DOI: 10.31038/AWHC.2019254

Bangladesh has achieved commendable progress in alleviation of poverty in recent years and, in association, the overall nutritional status of mothers and children has improved, and child mortality has fallen [1,2]. According to the regular Bangladesh Demographic and Health Surveys, the Stillbirth Rate (SBR) fell from 37 per 1000 pregnancies in 2004 to 21 in 2014 [3], and the Perinatal Mortality Rate (PMR) fell from 65 deaths per 1000 pregnancies in 2000 to 44 in 2014. And from 1994 to 2014, for every 1000 live births, the Neonatal Mortality Rate (NMR) fell from 52 to 28, the Infant Mortality Rate from 87 to 38, and the <5 year Child Mortality Rate from 133 to 46. While BDHS provide information from many regions, they do not provide specific data from the far north of Mymensingh District where two non-government agencies, Symbiosis International and the Mennonite Central Committee, have, for many years, been involved in developmental projects. In November 2018, in order to review their progress and to plan for the future, these organisations surveyed indices of child and maternal health, and related socio-economic factors, in 25 sites in that region. This paper reviews aspects of morbidity and mortality of children in those sites and compares them with BDHS and other sources. In other reports we examine anthropometry of children and factors relating to maternal health.

Methods

Over two consecutive weeks in November 2018 surveys were performed in 25 sites in and around the administrative centres of Haluaghat and Dhobaura, and the main obstetric facility, Joyramkura Hospital. Cross -sectional data was collected by two teams of Bangladeshi and Australian NGO workers, and senior medical students from Western Sydney University, under the supervision of an Australian general practitioner experienced in rural, remote and indigenous medicine, and the Professor of Paediatrics from Western Sydney University. Data was recorded on paper, transcribed to a computer, and later analysed with reference to WHO standards. Anthropometric data was gained by specially constructed stadiometers with fixed metal measuring ‘tapes’ and with portable weighing machines which were carried, along with other equipment, into the various villages. Anthropometric data of age, heights and weights was reported as Height Per Age (HAZ), Weight Per Age (WAZ) and Weight For Height (WHZ), and expressed as Standard Deviations (SD) from the mean of the reference population in accordance with World Health Organisation practice [4,5]. Moderate disturbances of anthropometry are defined as greater or less than 2SD from the mean: severe from 3SD. Reduced HAZ ratios denote the presence and degree of ‘stunting’, mostly due to chronic under-nutrition and or disease. Reduced WHZ suggests ‘wasting’ from acute malnutrition or disease, and reduced WAZ suggests ‘marasmus’ associated with acute or chronic loss of weight. SBR is defined as death of a foetus equal to or after 28 weeks of gestation. PMR is defined as the total of Stillbirths and Deaths in the first week of life per 1000 pregnancies. NMR is the number of deaths in the first month of life per 1000 live births. IMR is the number of deaths in the first year of life per 1000 live births. Under 5 CMR is the number of children dying <5 years of age per 1000 live births.No verbal post-mortem assessment of cause of death was undertaken: the mother’s simple explanation was accepted. Nor were undertaken any detailed histories of children suffering from chronic disease. Major clinical features were classified and some children were referred for further investigation at Joyramkura Hospital. Those with cardiac abnormalities confirmed by echocardiography were referred to higher centres.

Statistics

Data was cleansed and imported into a relational database enabling cross correlating queries to be executed. WHO anthropometric factors of Height Vs Age (HAZ), Weight Vs Age (WAZ), Weight Vs Height (WHZ) were calculated using the WHO published mathematical algorithms [6]. Outliers were identified according to WHO statements of limits and discarded as per WHO stated process. Data was converted to Z Scores and expressed as the Standard Deviation (SD) from the mean of the WHO reference standard population for both male and female [7]. Continuous unpaired data was analysed using zTest,. Count data was analysed using Chi-Squared Best Fit assuming equal proportions and trend data analysed using Chi-Squared Tend Analysis. Correlations were performed using Pearson’s correlation. In all tests sample size was > 30 and the null hypothesis rejected for results > 95% confidence, resultant P-Values are reported. We perform all comparisons against the combined male-female scores, unless otherwise stated. We used Minitab Express for all statistical analysis.

Ethics

The surveys were approved by governance of both Symbiosis International and Mennonite Central Committees as quality assurance of current programmes and preparation for future activity. Representatives of those NGOs visited the sites in advance, explained the aims and the process, and invited participation. Mothers and their children attended voluntarily. Data was de-identified for analysis but a list was kept in confidence in case of need to contact the parents eg with regard to medical concerns.

Results

Surveys were held in 25 sites, and included 2987 children and 1982 mothers.

