Prenatal development in rural South Africa: Relationship between birth weight and access to fathers and grandparents

Population Studies, Volume 64, No. 3, Year 2010

Interpretation

Birth weight is an indicator of prenatal development associated with health in infancy and childhood, and may be affected by the family environment experienced by the mother during pregnancy. Using data from KwaZulu-Natal, South Africa, we explore the importance of the mother’s access to the father and grandparents of the child during pregnancy. Controlling for household socio-economic indicators and maternal characteristics, the survival and residence of the biological father with the mother are positively associated with birth weight. The type of relationship seems to matter: married women have the heaviest newborns, but co-residence with a non-marital partner is also associated with higher birth weight. Access to the maternal grandmother may also be important: women whose mothers are alive have heavier newborns, but no additional benefit is observed from residing together. Co-residence with any grandparent is not associated with birth weight after controlling for the mother’s partnership. © 2010 Population Investigation Committee. We used data from the Africa Centre Demographic Information System (ACDIS), maintained by the Africa Centre for Health & Population Studies (http://www.africacentre.ac.za/). ACDIS has been described elsewhere (Hosegood and Timaeus 2005; Tanser et al. 2007). During bi-annual visits, detailed demographic and health data are collected on all resident and non-resident members of households in the Umkhanyakude district of KwaZulu-Natal. Household membership is distinguished from residence in the homestead. In this report, when we refer to household and homestead we are referring to the household (group of individuals) and homestead (compound) with which an individual was most closely associated by membership and residence at the time of the child’s birth. At each household visit, information is collected about all current and recently ended pregnancies. For live births, information about birth weight and other health indicators is recorded from the clinic Road-to-Health card if available or from recall by a parent or a care-giver. Our sample consisted of 3,993 children born between 2000 and 2003. The following were excluded: (i) multiple births, which have different patterns of birth weight (Garite et al. 2004), (ii) children whose mothers were not resident in the study area at the time of the birth, because no information about family and household exposures at their residence outside the surveillance area was available from ACDIS, and (iii) children for whom valid information on birth weight was not available. Our sample represented about half of all births to resident women during the period. Information on birth weight was missing for 4,404 births, including 148 cases of biologically impossible birth weights that were re-coded to ‘missing’. An examination of potential selection bias showed that children from the wealthiest households, those who would survive infancy, and those whose mothers were younger or married were more likely to be included. The main reasons for birth weight not being available is that the information was not recorded on the health card, the card was not available, or the informant did not know the birth weight. This was often the case if the mother did not deliver with an attendant or did not take the newborn to a clinic soon after the birth. We found that the likelihood of birth weight being recorded was associated with the proximity of a clinic or hospital and the mother’s use of antenatal care. Sensitivity analyses showed that the results were robust to different specifications and to estimation with a selection correction. Additionally, the observed distribution of birth weights is similar to that reported by a clinic-based study with more complete data from the same population (Rollins et al. 2007). The first time a child or adult is registered by ACDIS, information is collected on his or her biological parents, including their survival status, household membership and residency, and whether the parent has also been registered in ACDIS. Where parents are members of the same household as their child, their ACDIS records are linked together. Parents’ survival status is also recorded for each child at routine visits. Thus even though only 30 per cent of births were linked to a father registered in ACDIS and 60 per cent to a maternal grandmother, information on father’s and grandmother’s survival and co-residence with the child’s mother was available for most children. Using the above information, we classified the status of fathers, maternal grandmothers, and mother’s partners as follows: ‘co-resident’ if he or she was a resident of the same homestead as the mother at the interview round closest to the child’s birth; ‘residing elsewhere’ if he or she was alive but not identified with the same homestead as the mother; and as ‘deceased’ if his or her death had been directly or indirectly reported in ACDIS. In addition, we combined information on the mother’s partnership status (married, no partner, etc.) with the partner’s (if applicable) household and homestead information. Thus, for non-marital partners, we distinguished between those who were members of the same household and, presumably, subject to the obligations and transactions entailed in membership, and those who were also co-resident and thus in frequent, even daily contact with other members. We also took into account access to the maternal grandfather and the paternal grandparents. Where mothers were not members of the