Association between Wood and Other Biomass Fuels and Risk of Low Birthweight in Uganda: A Cross-Sectional Analysis of 2016 Uganda Demographic and Health Survey Data

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Study Justification:
This study aimed to investigate the association between the use of wood and other biomass cooking fuels and the risk of low birthweight (LBW) in Uganda. Previous research has shown that in utero exposure to household air pollution from polluting cooking fuels can lead to adverse pregnancy outcomes, including LBW. However, no previous study in Uganda has specifically examined the association between different types of biomass cooking fuels and LBW. Therefore, this study fills a gap in the existing literature and provides valuable insights into the potential health risks associated with the use of these fuels.
Highlights:
– The study used data from the 2016 Uganda Demographic and Health Survey, which included information on 15,270 live births within five years prior to the survey.
– The prevalence of LBW in Uganda was found to be 9.6% of all live births.
– Although the crude analysis suggested an association between wood fuel use and LBW compared to other biomass and kerosene fuel use, after adjusting for sociodemographic and obstetric factors, no association was observed.
– LBW was found to be significantly more likely among female neonates and neonates born to mothers living in larger households.
– LBW was significantly less likely among neonates delivered at term, born to women with secondary or tertiary education, and living in households with a higher wealth index.
– The study findings suggest inconclusive evidence regarding the association between the use of wood compared to other biomass and kerosene cooking fuels and the risk of LBW.
Recommendations for Lay Reader and Policy Maker:
Based on the study findings, it is recommended that:
– The Ugandan government should prioritize public health actions that support female education and broader sustainable development to improve household living standards.
– Further research is needed to better understand the potential health risks associated with the use of different types of biomass cooking fuels and to inform policy interventions aimed at reducing household air pollution and improving maternal and child health outcomes.
Key Role Players:
– Ugandan government agencies responsible for public health and environmental protection
– Non-governmental organizations working on sustainable development and women’s education
– Health professionals and researchers specializing in maternal and child health
Cost Items for Planning Recommendations:
– Funding for research studies to investigate the health risks associated with different types of biomass cooking fuels
– Resources for public health campaigns and interventions aimed at promoting female education and sustainable development
– Investments in infrastructure and technologies that reduce household air pollution, such as cleaner cooking fuels and improved ventilation systems

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because the study used a large representative sample and employed multivariable logistic regression to analyze the association between wood and other biomass cooking fuel use and low birthweight. However, the study findings were inconclusive, as no significant association was observed after adjusting for sociodemographic and obstetric factors. To improve the evidence, future studies could consider collecting more detailed information on cooking practices and household air pollution exposure, as well as conducting longitudinal studies to establish causal relationships.

In utero exposure to household air pollution (HAP) from polluting cooking fuels has been linked to adverse pregnancy outcomes including low birthweight (LBW). No previous study in Uganda has attempted to investigate the association between the different types of biomass cooking fuels and LBW. This study was conducted to investigate the association between wood and other biomass cooking fuel use with increased risk of LBW, using the 2016 Uganda Demographic and Health Survey for 15,270 live births within five years prior to interview. LBW, defined as birthweight of <2500 g, was estimated from maternal recall and health cards. Association between household exposure to the different solid biomass cooking fuels and LBW was determined using multivariable logistic regression. Biomass cooking fuels were used in 99.6% of the households, with few (0.3%) using cleaner fuels and 0.1% with no cooking, while the prevalence of LBW was 9.6% of all live-births. Although the crude analysis suggested an association between wood fuel use and LBW compared to other biomass and kerosene fuel use (AOR: 0.82; 95% CI: 0.67–1.00), after adjusting for sociodemographic and obstetric factors, no association was observed (AOR: 0.94; 95% CI: 0.72–1.22). LBW was significantly more likely among female neonates (AOR: 1.32 (95% CI: 1.13–1.55) and neonates born to mothers living in larger households (AOR: 1.03; 95% CI: 1.00–1.07). LBW was significantly less likely among neonates delivered at term (AOR: 0.39; 95% CI: 0.31–0.49), born to women with secondary or tertiary level of education (AOR: 0.80; 95% CI: 0.64–1.00), living in households with a higher wealth index (AOR: 0.69; 95% CI: 0.50–0.96), Eastern (AOR: 0.76; 95% CI:0.59–0.98) and Northern (AOR: 0.75; 95% CI: 0.57–0.99) regions. The study findings suggest inconclusive evidence regarding the association between the use of wood compared to other biomass and kerosene cooking fuels and risk of LBW. Given the close observed association between socioeconomic status and LBW, the Ugandan government should prioritize public health actions which support female education and broader sustainable development to improve household living standards in this setting.

