79–41.26)
Oregon
6.93 (6.57–7.30)
Utah
3.91 (3.20–4.77)
Washington Maternal age (years)
16.47 (15.04–18.00) d
i
25.85 (24.06–27.73) 74.15 (72.27–75.94)
Male
48.21 (46.09–50.34)
Female
51.79 (49.66–53.91)
a
Sample includes live, singleton, term (C37 gestational weeks) births among AI/AN women 20 years or older who in Alaska, Minnesota, Nebraska, New Mexico, Oklahoma, Oregon, Utah, and Washington from the Pregnancy Risk Assessment Monitoring System b
Percentage of sample (95 % Confidence Intervals)
c
Variable definition from both notations on the birth certificates and maternal self-report on the PRAMS questionnaire
d
Variable definition from notations on the birth certificates
e
Variable definition from maternal self-report on PRAMS questionnaire
20–24
42.15 (40.04–44.28)
25–29
32.23 (30.27–34.25)
Federal Poverty Level determined by maternal self-report of income and number of dependents from the year previous to infant’s birth
30–34
18.11 (16.55–19.78)
g
C35
7.52 (6.59–8.58)
Education (years)d 18.00 (16.49–19.61)
12
42.55 (40.42–44.69)
\12 Marital statusd
39.45 (37.38–41.56)
Married
44.16 (42.06–46.29)
Not married
55.84 (53.71–57.94) f
B138 % FPL
68.19 (66.12–70.19)
[138 % FPL
31.81 (29.81–33.88)
Prepregnancy body mass index (kg/m2)e \18.5 Underweight
2.84 (2.14–3.75)
18.5–24.9 Normal
40.08 (37.93–42.27)
25.0–29.9 Overweight
27.46 (25.60–29.41)
C30.0 Obese
29.62 (27.72–31.60)
Weight gain during pregnancy Inadequate
d, g
21.29 (19.46–23.24)
Appropriate
28.77 (26.76–30.87)
Excess
49.94 (47.68–52.20)
Parityc First birth
30.64 (28.67–32.68)
Second or later birth
69.36 (67.32–71.33)
Entry into prenatal caref 1st Trimester
82.43 (80.75–84.00)
2nd Trimester
14.95 (13.48–16.55)
3rd Trimester or no PNC Any reported diabetesc,
Weight gain categories determined by 2009 Institute of Medicine’s guidelines by each BMI category
h
[12
Federal poverty levele,
f
2.62 (2.04–3.35)
h
Yes
12.92 (11.60–14.37)
No
87.08 (85.63–88.40)
Notation on the birth certificate or self-report on the PRAMS questionnaire of diabetes (prepregnancy diabetes or gestational diabetes)
i
Notation on the birth certificate or self-report on the PRAMS questionnaire of smoking during pregnancy
increased odds of macrosomia (27 and 57 %, respectively) compared to normal weight women. These findings are consistent with other studies [2, 30]. However, Alaska Natives seemed to be driving the increased odds in overweight women, since overweight American Indians and mixed race women no longer showed significant increased odds of macrosomia. Obesity at conception remained a significant factor for macrosomia across all women. While obesity was seen in about 30 % of the study population, excess GWG beyond the 2009 IOM guidelines occurred in 50 % of all women. Consistent with other studies, we found excess GWG to be associated with increased odds of macrosomia independent of prepregnancy BMI [2, 25]. However, this analysis extended prior findings by examining the association of GWG with macrosomia for each pound a woman gained beyond recommended ranges. For every five pounds gained beyond appropriate, women increased their odds of macrosomia by 16 % compared to women who remained within appropriate ranges. Of those AI/AN women who did gain in excess, the average gain was 14 lbs. beyond appropriate, increasing the odds of macrosomia 53 % compared to appropriate weight gain. This relationship held true across
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Matern Child Health J Table 2 Prevalence of macrosomia by demographic and pregnancy characteristics among American Indians/ Alaska Natives, PRAMS, 8 states, 2004–2011a
Prevalence of Macrosomia Birth weight C 4000 grams % (95% CI)b Total sample
13.54 (12.60–14.54)
AI/ANsd 11.81 (10.87–12.82)
Alaska Natives
20.96 (19.42–22.59)
Mixed- AI/ANs with other
13.81 (10.47–18.01)
e
0.016*
20–24 25–29
12.38 (10.95–13.98) 12.94 (11.47–14.57)
30–34
15.93 (13.39–18.85)
C35
16.80 (13.85–20.22)
Education (years)e
0.286
[12
12.14 (10.32–14.23)
12
14.17 (12.71–15.76)
\12
13.35 (11.79–15.08)
Marital statuse
0.267
Married
14.17 (12.67–15.82)
Not married Federal poverty levelf,
13.03 (11.85–14.31) g
0.685
B138 % FPL
13.33 (12.11–14.66)
[138 % FPL
13.77 (12.19–15.52)
Prepregnancy body mass index (kg/m2)f \18.5 Underweight 18.5–24.9 Normal weight
\0.001* 5.00 (3.00–8.21) 9.97 (8.51–11.66)
25.0–29.9 Overweight
14.22 (12.58–16.04)
C30.0 Obese
17.80 (15.96–19.81)
Weight gain during pregnancye, Inadequate
h
\0.001* 9.58 (7.88–11.61)
Appropriate
9.38 (7.92–11.09)
Excess
16.93 (15.43–18.54)
Paritye
0.002*
First birth
11.20 (9.61–13.01)
Second or later birth Entry into prenatal care
14.60 (13.44–15.83) g
0.013*
1st Trimester
13.83 (12.76–14.99)
2nd Trimester
12.92 (10.63–15.62)
3rd Trimester or no PNC
7.37 (4.71–11.34)
Any reported diabetesi Yes No
\0.001* 19.87 (17.12–22.93) 12.60 (11.60–13.68)
Smoking during pregnancyj
123
– \0.001*
American Indians
Maternal age (years)
p valuec
\0.001*
Yes
10.35 (8.76–12.18)
No
14.65 (13.52–15.87)
Matern Child Health J Table 2 continued
Prevalence of Macrosomia Birth weight C 4000 grams % (95% CI)b Infant sexf
p valuec
\0.001*
Male
16.49 (15.01–18.08)
Female
10.79 (9.62–12.08)
a
Sample includes live, singleton, term (C37 gestational weeks) births among AI/AN women 20 years or older in Alaska, Minnesota, Nebraska, New Mexico, Oklahoma, Oregon, Utah, and Washington from the Pregnancy Risk Assessment Monitoring System
b
Prevalence of Macrosomia (95 % Confidence Interval)
c
Chi-square p value
d
Variable definition from both notations on the birth certificates and maternal self-report on the PRAMS questionnaire
e
Variable definition from notations on the birth certificates
f
Variable definition from maternal self-report on PRAMS questionnaire
g
Federal Poverty Level determined by maternal self-report of income and number of dependents from the year previous to infant’s birth
h
Weight gain categories determined by 2009 Institute of Medicine’s guidelines by each Body Mass Index category
i
Notation on the birth certificate or self-report on the PRAMS questionnaire of diabetes (prepregnancy diabetes or gestational diabetes) j
Notation on the birth certificate or self-report on the PRAMS questionnaire of smoking during pregnancy
* Statistically significant, alpha = 0.05
all races and seven states with adequate data. Although inadequate weight gain showed decreased odds of macrosomia, gaining weight below the guidelines would not be recommended. Among marginalized populations in the United States, AI/ANs are increasing in numbers and are experiencing some of the greatest health disparities across many chronic conditions [11]. The AI/AN population grew twice as fast as the US population overall between 2000 and 2010 [27]. More research is needed in the design and implementation of successful weight management programs tailored for the growing AI/AN population. Macrosomia creates an intergenerational cycle of chronic disease risk factors. Children are more likely to be obese later in life if they were born macrocosmic or born to mothers who were overweight/ obese at conception or who gained excess amount of weight during pregnancy [8, 18, 20, 30]. Reducing the prevalence of macrosomia, and potential subsequent obstetric risks, is especially important among AI/AN populations who may have limited access to emergency services. Healthcare access can be a challenge on reservations or in rural areas due to lack of transportation or proximity to healthcare facilities and in urban areas where IHS services are limited [5, 13]. One study in 2012 among singleton, full term births in California estimated that composite maternal and neonatal complications increased 2.3 times for infants above 4000 g and 6.3 times for infants above 4500 g compared to infants below 4000 g [17]. Macrosomia is difficult to predict