same households as these relatives no additional information was available about the characteristics of the grandparent in ACDIS. Furthermore, if a child’s record was not linked to that of the father, we could not link to the father’s regular updates about his parents’ survival, and therefore would not have data on those who had died. Consequently, other grandparents could be classified only as ‘co-resident’ or ‘not co-residing’, which meant assuming that a grandfather or paternal grandmother could not provide substantial help to the mother unless he or she resided in the same homestead. One of the challenges of understanding the effects of social support is that it is intertwined with other characteristics (Portes 2000), such as social and economic status. Studies from the USA have shown that low socio-economic status is associated with risk of low birth weight (Rutter and Quine 1990; Parker et al. 1994; Rini et al. 1999). This may be because wealthier households can provide a healthier environment, including better nutrition and less poverty-induced stress for the mother. We used information on the resources owned by the household in 2000/2001, at the time of or shortly before the pregnancy, asindicators of household wealth (house construction materials, household amenities, ownership of commodities) and ranked households into quintiles according to their relative long-term wealth, using principal components analysis (PCA) (following Dunteman 1989; Filmer and Pritchett 2001). Mother’s level of education was included as it has been shown to correlate with child health, including birth weight (Warner 1998; Rini et al. 1999; Feldman et al. 2000). A variable that had not been previously explored but that seemed likely to be important in a highly mobile population was the frequency of periods away from the homestead. This was seen as an indicator of the mother’s exposure to the household environment, access to family support, and also indicative of access to sources of income. Because negative financial shocks and health shocks may cause maternal stress, which may affect foetal development (Hoffman and Hatch 1996), we included indicators of whether the household had recently experienced a major financial shock (job loss or major loss of property owing to theft, fire, or flood) and an indicator of whether the household had reported recent experience of a major health shock (death or serious illness). The first set of analyses focused on the association between birth weight and the survival and residency status of the child’s biological father and maternal grandmother. We used ordinary least squares (OLS) regressions with robust standard errors. The dependent variable was weight in grams, measured as a continuous variable: On the right-hand side, categorical variables indicate the mother’s access during pregnancy to her own mother (vg) and to the child’s biological father (tp), each coded as follows: deceased; alive but not co-residing with the mother (omitted category), and co-residing with the mother at the child’s birth. In models examining the role of partnership patterns, we re-coded tp to indicate the mother’s partnership arrangement at the child’s birth as follows: married to partner; co-resident with non-marital partner who is also a household member; non-marital partner is a member of the same household but resides elsewhere; non-marital partner neither resides with mother nor is a member of the same household; and no partner (omitted category). In models estimating the effects of access to the other grandparents, we re-coded vg to indicate co-residence as follows: co-residing with the mother at the child’s birth, not co-residing with the mother, including deceased (omitted category). We included bio-demographic variables known to affect birth weight: the mother’s age at birth, whether this was her first live birth, and the child’s sex. These are denoted as Wc. Similarly, Xm captures the socio-economic characteristics of the mother: education and whether she was regularly away from home overnight. Yh is a vector of household characteristics, including the wealth quintile, the indicator of any financial shocks, and the indicator of any health shocks. Since residents in some locations, because of disease environments or lack of access to resources, may be more prone to poor health, we included Zi, a vector of dummy variables for each of the 24 traditional administrative units called isigodi. Finally, we included a series of dummy variables indicating the child’s year of birth, Uy, to capture secular trends in birth weight. In additional models, we added interactions to test the importance of the family environment in particular circumstances. We investigated whether grandchildren of grandmothers eligible for State old-age pensions (those aged 60 years and older) had a higher birth weight than those with younger grandmothers. To test this, we added a dummy variable indicating whether the grandmother was of pensionable age. We also investigated whether the importance of pension was affected by co-residence. In another test, we investigated whether access to the grandmother was more beneficial for inexperienced (firsttime) mothers. Finally, we tested whether the role of family environment differed by wealth status. Because many observations did not have information on birth weight, we re-estimated all models as Heckman selection correction models. In these, the non-selection hazard was estimated in the first stage on all covariates used in the study plus two exclusion variables to test the effect of two influences on whether birth weight data were obtained or retained by the family. The exclusion variables were