Anthropometry

Growth restriction was revealed to be a major problem. In summary, and with regard to combined sexes of children <5 years of age, 36.2% were moderately stunted (HAZ <-2SD) and 14.6% severely stunted (<-3SD). 32.5% were moderately (WAZ <-2SD) and 10.4% were severely underweight (<-3SD). 15.8% were moderately (WHZ<-2SD) and 6.5% were severely wasted (<-3SD). Similar restriction was found in children aged from 5–14 years: 25.6% were moderately (HAZ <-2SD) and 6.5% were severely stunted (<-3SD). 29.4% were moderately (WAZ<-2SD) and 10.7% were severely underweight (<-3SD). Growth restriction was also common in mothers, of whom 15.3% were stunted. That this rate increased to 25.6% in children 5–14, and then to 36.2% in those <5 years, suggests stunting is increasing in recent years.

Mortality

Of 4408 live births, 93 died in the first week of life, 122 within the first month, 199 in the first year, and 275 within the first five years of life. These statistics suggest a PMR of 109.0, and an NMR of 27.7, IMR of 17.5, and an <5 CMR of 62.4. These live born deaths had been preceded by 435 stillbirths, giving an SBR of 89.8. No data was collected on the sex of the deceased. Thus, the SBR in our surveyed sites would appear to be almost four times greater than the national average (89.8 vs 21), the PMR to be over twice the national rate (109 vs 44), the NMR similar to that rate (27.7 vs 28), the IMR half that rate (17.5 vs 38) and the CMR one and half times greater than the national rate (62.4 vs 46). The CMR was 25% higher in stunted mothers, than non stunted (p=0.0326), and was inversely related to family income (p=0.0179) which is a surrogate for maternal education.

Causes of Death

In almost half of the deaths (46.5%), the mother declared she did not know why her child had died. In 18.9% of cases, she thought deaths were related to the birth process. In 12.0%, she declared pneumonia to have been the cause. 6.9% of the children had drowned. 3.3% were reported to have become suddenly ‘sick’, and diarrhoea featured in another 2.2%. Trauma accounted for 1.8% of deaths, and others appeared to have died of malformations and malignancy.

Morbidity

Children with chronic disease were identified by the mother during the surveys, and significant abnormality was confirmed in 106 children. 52 were suffering from Cerebral Palsy (CP): 12 with serious cardiac disease; 12 with syndromes including 5 with Down Syndrome; 5 with unspecified developmental delay; 5 with external eye problems including severe strabismus and microphthalmia; 4 with deaf-mutism; 2 with congenital dislocation of the hip; and 2 with treated myelo-meningocoele . Overall, this gave a prevalence of physical impairment due to neurological causes of 18.1 per 1000. Sundry cases included one each of elephantiasis, rheumatoid arthritis, urinary incontinence, and renal hydronephrosis, while 5 had inguinal hernias, and one a huge omphalocoele.

Of the 52 children with cerebral palsy, 27 were boys and 25 girls with a mean age of 5.3 years. All were severely affected with Gross Motor Function Classifications in the range of 4 and 5 but none were receiving physiotherapy or special schooling, and none appeared to have mechanical aids. All appeared to have been associated with birthing difficulties, except for one associated with head injury at 3 years of age, another with febrile illness at 18/12 of age, and another with severe gastro-enteritis at a similar age. Under-nutrition was a major complication of CP: 42.3% were stunted (HAZ < -2SD) compared with 25% of local children without CP (PValue 0.0048) and 23.1% were <-3SD. 44.2% were underweight (WAZ<-2SD) compared with 23.7% without CP (PValue 0.0006), and 18 of that 23 were <-3SD. There was no gender difference. Cardiac abnormalities included 3 with Tetralogy of Fallot, 2 with cardiomyopathy, 3 with septal defects, 3 with valvular disease (2 stenoses and 1 incompetence) and 1 patent ductus arteriosus. All appeared to be of congenital origin.

Discussion

The survey revealed severe levels of morbidity and mortality in children in rural areas in the north of the Mymensingh District. The degree of growth restriction rivals the worst in the country and must be fundamental to the high rate of mortality. It is well known that under-nutrition reduces immunity and is associated with a three to four times greater rate of mortality. Our survey confirmed the presence of socio-economic correlations of under-nutrition that have international recognition. As reported elsewhere, the stunting of children was associated with stunting of mothers, and maternal stunting was associated with reduced age at marriage, income, education and proper disposal of human waste. As most of our children were born at home with untrained birth attendants, birth weights are unknown, but the 9% prevalence of in-utero growth restriction recorded in the private obstetric facility, Joyramkura Hospital, suggests a much higher rate in poor, undernourished mothers delivering in the villages. Intra-uterine growth restriction will contribute to the stunting of children and thus the mortality rate. That the survey revealed the rate of stunting to have increased from 15.3% in mothers, to 25.6% in children 5–14, and then to 36.2% in those <5 years, suggests its prevalence is increasing in the north, despite the nation’s fall in poverty. Why this should be so is not clear: perhaps poverty has worsened in the north. Alternatively, the rising prevalence of overweight in stunted and other mothers (as discussed in another report) might suggest disposable income is being spent on calorie dense foods and drinks which fatten the mother but do not provide sufficient nutrition for linear growth in the child.