prenatally, so risk factors need to be addressed before and during pregnancy to minimize these risks and reduce healthcare costs for complicated deliveries. Currently, weight management interventions for BMI exist for women of reproductive age and for GWG during pregnancy [23]. GWG management programs closely resemble lifestyle programs used for BMI control in non-pregnant women, and often include structured meal plans, behavioral modification strategies, and ongoing contact with healthcare providers [23]. One essential element to preventing excess GWG while providing adequate nutrition to the growing fetus is for women to receive counseling on dietary information during pregnancy. Currently, the American Congress of Obstetricians and Gynecologists (ACOG) recommends all women be educated on the GWG guidelines but does not mention nutritional consultations for normal weight women [1]. Counseling should encourage appropriate GWG and minimize additional gain beyond recommendations for all women of all BMI levels. Among our sample, the 40 % who were normal weight could benefit from counseling to reduce excess GWG, since 50 % of women gained in excess. ACOG recommends discussions begin early in prenatal care [1], which is opportunistic since in our study 82 % of women reported entering prenatal care during the first trimester. Designing interventions to address weight management specifically for AI/AN women will be challenging. Stratified analyses revealed racial and geographical variations in the prevalence of macrosomia and its association with risk
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Matern Child Health J Table 3 Average weight (lbs.) gained under appropriate among American Indian/Alaska Native women who gained an inadequate amount of weighta, PRAMS, 8 states, 2004–2011b
Total
Macrosomia (C4000 g)
No Macrosomia (\4000 g)
Meanc
(SD)d
Meanc
-7.28
(0.52)
p valuee
(SD)d
-8.04
(0.38)
0.232
-11.74
(2.71)
–
-8.69
(0.53)
0.512
Prepregnancy BMI (kg/m2)f \18.5 Underweight
–g
18.5–24.9 Normal weight
-9.28
(0.72)
25.0–29.9 Overweight
-6.93
(0.70)
-7.35
(0.58)
0.646
C30.0 Obese
-5.99
(0.75)
-6.15
(0.45)
0.855
a
Categorization of gestational weight gain according to the 2009 Institute of Medicine’s guidelines Sample includes live, singleton, term (C 37 gestational weeks) births among AI/AN women 20 years or older in Alaska, Minnesota, Nebraska, New Mexico, Oklahoma, Oregon, Utah, and Washington from the Pregnancy Risk Assessment Monitoring System
b
c
Mean (lbs.), continuous variable representing the number of pounds gained from the appropriate range of weight gain recommended for each prepregnancy BMI recommended by the 2009 Institute of Medicine’s guidelines
d
Standard deviation of the mean
e
p values for mean pairwise comparisons of average number of pounds gained outside of the recommended amount between macrosomic and non-macrosomic infants
f
Prepregnancy Body Mass Index from maternal self-report on PRAMS questionnaire
g
Inadequate cell size too small to produce estimate (n \ 5)
* Bonferonii adjusted statistically significant, alpha = 0.006
Table 4 Average weight (lbs.) gained above appropriate among American Indian/Alaska Native women who gained an excess amount of weighta, PRAMS, 8 statesb
p valuee
Macrosomia (C4000 g)
No Macrosomia (\4000 g)
Meanc
(SD)d
Meanc
(SD)d
17.24
(0.57)
13.47
(0.46)
\0.001*
\18.5 Underweight 18.5–24.9 Normal weight
9.60 14.85
(1.56) (1.01)
16.01 11.13
(6.21) (0.58)
0.317 0.001*
25.0–29.9 Overweight
18.89
(1.00)
14.88
(0.99)
0.004*
C30.0 Obese
17.57
(0.86)
14.26
(0.64)
0.002*
Total Prepregnancy BMI (kg/m2)f
a
Categorization of gestational weight gain according to the 2009 Institute of Medicine’s guidelines
b
Sample includes live, singleton, term (C 37 gestational weeks) births among AI/AN women 20 years or older in Alaska, Minnesota, Nebraska, New Mexico, Oklahoma, Oregon, Utah, and Washington from the Pregnancy Risk Assessment Monitoring System
c
Mean (lbs.), continuous variable representing the number of pounds gained from the appropriate range of weight gain recommended for each prepregnancy BMI recommended by the 2009 Institute of Medicine’s guidelines
d
Standard deviation of the mean
e
p values for mean pairwise comparisons of average number of pounds gained outside of the recommended amount between macrosomic and non-macrosomic infants
f
Prepregnancy Body Mass Index from maternal self-report on PRAMS questionnaire
g
Inadequate cell size too small to produce estimate (n \ 5)
* Bonferonii adjusted statistically significant, alpha = 0.006
factors. Only Alaska, Nebraska, Oklahoma, and Oregon maintained significant associations with macrosomia among obese women. Alaska Natives seem to be higherrisk group compared to American Indians and mixed race women, with the highest prevalence of macrosomia and increased odds of macrosomia among overweight and
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obese women and women with excess GWG. Other studies have warned against aggregating American Indians and Alaska Natives due to such variations; birth outcomes and prenatal care utilization also differ among AI/ANs by geographical region, and by rural versus urban households [3, 5, 13].
Matern Child Health J Table 5 Adjusted odds ratios of macrosomia among American Indian/Alaska Native women, PRAMS, 8 states, 2004–2011a Aggregated aOR (95 % CI)b
American Indians aOR (95 % CI)c
Alaska Natives aOR (95 % CI)d
Mixed-AI/ANs with other aOR (95 % CI)e
\18.5 Underweight
0.64 (0.35–1.16)
0.95 (0.51–1.78)
–g
–
18.5–24.9 Normal weight
ref
ref
ref
ref
25.0–29.9 Overweight
1.27 (1.01–1.59)*
1.30 (1.00–1.68)
1.46 (1.11–1.92)*
1.13 (0.54–2.35)
C30.0 Obese
1.57 (1.25–1.96)*
1.60 (1.23–2.07)*
1.88 (1.43–2.46)*
1.23 (0.59–2.56)
Every 5 lbs. under appropriate Appropriate
0.86 (0.84–0.89)* ref
0.86 (0.83–0.90)* ref
0.82 (0.78–0.86)* ref
0.90 (0.84–0.98)* ref
Every 5 lbs. over appropriate
1.16 (1.13–1.20)*
1.16 (1.12–1.21)*
1.22 (1.17–1.28)*
1.11 (1.02–1.19)*
Prepregnancy BMI (kg/m2)f
Gestational weight gainh
a
Sample includes live, singleton, term (C37 gestational weeks) births among AI/AN women 20 years or older in Alaska, Minnesota, Nebraska, New Mexico, Oklahoma, Oregon, Utah, and Washington from the Pregnancy Risk Assessment Monitoring System
b
Adjusted Odds Ratios (95 % Confidence Intervals) determined by multivariable logistic regression, variables included were prepregnancy body mass index, gestational weight gain, AI/AN race, maternal age, parity, any diabetes during pregnancy, smoking during pregnancy, and state of residence (n = 7771)
c
Adjusted Odds Ratios (95 % Confidence Intervals) determined by multivariable logistic regression, variables included were prepregnancy body mass index, gestational weight gain, maternal age, parity, any diabetes during pregnancy, smoking during pregnancy, and state of residence (n = 4391)
d
Adjusted Odds Ratios (95 % Confidence Intervals) determined by multivariable logistic regression, variables included were prepregnancy body mass index, gestational weight gain, maternal age, parity, any diabetes during pregnancy, and smoking during pregnancy (n = 1990)
e
Adjusted Odds Ratios (95 % Confidence Intervals) determined by multivariable logistic regression, variables included were prepregnancy body mass index, gestational weight gain, maternal age, parity, any diabetes during pregnancy, smoking during pregnancy, and state of residence (n = 1390)
f
Prepregnancy Body Mass Index from maternal self-report on PRAMS questionnaire
g
Inadequate cell size, too small to produce estimate (n \ 5) Gestational Weight Gain extracted from birth certificates, representing 5 lb. intervals above and below the appropriate weight gain range recommended by the 2009 Institute of Medicine’s gestational weight gain guidelines based on prepregnancy BMI
h
* Statistically significant adjusted odds ratio
State of residence is only one contextual concern; addressing risk factors before and during pregnancy among AI/AN women requires culturally sensitive approaches and integration with current public health systems. Adaptors of currently successful programs or developers of novel interventions will need to work with local communities to address current attitudes, tribal and regional differences, political diversity, and socioeconomic environment [3]. Aside from design, implementation of such interventions will present additional problems. Disparities in healthcare access and utilization are engendered by systematic barriers along with cultural beliefs and practices that conflict with modern medical care [15]. Some potential barriers include matching literacy levels, improving local resources, incorporating tribal/cultural practices, and increasing healthcare utilization [19, 20, 24]. Identifying protective factors within each community could help direct interventions as well. Of note, 40 % of AI/AN women in this study entered pregnancy at normal weight and 30 % did gain weight within recommended guidelines.