AI Digest

Study Justification:

The study aims to investigate the relationship between birth weight and access to fathers and grandparents during pregnancy in rural South Africa. Birth weight is an important indicator of prenatal development and is associated with health outcomes in infancy and childhood. Understanding the family environment experienced by the mother during pregnancy can provide valuable insights into factors that may affect birth weight.

Highlights:

– The study found that the survival and residence of the biological father with the mother during pregnancy were positively associated with birth weight.
– Married women had the heaviest newborns, but co-residence with a non-marital partner was also associated with higher birth weight.
– Access to the maternal grandmother was also found to be important, with women whose mothers were alive having heavier newborns.
– Co-residence with any grandparent was not associated with birth weight after controlling for the mother’s partnership.

Recommendations:

– Policy makers should consider the importance of the family environment during pregnancy in promoting healthy birth weights.
– Programs and interventions should focus on supporting and strengthening relationships between fathers and mothers during pregnancy.
– Support systems for single mothers should be developed to ensure access to resources and support from extended family members, such as grandparents.
– Further research is needed to explore the specific mechanisms through which the family environment influences birth weight.

Key Role Players:

– Policy makers and government agencies responsible for maternal and child health programs.
– Healthcare providers, including doctors, nurses, and midwives, who can provide guidance and support to pregnant women.
– Community organizations and NGOs that can provide resources and support to families during pregnancy.
– Researchers and academics who can continue to study and evaluate the relationship between birth weight and the family environment.

Cost Items:

– Funding for research and data collection, including survey administration and data analysis.
– Development and implementation of programs and interventions to support healthy birth weights.
– Training and education for healthcare providers to ensure they have the knowledge and skills to support pregnant women.
– Resources and materials for community organizations and NGOs to provide support to families during pregnancy.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is relatively strong, but there are some areas for improvement. The study uses data from a well-established demographic information system and includes a large sample size. The authors control for various socio-economic indicators and maternal characteristics. However, the study relies on self-reported birth weight, which may introduce measurement error. Additionally, the abstract does not provide information on the statistical significance of the findings or the effect sizes. To improve the evidence, the authors could consider using more objective measures of birth weight and reporting the statistical significance and effect sizes of the associations.

