Contextual risk factors for low birth weight: A multilevel analysis

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Study Justification:
The study aimed to identify contextual risk factors for low birth weight (LBW) in Ghana. LBW is a leading cause of neonatal death and contributes to infant and under-five mortality. Despite efforts to reduce LBW, its prevalence has not declined in sub-Saharan Africa (SSA) and Asia. This study aimed to fill the knowledge gap on contextual risk factors for LBW, particularly in SSA.
Highlights:
– The study utilized data from the 2003 and 2008 Demographic and Health Surveys in Ghana.
– Contextual risk factors for LBW were identified through multivariable multilevel logistic regression analysis.
– The study found that being a rural dweller increased the likelihood of having a LBW infant by 43%.
– Living in poverty-concentrated communities doubled the risk of having a LBW infant.
– Neighbourhoods with a high coverage of safe water supply reduced the odds of having a LBW infant by 28%.
– The study concluded that implementing appropriate community-based intervention programs could reduce the occurrence of LBW infants.
Recommendations:
– Implement community-based intervention programs targeting rural areas to reduce the prevalence of LBW infants.
– Address poverty concentration in communities to decrease the risk of LBW.
– Improve access to safe water supply in neighborhoods to reduce the likelihood of LBW.
Key Role Players:
– Health policymakers and government officials responsible for implementing interventions to reduce LBW.
– Community health workers and healthcare providers involved in delivering community-based interventions.
– Non-governmental organizations (NGOs) working in the field of maternal and child health.
Cost Items for Planning Recommendations:
– Funding for community-based intervention programs, including personnel salaries, training, and program implementation.
– Resources for poverty alleviation initiatives in targeted communities.
– Investment in improving water supply infrastructure in neighborhoods.
Please note that the cost items provided are general categories and not actual cost estimates. The specific costs will depend on the scale and scope of the interventions implemented.

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on a population-based study that utilized a combined dataset of the 2003 and 2008 Ghana Demographic and Health Survey (GDHS). The study used multivariable multilevel logistic regression analysis to identify contextual risk factors for low birth weight (LBW) in Ghana. The study included a large sample size of 6,900 mothers dwelling in 412 communities. The results showed that contextual-level factors such as rural dwelling, poverty-concentrated communities, and low coverage of safe water supply were significantly associated with LBW. The study provides specific odds ratios, confidence intervals, and p-values to support the findings. To improve the evidence, it would be helpful to include more information on the sampling techniques and procedures for the GDHS data collection, as well as details on the questionnaires used for data collection. Additionally, providing information on the response rate and any potential limitations of the study would further strengthen the evidence.

Background: Low birth weight (LBW) remains to be a leading cause of neonatal death and a major contributor to infant and under-five mortality. Its prevalence has not declined in the last decade in sub-Saharan Africa (SSA) and Asia. Some individual level factors have been identified as risk factors for LBW but knowledge is limited on contextual risk factors for LBW especially in SSA. Methods: Contextual risk factors for LBW in Ghana were identified by performing multivariable multilevel logistic regression analysis of 6,900 mothers dwelling in 412 communities that participated in the 2003 and 2008 Demographic and Health Surveys in Ghana. Results: Contextual-level factors were significantly associated with LBW: Being a rural dweller increased the likelihood of having a LBW infant by 43% (OR 1.43; 95% CI 1.01-2.01; P-value <0.05) while living in poverty-concentrated communities increased the risk of having a LBW infant twofold (OR 2.16; 95% CI 1.29-3.61; P-value <0.01). In neighbourhoods with a high coverage of safe water supply the odds of having a LBW infant reduced by 28% (OR 0.74; 95% CI 0.57-0.96; P-value <0.05). Conclusion: This study showed contextual risk factors to have independent effects on the prevalence of LBW infants. Being a rural dweller, living in a community with a high concentration of poverty and a low coverage of safe water supply were found to increase the prevalence of LBW infants. Implementing appropriate community-based intervention programmes will likely reduce the occurrence of LBW infants.