Although this study has important and clinically relevant findings for AI/AN women, it does have some limitations. Not all states with large AI/AN populations, such as Arizona and California, participate in PRAMS, and findings are only generalizeable to the states included in the analysis. In addition, PRAMS only samples women who have had a live birth, so the findings cannot be applied to women with pregnancy loss. Due to PRAMS sampling methodology, we were only able to obtain data on AI/AN women at the state level, so smaller regional or tribal differences could not be assessed. Even at the state level, we had inadequate cell sizes for some regressions to be meaningful. Also, there may be some misclassification of prepregnancy BMI, likely a lower report than truth, due to self-reported post hoc assessment of prepregnancy weight at the time of the questionnaire (about 4 months postpartum). There may be some misclassification of race from extraction from the birth certificate as well. We do not expect these potential misclassifications to differ by delivery of a macrosomia infant. The largest limitation to
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123
20.85 (19.31–22.48)
1.92 (1.45–2.53)*
25.0–29.9 Overweight
C30.0 Obese
ref 1.22 (1.17–1.28)*
Appropriate
Every 5 lbs. gained over appropriate
1.16 (1.06–1.27)*
ref
0.86 (0.79–0.94)*
1.72 (0.68–4.32)
1.46 (0.75–2.84)
ref
–
aOR (95 % CI)c
1.16 (1.07–1.27)*
ref
0.86 (0.79–0.94)*
1.84 (1.11–3.04)*
1.54 (0.82–2.88)
ref
–
1.22 (1.04–1.44)*
ref
0.82 (0.69–0.97)*
1.09 (0.39–3.08)
1.23 (0.48–3.14)
ref
–
1.16 (1.09–1.24)*
ref
0.86 (0.81–0.92)*
1.48 (1.01–2.16)*
1.09 (0.74–1.62)
ref
0.90 (0.36–2.27)
1.11 (1.06–1.16)*
ref
0.90 (0.86–0.95)*
1.82 (1.30–2.56)*
1.36 (0.93–2.00)
ref
0.99 (0.42–2.36)
Washington % (95 %CI)b
1.16 (0.93–1.45)
ref
–
–
5.05 (0.44–57.58)
ref
–
aOR (95 % CI)c
1.11 (1.04–1.18)*
ref
0.90 (0.85–0.97)*
1.22 (0.64–2.33)
1.04 (0.52–2.10)
ref
–
aOR (95 % CI)c
8.74 (4.84–15.28) 16.51 (13.07–20.65)
Utah % (95 % CI)b
aOR (95 % CI)c
14.53 (12.92–16.30)
Oregon % (95 % CI)b
aOR (95 % CI)c
9.91 (8.82–11.12)
Oklahoma % (95 % CI)b
aOR (95 % CI)c
10.43 (7.70–13.97)
New Mexico % (95 % CI)b
aOR (95 % CI)c
14.43 (11.97–17.31)
Nebraska % (95 % CI)b
Prevalence of Macrosomia (95 % Confidence Interval)
Prepregnancy Body Mass Index from maternal self-report on PRAMS questionnaire
Inadequate cell size, too small to produce estimate (n \ 5)
* Statistically significant adjusted odds ratio
Gestational Weight Gain extracted from birth certificates, presented by 5 lb. intervals above and below the appropriate weight gain range recommended by the 2009 Institute of Medicine’s gestational weight gain guidelines based on prepregnancy BMI
f
e
d
Adjusted Odds Ratios (95 % Confidence Intervals) determined by multivariable logistic regression, variables included were prepregnancy body mass index, gestational weight gain, AI/AN race, maternal age, parity, any diabetes during pregnancy, and smoking during pregnancy
c
b
a Sample includes live, singleton, term (C37 gestational weeks) births among AI/AN women 20 years or older in Alaska, Minnesota, Nebraska, New Mexico, Oklahoma, Oregon, Utah, and Washington from the Pregnancy Risk Assessment Monitoring System
0.82 (0.78–0.86)*
Every 5 lbs. under appropriate
Gestational weight gainf
ref 1.43 (1.09–1.89)*
18.5–24.9 Normal weight
–e
aOR (95 % CI)c
14.93 (11.50–19.18)
Minnesota % (95 % CI)b
\18.5 Underweight
Prepregnancy BMI (kg/m2)d
Prevalence of macrosomia
Alaska % (95 % CI)b
Table 6 Adjusted odds ratios for macrosomia among American Indian/Alaska Native women by state of residence, PRAMS, 8 states, 2004–2011a
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this study is the inability to control for history of previous macrosomia, a well-known risk factor for macrosomia reflecting both environmental and genetic factors [10]. This study was able to capture a large population-based sample of AI/AN women using a validated questionnaire to obtain exposure data and many variables not found on birth certificates. This study also used a new method of classifying GWG to see the effects of each pound gained beyond appropriate compared to traditional GWG classification in three tiers (inadequate, appropriate, and excess). In addition, the PRAMS sampling methodology allows estimates to represent the entire state and allows inter-state comparisons. The ability to stratify in the analysis revealed important racial and geographical variations in the prevalence of macrosomia and associations with BMI and GWG. This study could have many implications for development of public health efforts, and encourage future research on this unique population. Prepregnancy BMI and GWG should be considered independent risk factors for delivery of a macrosomic infant among AI/AN women. Prepregnancy BMI needs to be addressed throughout the life course so women can enter the pregnancy at a normal weight. Among all AI/AN women, excess GWG should be addressed and weight management interventions should begin early in prenatal care. To help reduce the prevalence of high-risk pregnancies and improve pregnancy outcomes, development and implementation of weight management interventions addressing cultural and behavioral variations among AI/ AN women will be challenging and should be a priority for future research. Acknowledgments I would like to acknowledge the entire PRAMS working group in Alaska, Minnesota, Nebraska, New Mexico, Oklahoma, Oregon, Utah, Washington, and the CDC. In particular, thank you to Paul Weiss from Emory University and Denise D’Angelo from CDC on their guidance at the start of this project.
4.
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13.
14.
Compliance with Ethical Standards Conflict of interest of interest.