Abstract

We used data from the Africa Centre Demographic Information System (ACDIS), maintained by the Africa Centre for Health & Population Studies (http://www.africacentre.ac.za/). ACDIS has been described elsewhere (Hosegood and Timaeus 2005; Tanser et al. 2007). During bi-annual visits, detailed demographic and health data are collected on all resident and non-resident members of households in the Umkhanyakude district of KwaZulu-Natal. Household membership is distinguished from residence in the homestead. In this report, when we refer to household and homestead we are referring to the household (group of individuals) and homestead (compound) with which an individual was most closely associated by membership and residence at the time of the child’s birth. At each household visit, information is collected about all current and recently ended pregnancies. For live births, information about birth weight and other health indicators is recorded from the clinic Road-to-Health card if available or from recall by a parent or a care-giver. Our sample consisted of 3,993 children born between 2000 and 2003. The following were excluded: (i) multiple births, which have different patterns of birth weight (Garite et al. 2004), (ii) children whose mothers were not resident in the study area at the time of the birth, because no information about family and household exposures at their residence outside the surveillance area was available from ACDIS, and (iii) children for whom valid information on birth weight was not available. Our sample represented about half of all births to resident women during the period. Information on birth weight was missing for 4,404 births, including 148 cases of biologically impossible birth weights that were re-coded to ‘missing’. An examination of potential selection bias showed that children from the wealthiest households, those who would survive infancy, and those whose mothers were younger or married were more likely to be included. The main reasons for birth weight not being available is that the information was not recorded on the health card, the card was not available, or the informant did not know the birth weight. This was often the case if the mother did not deliver with an attendant or did not take the newborn to a clinic soon after the birth. We found that the likelihood of birth weight being recorded was associated with the proximity of a clinic or hospital and the mother’s use of antenatal care. Sensitivity analyses showed that the results were robust to different specifications and to estimation with a selection correction. Additionally, the observed distribution of birth weights is similar to that reported by a clinic-based study with more complete data from the same population (Rollins et al. 2007). The first time a child or adult is registered by ACDIS, information is collected on his or her biological parents, including their survival status, household membership and residency, and whether the parent has also been registered in ACDIS. Where parents are members of the same household as their child, their ACDIS records are linked together. Parents’ survival status is also recorded for each child at routine visits. Thus even though only 30 per cent of births were linked to a father registered in ACDIS and 60 per cent to a maternal grandmother, information on father’s and grandmother’s survival and co-residence with the child’s mother was available for most children. Using the above information, we classified the status of fathers, maternal grandmothers, and mother’s partners as follows: ‘co-resident’ if he or she was a resident of the same homestead as the mother at the interview round closest to the child’s birth; ‘residing elsewhere’ if he or she was alive but not identified with the same homestead as the mother; and as ‘deceased’ if his or her death had been directly or indirectly reported in ACDIS. In addition, we combined information on the mother’s partnership status (married, no partner, etc.) with the partner’s (if applicable) household and homestead information. Thus, for non-marital partners, we distinguished between those who were members of the same household and, presumably, subject to the obligations and transactions entailed in membership, and those who were also co-resident and thus in frequent, even daily contact with other members. We also took into account access to the maternal grandfather and the paternal grandparents. Where mothers were not members of the same households as these relatives no additional information was available about the characteristics of the grandparent in ACDIS. Furthermore, if a child’s record was not linked to that of the father, we could not link to the father’s regular updates about his parents’ survival, and therefore would not have data on those who had died. Consequently, other grandparents could be classified only as ‘co-resident’ or ‘not co-residing’, which meant assuming that a grandfather or paternal grandmother could not provide substantial help to the mother unless he or she resided in the same homestead. One of the challenges of understanding the effects of social support is that it is intertwined with other characteristics (Portes 2000), such as social and economic status. Studies from the USA have shown that low socio-economic status is associated with risk of low birth weight (Rutter and Quine 1990; Parker et al. 1994; Rini et al. 1999). This may be because wealthier households can provide a healthier environment, including better nutrition and less poverty-induced stress for the mother. We used information on the resources owned by the household in 2000/2001, at the time of or shortly before the pregnancy, asindicators of household wealth (house construction materials, household amenities, ownership of commodities) and ranked households into quintiles according to their relative long-term wealth, using principal components analysis (PCA) (following Dunteman 1989; Filmer and Pritchett 2001). Mother’s level of education was included as it has been shown to correlate with child health, including birth weight (Warner 1998; Rini et al. 1999; Feldman et al. 2000). A variable that had not been previously explored but that seemed likely to be important in a highly mobile population was the frequency of periods away from the homestead. This was seen as an indicator of the mother’s exposure to the household environment, access to family support, and also indicative of access to sources of income. Because negative financial shocks and health shocks may cause maternal stress, which may affect foetal development (Hoffman and Hatch 1996), we included indicators of whether the household had recently experienced a major financial shock (job loss or major loss of property owing to theft, fire, or flood) and an indicator of whether the household had reported recent experience of a major health shock (death or serious illness). The first set of analyses focused on the association between birth weight and the survival and residency status of the child’s biological father and maternal grandmother. We used ordinary least squares (OLS) regressions with robust standard errors. The dependent variable was weight in grams, measured as a continuous variable: On the right-hand side, categorical variables indicate the mother’s access during pregnancy to her own mother (vg) and to the child’s biological father (tp), each coded as follows: deceased; alive but not co-residing with the mother (omitted category), and co-residing with the mother at the child’s birth. In models examining the role of partnership patterns, we re-coded tp to indicate the mother’s partnership arrangement at the child’s birth as follows: married to partner; co-resident with non-marital partner who is also a household member; non-marital partner is a member of the same household but resides elsewhere; non-marital partner neither resides with mother nor is a member of the same household; and no partner (omitted category). In models estimating the effects of access to the other grandparents, we re-coded vg to indicate co-residence as follows: co-residing with the mother at the child’s birth, not co-residing with the mother, including deceased (omitted category). We included bio-demographic variables known to affect birth weight: the mother’s age at birth, whether this was her first live birth, and the child’s sex. These are denoted as Wc. Similarly, Xm captures the socio-economic characteristics of the mother: education and whether she was regularly away from home overnight. Yh is a vector of household characteristics, including the wealth quintile, the indicator of any financial shocks, and the indicator of any health shocks. Since residents in some locations, because of disease environments or lack of access to resources, may be more prone to poor health, we included Zi, a vector of dummy variables for each of the 24 traditional administrative units called isigodi. Finally, we included a series of dummy variables indicating the child’s year of birth, Uy, to capture secular trends in birth weight. In additional models, we added interactions to test the importance of the family environment in particular circumstances. We investigated whether grandchildren of grandmothers eligible for State old-age pensions (those aged 60 years and older) had a higher birth weight than those with younger grandmothers. To test this, we added a dummy variable indicating whether the grandmother was of pensionable age. We also investigated whether the importance of pension was affected by co-residence. In another test, we investigated whether access to the grandmother was more beneficial for inexperienced (firsttime) mothers. Finally, we tested whether the role of family environment differed by wealth status. Because many observations did not have information on birth weight, we re-estimated all models as Heckman selection correction models. In these, the non-selection hazard was estimated in the first stage on all covariates used in the study plus two exclusion variables to test the effect of two influences on whether birth weight data were obtained or retained by the family. The exclusion variables were