The UDHS (2016) is a cross-sectional population-based national dataset funded by the U.S. Agency for International Development, with the birth recode (a file produced by DHS where each observation is an individual birth within the last five years) and relevant variables from the individual recode being extracted for this study [28]. The birth recodes contained birth history data, while individual recode (each observation is every woman within the survey) provided information on socio-demographic and household characteristics [28]. A two-stage stratified sampling methodology was employed to randomly select a representative sample [28]. Any woman residing in the selected household of reproductive age (15–49 years) was interviewed and asked to report their birth history (including live and still births) for the five years preceding the survey. A total of 19,588 households and 18,506 women were surveyed [28], with response rates of 97% (18,506/19,088) for the individual (women’s) dataset and 67% (10,429/15,522) of the birth records respectively. Additional information regarding the UDHS has been described elsewhere [28]. Singleton live births, occurring at term (≥37 weeks gestation) and/or pre-term (<37 weeks gestation) which occurred in the last five years (2012–2016) from the time of interview were included in the study. Multiple births were excluded from the analysis because of the high risk of LBW among multiple pregnancies [31]. The wealth index provided by DHS is calculated through principal component analysis (PCA), including assets, toilet facility, drinking water sources, cooking fuel, and house construction as predictor variables [32], with the final variable containing wealth quintiles (lowest, low, middle, high, and highest). As cooking fuel was the exposure of interest within this study, the wealth index (categorized as low, second, middle, fourth, and higher) was recalculated using the methods provided by the DHS [33] in SPSS [34] to remove cooking fuel to prevent circularity [35]. Self-reported main household cooking fuel was categorized into cleaner fuels (LPG), electricity, biogas, and no cooking), and biomass and kerosene fuels (kerosene, coal/lignite, charcoal, wood, straw/shrubs/grass, agricultural crop, animal dung). Households, where cooking was not done, were classified as using cleaner fuel because it was assumed that HAP levels in these households would be comparable to households that used cleaner fuels. The exposure variable was the biomass or kerosene cooking fuels, categorized into wood and other biomass and kerosene fuels (kerosene, charcoal, straw/shrubs/grass, agricultural crop, animal dung). The outcome variable was LBW defined as a birthweight of less than 2500 g, obtained from either the health card (34%) or the maternal recall of child’s weight at birth (66%). Covariates were identified from the literature [21,36,37,38] as those potentially associated with HAP or LBW. The covariates from household and contextual characteristics included age of the household head, access to electricity, place of residence (urban, rural), geographical region (central, east, north, west), household smoking status (yes, no), place of cooking (in the main house, separate house, outdoors) and wealth index (low, second, middle, fourth, or highest). The 15 sub-regions in Uganda were categorized into four regions which included central, east, west, and north, which are defined based on ethnicity, poverty index, and geographical location [39]. Information from the respondents included age (15–19, 20–34, 35–49 years), level of education (no or primary education, secondary or tertiary). Pregnancy-related maternal covariates considered included parity (primigravida or multigravida), birth order (continuous variable), sex of the baby (male or female), and body mass index (BMI) (5 months), number of ANC visits (≥4 or <4), sulphadoxine-pyrimethamine (SP) (yes or no), birth interval (<24 months or ≥24 months), iron-folate supplementation (yes or no), deworming during pregnancy (yes or no), birth interval, based on WHO categorization, of less than 24 months or birth intervals of ≥24 months was used [40]. BMI, measured in kg/m2, was categorized as low when BMI was <18.5 or normal when BMI was ≥18.5 [41], as there is a higher risk of LBW for BMI of less than 18.5 [42,43]. Categorical variables were summarized using frequencies and proportions. Skewed continuous variables were summarized using the median and inter-quartile range (IQR), while normally distributed continuous variables were summarized using means and standard deviations. Bivariate and multivariate logistic regression, using survey commands to adjust for the complex sampling structure, was deployed to determine the association between exposure to wood and other forms of polluting fuels and LBW. The odds ratios (OR), 95% confidence intervals (95% CI), and p-values were reported. Clinically relevant variables, those with a p-value was less than 0.2, and variables without high levels of missing, in the bivariate analyses were included in the multivariate logistic regression model. Missing values were handled by case-wise deletion. Sensitivity analyses were undertaken to ensure robustness of study findings and to further investigate confounding factors (e.g., BMI) that could not be accounted for in the main analysis due to a large proportion of missing data (Figure 1). Further stratified analyses were undertaken according to residence (rural or urban), cooking location (indoor, outdoor), and maternal BMI (≤18.5). Multivariable linear regression model was performed using birthweight as a continuous variable. Stata software (version 16.1) [44] was used to analyze the data. Description of sensitivity analysis. USAID obtained ethical approvals from the relevant authorities in Uganda to collect the data. Permission was obtained from the USAID to gain access to the anonymized and aggregated freely available dataset from the DHS online data archive [45].