The authors declare that they have no conflict
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The Effects of Prepregnancy Body Mass Index and Gestational Weight Gain on Fetal Macrosomia Among American Indian/ Alaska Native Women Karilynn Rockhill1,2 • Haley Dorfman1,3 • Meghna Srinath1,4 • Carol Hogue1,5
Ó Springer Science+Business Media New York 2015
Abstract Objectives The American Indian/Alaska Native (AI/AN) population is a high-risk group across many health indicators, including fetal macrosomia. We aimed to investigate the effects of prepregnancy body mass index (BMI) and gestational weight gain (GWG) on macrosomia and explore possible racial and geographical variations among AI/AN women. Methods This retrospective cohort study was conducted from the Pregnancy Risk Assessment Monitoring System in eight states (2004–2011) among live, singleton, term births to AI/AN women 20 years or older. Prevalence of macrosomia (birth weight C 4000 g) by select characteristics were estimated; differences were assessed with Chisquares. Multivariable logistic regression was conducted to calculate adjusted odds ratios (aOR) for effects on macrosomia of BMI and GWG (enumerating the pounds women deviated from the Institute of Medicine guidelines for GWG) controlling for other factors in the total sample and stratified by race and state of residence. Results The prevalence of macrosomia was 14 %, ranging from 8 to 21 % (Utah–Alaska). Among AI/AN women, 30 % were obese prepregnancy and 50 % had excess GWG. Significant independent effects were found for macrosomia of prepregnancy overweight (aOR 1.27; 95 % Confidence Interval 1.01–1.59), obesity (aOR 1.63; & Karilynn Rockhill [email protected] 1
Emory University, Atlanta, GA, USA
2
1514 Sheridan Road, Apt. 4310, Atlanta, GA 30324, USA
3
789 Hammond Drive #321, Atlanta, GA 30328, USA
4
611 East 11th Street, Apt. 4A, New York, NY 10009, USA
5
1513 Clifton Road NW, Atlanta, GA 30322, USA
1.29–2.07), and excess GWG (aOR 1.16; 1.13–1.20 per five pounds gained beyond appropriate). Adjusted estimates varied between race and state. Conclusions Prepregnancy BMI and GWG are independent factors for macrosomia among AI/AN women. Future research should prioritize development, testing, and implementation of weight management programs, which account for variations among AI/AN women, both before and during pregnancy for BMI regulation and GWG control. Keywords Macrosomia Body mass index Gestational weight gain American Indian/Alaska Native PRAMS
Significance American Indians/Alaska Natives (AI/AN) are a growing population experiencing some of the greatest health disparities. Fetal macrosomia has great risks to the mother and child. High body mass index (BMI) and excess gestational weight gain (GWG) are well-established risk factors for macrosomia in other populations. Data show high obesity rates among AI/ AN women, but population-based estimates on GWG are missing. This study aimed to provide surveillance estimates of BMI and GWG, and assess the contributions on macrosomia among AI/AN women; stratifying to explore possible racial and regional variations. We utilized a new method using incremental weight measurements to assess GWG.
Introduction American Indians and Alaska Natives (AI/ANs) comprised 1.7 % of the US population in 2010 and is a diverse group consisting of 566 federally recognized tribes [22, 27]. The
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federal Indian Health Service (IHS), created in 1955, delivers health services in direct IHS, tribally operated, and urban clinics [22]. While the health status of AI/ANs has improved, racial health disparities still persist today [3, 15]. These health disparities are commonly explained by inadequate education, high rates of poverty, high unemployment rates, poor living conditions, and other barriers to receiving health services [5, 11, 13]. Federal relocation and terminations policies of the 1950’s along with opportunities in urban areas, created a residential shift away from reservations. By 2010, 20 % of AI/ANs lived inside AI/AN designated areas [5, 11, 27]. This residential shift leaves many with reduced access to tribal healthcare services since only 1 % of the IHS budget is allocated to healthcare off reservations [3, 5, 11]. The maternal AI/AN population is characterized as a high-risk group for poor pregnancy outcomes across many health indicators and risk factors, including higher prevalence of obesity during pregnancy, inadequate prenatal care, and younger age during pregnancy [3, 7, 11]. Fetal macrosomia, infants with excessive intrauterine growth, comes with many risks to the mother and child. Common short-term maternal concerns include longer first and second stages of labor, instrumental vaginal delivery, emergency cesarean sections, perineal trauma, and postpartum hemorrhage [4, 14, 17]. Delivery complications include shoulder dystocia, brachial plexus injuries, admissions to the neonatal unit, and infant death [4, 14, 17]. Long-term, macrosomic babies have higher risks of diabetes, overweight/obesity during childhood, asthma, and persistent plexus injuries [8, 9, 18]. In 2011, AI/AN women had two percentage points more macrosomic births than the national average [21]. Macrosomia is diagnosed retrospectively after delivery; however, predicting fetal weight is clinically useful to prepare for potential obstetric risks and determine the course of action for the delivery [10, 28]. Addressing possible risk factors before and during pregnancy to reduce the likelihood of inordinate fetal growth is important. High prepregnancy BMI and excess GWG are both well-established risk factors for macrosomia in other racial populations [2, 25, 30]. Surveillance data have shown high obesity prevalence among AI/AN women of reproductive age, but population-based estimates on GWG are still missing [11]. Moreover, contributions of BMI and GWG on macrosomia among AI/ANs are still lacking. This study utilized a new method to assess weight gain by using incremental weight gain measurements. Recognizing the need for more research and surveillance in this population, the two objectives of this study were: (1) to describe the prevalence and investigate the effects of high prepregnancy BMI and excess GWG on fetal macrosomia among AI/AN women and (2) to estimate the
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associations of BMI and GWG with fetal macrosomia by race and state to explore possible racial and regional variations.
Methods This retrospective cohort study was conducted among women who delivered a live birth between 2004 and 2011. Data were analyzed from the Pregnancy Risk Assessment Monitoring System (PRAMS), a population-based survey conducted annually by the Centers for Disease Control and Prevention in collaboration with state health departments. PRAMS used standardized methodology to select a representative sample of all live births in the state from birth certificates. Women are sent a questionnaire by mail about their attitudes and experiences before, during, and after pregnancy and are followed up by telephone. To minimize non-response bias, PRAMS data include only states that reach certain response rate thresholds, C70 % for 2004–2006 and C65 % from 2007 to 2011, but do not supply race-specific rates. To be included in this analysis, at least 5 % of a state’s live births had to be to AI/AN women: Alaska (2004–2010), Minnesota (2004–2011), Nebraska (2004–2011), New Mexico (2004–2005, 2011), Oklahoma (2004–2011), Oregon (2004–2011), Utah (2004–2011), and Washington (2004–2011) [16]. A more detailed explanation of the PRAMS methodology can be found elsewhere [26]. AI/AN women were defined by birth certificate classification as either single race AI or AN or as mixed race with any identification of AI/AN. We included only women who were 20 years or older who delivered a live, singleton, term (C37 gestational weeks) infant (n = 8663). Women under age 20 follow separate BMI classification schemes and therefore were excluded [6]. Preterm infants were excluded because macrosomia may have other implications for those births. Gestational age was extracted from the birth certificate. Variable Definitions Consistent with other studies, macrosomia was defined as birth weight C4000 g as reported on the birth certificate [2, 30]. Prepregnancy BMI was based on maternal self-report on the PRAMS survey and categorized as underweight (\18.5 kg/m2), normal (18.50–24.99 kg/m2), overweight (25.00–29.99 kg/m2), or obese (C30.00 kg/m2) [29]. BMIs \ 13 kg/m2 or [70 kg/m2 were considered implausible values. GWG was extracted from the birth certificate and both a continuous and categorical variable were created. All women gaining 97 lbs. or more are collapsed by PRAMS. Women were categorized based on their
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prepregnancy BMI following the 2009 Institute of Medicine’s (IOM) guidelines for recommended weight gain during pregnancy. The recommended ranges are 28–40 lbs. for underweight women, 25–35 lbs. for normal weight women, 15–25 lbs. for overweight women, and 11–20 lbs. for obese women [12]. GWG was defined as inadequate if gain was below the recommended range, appropriate if gain was within the range, or excess if gain was beyond the range. The continuous variable represented the number of pounds above or below the recommended appropriate range. For this variable women gaining within recommended ranges were coded as having zero pounds over appropriate weight gain. Data on maternal age, education level, marital status, parity, and infant sex were extracted from the birth certificate; federal poverty level and timing of entry into prenatal care were based on the PRAMS questionnaire. A positive diabetes status was defined as gestational or prepregnancy diabetes listed on either source. Women were classified as smokers during pregnancy if they were identified as smokers on either source.