Materials

N/A

Innovations

Based on the provided information, here are some potential innovations that could improve access to maternal health:

1. Mobile Health Clinics: Implementing mobile health clinics that can travel to rural areas in South Africa, providing prenatal care and health services to pregnant women who may not have easy access to healthcare facilities.

2. Telemedicine: Utilizing telemedicine technology to connect pregnant women in rural areas with healthcare professionals, allowing them to receive virtual prenatal consultations and guidance without having to travel long distances.

3. Community Health Workers: Training and deploying community health workers in rural areas to provide education, support, and basic prenatal care to pregnant women, bridging the gap between healthcare facilities and remote communities.

4. Maternal Health Vouchers: Introducing a voucher system that provides pregnant women in rural areas with financial assistance to access maternal health services, including prenatal care, delivery, and postnatal care.

5. Public Transportation Services: Improving public transportation infrastructure and services in rural areas to ensure that pregnant women have reliable and affordable transportation options to reach healthcare facilities for prenatal care and delivery.

6. Health Education Programs: Implementing comprehensive health education programs in rural communities, focusing on prenatal care, nutrition, and maternal health, to empower women with knowledge and resources to take care of themselves and their unborn babies.

7. Maternity Waiting Homes: Establishing maternity waiting homes near healthcare facilities in rural areas, providing a safe and comfortable place for pregnant women to stay during the final weeks of pregnancy, ensuring timely access to skilled birth attendants and emergency obstetric care.

8. Mobile Apps for Maternal Health: Developing mobile applications that provide pregnant women with information, reminders, and resources related to prenatal care, nutrition, and maternal health, helping them stay informed and engaged in their own healthcare.

9. Partnerships with Traditional Birth Attendants: Collaborating with traditional birth attendants in rural communities, training them in safe delivery practices and integrating them into the formal healthcare system, to ensure that pregnant women have access to skilled birth attendants even in remote areas.

10. Maternal Health Outreach Programs: Organizing regular outreach programs in rural communities, where healthcare professionals visit and provide prenatal care, health screenings, and education to pregnant women, increasing access to essential maternal health services.

