Predictors of low birth weight and preterm birth in rural Uganda: Findings from a birth cohort study

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Study Justification:
The study aimed to identify predictors of low birth weight (LBW) and preterm birth in rural Uganda. This is important because LBW and preterm birth are associated with increased risks for neonatal mortality, morbidity, and long-term health issues. By understanding the predictors, interventions can be developed to reduce the rates of LBW and preterm birth, improving the health outcomes of infants in rural Uganda.
Study Highlights:
– The study found that 4.3% of infants in the study were born with LBW and 19.4% were born preterm.
– Maternal factors such as height, multigravida status, and adequate birth spacing were associated with lower odds of delivering a LBW infant.
– Maternal factors such as severe household food insecurity and malaria infection during pregnancy were associated with higher odds of delivering a LBW infant.
– Maternal factors such as residing in the Southwest region, being ≥20 years old, having adequate birth spacing, and attending ≥4 antenatal care visits were associated with lower odds of delivering a preterm infant.
– Maternal factors such as being unmarried or delivering at home were associated with higher odds of delivering a preterm infant.
– Severe household food insecurity, adolescent pregnancy, inadequate birth spacing, malaria infection, suboptimal antenatal care attendance, and home delivery were identified as modifiable risk factors for higher rates of LBW and/or preterm birth in rural Uganda.
Study Recommendations:
Based on the findings, the study recommends the following:
1. Interventions to address household food insecurity should be implemented to reduce the risk of LBW and preterm birth.
2. Programs targeting adolescent pregnancy should be developed to reduce the rates of LBW and preterm birth.
3. Education and awareness campaigns on the importance of adequate birth spacing should be conducted to promote healthier birth outcomes.
4. Efforts should be made to improve malaria prevention and treatment during pregnancy to reduce the risk of LBW.
5. Strategies to enhance antenatal care attendance, including increasing access and promoting the recommended number of visits, should be implemented to reduce the rates of preterm birth.
6. Initiatives to promote institutional deliveries and discourage home deliveries should be implemented to reduce the risk of preterm birth.
Key Role Players:
To address the recommendations, the following key role players are needed:
1. Government health departments and policymakers to develop and implement interventions and programs.
2. Healthcare providers to deliver appropriate antenatal care, including malaria prevention and treatment.
3. Community health workers to conduct education and awareness campaigns and provide support to pregnant women.
4. Non-governmental organizations (NGOs) and international agencies to provide funding and technical support for interventions.
5. Researchers and academics to conduct further studies and evaluate the effectiveness of interventions.
Cost Items for Planning Recommendations:
While the actual cost will depend on the specific interventions and programs implemented, the following cost items should be considered in planning:
1. Funding for the development and implementation of interventions, including personnel, training, and materials.
2. Costs associated with healthcare services, such as antenatal care visits, malaria prevention and treatment, and institutional deliveries.
3. Costs for education and awareness campaigns, including materials, community engagement, and training of community health workers.
4. Monitoring and evaluation costs to assess the impact and effectiveness of interventions.
5. Administrative and coordination costs for government departments, NGOs, and international agencies involved in implementing the recommendations.

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is rated 8 out of 10 because it provides detailed information about the study design, data collection methods, and statistical analysis. The abstract includes specific results and associations between various factors and low birth weight (LBW) and preterm birth. However, it does not mention the sample size or the statistical significance of the associations. To improve the evidence, the abstract could include the sample size and p-values for the associations found in the study.

Background Approximately 20.5 million infants were born weighing <2500 g (defined as low birthweight or LBW) in 2015, primarily in low- and middle-income countries (LMICs). Infants born LBW, including those born preterm (150 cm) (adjusted Odds Ratio (aOR) = 0.42 (95% CI = 0.24, 0.72)), multigravida (aOR = 0.62 (95% CI = 0.39, 0.97)), or with adequate birth spacing (>24 months) (aOR = 0.60 (95% CI = 0.39, 0.92)) had lower odds of delivering a LBW infant Mothers with severe household food insecurity (aOR = 1.84 (95% CI = 1.22, 2.79)) or who tested positive for malaria during pregnancy (aOR = 2.06 (95% CI = 1.10, 3.85)) had higher odds of delivering a LBW infant. In addition, in multivariable analysis, mothers who resided in the Southwest (aOR = 0.64 (95% CI = 0.54, 0.76)), were ≥20 years old (aOR = 0.76 (95% CI = 0.61, 0.94)), with adequate birth spacing (aOR = 0.76 (95% CI = 0.63, 0.93)), or attended ≥4 antenatal care (ANC) visits (aOR = 0.56 (95% CI = 0.47, 0.67)) had lower odds of delivering a preterm infant; mothers who were neither married nor cohabitating (aOR = 1.42 (95% CI = 1.00, 2.00)) or delivered at home (aOR = 1.25 (95% CI = 1.04, 1.51)) had higher odds. Conclusions In rural Uganda, severe household food insecurity, adolescent pregnancy, inadequate birth spacing, malaria infection, suboptimal ANC attendance, and home delivery represent modifiable risk factors associated with higher rates of LBW and/or preterm birth. Future studies on interventions to address these risk factors may be warranted.