The very high rate of stunting and underweight in children with CP confirms their special vulnerability. Reasons for under-nutrition include difficulties in feeding and swallowing, problems with consistency of the diet, problems with understanding the cause of CP, and poverty [8]. It was reassuring the rate of undernutrition was similar for males and females because, sometimes, the latter are perceived as a greater burden. Home births, attended by un-trained personnel, distant in time and location from the obstetric facility at Joyramkura, must be the basis of the appalling rate of still and perinatal death rates. The gestational age of babies still born in poor rural areas is often unknown [9] but up to 50% of such deaths are likely to be due to intra-partum difficulties [10]. The low rate of hypertensive and diabetic complications at Joyramkura Hospital would support this concept of ‘mechanical’ rather than medical causation [11]. Therefore, prolonged and obstructed labour is likely to be a major cause of deaths of children of all ages, and of cerebral palsy in survivors [12].

Given the extraordinary Stillbirth Rate, it may not be reassuring that the NMR and IMR in northern Mymensingh approximate the national rate. It could merely signify that babies are expiring during rather than after birth. The <5 CMR of 62.4 in northern Mymensingh, however, rivals the highest rate in Bangladesh (67 per 1000) reported from rural Sylhet [13]. The striking feature in our survey is that the cause of almost half of the deaths was unknown to the mother. This would suggest reliance on village ‘physicians’ without recourse to trained opinion. In confirmation, only 1452 emergency visits and 1578 admissions of children <5 years were recorded in 2018 in the government hospital at Dhobaura which serves a population of 225,399 [14]. In the same year, only 1331 emergency visits and 875 admissions of children <5years were recorded in the government hospital in Haluaghat, the other administrative centre in the region, serving a population of 333,065 inhabitants [15]. Only 8 visits a day from a population of over half a million people supports the concept of help being sought elsewhere. While almost half the mothers seemed to have little understanding of why their children had died, some deaths were ascribed to unspecified problems at birth, and others to such recognised causes as pneumonia [16] and drowning [17]. Preventable infections must have played a role: though almost all mothers declared their children had been vaccinated, no details were available, and a ‘drop out’ rate of around 50% of Bacille Calmette Guerin, Pentavalent, Measles and even poliomyelitis vaccinations was reported by health authorities in Dhubaura. Similarly, there was no guarantee that government distribution of vitamin A had been successful, though gastro-enteritis was not a prominent cause of death. 52 cases of cerebral palsy from 4408 live births suggests an extremely high rate of CP (11.8 per 1000) compared to the international rate of around 2 per 1000 live births. Regrettably, rates 5 to 10 times higher than the international one may not be unusual in Low to Middle Income Countries [18,19]. In one study in Bangladesh it was calculated to be 3.4 per 1000, but estimated to be much higher because of early deaths and difficulties in diagnosis [20]. The overall rate of Physical Impairment (PI) due to neurological disease in our ‘broad brush’ survey was 18.1 per 1000, three times higher than the 6.1 per 1000 revealed in a much more sensitive study in Bangladesh [21]. Most likely, our study has seriously underestimated the prevalence of PI. We only counted survivors with gross impairment. Consideration should be given to cause of death, and sensitive screening should be performed to assess true prevalence and plan for intervention.

The consequences of cerebral palsy extend beyond the child to include psychological and financial costs to the family, especially to the mother [22,23]. The quality of maternal life has been shown to be much reduced in association with a child with CP. Early diagnosis, therefore, may provide opportunity for positive intervention: for neurological improvement by taking advantage of cerebral plasticity, for reduction of spasticity, for provision of aids from eye glasses to wheelchairs, for community understanding and cultural acceptance of handicap, and thus for improvement in maternal quality of life. Such early diagnosis might be achieved by the ‘Key Informant’s Method’ developed by the Child Sight Foundation in Shajadpur, Bangladesh, in which ‘knowledgeable members of the community (key informants) are trained to effectively identify children with moderate-severe physical impairments, sensory impairments and epilepsy’ [24]. Identified children and carers then gather for evaluation and low cost, family and community based intervention. Details are recorded for the creation of a Bangladesh Cerebral Palsy Register [25] which is intended to reveal causes and initiate response. CP incurs cultural, financial, logistic and staffing challenges but surveys should progress from prevalence to intervention [26–28]. At present, interventions are limited. In the Shajadhur study, 78.2% of children had never received rehabilitation, and only a fifth were attending regular school [29]. In northern Mymensingh District, such services do not existent. Our survey revealed 10 children with congenital heart disease and another 2 with dilated cardiomyopathy of possible congenital origin. The mean age was 7.2 years raising the likelihood of under-estimation because of survivor bias. Therefore, our rate of 3 per 1000 does not confront reports of a ‘normal’ incidence of moderate to severe abnormalities of around 6 per thousand [30]. In rural Bangladesh, one problem is diagnosis, the other is cost of surgery when even the bus fare to Dhaka may be prohibitive.