AI Innovations Description

Based on the description provided, the recommendation to improve access to maternal health would be to prioritize and promote the involvement of fathers and grandparents during pregnancy. This can be done through various strategies, such as:

1. Education and awareness: Implement programs that educate expectant mothers and their families about the importance of father and grandparent involvement during pregnancy. This can include information on the positive impact on birth weight and overall maternal health.

2. Supportive policies: Advocate for policies that encourage and support fathers and grandparents to be actively involved in prenatal care. This can include flexible work arrangements, parental leave policies, and incentives for involvement in prenatal appointments and classes.

3. Community engagement: Establish community-based support groups or networks that provide a platform for fathers and grandparents to connect, share experiences, and learn from each other. This can help create a supportive environment and encourage active involvement.

4. Healthcare provider training: Provide training to healthcare providers on the importance of involving fathers and grandparents in maternal health care. This can include strategies for engaging and supporting them throughout the pregnancy journey.

5. Accessible resources: Develop and distribute informational resources, such as brochures or online materials, that highlight the benefits of father and grandparent involvement and provide practical tips for how they can support the expectant mother during pregnancy.

By implementing these recommendations, it is expected that access to maternal health will be improved, leading to better birth outcomes and overall maternal well-being.

AI Innovations Methodology

Based on the provided information, here are some potential recommendations for improving access to maternal health:

1. Strengthening antenatal care services: Enhance the availability and quality of antenatal care services, including regular check-ups, screenings, and education for pregnant women. This can help identify and address any potential health issues early on.

2. Mobile health (mHealth) interventions: Utilize mobile technology to provide maternal health information, reminders, and support to pregnant women, especially those in remote or underserved areas. This can help bridge the gap in access to healthcare services.

3. Community-based interventions: Implement community-based programs that focus on educating and empowering women and their families about maternal health. This can involve training local community health workers, organizing support groups, and promoting awareness campaigns.

4. Transportation support: Improve transportation infrastructure and provide transportation assistance to pregnant women, particularly in rural areas where access to healthcare facilities may be limited. This can help ensure timely access to prenatal care and emergency obstetric services.

5. Telemedicine services: Establish telemedicine services that allow pregnant women to consult with healthcare professionals remotely, reducing the need for physical travel to healthcare facilities. This can be particularly beneficial for women in remote or underserved areas.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Define the target population: Identify the specific population group or geographic area that will be the focus of the simulation.

2. Collect baseline data: Gather relevant data on the current state of maternal health access in the target population, including indicators such as maternal mortality rates, antenatal care coverage, and distance to healthcare facilities.

3. Define indicators: Determine the key indicators that will be used to measure the impact of the recommendations, such as changes in antenatal care utilization, reduction in maternal mortality rates, or improvements in birth outcomes.

4. Develop a simulation model: Create a simulation model that incorporates the various factors influencing access to maternal health, such as distance to healthcare facilities, availability of transportation, and availability of healthcare providers. The model should also incorporate the potential impact of the recommended interventions.

5. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to assess the potential impact of the recommendations. This can involve adjusting different variables, such as the coverage of antenatal care services or the availability of transportation support, to see how they affect the outcomes.

6. Analyze results: Analyze the simulation results to determine the potential impact of the recommendations on improving access to maternal health. This can involve comparing the outcomes of different scenarios and identifying the most effective interventions.

7. Refine and validate the model: Continuously refine and validate the simulation model based on real-world data and feedback from experts in the field. This will help ensure the accuracy and reliability of the simulation results.

8. Communicate findings: Present the findings of the simulation study to relevant stakeholders, such as policymakers, healthcare providers, and community organizations. This can help inform decision-making and prioritize interventions that have the greatest potential for improving access to maternal health.

Author

Dima Health

Statistics:

Citations: 22

Identifiers:

DOI: 10.1080/00324728.2010.510201

ISSN: 00324728

Research Areas:

Disparities, Environmental, Food Security, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

South Africa

Study Design:

Cross Sectional Study

Participants Gender:

Female