Study approval was obtained from the Makerere University Research Ethics Committee at the School of Public Health in Kampala, Uganda; the Uganda National Council for Science and Technology in Kampala, Uganda; the Tufts Health Sciences Institutional Review Board in Boston, MA; and the Institutional Review Board at Harvard T.H. Chan School of Public Health, Boston, MA. Before enrollment into the study, written informed consent was obtained from all participants. The Uganda Birth Cohort Study (UBCS, {“type”:”clinical-trial”,”attrs”:{“text”:”NCT04233944″,”term_id”:”NCT04233944″}}NCT04233944) was a prospective birth cohort study conducted from 2014–2016 in 12 districts/16 sub-counties in rural northern and southwestern Uganda. The study, which enrolled 5,044 pregnant women, was designed to assess the impact of the Uganda Community Connector Program (UCCP), a five-year agriculture, livelihoods, and nutrition program funded by the United States Agency for International Development (USAID) which aimed to improve the nutritional status of women and children and the livelihoods of vulnerable populations in rural Uganda. The enrollment period for the UBCS lasted approximately 12 months. Eligible women, who were identified as pregnant from a urine pregnancy test administered by village health team (VHT) workers, were referred to study staff for enrollment into the main study. Following enrollment, which occurred primarily during the second or third trimester of pregnancy, mother-infant pairs were prospectively followed every three months until infants reached six or nine months of age. Data collected in the UBCS included information on demographics and household characteristics [e.g., water, sanitation and hygiene (WASH) practices, food security, agricultural production; and gender dynamics], maternal dietary intake and diversity, pregnancy history and outcomes, breastfeeding and complementary feeding, and infant morbidity and mortality. Maternal and infant anthropometry, including infant birth weight, were also collected. Pregnant women 15–49 years of age were eligible to participate in the UBCS if they planned to reside in the study area through the completion of follow-up and provided written informed consent. The inclusion, exclusion, referral, and termination criteria for the UBCS are presented in S1 Table. The target enrollment for the UBCS was 5,152 pregnant women (i.e., 322 pregnant women in each of the 16 participating sub-counties). This sample size allowed for a detection of a 0.14-unit difference in child length-for-age Z-score (LAZ) (the primary outcome variable of the parent study) with 80% power and a 0.05 level of significance, assuming 30% attrition between enrollment of pregnant women and delivery for reasons such as maternal death, fetal loss, household migration, temporary relocation of the mother for delivery, withdrawal, and loss to follow-up. Furthermore, it assumed 10% attrition among live births between delivery and completion of follow-up. Fig 1 shows the study profile for this analysis. In total, 5,044 women met the eligibility criteria and were enrolled into the UBCS. Of these, women were excluded from this analysis if they had a missing enrollment visit (n = 95) or a missing birth visit (n = 851). Furthermore, they were excluded if their infant was not born alive (n = 120) or if they had a multiple birth (n = 8). Women were excluded from the LBW analysis if birth weight of the newborn was not recorded within 72 hours (n = 633) after birth and from the preterm birth analysis if gestational age data were missing (n = 129). After exclusion criteria were applied, a total of 3,337 women were included in the LBW analysis and 3,841 in the preterm birth analysis. S2 Table presents the breakdown of enrollment by region, district, and sub-county for both the UBCS and this analysis. The UBCS questionnaires consisted of 13 modules which were programmed onto handheld Android devices using Open Data Kit (ODK) software. Trained enumerators conducted household visits every three months from the time of enrollment until the child reached six or nine months of age. With the exception of pregnancy