A significant positive correlation was also measured between total body weight gain during pregnancy and IBW (r = 0.32; p < 0.00). Although levels of food insecurity were low, food insecurity meant an undiversified diet among pregnant women and resulted in both inadequate and excessive weight gain during pregnancy.
- Aim of the study
- Research Objectives
- Research Hypotheses
- Study parameters
- Assumptions
- Definitions
- Abbreviations
- Summary
Optimal gestational weight gain (GWG) is associated with improved health, reduced maternal obesity, uncomplicated deliveries and increased survival and health of newborn infants (Ochsenbein-Ko¨lble, Roos, Gasser & Zimmermann 2007). Antenatal care: "Routine health check-up of presumed healthy pregnant women without symptoms (screening), in order to diagnose diseases or complicated obstetric conditions without symptoms, and to provide information about lifestyle, pregnancy and childbirth" ( Backe, Pay, Klovning & Sand 2015).
Background on the maternal and infant/child health in South Africa
One in three South African women experience a mental health problem during or soon after pregnancy (Field & Honikman 2015; PMHP 2010). One reason why mental illness is common among South African women is that many develop psychological stress during and after pregnancy (Field & Honikman 2015).
Malnutrition across the life span in relation to the UNICEF Conceptual
Root causes such as political, legal and cultural factors can limit the achievement of good nutrition. Vulnerable groups such as women and children need adequate care and support, access to good health services and living in a healthy and hygienic environment.
Nutrition during pregnancy
- Nutrient requirements and diet-related practices during pregnancy
- Nutrition related diseases and pregnancy
- HIV, nutrition and pregnancy
- Dietary diversity
- Dietary intake and nutritional status of pregnant women
The IOM recommends that pregnant women drink 2.4 liters (about 10 glasses) of fluids per day (IOM 2005). A South African study conducted in rural Limpopo in 2006 among black pregnant women reported the prevalence of GDM as 8.8% (Mamabolo et al 2006).
Maternal nutrition interventions
The dietary diversity of pregnant South African women has not been documented and will therefore be explored in more detail. The South African NDoH also provides calcium supplementation for pregnant women (Labuschagne et al 2012), as it is beneficial in preventing hypertensive disorders such as preeclampsia (WHO 2013).
Anthropometric status of pregnant women
- Maternal height and pre-gravid body mass index
- Maternal mid upper arm circumference
- Gestational weight gain
According to Drehmer, Duncan, Kac & Schmidt (2013), a high pre-pregnancy BMI and pre-pregnancy overweight/obesity is a risk factor for excessive weight gain. Pre-pregnancy obesity has been linked to numerous adverse pregnancy outcomes (Heude et al 2012; Tenenbaum-Gavish & Hod 2012; Cnattingius, Bergström, Lipworth & Kramer 1998), these include: GDM, pre-eclampsia, emergency caesarean section, PTB, LGA and SB (Heude et al 2012; Choi, Park & Shin 2011).
Household food security
- Household food security in South Africa
- Household food security and dietary intake and gestational weight gain
- Household food insecurity and depression in pregnant women
Another study reported that 32.9% of mothers suffered from depression, of those who were depressed, 67.1% were food insecure (Casey, Goolsby, Berkowitz, Frank, Cook, Cutts, Black, Zaldivar, Levenson, Heeren & Meyers 2004). In South Africa, there appears to be a lack of published research that has examined both household FI and depression in pregnant women.
Socio-economic status, food insecurity, dietary diversity and pregnancy
Antenatal Depression
- Global prevalence, signs and risk factors for antenatal depression
- Antenatal depression in South Africa
- Impact of antenatal depression on pregnant women and their offspring
Pregnant women who regularly use pain medication have four times the risk of antenatal depression (Alder et al 2011). HIV is a risk factor for the development of antenatal depression in pregnant women (Rochat, Tomlinson, Newell & Stein 2013; PMHP 2010).