Furthermore, we stratified separately by race and state of residence to examine if aggregated data masked any racial or regional variations regarding macrosomia. Race-specific aORs for American Indians (n = 4391), Alaska Natives (n = 1990), and mixed race (n = 1390) women were calculated controlling for the same confounders. The model for Alaska Natives removed state of residence since all Alaska Natives resided in Alaska. We also calculated statestratified prevalence estimates and conducted regression analyses; all models included the same confounders reported above without state of residence. Statistical significance was defined as alpha B0.05. All estimates for strata with inadequate cell sizes (\5) were not displayed. This study was not based on a clinical study and was approved by Emory University’s Institutional Review Board. All analyses were performed using SAS 9.3 and SUDAAN to accommodate the complex survey design of PRAMS (Cary, NC, USA).
Data Analysis
Among all women in the sample, 13.4 % identified as American Indian, Alaska Native, or mixed race with AI/AN. Restricting to live, singleton, term births to women 20 years or older, the sample consisted of 67.9 % of all AI/AN women (Fig. 1). The sample was predominately American Indian women. Most of the sample resided in Oklahoma (39.0 %), followed by Washington (16.5 %) and Alaska (15.4 %) (Table 1). Each of the other states represented less than 10 % of the sample. While all women who identified as Alaska Native resided in Alaska, 2 % in Alaska identified as American Indian. New Mexico was primarily American Indian with only 4 % of women identifying as mixed race. Other states had larger proportions of mixed race women. Mean prepregnancy BMI for the overall sample was overweight at 27.5 kg/m2 [standard deviation (SD): 0.15]. However, the largest proportion of women classified as normal weight (40.1 %), and approximately equal proportions of overweight and obese women (27.5 and 29.6 %, respectively) (Table 1). Almost half the sample gained an excess amount of weight during pregnancy (49.9 %), and only 28.8 % of women gained within the IOM guidelines (Table 1). For the main variables of interest, missing data on birth weight was 0.2 %, prepregnancy BMI was 4.0 %, and GWG was 10.0 % of women. Macrosomia estimates did not differ for BMI and GWG for women missing data. Overall, Alaska Natives had a statistically significantly higher prevalence of macrosomia compared to American Indians and mixed race women (Table 2). The prevalence of macrosomia was also statistically significantly different among the prepregnancy BMI groups and IOM’s GWG categories (Table 2).
The prevalence of macrosomia and 95 % confidence intervals (CI) were calculated for selected demographic and pregnancy characteristics; differences were assessed by Chi-square tests. Using the continuous GWG measure, the mean number of pounds gained above and below appropriate was also calculated for mothers delivering a macrosomic and non-macrosomic infant and stratified by BMI. Pairwise comparisons of the means were calculated for each stratum to see differences in magnitude of GWG using Bonferroni’s method to determine the alpha value (p = 0.006). Multivariable logistic regression was conducted to quantify the association of macrosomia with categorical BMI and continuous GWG adjusted for all possible confounders. Confounders considered were chosen from observed crude associations and potential causal variables from the literature. At first, an interaction term for BMI and GWG was included to assess the potential for effect modification, which was found insignificant. The reported adjusted odds ratios (aOR) examined the independent associations between fetal macrosomia with prepregnancy BMI and GWG (assessed as every five pounds of weight gained above or below the recommended ranges) controlling for: AI/AN race, maternal age, parity, diabetes status, smoking during pregnancy, and state of residence. State of residence was included in the model as a proxy for many regional/state differences within the AI/AN population. Only women with no missing covariate information were included in the regression (n = 7771).
Results
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Matern Child Health J Fig. 1 Flow chart for inclusion/ exclusion criteria for study population: PRAMS, Alaska, Minnesota, Nebraska, New Mexico, Oklahoma, Oregon, Utah, and Washington, 2004–2011
Among women who delivered a macrosomic infant, the mean prepregnancy BMI was statistically higher than among women delivering non-macrosomic infants (29.17 vs. 27.23 kg/m2, respectively; p \ 0.001). Once BMI and the recommended weight gain guidelines were taken into account, no difference was seen in the mean number of pounds gained below the recommended range for women who gained an inadequate amount (Table 3). However, women with excess GWG delivering a macrosomic infant overall gained statistically significantly more weight than women who delivered a non-macrosomic infant (Table 4). No effect modification was found in the total sample between BMI and GWG (interaction term: p = 0.103); therefore the final regression model shows their independent effects for macrosomia. Among the other predictors in the model, the ones which also showed significant associations with macrosomia included a positive diabetes status during pregnancy versus no diabetes (aOR 1.52; 95 % CI 1.22–1.90), multiparity (aOR 1.68; 1.37–2.06), and no smoking versus smoking during pregnancy (aOR 0.55; 0.43–0.70). Compared to the odds for normal weight women, the odds of macrosomia increased in overweight women by 27 % and obese women by 57 %, controlling for GWG and other potential confounders (Table 5). Compared to the odds of women who gained appropriate weight, excess GWG increased the odds of macrosomia; for every five additional pounds gained over the recommended range, the risk for macrosomia increased 16 % (Table 5). Of those women who did gain in excess, AI/AN women gained on average 14.11 lbs. (SD: 0.39) beyond appropriate. A woman representing this average gain would increase her odds of
123
macrosomia by 53 % compared to a woman who gained within recommendations adjusting for BMI and other potential confounders (aOR 1.53; 95 % CI 1.40–1.66). When stratified, in all three race categories obesity and excess GWG remained determinants of macrosomia. However, only Alaska Natives showed significant increased odds for macrosomia among overweight women (Table 5). Prevalence of macrosomia ranged from 8.7 % in Utah to 20.9 % in Alaska (Table 6). Utah had a low prevalence of macrosomia and inadequate cell sizes made estimates uninformative. Among the other states, prepregnancy BMI increased the odds of macrosomia among overweight women in Alaska only and among obese women in Alaska, Nebraska, Oklahoma, and Oregon (Table 6). Similar to the aggregate analysis, statistically significant increased odds of macrosomia were found for every five pounds of GWG gained beyond appropriate in all seven states (Table 6).