Our survey has limitations. First, given the propensity for home births attended by untrained personnel, dates, weights and gestations were usually unrecorded and as lamented in BDHS 2014, ‘any memory of ages and causes of death, even of stillbirths themselves, dims with passing years in retrospective, cross sectional studies’. Second, associated with decreasing memory is the tendency to ‘heap’ ages at standard digits of, say, 6 months or 1 year. We sought to minimise this limitation by expressing age in half years. For example, if a mother declared the child to be 3 years old, we calculated on the basis of it being 3 1/2. We were reassured, however, by the BDHS declaration that the ratio of deaths under one week to those under one month should be around 70 % if estimation of age is reasonable. Our ratio was 76%, probably reflecting increased obstetric difficulties. A third limitation is that we relied on mothers’ statements as interpreted through a single translator.

Conclusion

Despite limitations, our survey has revealed major problems of morbidity and mortality of children in northern Mymensingh District. Stunting is prevalent and inter-generational. Home deliveries prevail and are rewarded by an extra-ordinary rate of still and perinatal death, and subsequent development of cerebral palsy. There is also a high rate of death in older children, for reasons often unknown to the mothers. Attendance rates at local government hospitals suggest reliance on village ‘physicians’. There is an urgent need for timely referral of obstetric and child health problems to an equipped facility. Screening for cerebral palsy and provision for intervention should be performed…and will be in the near future.