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

1. Clean Cooking Solutions: Develop and promote cleaner cooking technologies, such as improved cookstoves or alternative fuels, to reduce household air pollution and minimize the risk of adverse pregnancy outcomes like low birthweight.

2. Education and Awareness Campaigns: Implement educational programs and awareness campaigns to educate pregnant women and their families about the health risks associated with traditional biomass cooking fuels and the benefits of using cleaner alternatives.

3. Subsidies and Incentives: Provide financial incentives or subsidies to encourage households to switch to cleaner cooking fuels or technologies, making them more affordable and accessible to low-income families.

4. Infrastructure Development: Improve infrastructure in rural areas, such as access to electricity or piped gas, to enable households to transition to cleaner cooking fuels more easily.

5. Maternal Health Clinics: Establish and strengthen maternal health clinics in underserved areas, providing comprehensive prenatal care, including education on the importance of clean cooking practices and the risks of household air pollution.

6. Research and Data Collection: Conduct further research and data collection to better understand the specific health impacts of different biomass cooking fuels on maternal and child health outcomes, enabling more targeted interventions and policies.

7. Policy and Advocacy: Advocate for policy changes at the national level to prioritize maternal health and promote the use of cleaner cooking technologies, including regulations on the use of traditional biomass fuels.

It is important to note that these recommendations are based on the specific context of Uganda and the findings of the study mentioned. Implementing these innovations would require collaboration between government agencies, NGOs, and other stakeholders to ensure their effectiveness and sustainability.
AI Innovations Description
The study mentioned in the description explores the association between the use of wood and other biomass cooking fuels and the risk of low birthweight (LBW) in Uganda. The study used data from the 2016 Uganda Demographic and Health Survey (UDHS) and included 15,270 live births within five years prior to the survey.

The findings of the study suggest that there is no conclusive evidence of an association between the use of wood compared to other biomass and kerosene cooking fuels and the risk of LBW. However, the study did find other factors that were significantly associated with LBW. Female neonates, neonates born to mothers living in larger households, and neonates delivered at term were more likely to have LBW. On the other hand, neonates born to women with secondary or tertiary education, living in households with a higher wealth index, and residing in the Eastern and Northern regions of Uganda were less likely to have LBW.

Based on these findings, the study recommends that the Ugandan government prioritize public health actions that support female education and broader sustainable development to improve household living standards. These actions can help address the socioeconomic factors associated with LBW and potentially improve maternal and child health outcomes.

It is important to note that the study used cross-sectional data, which limits the ability to establish causality. Further research and interventions are needed to better understand and address the factors influencing LBW in Uganda and improve access to maternal health services.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations for improving access to maternal health:

1. Promote the use of cleaner cooking fuels: Encourage households to switch from biomass and kerosene fuels to cleaner alternatives such as LPG, electricity, or biogas. This can help reduce household air pollution and potentially improve pregnancy outcomes.

2. Increase awareness and education: Implement public health campaigns to raise awareness about the risks of household air pollution and the importance of using clean cooking fuels. Provide education on the benefits of cleaner fuels for maternal and child health.

3. Improve access to clean cooking technologies: Ensure that clean cooking technologies and fuels are readily available and affordable for households, especially in rural areas. This may involve subsidies, distribution programs, or partnerships with local organizations.

4. Strengthen antenatal care services: Enhance antenatal care services to include education on the risks of household air pollution and the promotion of clean cooking fuels. This can be done through training healthcare providers and integrating this information into existing maternal health programs.

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 specific indicators that can measure the impact of the recommendations, such as the percentage of households using clean cooking fuels, the reduction in low birthweight rates, or the increase in antenatal care attendance.

2. Collect baseline data: Gather data on the current status of access to maternal health and the use of cooking fuels in the target population. This can be done through surveys, interviews, or existing data sources.

3. Develop a simulation model: Create a simulation model that incorporates the identified indicators and their relationships. This model should consider factors such as population size, socio-economic characteristics, geographical distribution, and healthcare infrastructure.

4. Input the recommendations: Introduce the recommended interventions into the simulation model. This can be done by adjusting relevant variables, such as the percentage of households using clean cooking fuels or the coverage of antenatal care services.

5. Run the simulation: Execute the simulation model to project the potential impact of the recommendations over a specified time period. This can provide estimates of the expected changes in the selected indicators.

6. Analyze the results: Analyze the simulation results to assess the potential impact of the recommendations on improving access to maternal health. This can involve comparing the projected outcomes with the baseline data and identifying any significant changes or improvements.

7. Refine and validate the model: Validate the simulation model by comparing the projected outcomes with real-world data, if available. Refine the model as needed to improve its accuracy and reliability.

8. Communicate the findings: Present the simulation results in a clear and concise manner, highlighting the potential benefits of the recommendations for improving access to maternal health. This information can be used to inform policy decisions and guide the implementation of interventions.

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