Discussion Studies have shown AI/ANs have higher birth weights on average compared to other races [3]. The results of this study suggest that higher birth weights among AI/AN women are related to both prepregnancy overweight and obesity and excessive GWG during pregnancy; with no modification of GWG risk by prepregnancy BMI. Women who delivered macrosomic infants had on average higher self-reported prepregnancy BMIs and gained more weight throughout pregnancy. Among AI/AN women, being overweight or obese at conception was significantly associated with
Matern Child Health J Table 1 Sample characteristics among American Indians/Alaska Natives, PRAMS, 8 states, 2004–2011a % (95% CI)
Table 1 continued % (95% CI)b
b
Smoking during pregnancyc,
AI/ANsc
Yes
American Indian
67.84 (65.99–69.65)
Alaska Native
15.15 (14.51–15.82)
Mixed- AI/AN with other
17.00 (15.24–18.92)
No Infant sexd
State of residenced Alaska
15.42 (14.7–16.10)
Minnesota Nebraska
7.57 (6.78–8.44) 3.55 (3.34–3.77)
New Mexico
7.16 (6.54–7.82)
Oklahoma
39.00 (36.79–41.26)
Oregon
6.93 (6.57–7.30)
Utah
3.91 (3.20–4.77)
Washington Maternal age (years)
16.47 (15.04–18.00) d
i
25.85 (24.06–27.73) 74.15 (72.27–75.94)
Male
48.21 (46.09–50.34)
Female
51.79 (49.66–53.91)
a
Sample includes live, singleton, term (C37 gestational weeks) births among AI/AN women 20 years or older who in Alaska, Minnesota, Nebraska, New Mexico, Oklahoma, Oregon, Utah, and Washington from the Pregnancy Risk Assessment Monitoring System b
Percentage of sample (95 % Confidence Intervals)
c
Variable definition from both notations on the birth certificates and maternal self-report on the PRAMS questionnaire
d
Variable definition from notations on the birth certificates
e
Variable definition from maternal self-report on PRAMS questionnaire
20–24
42.15 (40.04–44.28)
25–29
32.23 (30.27–34.25)
Federal Poverty Level determined by maternal self-report of income and number of dependents from the year previous to infant’s birth
30–34
18.11 (16.55–19.78)
g
C35
7.52 (6.59–8.58)
Education (years)d 18.00 (16.49–19.61)
12
42.55 (40.42–44.69)
\12 Marital statusd
39.45 (37.38–41.56)
Married
44.16 (42.06–46.29)
Not married
55.84 (53.71–57.94) f
B138 % FPL
68.19 (66.12–70.19)
[138 % FPL
31.81 (29.81–33.88)
Prepregnancy body mass index (kg/m2)e \18.5 Underweight
2.84 (2.14–3.75)
18.5–24.9 Normal
40.08 (37.93–42.27)
25.0–29.9 Overweight
27.46 (25.60–29.41)
C30.0 Obese
29.62 (27.72–31.60)
Weight gain during pregnancy Inadequate
d, g
21.29 (19.46–23.24)
Appropriate
28.77 (26.76–30.87)
Excess
49.94 (47.68–52.20)
Parityc First birth
30.64 (28.67–32.68)
Second or later birth
69.36 (67.32–71.33)
Entry into prenatal caref 1st Trimester
82.43 (80.75–84.00)
2nd Trimester
14.95 (13.48–16.55)
3rd Trimester or no PNC Any reported diabetesc,
Weight gain categories determined by 2009 Institute of Medicine’s guidelines by each BMI category
h
[12
Federal poverty levele,
f
2.62 (2.04–3.35)
h
Yes
12.92 (11.60–14.37)
No
87.08 (85.63–88.40)
Notation on the birth certificate or self-report on the PRAMS questionnaire of diabetes (prepregnancy diabetes or gestational diabetes)
i
Notation on the birth certificate or self-report on the PRAMS questionnaire of smoking during pregnancy
increased odds of macrosomia (27 and 57 %, respectively) compared to normal weight women. These findings are consistent with other studies [2, 30]. However, Alaska Natives seemed to be driving the increased odds in overweight women, since overweight American Indians and mixed race women no longer showed significant increased odds of macrosomia. Obesity at conception remained a significant factor for macrosomia across all women. While obesity was seen in about 30 % of the study population, excess GWG beyond the 2009 IOM guidelines occurred in 50 % of all women. Consistent with other studies, we found excess GWG to be associated with increased odds of macrosomia independent of prepregnancy BMI [2, 25]. However, this analysis extended prior findings by examining the association of GWG with macrosomia for each pound a woman gained beyond recommended ranges. For every five pounds gained beyond appropriate, women increased their odds of macrosomia by 16 % compared to women who remained within appropriate ranges. Of those AI/AN women who did gain in excess, the average gain was 14 lbs. beyond appropriate, increasing the odds of macrosomia 53 % compared to appropriate weight gain. This relationship held true across
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Matern Child Health J Table 2 Prevalence of macrosomia by demographic and pregnancy characteristics among American Indians/ Alaska Natives, PRAMS, 8 states, 2004–2011a
Prevalence of Macrosomia Birth weight C 4000 grams % (95% CI)b Total sample
13.54 (12.60–14.54)
AI/ANsd 11.81 (10.87–12.82)
Alaska Natives
20.96 (19.42–22.59)
Mixed- AI/ANs with other
13.81 (10.47–18.01)
e
0.016*
20–24 25–29
12.38 (10.95–13.98) 12.94 (11.47–14.57)
30–34
15.93 (13.39–18.85)
C35
16.80 (13.85–20.22)
Education (years)e
0.286
[12
12.14 (10.32–14.23)
12
14.17 (12.71–15.76)
\12
13.35 (11.79–15.08)
Marital statuse
0.267
Married
14.17 (12.67–15.82)
Not married Federal poverty levelf,
13.03 (11.85–14.31) g
0.685
B138 % FPL
13.33 (12.11–14.66)
[138 % FPL
13.77 (12.19–15.52)
Prepregnancy body mass index (kg/m2)f \18.5 Underweight 18.5–24.9 Normal weight
\0.001* 5.00 (3.00–8.21) 9.97 (8.51–11.66)
25.0–29.9 Overweight
14.22 (12.58–16.04)
C30.0 Obese
17.80 (15.96–19.81)
Weight gain during pregnancye, Inadequate
h
\0.001* 9.58 (7.88–11.61)
Appropriate
9.38 (7.92–11.09)
Excess
16.93 (15.43–18.54)
Paritye
0.002*
First birth
11.20 (9.61–13.01)
Second or later birth Entry into prenatal care
14.60 (13.44–15.83) g
0.013*
1st Trimester
13.83 (12.76–14.99)
2nd Trimester
12.92 (10.63–15.62)
3rd Trimester or no PNC
7.37 (4.71–11.34)
Any reported diabetesi Yes No
\0.001* 19.87 (17.12–22.93) 12.60 (11.60–13.68)
Smoking during pregnancyj
123
– \0.001*
American Indians
Maternal age (years)
p valuec
\0.001*
Yes
10.35 (8.76–12.18)
No
14.65 (13.52–15.87)
Matern Child Health J Table 2 continued
Prevalence of Macrosomia Birth weight C 4000 grams % (95% CI)b Infant sexf
p valuec
\0.001*
Male
16.49 (15.01–18.08)
Female
10.79 (9.62–12.08)
a
Sample includes live, singleton, term (C37 gestational weeks) births among AI/AN women 20 years or older in Alaska, Minnesota, Nebraska, New Mexico, Oklahoma, Oregon, Utah, and Washington from the Pregnancy Risk Assessment Monitoring System
b
Prevalence of Macrosomia (95 % Confidence Interval)
c
Chi-square p value
d
Variable definition from both notations on the birth certificates and maternal self-report on the PRAMS questionnaire
e
Variable definition from notations on the birth certificates
f
Variable definition from maternal self-report on PRAMS questionnaire
g
Federal Poverty Level determined by maternal self-report of income and number of dependents from the year previous to infant’s birth
h
Weight gain categories determined by 2009 Institute of Medicine’s guidelines by each Body Mass Index category
i
Notation on the birth certificate or self-report on the PRAMS questionnaire of diabetes (prepregnancy diabetes or gestational diabetes) j
Notation on the birth certificate or self-report on the PRAMS questionnaire of smoking during pregnancy
* Statistically significant, alpha = 0.05
all races and seven states with adequate data. Although inadequate weight gain showed decreased odds of macrosomia, gaining weight below the guidelines would not be recommended. Among marginalized populations in the United States, AI/ANs are increasing in numbers and are experiencing some of the greatest health disparities across many chronic conditions [11]. The AI/AN population grew twice as fast as the US population overall between 2000 and 2010 [27]. More research is needed in the design and implementation of successful weight management programs tailored for the growing AI/AN population. Macrosomia creates an intergenerational cycle of chronic disease risk factors. Children are more likely to be obese later in life if they were born macrocosmic or born to mothers who were overweight/ obese at conception or who gained excess amount of weight during pregnancy [8, 18, 20, 30]. Reducing the prevalence of macrosomia, and potential subsequent obstetric risks, is especially important among AI/AN populations who may have limited access to emergency services. Healthcare access can be a challenge on reservations or in rural areas due to lack of transportation or proximity to healthcare facilities and in urban areas where IHS services are limited [5, 13]. One study in 2012 among singleton, full term births in California estimated that composite maternal and neonatal complications increased 2.3 times for infants above 4000 g and 6.3 times for infants above 4500 g compared to infants below 4000 g [17]. Macrosomia is difficult to predict