References

1. Headey D, Hoddinott J, Ali D, Tesfaye R, Dereje M (2015) The Other Asian Enigma: Explaining the Rapid Reduction of Undernutrition in Bangladesh. World Development 66: 749–761.
2. World Bank (2018) Bangladesh continues to reduce poverty but at a decreased rate. Available online: https://www.worldbank.org/en/news/feature/2017/10/24/bangladesh-continues-to-reduce-poverty-but-at-slower-pace (accessed June 7, 2019).
3. Abir T, Agho KE, Ogbo FA, Stevens GJ, Page A, et al. (2017) Predictors of stillbirths in Bangladesh: evidence from the 2004–2014 nation-wide household surveys. Glob Health Action 10: 1410048.
4. WHO Multicentre Growth Reference Study Group (2006) Reliability of anthropometric measurements in the WHO Multicentre Growth Reference Study. Acta Paediatr Suppl 450: 38–46.
5. The WHO Anthro Survey Analyser. https://www.who.int/nutgrowthdb/software/en/ (accessed June 7, 2019).
6. de Onis M, Adelheid Onyango , Elaine Borghi , Amani Siyam , Monika Blössner, et al. (2012) Worldwide implementation of the WHO Child Growth Standards. Public Health Nutrition 15: 1603–1610.
7. de Onis M (2006) Reliability of anthropometric measurements in the WHO Multicentre Growth Reference Study. Acta Pædiatrica 95: 38–46.
8. Jahan I, Muhit M, Karim T, Smithers-Sheedy H, Novak I, et al. (2019) What makes children with cerebral palsy vulnerable to malnutrition? Findings from the Bangladesh cerebral palsy register (BCPR). Disabil Rehabil 41: 2247–2254.
9. Lawn JE, Yakoob MY, Haws RA, Soomro T, Darmstadt GL, (2009) 3.2 million stillbirths: epidemiology and overview of the evidence review. BMC Pregnancy Childbirth 9: 1.
10. Campbell O, Gipson R, el-Mohandes A, Issa AH, Matta N, et al. (2004) The Egypt National Perinatal/Neonatal Mortality Study 2000. J Perinatol 24: 284–289.
11. Joyramkura Hospital Annual Report; Annual Report (2018) Joyramkura Hospital: Joyramkura, Bangladesh.
12. Baqui AH, Choi Y, Williams EK, Arifeen SE, Mannan I, et al. (2011) Levels, timing, and etiology of stillbirths in Sylhet district of Bangladesh. BMC Pregnancy Childbirth 11: 25.
13. National Institute of Population Research and Training, Mitra and Associates, ICF International (2016) Bangladesh Demographic and Health Survey 2014; Dhaka, Bangladesh and Rockville, Maryland, USA.
14. Local Health Bulletin 2018 for Dhobaura Upazila Health Complex (2018) Health Bulletin for Ministry of Health and Family Welfare: Mymensingh, Bangladesh.
15. Local Health Bulletin 2018 for Haluaghat Upazila Health Complex (2018) Health Bulletin for Ministry of Health and Family Welfare: Mymensingh, Bangladesh.
16. Baqui AH, Black RE, Arifeen SE (1998) Pneumonia mortality and healthcare utilization in young children in rural Bangladesh a prospective verbal autopsy. Bull World Health Organ 76: 161–171.
17. Rahman A, Giashuddin SM, Svanstrom L, Rahman F (2006) Drowning–a major but neglected child health problem in rural Bangladesh: implications for low income countries. Int J Inj Contr Saf Promot 13: 101–105.
18. Couper J (2002) Prevalence of childhood disability in rural KwaZulu-Natal. S Afr Med J 92: 549–552.
19. Cruz M, Jenkins R, Silberberg D (2006) The burden of brain disorders. Science 312: 53.
20. Khandaker G, Muhit M, Karim T, Smithers-Sheedy H, Novak I, et al. (2019) Epidemiology of cerebral palsy in Bangladesh: a population-based surveillance study. Dev Med Child Neurol 61: 601–609.
21. Murthy GV, Mactaggart I, Mohammad M, Islam J, Noe C (2014) Bangladesh, K.I.M.S.G. Assessing the prevalence of sensory and motor impairments in childhood in Bangladesh using key informants. Arch Dis Child 99: 1103–1108.
22. Mobarak R, Khan NZ, Munir S, Zaman SS, McConachie H (2000) Predictors of stress in mothers of children with cerebral palsy in Bangladesh. J Pediatr Psychol 25: 427–433.
23. Ones K, Yilmaz E, Cetinkaya B, Caglar N (2005) Assessment of the quality of life of mothers of children with cerebral palsy (primary caregivers). Neurorehabil Neural Repair 19: 232–237.
24. Mackey S, Murthy GV, Muhit MA, Islam JJ, Foster A (2012) Validation of the key informant method to identify children with disabilities: methods and results from a pilot study in Bangladesh. J Trop Pediatr 58: 269–274.
25. Khandaker G, Smithers-Sheedy H, Islam J, Alam M, Jung J, et al. (2015) Bangladesh Cerebral Palsy Register (BCPR): a pilot study to develop a national cerebral palsy (CP) register with surveillance of children for CP. BMC Neurol 15: 173.
26. Maloni PK, Despres ER, Habbous J, Primmer AR, Slatten JB, (2010) Perceptions of disability among mothers of children with disability in Bangladesh: implications for rehabilitation service delivery. Disabil Rehabil 32: 845–854.
27. McConachie H, Huq S, Munir S, Kamrunnahar, Akhter N et al. (2001) Difficulties for mothers in using an early intervention service for children with cerebral palsy in Bangladesh. Child Care Health Dev 27: 1–12.
28. Yousafzai AK, Lynch P, Gladstone M (2014) Moving beyond prevalence studies: screening and interventions for children with disabilities in low-income and middle-income countries. Arch Dis Child 99: 840–848.
29. Khandaker G, Muhit M, Rashid H, Khan A, Islam J, et al. (2014) Infectious causes of childhood disability: results from a pilot study in rural Bangladesh. J Trop Pediatr 60: 363–369.
30. Hoffman JI, Kaplan S (2002) The incidence of congenital heart disease. J Am Coll Cardiol 39: 1890–1900.

DOI: 10.31038/AWHC.2019253

Abstract

Maternal prenatal weight data can be used to estimate infant birth weight, connect weight concerns of the women before and after pregnancy, and prevent offspring obesity. Prenatal weight data can be interpreted in relation to many contexts, such as BMI, weight loss or gain, body fat, and patient perspectives. The primary goals for weight management during pregnancy include helping pregnant women achieve an optimal weight but to avoid excessive gestational weight gain. To prevent excessive gestational weight gain among pregnant women, healthcare providers can focus on utilizing integrated and inter professional collaborative team care approach, the 5A’s method for lifestyle counseling, and self-monitoring strategy to empower pregnant women for behavioral change. Prenatal weight management is important for a healthy pregnancy with short- and long-term health implications for the mother and the baby.