Infant birth outcomes
- Maternal diet and infant birth outcomes
- Pre-gravid body mass index and infant birth weight
- Gestational weight gain and infant birth weight
- Household food insecurity and infant birth weight
- Prevalence/presence of depression and infant birth outcomes
Five percent of the calculated sample size (N = 255) of pregnant women were interviewed during the pilot study (n = 15). The fourth and final null hypothesis in the first chapter stated that, "the presence of antenatal depression will not result in insufficient/excessive weight gain in pregnant women when compared to IOM guidelines."
Conclusion
- Background on location of the study
- Study design
- Study population and sample selection
- Study population
- Sample selection
- Study methods and materials
- Research instruments
- Fieldworker recruitment
- Pilot study
- Data collection
- Variables included in the study, data capturing and statistical analysis
- Statistical analysis
- Data quality control
- Reduction of bias
- Ethical considerations
- Conclusion
Imbalenhle CHC is one of two CHCs that fall within the uMgungundlovu district and is located in the town of Imbali. During the interview, the weight and height of the participants were also measured. On the day of the interview, anthropometric measurements of weight and standing height were also determined.
At the end of the interview, the field worker calculated questionnaire scores to determine whether a participant needed a referral. An extensive theoretical background discussion of concepts included in the socio-demographic questionnaire ensured the reliability of the questionnaire. The electronic scale used to measure weight on the day of the interview was calibrated with a.
Socio-demographic results
This percentage consists of 19.2% of participants who went to college, 4.7% who went to a technical school and 7.6% who have university education. The majority of participants (73%) had regular household income not linked to a government grant. A large number of participants were the main breadwinners in their families (24%) followed by participants' mothers, spouses, participants' fathers, siblings, significant others, partners, in-laws and finally grandparents.
Approximately 51% of participants did not have access to a grant, while the remainder reported having access to a grant. The percentage of participants who did not have access to running water (8%) reported that they use water from rivers and streams. Approximately 8% of participants had previously delivered a full-term LBW infant, while 5% had delivered a preterm infant who also had an LBW.
Anthropometric results
- Total weight gain correlations
When comparing Tables 4.5 and 4.6, there was no significant difference in the height and BMI of the study population based on measurements taken by enrolled nursing staff and measurements taken by the field workers in the current study. About 26% of women had inadequate weight gain, 40% had adequate weight gain, and 34% had excessive weight gain during pregnancy. The frequency of participants with inadequate, adequate and excessive GWG in relation to BMI classification is shown in Table 4.7.
A large number of participants with an obese BMI before pregnancy also had excessive weight gain. There was also a significant positive correlation between pre-existing health conditions before pregnancy and overall weight gain (r = 0.29; p = 0.00). No significant correlations were observed between total weight gain and maternal age, household income, employment status, health status during pregnancy, pre-pregnancy BMI, DDS, HFIAS and EPDS.
Dietary diversity
- Dietary diversity and socio-demographic characteristics
- Dietary diversity and household food security
The most consumed food groups were grains/roots/tubers, fats and oils and meat/poultry/fish. The least consumed food groups were vitamin A-rich fruits and vegetables, eggs and legumes, and nuts. The DDS classification of food groups and their consumption in relation to various socio-demographic characteristics and health indicators is presented in table 4.10.
A chi-square test was performed to determine if there was a statistically significant difference between participant DDS and socio-demographic characteristics. No statistically significant association was found for most of the socio-demographic characteristics, with the exception of level of education (p-value = 0.00) and household income (p-value = 0.04). In table 4.11, the dietary diversity classification is presented in relation to the average HFIAS score.
Household food security status of pregnant women
- Household food security status and socio-demographic characteristics
- Household food security status and anthropometric status
A Chi-square test was also performed to determine the association between the HFIAS and the socio-demographic characteristics. The participants' food security status in the household in relation to anthropometric measurements is reported in table 4.14. A statistically significant difference could not be found in the mean participant anthropometric measurements under the HFIAS categories.