This is a population-based study that utilized a combined dataset of the 2003 and 2008 Ghana Demographic and Health Survey (GHDS) to identify contextual risk factors for LBW in Ghana. Comprehensive information on the sampling techniques and procedures for the GDHS data collection have been published elsewhere [37], [38]. Detailed information on all under-five children in the last five years was captured in both surveys and 12,474 households, 11,045 women and 10,114 men were identified for interviews. Face-to-face interviews were conducted for all women aged 15 to 49 years and men aged 15 to 59 years in the sampled households by use of questionnaires covering socioeconomic, demographic and health indicators. Mothers were asked to recall the birth weight of their infants or provide hospital cards to confirm it. In case they could neither recall the birth weight of their infants nor provide a hospital card, they were asked whether the birth weight of their babies was very big, bigger than average, average, smaller than average or very small. For the purpose of the current analysis we classified infants with a birth weight smaller than average and very small as LBW infants [37], [38]. We referred to the primary sampling unit (PSU) of the DHS data as a community. The impact of the community context on low birth weight was examined by considering place of residence (rural/urban), proportion of the community that were having access to healthcare and safe water coverage, and proportion of illiterate (those that can neither read nor write in any language) and those living in extreme poverty in the community (estimated asset index <20% poorest quintile) as contextual factors. Un-confounded effects of contextual risk factors on LBW were estimated after considering potential confounders based on epidemiological knowledge, prior studies, and the available information in the GDHS. Maternal age, parity, birth interval, unplanned pregnancy, ethnicity, anaemia in pregnancy, use of antenatal care, use of antimalarial or mosquito nets during pregnancy, smoking, body mass index, maternal education, occupation, wealth status and marital status were considered as potential confounders in the analysis. Marital status was classified as currently, formerly and never married. Maternal educational attainment was categorized into no education, primary, and secondary or higher education. The GDHS applied an asset-based approach to estimate household wealth status [39], similar to previous studies conducted [40], [41]. In the descriptive analyses, the characteristics of the study population were expressed in terms of numbers and percentages. The prevalences of LBW across the categories of the explanatory variables were estimated in terms of numbers and percentages. We applied a two-level multivariable multilevel logistic regression analysis, fitting three models different models. Model 1 (empty or null model) has no explanatory variable and we used it to decompose the total variance of LBW between the contextual and individual level. Model 2 contained the contextual-level factors and we extended this model to form model 3 by accommodating all the potential confounders (individual-level factors). Sensitivity analysis was conducted to assess whether the results of the analyses were consistent with the group of LBW infants classified to be of very small birth weight. This was necessitated by the potential risk of having a misclassified outcome by maternal self-report. Measures of association between the contextual risk factors and LBW were reported in terms of odds ratios (OR) with their P-values and 95% confidence interval (CI) after considering potential confounders. Random effects were expressed in terms of Area variance (AV), Median Odds Ratio (MOR) and Intra-Cluster Correlation (ICC)/Variance Partition Coefficient (VPC). The fitness of the model was assessed using Akaike Information Criterion (AIC) while Variance Inflation Factor(VIF) was used to check for multicollinarity in the model. Two-tailed Wald test at significance level of alpha equal to 5% was used to determine the statistical significance of the determinants and all the analyses were performed with StataSE 11 software package, StataCorp LP, Texas, United States. Ethical clearance to conduct GDHS was obtained from the Ethics Review Committee, Ghana Health Service, Accra, Ghana and the Ethics Committee of ICF Macro in Calverton, United States. GDHS data are public access data and were made available to us upon request by Measure DHS.

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

1. Community-based intervention programs: Implementing appropriate community-based intervention programs can help reduce the occurrence of low birth weight (LBW) infants. These programs can focus on providing education and support to pregnant women in rural areas, where the prevalence of LBW infants is higher.

2. Improving access to healthcare: Increasing access to healthcare facilities and services in rural areas can help address the risk factors associated with LBW. This can involve setting up mobile clinics or improving transportation infrastructure to ensure that pregnant women have access to prenatal care and medical assistance when needed.

3. Safe water supply initiatives: Since living in communities with a low coverage of safe water supply increases the risk of having a LBW infant, initiatives to improve access to safe water can be implemented. This can include building water treatment facilities, promoting hygiene practices, and providing clean water sources in rural areas.

4. Poverty alleviation programs: Living in poverty-concentrated communities has been found to increase the risk of LBW infants. Implementing poverty alleviation programs can help improve the overall living conditions and socioeconomic status of communities, thereby reducing the prevalence of LBW infants.

5. Maternal education and empowerment: Promoting maternal education and empowerment can have a positive impact on maternal health outcomes. Providing educational resources and support to pregnant women can help them make informed decisions about their health and improve their overall well-being during pregnancy.

6. Targeted antenatal care: Ensuring that pregnant women have access to quality antenatal care is crucial for identifying and managing risk factors for LBW. Implementing targeted antenatal care programs that specifically address the needs of women in rural areas can help improve maternal and neonatal health outcomes.