Keywords

Weight Gain, Weight Management, Excessive Gestational Weight Gain, 5a’s Method, Self-Monitoring, Obesity

There are many reasons for why prenatal weight management is needed. First, maternal prenatal weight gain can be used to predict the size of an infant. A previous population study, based on 2,438 pregnant women from four ethnic groups and with a viable singleton pregnancy, found an increased risk of giving birth to a baby of either small or large for gestational age among women with below or above the average of gestational weight gain [1]. Second, prenatal weight management is part of the life course weight control intervention for women. Overweight and obesity before pregnancy is strongly associated with excessive gestational weight gain during pregnancy [2]. For some women, concerns about body weight before pregnancy may continue or intensify throughout the pregnancy. Moreover, three-quarter of women retain, on average, 3.5 kg of pregnancy weight at 6 months postpartum and those who gain excessive weight during pregnancy are likely to retain weight after birth [3]. The third reason is that prenatal weight management may prevent intergenerational obesity in the offspring. Growing evidence has pointed to the link between unfavorable intrauterine environment (e.g., excessive gestational weight gain, maternal obesity, and over nutrition) and offspring excess adiposity and childhood obesity [4]. Prevention of childhood obesity can start in the fetal stage by avoiding maternal excessive gestational weight gain through maternal healthy lifestyle. Prenatal weight, therefore, has short- and long-term health implications for the mother and the baby. Healthcare providers should be cognizant of prenatal weight management.

Prenatal Weight and Objective and Subjective Reference Value

Body Mass Index

Body mass index (BMI) is the most commonly used reference value to define adults who are underweight (BMI < 18.5 kg/m²), normal weight (BMI = 18.5–24.9 kg/m²), overweight (BMI = 25–29.9 kg/m²), or obese (BMI ≥ 30 kg/m²) [5]. Assessing prepregnancy BMI is important because optimal gestational weight gain recommended for each woman is individualized and based on her prepregnancy BMI. For instance, gestational weight gain recommendations by the Institute of Medicine [6] are 38–40 lb, 25–35 lb, 15–25 lb, and 11–20 lb for women of underweight, normal weight, overweight, and obese, respectively.

Weight Change

Body weight can also be assessed in the context of change, such as weight loss or weight gain between two time points. In non-pregnant women with overweight or obesity, a weight loss of 2.5–5.5 kg in two or more years can reduce type 2 diabetes by 30% to 60% [7]. Weight loss, however, is not recommended for pregnant women. A weight gain of more than 40 pounds for a singleton pregnancy from the beginning to the end of pregnancy is not favorable regardless of pre-pregnancy BMI [6]. Prenatal weight change can also be evaluated weekly. For instance, based on the IOM weight gain recommendations, a woman with a prepregnancy BMI ≥ 30 kg/m² in the second and third trimester is to gain 0.4 to 0.6 lb per week. A recent study indicates that pregnant women not receiving weight gain advice from their healthcare providers are likely to gain inadequate or excessive weight during pregnancy [8]. Care providers should have a conversation with pregnant women regarding their optimal recommended gestational weight gain. Furthermore, accuracy of a weight scale may alter weight readings.

Stein et al. [9] found that about 21% of the 223 weight scales collected from primary care clinics, diabetology/endocrinology clinics, weight loss facilities, and fitness centers were inaccurate by more than 6.0 lb when the scales were tested using the 250.0 lb. (113.6 kg) test. Regular calibration of weight scales following manufacture instructions can increase precision of weight measures.

Body Fat

Another context to consider when evaluating body weight is body fat. About 23.5% of US adults with normal weight are metabolically abnormal (normal-weight obesity) and it is their body fat that makes them susceptible to developing a metabolic disorder [10]. Increased body weight from body fat, particularly from abdominal adiposity, is of concern. A non-pregnant woman with a waist circumference of 35 inches (88 cm) has an increased risk for cardiometabolic disease [11]. For pregnant women, there is no agreeable reference value for body fat. A previous study found that an increase of 0.14 kg per week in abdominal fat among pregnant women is associated with an odds ratio of 1.31 (95% CI: 1.10–1.56) for gestational diabetes [12]. There are many ways to measure body fat and other body compositions, such as dual-energy x-ray absorptiometry, bio-impedance analysis, computed tomography, and magnetic resonance imaging [13]. Some of these methods may be cumbersome, expensive, and impractical in practice but are objective measures often performed in research.

Patient Perspectives

A previous study found that body ‘fatness’ and ‘excess fat’ were not desirable terms perceived by study participants with obesity; instead, they preferred a neutral term such as ‘weight’ [14]. These findings suggest that care providers need to be skillful and sensitive during clinical encounters with pregnant women when addressing gestational weight gain issues. Assessing weight in the context of patient perspectives is essential. Body weight change may connote physical, emotional, and relationship issues. A pregnant woman may report difficulty in climbing staircases, walking longer distances, playing with her young children at length, or lying down to sleep due to the weight she gains during pregnancy. An increase in body weight may also make women feel dissatisfied with their bodies or not want to socialize with their friends. Subjective personal perspective about weight gain from pregnant women can also help healthcare providers create individualized care plans that are meaningful to their pregnant patients.