Levene's test for equality of variances and an independent samples t-test were performed to compare the HFIAS with the anthropometric measurements. It can therefore be concluded that the variances in HFIAS between the anthropometric measurements are equal. However, the t-test results of the independent samples indicate that there is no significant difference between the HFIAS scores and the individual anthropometric measurements (p-value > 0.05).
Presence of antenatal depression
- Presence of antenatal depression and socio-demographic characteristics
- Presence of antenatal depression and anthropometric measurements
- Presence of antenatal depression and participant dietary diversity
An independent t test was performed to compare EPDS scores with maternal anthropometric measurements. There was no statistically significant difference between the EPDS and anthropometric measurements, with the exception of delivery weight. The frequency of participants who are in the lower 50% EPDS category according to their HFIAS score classification and those who are in the upper 50% EPDS according to their HFIAS score classification is shown in Table 4.21.
Levene's test for equality of variance and an independent sample t test were performed to compare the EPDS scores with the HFIAS scores. It can therefore be concluded that the variances in EPDS scores between the HFIAS scores are equal. However, the independent samples t-test results indicate that there is no significant difference between the EPDS scores and the HFIAS scores (p-value > 0.05).
Infant birth outcomes
- Infant birth weight in relation to socio-demographic characteristics
- Infant birth weight and dietary diversity
- Infant birth weight and household food security
- Infant birth weight and presence of possible antenatal depression
The mean weight gain and mean IBW according to the mother's pre-pregnancy BMI classification are shown in Table 4.23. The table shows that all infants born to women with an underweight pre-pregnancy BMI had normal birth weight infants. There was no statistically significant association between IBW and maternal weight, height, BMI and MUAC before pregnancy (see Table 4.25).
The table indicates that the same percentage of participants with both low and high HFIAS scores had children with normal birth weight. The table indicates that the majority of participants with children of normal birth weight, LBW, PTB, PTB (normal weight), and LGA had lower EPDS scores. The majority of those with higher EPDS scores had children with normal birth weight, equal amounts of LBW, and less PTB, normal weight PTB, and LGA compared to participants with lower EPDS scores.
Conclusion
The majority of participants with low EPDS scores were food secure, while those at risk for FI or food insecure had higher EPDS scores. The majority of participants had sufficient weight gain during pregnancy according to WHO classification and IOM guidelines. Participants with overweight pre-pregnancy BMI and who generally had mildly excessive weight gain during pregnancy had the greatest proportion of LGA infants.
Most LBW babies are born to women with normal BMI and overweight before pregnancy. The average reported by IBW indicates that most babies had an ideal birth weight. However, a statistically positive association was found between maternal birth weight, total gestational weight gain and IBW.
- Household food security status of pregnant women
- The presence of antenatal depression among pregnant women
- Pattern of weight gain
- Infant birth weight
- Testing of Hypotheses
However, in the current study, none of the participants were enrolled in a nutritional supplementation program. The results of the current study showed no association between socio-demographic characteristics. These findings suggest that young maternal age was not a risk factor for antenatal depression in the current study sample.
In the case of the present study, almost half of the LBW infants were also PTB infants. Findings from the present study indicate that women with average DDS had more PTB. Food insecurity was not associated with LBW infants, but with normal birth weight and LGA infants in the present study.
Critique of study
- Study Limitations
- Recommendations for improvement of study
- Recommendations for nutrition practice
- Recommendations for policy
- Implications for further research
Household Food Insecurity Access Scale in isiZulu
2 Emasontweni amane adlule, kuke kwaba nesikhathi lapho wena noma elinye ilungu lomndeni beningakwazi ukudla uhlobo lokudla oluthandayo ngenxa yokuntula imali? 4 Ingabe wena noma elinye ilungu lomndeni saphoqeleka ukuba nidle ukudla emasontweni amane edlule? Sicela uphawule impendulo esondela kakhulu endleleni ozizwe ngayo EZULWINI EZILI-7, hhayi nje indlela ozizwa ngayo namuhla.
As much as I always can. Not so much now. Certainly not so much now. Not at all.