It is important to note that these are general recommendations based on the information provided. The specific implementation of these innovations would require further research, planning, and collaboration with relevant stakeholders in the field of maternal health.
AI Innovations Description
Based on the study “Contextual risk factors for low birth weight: A multilevel analysis,” the following recommendation can be developed into an innovation to improve access to maternal health:

1. Implement community-based intervention programs: The study found that being a rural dweller and living in a community with a high concentration of poverty increased the prevalence of low birth weight (LBW) infants. To address this, innovative community-based intervention programs can be developed and implemented. These programs should focus on providing accessible and affordable maternal healthcare services, including antenatal care, nutrition support, and education on healthy pregnancy practices.

2. Improve access to safe water supply: The study also found that living in neighborhoods with a high coverage of safe water supply reduced the odds of having a LBW infant. To improve access to safe water supply, innovative solutions such as water purification systems, community water treatment plants, or mobile water supply units can be implemented in rural and impoverished communities. These solutions should be sustainable, cost-effective, and easily accessible to ensure that pregnant women have access to clean and safe water for drinking, cooking, and hygiene purposes.

3. Enhance maternal education and awareness: The study identified maternal education as a potential confounder in the analysis. To address this, innovative approaches can be developed to enhance maternal education and awareness on the importance of antenatal care, nutrition, and healthy lifestyle choices during pregnancy. This can be achieved through the use of mobile health applications, community health workers, and targeted educational campaigns that provide accurate and culturally sensitive information to pregnant women.

4. Strengthen healthcare infrastructure in rural areas: Since being a rural dweller was found to increase the likelihood of having a LBW infant, it is crucial to strengthen healthcare infrastructure in rural areas. This can be done through the establishment of well-equipped and staffed maternal health clinics, mobile health units, and telemedicine services. Innovative solutions such as teleconsultations, remote monitoring devices, and mobile clinics can help bridge the gap in access to healthcare services for pregnant women in remote areas.

By implementing these recommendations as innovative solutions, access to maternal health can be improved, leading to a reduction in the prevalence of LBW infants and ultimately contributing to better maternal and child health outcomes.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations for improving access to maternal health:

1. Increase access to healthcare facilities: Improve the availability and accessibility of healthcare facilities, particularly in rural areas where the likelihood of having a low birth weight (LBW) infant is higher. This can be achieved by building more healthcare centers, deploying mobile clinics, and providing transportation services for pregnant women.

2. Enhance community-based intervention programs: Implement community-based programs that focus on educating and empowering pregnant women and their families about the importance of antenatal care, proper nutrition, and healthy lifestyle choices. These programs can also provide support and resources for women living in poverty-concentrated communities.

3. Improve safe water supply: Increase the coverage of safe water supply in communities to reduce the risk of LBW infants. This can be done through infrastructure development, such as building water treatment plants and improving water distribution systems.

4. Address socioeconomic factors: Implement measures to alleviate poverty and improve literacy rates in communities. This can include providing vocational training, microfinance programs, and educational initiatives to empower individuals and families.

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

1. Define the indicators: Identify key indicators that measure access to maternal health, such as the number of healthcare facilities per population, percentage of pregnant women receiving antenatal care, and percentage of LBW infants.

2. Collect baseline data: Gather data on the current status of the indicators in the target population. This can be done through surveys, interviews, and data collection from healthcare facilities and relevant government agencies.

3. Develop a simulation model: Create a simulation model that incorporates the identified recommendations and their potential impact on the indicators. This model should consider factors such as population size, geographic distribution, and existing infrastructure.

4. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to assess the impact of the recommendations. This can involve adjusting variables such as the number of healthcare facilities, coverage of safe water supply, and implementation of community-based programs.

5. Analyze results: Analyze the simulation results to determine the potential impact of the recommendations on improving access to maternal health. This can include assessing changes in the indicators and identifying any potential challenges or limitations.

6. Refine and validate the model: Refine the simulation model based on the analysis of the results and validate it using additional data and expert input. This can help ensure the accuracy and reliability of the simulation.

7. Communicate findings and make recommendations: Present the findings of the simulation study, including the potential impact of the recommendations on improving access to maternal health. Use this information to make evidence-based recommendations for policy and programmatic interventions.

It is important to note that the methodology described above is a general framework and may need to be adapted based on the specific context and available data.

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