prenatally, so risk factors need to be addressed before and during pregnancy to minimize these risks and reduce healthcare costs for complicated deliveries. Currently, weight management interventions for BMI exist for women of reproductive age and for GWG during pregnancy [23]. GWG management programs closely resemble lifestyle programs used for BMI control in non-pregnant women, and often include structured meal plans, behavioral modification strategies, and ongoing contact with healthcare providers [23]. One essential element to preventing excess GWG while providing adequate nutrition to the growing fetus is for women to receive counseling on dietary information during pregnancy. Currently, the American Congress of Obstetricians and Gynecologists (ACOG) recommends all women be educated on the GWG guidelines but does not mention nutritional consultations for normal weight women [1]. Counseling should encourage appropriate GWG and minimize additional gain beyond recommendations for all women of all BMI levels. Among our sample, the 40 % who were normal weight could benefit from counseling to reduce excess GWG, since 50 % of women gained in excess. ACOG recommends discussions begin early in prenatal care [1], which is opportunistic since in our study 82 % of women reported entering prenatal care during the first trimester. Designing interventions to address weight management specifically for AI/AN women will be challenging. Stratified analyses revealed racial and geographical variations in the prevalence of macrosomia and its association with risk
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Matern Child Health J Table 3 Average weight (lbs.) gained under appropriate among American Indian/Alaska Native women who gained an inadequate amount of weighta, PRAMS, 8 states, 2004–2011b
Total
Macrosomia (C4000 g)
No Macrosomia (\4000 g)
Meanc
(SD)d
Meanc
-7.28
(0.52)
p valuee
(SD)d
-8.04
(0.38)
0.232
-11.74
(2.71)
–
-8.69
(0.53)
0.512
Prepregnancy BMI (kg/m2)f \18.5 Underweight
–g
18.5–24.9 Normal weight
-9.28
(0.72)
25.0–29.9 Overweight
-6.93
(0.70)
-7.35
(0.58)
0.646
C30.0 Obese
-5.99
(0.75)
-6.15
(0.45)
0.855
a
Categorization of gestational weight gain according to the 2009 Institute of Medicine’s guidelines Sample includes live, singleton, term (C 37 gestational weeks) births among AI/AN women 20 years or older in Alaska, Minnesota, Nebraska, New Mexico, Oklahoma, Oregon, Utah, and Washington from the Pregnancy Risk Assessment Monitoring System
b
c
Mean (lbs.), continuous variable representing the number of pounds gained from the appropriate range of weight gain recommended for each prepregnancy BMI recommended by the 2009 Institute of Medicine’s guidelines
d
Standard deviation of the mean
e
p values for mean pairwise comparisons of average number of pounds gained outside of the recommended amount between macrosomic and non-macrosomic infants
f
Prepregnancy Body Mass Index from maternal self-report on PRAMS questionnaire
g
Inadequate cell size too small to produce estimate (n \ 5)
* Bonferonii adjusted statistically significant, alpha = 0.006
Table 4 Average weight (lbs.) gained above appropriate among American Indian/Alaska Native women who gained an excess amount of weighta, PRAMS, 8 statesb
p valuee
Macrosomia (C4000 g)
No Macrosomia (\4000 g)
Meanc
(SD)d
Meanc
(SD)d
17.24
(0.57)
13.47
(0.46)
\0.001*
\18.5 Underweight 18.5–24.9 Normal weight
9.60 14.85
(1.56) (1.01)
16.01 11.13
(6.21) (0.58)
0.317 0.001*
25.0–29.9 Overweight
18.89
(1.00)
14.88
(0.99)
0.004*
C30.0 Obese
17.57
(0.86)
14.26
(0.64)
0.002*
Total Prepregnancy BMI (kg/m2)f
a
Categorization of gestational weight gain according to the 2009 Institute of Medicine’s guidelines
b
Sample includes live, singleton, term (C 37 gestational weeks) births among AI/AN women 20 years or older in Alaska, Minnesota, Nebraska, New Mexico, Oklahoma, Oregon, Utah, and Washington from the Pregnancy Risk Assessment Monitoring System
c
Mean (lbs.), continuous variable representing the number of pounds gained from the appropriate range of weight gain recommended for each prepregnancy BMI recommended by the 2009 Institute of Medicine’s guidelines
d
Standard deviation of the mean
e
p values for mean pairwise comparisons of average number of pounds gained outside of the recommended amount between macrosomic and non-macrosomic infants
f
Prepregnancy Body Mass Index from maternal self-report on PRAMS questionnaire
g
Inadequate cell size too small to produce estimate (n \ 5)
* Bonferonii adjusted statistically significant, alpha = 0.006
factors. Only Alaska, Nebraska, Oklahoma, and Oregon maintained significant associations with macrosomia among obese women. Alaska Natives seem to be higherrisk group compared to American Indians and mixed race women, with the highest prevalence of macrosomia and increased odds of macrosomia among overweight and
123
obese women and women with excess GWG. Other studies have warned against aggregating American Indians and Alaska Natives due to such variations; birth outcomes and prenatal care utilization also differ among AI/ANs by geographical region, and by rural versus urban households [3, 5, 13].
Matern Child Health J Table 5 Adjusted odds ratios of macrosomia among American Indian/Alaska Native women, PRAMS, 8 states, 2004–2011a Aggregated aOR (95 % CI)b
American Indians aOR (95 % CI)c
Alaska Natives aOR (95 % CI)d
Mixed-AI/ANs with other aOR (95 % CI)e
\18.5 Underweight
0.64 (0.35–1.16)
0.95 (0.51–1.78)
–g
–
18.5–24.9 Normal weight
ref
ref
ref
ref
25.0–29.9 Overweight
1.27 (1.01–1.59)*
1.30 (1.00–1.68)
1.46 (1.11–1.92)*
1.13 (0.54–2.35)
C30.0 Obese
1.57 (1.25–1.96)*
1.60 (1.23–2.07)*
1.88 (1.43–2.46)*
1.23 (0.59–2.56)
Every 5 lbs. under appropriate Appropriate
0.86 (0.84–0.89)* ref
0.86 (0.83–0.90)* ref
0.82 (0.78–0.86)* ref
0.90 (0.84–0.98)* ref
Every 5 lbs. over appropriate
1.16 (1.13–1.20)*
1.16 (1.12–1.21)*
1.22 (1.17–1.28)*
1.11 (1.02–1.19)*
Prepregnancy BMI (kg/m2)f
Gestational weight gainh
a
Sample includes live, singleton, term (C37 gestational weeks) births among AI/AN women 20 years or older in Alaska, Minnesota, Nebraska, New Mexico, Oklahoma, Oregon, Utah, and Washington from the Pregnancy Risk Assessment Monitoring System
b
Adjusted Odds Ratios (95 % Confidence Intervals) determined by multivariable logistic regression, variables included were prepregnancy body mass index, gestational weight gain, AI/AN race, maternal age, parity, any diabetes during pregnancy, smoking during pregnancy, and state of residence (n = 7771)
c
Adjusted Odds Ratios (95 % Confidence Intervals) determined by multivariable logistic regression, variables included were prepregnancy body mass index, gestational weight gain, maternal age, parity, any diabetes during pregnancy, smoking during pregnancy, and state of residence (n = 4391)
d
Adjusted Odds Ratios (95 % Confidence Intervals) determined by multivariable logistic regression, variables included were prepregnancy body mass index, gestational weight gain, maternal age, parity, any diabetes during pregnancy, and smoking during pregnancy (n = 1990)
e
Adjusted Odds Ratios (95 % Confidence Intervals) determined by multivariable logistic regression, variables included were prepregnancy body mass index, gestational weight gain, maternal age, parity, any diabetes during pregnancy, smoking during pregnancy, and state of residence (n = 1390)
f
Prepregnancy Body Mass Index from maternal self-report on PRAMS questionnaire
g
Inadequate cell size, too small to produce estimate (n \ 5) Gestational Weight Gain extracted from birth certificates, representing 5 lb. intervals above and below the appropriate weight gain range recommended by the 2009 Institute of Medicine’s gestational weight gain guidelines based on prepregnancy BMI
h
* Statistically significant adjusted odds ratio
State of residence is only one contextual concern; addressing risk factors before and during pregnancy among AI/AN women requires culturally sensitive approaches and integration with current public health systems. Adaptors of currently successful programs or developers of novel interventions will need to work with local communities to address current attitudes, tribal and regional differences, political diversity, and socioeconomic environment [3]. Aside from design, implementation of such interventions will present additional problems. Disparities in healthcare access and utilization are engendered by systematic barriers along with cultural beliefs and practices that conflict with modern medical care [15]. Some potential barriers include matching literacy levels, improving local resources, incorporating tribal/cultural practices, and increasing healthcare utilization [19, 20, 24]. Identifying protective factors within each community could help direct interventions as well. Of note, 40 % of AI/AN women in this study entered pregnancy at normal weight and 30 % did gain weight within recommended guidelines.