and birth outcome characteristics, data for this analysis came from the UBCS questionnaire administered at the enrollment visit, which occurred during the second or third trimester of pregnancy. Household food security status was assessed using the Household Food Insecurity Access Scale (HFIAS) [15], a validated tool for use in populations across different cultural contexts, including in rural East Africa [16]. The HFIAS covers a recall period of 30 days and consists of two types of questions: nine “occurrence” and nine “frequency-of-occurrence” questions. The respondent is first asked if a given condition was experienced (yes/no) and, if it was, then with what frequency (rarely, sometimes, or often). The resulting responses can be transformed into either a continuous or categorical indicator of food security. Categorically, households are characterized into four distinct categories: food secure, mildly food insecure, moderately food insecure, or severely food insecure. Dietary diversity during pregnancy was assessed from dietary recall data collected using the Food and Agriculture Organization (FAO) Minimum Dietary Diversity for Women (MDD-W) index [17]. Scores were computed as the sum of 10 food groups (grains, white roots and tubers, and plantains; legumes; nuts and seeds; dairy; meats, poultry and fish; eggs; vitamin A rich dark green vegetables; other vitamin A rich fruits and vegetables; other vegetables; and, other fruits) based on whether or not they were consumed in the previous 24-hours. At the enrollment visit, tests for maternal malaria infection and hemoglobin status in pregnancy were conducted by trained nursing staff at participants’ households. Malaria infection was diagnosed using a rapid diagnostic test (RDT, SD Bioline Malaria Ag P.f/Pan test, Standard Diagnostics, Inc., Republic of Korea), and hemoglobin levels were measured using a portable hemoglobinometer (HemoCue 301; HemoCue America, Brea, CA, USA). Depending on the results, appropriate counseling, treatment, and/or referral to local health facilities were provided in accordance with UBCS standard operating procedure (SOP). Gestational age was calculated from the first day of mothers’ last menstrual period (LMP). Maternal height was measured to the nearest 0.1 cm using a portable height board (ShorrBoard® infant/child/adult portable height-length measuring board; Weigh and Measure, LLC, Olney, MD). Infant birth weight was measured within 72 hours to the nearest and 0.1 kg using an electronic scale (Seca model 874, Seca Corporation, Hanover, MD). In all anthropometric measures, triplicate measurements were averaged to provide a single measurement. For the purpose of this analysis, infants born <2,500 grams were considered LBW, and infants born <37 weeks gestation were considered preterm. Adolescent pregnancy was defined as 24 months [18] and adequate ANC care was defined as ≥4 visits per the previous four-visit ANC (FANC) model [19]. Prior to regression analyses, categorical summary statistics for household (location, household head sex, household head marital status, household head education, household food security, water source, UCCP participation), maternal (age, height, education, dietary diversity, gravida, birth spacing, ANC visits, deworming medication, iron tablets, hemoglobin, malaria status, HIV status), and infant (sex, location of delivery) characteristics were cross tabulated among LBW and non-LBW infants and among preterm and non-preterm infants. Bivariate logistic regression analyses were conducted to identify the relationship between independent household, maternal, and infant characteristics and the birth outcomes of interest (i.e., LBW and preterm birth). All variables in the bivariate analysis were considered for multivariable logistic regression analysis. Backward stepwise logistic regression models, which produced adjusted odds ratios (aORs), with a 0.05 cut-off for inclusion, were used to test for the predictors of LBW and preterm birth. All analyses were conducted using STATA 15 software (Stata Corps, College Station, TX, USA). In all cases, p<0.05 was considered statistically significant.