Prenatal Weight Management

The US Preventive Services Task Force [15] recommends clinicians offer or refer patients with a BMI of 30 kg/m² or higher to intensive, multicomponent behavioral interventions. This weight management recommendation is based on the evidence that obesity is a risk factor for many chronic diseases and that behavioral interventions can help people lose weight and therefore decrease disease risk. Weight management for pregnant women, however, is unique and challenging due to several reasons. First, currently there are no agreeable guidelines for weight loss during pregnancy. For pregnant women with a BMI of 30 kg/m² or higher, the American College of Obstetricians and Gynecologists or ACOG [16] suggests dietary control, exercise, and behavioral modifications be the primary weight management strategies. Second, one half of pregnant women gain excessive weight during pregnancy [2] and each woman’s optimal amount of weight gain is individualized based on her prepregnancy BMI. Prenatal weight management, therefore, should be discussed between all pregnant women and their healthcare providers and this demand may require modifications of practice for some care systems. Third, some providers may be reluctant to discuss with their patients weight management due to time constraint and lack of counseling knowledge and skills [17]. As reported in a previous study, patients wanted their primary care providers to be partners in their weight management efforts, but they recognized limitations of provider’s time and expertise [18].

To help pregnant women adhere to IOM weight gain recommendations and prevent excessive weight gain, care providers may consider an integrated approach as addressed in the ACOG [16] clinical guidelines. This approach begins before conception and through pregnancy and postpartum, especially for women with a BMI in the obesity category. As implied in this approach, care providers in various practice settings, such as primary care centers and in-patient units serving reproductive-age women in-between pregnancy, during pregnancy, and after pregnancy, should discuss weight management with the women.

Another approach is to encourage interprofessional collaboration to maximize support for the women from nurses, physicians, nutritionists, health coaches, and other professionals. A practice guide published by the National Blood, Heart, and Lung Institute [19] states that a practitioner is encouraged to involve other professionals in lifestyle counseling. In fact, primary care providers in a previous study reported they were comfortable to involve ancillary staff who had the skills to do lifestyle counseling for patients [17]. The 5 A’s model (ask, advise, assess, assist, and arrange), endorsed by the Agency for Healthcare Research and Quality [20] and originally developed for smoking cessation, is a useful guide for behavior counseling and can be used in prenatal weight and lifestyle counseling. For instance, Eaton et al. [21] used the 5A’s (address the agenda, assess, advise, assist, and arrange) to do nutrition counseling in primary care setting. Vallis et al. [22] provided detailed definitions for the 5 A’s (ask, assess, advice, agree, and assist) for obesity counseling and they added an “agree” step to invite clients to develop a workable, measurable, and achievable plan.

The American Association of Clinical Endocrinologists and the American College of Endocrinology clinical guidelines [23] suggest self-monitoring, goal setting, stress reduction, stimulus control, behavioral contracting, cognitive restructuring, and social support as behavioral change strategies. Among them, self-monitoring is the most used strategy and requires minimal skills of the women. It has also been found that self-monitoring strategy is the most effective strategy as compared to other strategies in promoting health eating and physical activity and postpartum weight loss [24, 25]. Care providers can empower pregnant women by encouraging them to use self-monitoring strategy to monitor body weight change and weight-related behavior, such as food intake and physical activity. Care providers and pregnant women can then look at self-monitored data and collectively decide ‘problem behaviors’ and propose potential solutions.

Summary

Weight gain in pregnancy is an indicator for fetal growth and infant birthweight. Prenatal weight assessment and management should be viewed as part of a life course intervention and as an opportunity to prevent potential obesity in the offspring and the mother. Due to its health implications in the mother and the baby, prenatal weight assessment and management should be incorporated in prenatal practice. Healthcare providers should aim to help pregnant women achieve optimal weight recommended by the Institute of Medicine and avoid excessive gestational weight gain. Healthcare providers can approach prenatal weight management using an integrated team care model, interprofessional collaborative practice, the 5 A’s method for lifestyle behavior counseling, and a self-monitoring strategy to identify problem areas in eating and physical activity, and solutions to modify unfavorable behaviors.

Acknowledgement

The author received no funding for this work. The author thanks Elisabeth Ferris-Rowe, MD, MS for her valuable input and Rosa Lindsey, MA for her assistance with manuscript editing.