Although this study has important and clinically relevant findings for AI/AN women, it does have some limitations. Not all states with large AI/AN populations, such as Arizona and California, participate in PRAMS, and findings are only generalizeable to the states included in the analysis. In addition, PRAMS only samples women who have had a live birth, so the findings cannot be applied to women with pregnancy loss. Due to PRAMS sampling methodology, we were only able to obtain data on AI/AN women at the state level, so smaller regional or tribal differences could not be assessed. Even at the state level, we had inadequate cell sizes for some regressions to be meaningful. Also, there may be some misclassification of prepregnancy BMI, likely a lower report than truth, due to self-reported post hoc assessment of prepregnancy weight at the time of the questionnaire (about 4 months postpartum). There may be some misclassification of race from extraction from the birth certificate as well. We do not expect these potential misclassifications to differ by delivery of a macrosomia infant. The largest limitation to
123
123
20.85 (19.31–22.48)
1.92 (1.45–2.53)*
25.0–29.9 Overweight
C30.0 Obese
ref 1.22 (1.17–1.28)*
Appropriate
Every 5 lbs. gained over appropriate
1.16 (1.06–1.27)*
ref
0.86 (0.79–0.94)*
1.72 (0.68–4.32)
1.46 (0.75–2.84)
ref
–
aOR (95 % CI)c
1.16 (1.07–1.27)*
ref
0.86 (0.79–0.94)*
1.84 (1.11–3.04)*
1.54 (0.82–2.88)
ref
–
1.22 (1.04–1.44)*
ref
0.82 (0.69–0.97)*
1.09 (0.39–3.08)
1.23 (0.48–3.14)
ref
–
1.16 (1.09–1.24)*
ref
0.86 (0.81–0.92)*
1.48 (1.01–2.16)*
1.09 (0.74–1.62)
ref
0.90 (0.36–2.27)
1.11 (1.06–1.16)*
ref
0.90 (0.86–0.95)*
1.82 (1.30–2.56)*
1.36 (0.93–2.00)
ref
0.99 (0.42–2.36)
Washington % (95 %CI)b
1.16 (0.93–1.45)
ref
–
–
5.05 (0.44–57.58)
ref
–
aOR (95 % CI)c
1.11 (1.04–1.18)*
ref
0.90 (0.85–0.97)*
1.22 (0.64–2.33)
1.04 (0.52–2.10)
ref
–
aOR (95 % CI)c
8.74 (4.84–15.28) 16.51 (13.07–20.65)
Utah % (95 % CI)b
aOR (95 % CI)c
14.53 (12.92–16.30)
Oregon % (95 % CI)b
aOR (95 % CI)c
9.91 (8.82–11.12)
Oklahoma % (95 % CI)b
aOR (95 % CI)c
10.43 (7.70–13.97)
New Mexico % (95 % CI)b
aOR (95 % CI)c
14.43 (11.97–17.31)
Nebraska % (95 % CI)b
Prevalence of Macrosomia (95 % Confidence Interval)
Prepregnancy Body Mass Index from maternal self-report on PRAMS questionnaire
Inadequate cell size, too small to produce estimate (n \ 5)
* Statistically significant adjusted odds ratio
Gestational Weight Gain extracted from birth certificates, presented by 5 lb. intervals above and below the appropriate weight gain range recommended by the 2009 Institute of Medicine’s gestational weight gain guidelines based on prepregnancy BMI
f
e
d
Adjusted Odds Ratios (95 % Confidence Intervals) determined by multivariable logistic regression, variables included were prepregnancy body mass index, gestational weight gain, AI/AN race, maternal age, parity, any diabetes during pregnancy, and smoking during pregnancy
c
b
a Sample includes live, singleton, term (C37 gestational weeks) births among AI/AN women 20 years or older in Alaska, Minnesota, Nebraska, New Mexico, Oklahoma, Oregon, Utah, and Washington from the Pregnancy Risk Assessment Monitoring System
0.82 (0.78–0.86)*
Every 5 lbs. under appropriate
Gestational weight gainf
ref 1.43 (1.09–1.89)*
18.5–24.9 Normal weight
–e
aOR (95 % CI)c
14.93 (11.50–19.18)
Minnesota % (95 % CI)b
\18.5 Underweight
Prepregnancy BMI (kg/m2)d
Prevalence of macrosomia
Alaska % (95 % CI)b
Table 6 Adjusted odds ratios for macrosomia among American Indian/Alaska Native women by state of residence, PRAMS, 8 states, 2004–2011a
Matern Child Health J
Matern Child Health J
this study is the inability to control for history of previous macrosomia, a well-known risk factor for macrosomia reflecting both environmental and genetic factors [10]. This study was able to capture a large population-based sample of AI/AN women using a validated questionnaire to obtain exposure data and many variables not found on birth certificates. This study also used a new method of classifying GWG to see the effects of each pound gained beyond appropriate compared to traditional GWG classification in three tiers (inadequate, appropriate, and excess). In addition, the PRAMS sampling methodology allows estimates to represent the entire state and allows inter-state comparisons. The ability to stratify in the analysis revealed important racial and geographical variations in the prevalence of macrosomia and associations with BMI and GWG. This study could have many implications for development of public health efforts, and encourage future research on this unique population. Prepregnancy BMI and GWG should be considered independent risk factors for delivery of a macrosomic infant among AI/AN women. Prepregnancy BMI needs to be addressed throughout the life course so women can enter the pregnancy at a normal weight. Among all AI/AN women, excess GWG should be addressed and weight management interventions should begin early in prenatal care. To help reduce the prevalence of high-risk pregnancies and improve pregnancy outcomes, development and implementation of weight management interventions addressing cultural and behavioral variations among AI/ AN women will be challenging and should be a priority for future research. Acknowledgments I would like to acknowledge the entire PRAMS working group in Alaska, Minnesota, Nebraska, New Mexico, Oklahoma, Oregon, Utah, Washington, and the CDC. In particular, thank you to Paul Weiss from Emory University and Denise D’Angelo from CDC on their guidance at the start of this project.
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Compliance with Ethical Standards Conflict of interest of interest.
The authors declare that they have no conflict
References 1. ACOG Committee opinion no. 549: obesity in pregnancy. (2013). Obstetrics Gynecology, 121(1), 213–217. doi: 10.1097/01.AOG. 0000425667.10377.60. 2. Alberico, S., Montico, M., Barresi, V., Monasta, L., Businelli, C., Soini, V., et al. (2014). The role of gestational diabetes, prepregnancy body mass index and gestational weight gain on the risk of newborn macrosomia: Results from a prospective multicentre study. BMC Pregnancy Childbirth, 14, 23. doi:10.1186/ 1471-2393-14-23. 3. Alexander, G. R., Wingate, M. S., & Boulet, S. (2008). Pregnancy outcomes of American Indians: Contrasts among regions
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