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

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide pregnant women with access to information and resources related to maternal health, including prenatal care, nutrition, and birth preparedness. These apps can also send reminders for antenatal visits and provide personalized health advice.

2. Telemedicine: Implement telemedicine programs that allow pregnant women in rural areas to consult with healthcare professionals remotely. This can help overcome geographical barriers and provide access to specialized care and advice.

3. Community Health Workers: Train and deploy community health workers in rural areas to provide education, support, and basic healthcare services to pregnant women. These workers can conduct regular check-ups, provide prenatal care, and refer women to higher-level healthcare facilities when necessary.

4. Mobile Clinics: Establish mobile clinics that travel to remote areas to provide maternal health services, including prenatal care, vaccinations, and health screenings. This can help reach women who have limited access to healthcare facilities.

5. Maternal Health Vouchers: Introduce voucher programs that provide pregnant women with financial assistance to access maternal health services. These vouchers can cover the cost of antenatal care, delivery, and postnatal care, making healthcare more affordable and accessible.

6. Health Education Programs: Develop and implement comprehensive health education programs that target pregnant women and their families. These programs can provide information on nutrition, hygiene, breastfeeding, and birth preparedness, empowering women to make informed decisions about their health.

7. Improved Transportation: Enhance transportation infrastructure in rural areas to ensure that pregnant women can easily access healthcare facilities. This can involve improving roads, providing transportation subsidies, or implementing emergency transportation systems for pregnant women in need of urgent care.

8. Maternal Health Hotlines: Establish toll-free hotlines staffed by healthcare professionals who can provide guidance, answer questions, and offer support to pregnant women. This can be particularly beneficial for women in remote areas who may have limited access to healthcare facilities.

9. Maternal Health Awareness Campaigns: Launch targeted awareness campaigns to educate communities about the importance of maternal health and the available resources and services. These campaigns can help reduce stigma, increase awareness of available care, and encourage women to seek timely and appropriate healthcare.

10. Public-Private Partnerships: Foster collaborations between government agencies, non-profit organizations, and private sector entities to improve access to maternal health services. These partnerships can leverage resources, expertise, and technology to develop innovative solutions and expand healthcare coverage in underserved areas.
AI Innovations Description
Based on the study findings, here is a recommendation that can be developed into an innovation to improve access to maternal health:

1. Implement targeted interventions to address modifiable risk factors: The study identified several modifiable risk factors associated with higher rates of low birth weight (LBW) and preterm birth in rural Uganda, including severe household food insecurity, adolescent pregnancy, inadequate birth spacing, malaria infection, suboptimal antenatal care (ANC) attendance, and home delivery. To improve access to maternal health, targeted interventions should be developed to address these risk factors. For example, programs can be implemented to improve household food security, provide education and support for adolescent mothers, promote family planning and birth spacing, increase access to malaria prevention and treatment during pregnancy, enhance ANC services, and encourage facility-based deliveries.

By addressing these specific risk factors, it is possible to reduce the incidence of LBW and preterm birth, leading to improved maternal and child health outcomes. These interventions can be implemented through collaborations between healthcare providers, community organizations, and government agencies, with a focus on reaching rural populations in Uganda. Monitoring and evaluation should be conducted to assess the effectiveness of these interventions and make necessary adjustments for continuous improvement.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Increase access to antenatal care (ANC): Promote and facilitate regular ANC visits for pregnant women, with a focus on ensuring that women attend at least four ANC visits. This can be achieved through community outreach programs, mobile clinics, and transportation support.

2. Improve maternal nutrition: Implement interventions to address household food insecurity and promote dietary diversity during pregnancy. This can include providing nutritional education, promoting local food production, and supporting income-generating activities to improve access to nutritious food.

3. Enhance malaria prevention and treatment: Strengthen efforts to prevent and treat malaria during pregnancy, as it is associated with higher odds of delivering a low birth weight infant. This can involve distributing insecticide-treated bed nets, providing antimalarial medication, and promoting awareness of preventive measures.

4. Promote birth spacing: Encourage adequate birth spacing (>24 months) between pregnancies to reduce the risk of low birth weight and preterm birth. This can be achieved through family planning services, counseling, and education on the benefits of birth spacing.

5. Improve access to skilled birth attendants: Increase access to skilled birth attendants, particularly for women who deliver at home. This can be done by training and deploying more midwives and other skilled health personnel, as well as improving transportation infrastructure to ensure timely access to healthcare facilities.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define indicators: Identify key indicators to measure the impact, such as the percentage of pregnant women attending at least four ANC visits, the percentage of women with adequate birth spacing, the prevalence of low birth weight and preterm birth, and the percentage of women delivering with skilled birth attendants.

2. Collect baseline data: Gather baseline data on the selected indicators from the target population. This can be done through surveys, interviews, and health facility records.

3. Implement interventions: Introduce the recommended interventions in the target population. This may involve implementing community-based programs, training healthcare providers, and improving infrastructure and resources.

4. Monitor and evaluate: Continuously monitor the implementation of interventions and collect data on the selected indicators. This can be done through regular surveys, health facility records, and monitoring systems.

5. Analyze data: Analyze the collected data to assess the impact of the interventions on the selected indicators. This can involve comparing the baseline data with the post-intervention data to determine any changes or improvements.

6. Interpret results: Interpret the results to understand the effectiveness of the interventions in improving access to maternal health. Identify any gaps or areas that require further attention or modification.

7. Refine interventions: Based on the findings, refine the interventions as needed to optimize their impact on improving access to maternal health.

8. Repeat the process: Continuously repeat the monitoring and evaluation process to assess the long-term impact of the interventions and make further improvements.

By following this methodology, it would be possible to simulate the impact of the recommended interventions on improving access to maternal health and identify effective strategies for addressing the identified risk factors.

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