References

1. Pugh SJ, Hinkle SN, Kim S, Albert PS (2018) Combined influence of gestational weight gain and estimated fetal weight on risk assessment for small-or large for-gestational -age birth weight. A prospective cohort study. J of Ultrasound Med 37: 935–940.
2. Branum AM, Sharma AJ, Deputy NP (2016) QuickStats. Gestational weight gain among women with full-term, singleton births, compared with recommendations – 48 States and the District of Columbia, 2015. MMWR 65: 1121.
3. Hollis JL, Crozier SR, Inskip HM, Cooper C (2017) Modifiable risk factors of maternal postpartum weight retention: An analysis of their combined impact and potential opportunities for prevention. Int J Obes (London) 41: 1091–1098.
4. Ma RCW, Popkin BM (2017) Intergenerational diabetes and obesity—A cycle to break? PLoS Med 14: e1002415.
5. World Health Organization (2019) BMI classification. [Internet] [2019 September 30]. Available: https://www.who.int/gho/ncd/risk_factors/bmi_text/en/
6. Institute of Medicine (2009) Weight gain during pregnancy: Reexamining the guidelines. Washington, DC: The National Academies Press. [Internet] [2019 September 30]. Available: http://nationalacademies.org/hmd/reports/2009/weight-gain-during-pregnancy-reexamining-the-guidelines.aspx
7. National Heart, Lung and Blood Institute (2013) Managing overweight and obesity in adults. Systematic evidence review from the expert panel [Internet]. [2019 September 30]. Available: https://www.nhlbi.nih.gov/sites/default/files/media/docs/obesity-evidence-review.pdf
8. Deputy NP,  Sharma AJ, Kim SY, Olson CK (2018) Achieving appropriate gestational weight gain: The role of healthcare provider advice. J Womens Health (Larchmt) 27: 552–560.
9. Stein RJ, Haddock CK, Poston WSC, Catanese D (2005) Precision in weighing: A comparison of scales found in physician offices, fitness centers, and weight loss centers. Public Health Rep 120: 266–270.
10. Rubin R (2018) What’s the best way to treat normal-weight people with metabolic abnormalities? JAMA 320: 223–225.
11. Klein S, Allison DB, Heymsfield SB, Kelley DE (2007) Waist circumference and cardiometabolic risk: A consensus statement from Shaping America’s Health: Association for Weight Management and Obesity Prevention; NAASO, the Obesity Society; the American Society for Nutrition; and the American Diabetes Association. Obesity 15: 1061–1067.
12. Sommer C, Mørkrid K, Jenum AK, Sletner L (2014) Weight gain, total fat gain and regional fat gain during pregnancy and the association with gestational diabetes: a population-based cohort study. Int J Obes 38: 76–81.
13. McCarthy MA, Strauss BJG, Walker SP, Pernezel M (2004) Determination of maternal body composition in pregnancy and its relevance to perinatal outcomes. Obstet Gynecol Surv 59: 731–742.
14. Volger S, Vetter ML, Dougherty M, Panigrahi E (2012) Patients’ preferred terms for describing their excess weight: Discussing obesity in clinical practice. Obesity (Silver Spring) 20: 147–150.
15. U.S. Preventive Services Task Force (2018) Weight Loss to Prevent Obesity-Related Morbidity and Mortality in Adults: Behavioral Interventions. [Internet] [2019 September 30]. Available: https://www.uspreventiveservicestaskforce.org/Page/Document/RecommendationStatementFinal/obesity-in-adults-interventions1
16. American College of Obstetricians and Gynecologists (2015) Obesity in Pregnancy. Practice Bulletin number 156. Obstet & Gynecol 126: 112–126.
17. Bennett WL, Gudzune KA, Appel LA, Clark JM (2013) Insights from the POWER practice-based weight loss trial: A focus group study on the PCP’s role in weight management. J Gen Intern Med 29: 50–58.
18. Bloom K, Adler J, Bridges C, Bernstein J (2018) Examining patient perspectives on weight management support in the primary care setting. J Primary Prevention 39: 387–399.
19. National Heart, Lung and Blood Institute (2010) The practical guide for identification, evaluation, and treatment of overweight and obese adults. NIH Publication Number 00-4084. [Internet] [2019 September 30].Available: Retried September 18, 2018 from https://www.nhlbi.nih.gov/files/docs/guidelines/prctgd_c.pdf
20. Agency for Healthcare Research and Quality (2012). Five Major Steps to Intervention (The “5 A’s”). [Internet] [2019 September 30]. Available:  https://www.ahrq.gov/prevention/guidelines/tobacco/5steps.html
21. Eaton CB, McBride PE, Gans KA, Underbakke GL (2003) Teaching Nutrition Skills to Primary Care Practitioners. J Nutr133: 563–566.
22. Vallis M, Piccinini-Vallis H, Sharma AM, Freedhoff Y (2013) Modified 5 as minimal intervention for obesity counseling in primary care. Can Fam Physician 59: 27–31.
23. Garvey WT, Mechanick JI, Brett EM, Garber AJ (2016) American Association of Clinical Endocrinologists and American College of endocrinology Comprehensive clinical practice guidelines for medical care of patient with obesity. Endocr Prac 22: 1–203.
24. Lim S, O’Reilly S, Behrens H, Skinner T (2015) Effective strategies for weight loss in post-partum women: a systematic review and meta-analysis. Obes Rev 16: 972–987.
25. Michie S, Abraham C, Whittington C, McAteer J (2009). Effective techniques in healthy eating and physical activity interventions: A meta-regression. Health Psychol 28: 690–701.