Association between household environmental conditions and nutritional status of women of childbearing age in Nigeria

PLoS ONE, Volume 15, No. 12 December, Year 2020

Interpretação

Maternal undernutrition remains a leading cause of morbidity and mortality in Nigeria. Yet, most interventional programmes are focused on infant and child nutrition outcomes and not on maternal nutrition-related outcomes. Evidence suggests that the integration of household environmental interventions into nutrition actions can make a difference in reducing the burden of maternal undernutrition. This study examined the influence of household environmental conditions (HHEC) on the nutritional status of women of childbearing age in Nigeria using secondary data from the 2013 Nigeria Demographic and Health Survey. The original sample of 38,948 women age 15–49 years was selected using multi-stage probability sampling. The sample for the current analysis was 23,344 after exclusion of women due to health status or provision of incomplete information. The dependent and main independent variables were undernutrition (defined as Body Mass Index below 18.5) and HHEC (generated from cooking fuel, toilet type, source of drinking water, and housing materials) respectively. Data were analysed using descriptive statistics, Chi-square, and logistic regression model at 5% level of significance. The prevalence of undernutrition among women living in houses with unimproved and improved HHEC was 17.2% and 7.2% respectively. The adjusted odds of undernutrition was significantly higher among women who lived in houses with unimproved HHEC (aOR = 2.02, C.I = 1.37–2.97, p <0.001). The odds of undernutrition are greater in young women (aOR = 2.38, C.I. = 1.88–3.00, p <0.001) compared to older, and those of lower wealth status (aOR = 2.14, CI = 1.69–2.71, p <0.001) compared to higher. Other predictors of undernutrition in women of reproductive age in Nigeria include the level of education, marital status, and working status. Living in a house with unimproved environmental conditions is a predictor of undernutrition in women. The integration of environmental and nutrition programmes could assist in addressing this burden in Nigeria. Ethical approval was obtained from the National Ethical Review Board of the Federal Ministry of Health before conducting this survey by the data originators (NHREC/2008/07). Written informed consent was obtained from women of reproductive age (15–49 years) at the point of data collection and were assured of the confidentiality and anonymity of the information provided. This cross-sectional design study involved analysis of secondary data collected on women during the 2013 Nigeria Demographic and Health Survey (NDHS). The survey was carried out to provide reliable information about maternal and child health and family planning services in the urban and rural areas, across the country’s six geographical zones, and each of the 36 states and the Federal Capital Territory. For this nationally representative sample survey that covered the entire population residing in non-institutional dwelling units in Nigeria, a multi-stage probability sampling approach was used to select the eligible respondents, who were women of reproductive age (15–49 years). The sampling frame used was the list of enumeration areas (EAs). The primary sampling unit (PSU), referred to as a cluster in the survey, was defined based on EAs and the sample was selected using a stratified three-stage cluster design consisting of 904 clusters, 372 in urban areas and 532 in rural areas. A representative sample of 40,680 households was selected for the survey [20]. The eligible women that were interviewed were either permanent residents of selected households or visitors that were present in the households on the night before the survey. The household and eligible women’s response rates for the 2013 NDHS were 99.0% and 97.6% respectively [20]. The Global Positioning System (GPS) receivers were used in calculating the coordinates of the sampled clusters. A fixed sample take of 45 households was selected in every urban and rural cluster through equal probability systematic sampling. The overall sample size was 38,948 and this number reduced to 23,344 when women with incomplete information on the variables that were used to generate the main independent variable (Household Environmental Conditions) and those who were either ‘breastfeeding’ or ‘pregnant’ at the period of the survey were excluded from the sample. Exclusion of these women was to reduce bias that may be introduced as a result of loss of weight due to breastfeeding, and weight gain or weight loss because of the fetus for pregnant women. In this survey, 12.1% (4710) and 25.4% (9909) were currently pregnant and currently breast-feeding respectively. Only 1.5% (600) did not have information on either their weight or height, thus making the computation of body mass index impossible and 0.9% (384) have missing information in one or more of the variables used for the generation of household environmental conditions. The characteristics of women excluded did not differ significantly from the remaining sample. We therefore regard the resulting sample as being representative of the general population. Anthropometry is a universally acceptable, inexpensive and non-invasive technique that is useful for the evaluation of physical growth and the nutritional status of individuals. The dependent variable was the Body Mass Index (BMI). It was used as a measure of nutritional status following the WHO stipulated guidelines [21]. This variable was created from the information on the weight and height of women. Weight was measured in kilogrammes by trained field workers using a calibrated weighing balance with a capacity of 200 kg to the nearest 0.1 kg. Height was measured in meter to the nearest 1 cm using a tape rule. Thus, the BMI was determined using the formula; BMI = Weight/(Height)2 [21]. The nutritional status was obtained as follows; Underweight if BMI<18.5kg/m2, Normal if 18.5kg/m2≤BMI<25.0kg/m2, Overweight if 25.0kg/m2≤BMI<30.0kg/m2 and Obesity if BMI≥30.0kg/m2 [21]. The analysis reported in this study was based on the underweight category. The main independent variable was the Household Environmental Conditions (HHEC). This variable was generated from information on; cooking fuel, toilet type, source of drinking water, and housing materials. The classification of the variable HHEC was based on the information found in the literature on the association between cooking fuel, toilet type, source of drinking water, housing materials, and undernutrition [9,15,22,23]. In the original questionnaire used for the study, respondents were asked to select the main type of; cooking fuel, toilet type, source of drinking water, and main floor material, main roof material, main wall material from the list of options in Table 1. The categories of environmental factors shown in Table 1 were adapted from the groupings of variables from the year 2013 Nigeria National Demographic Health Survey and the year 2010 WHO and UNICEF report on sanitation and drinking water [20,24]. Each woman was assessed based on the re-categorized score in each of HHEC, thus, the maximum score attainable is 6. Consequently, this overall score was disaggregated into 4 categories as: Other independent variables include; Age, place of residence, region of residence, level of education, household wealth index, ethnicity, work status, parity, marital status and sex of household head. The variable household wealth was created as a composite measure of a household's cumulative living standard using Principal Component Analysis (PCA). It was generated using data on a household’s ownership of selected assets as presented in the DHS guide [25–28]. However, some variables used in the creation of the original wealth index in DHS included the variables used in the calculation of household environmental conditions (source of drinking water, toilet facilities, flooring materials, wall materials, and roof materials). Therefore, such variables were excluded from the newly generated household wealth to avoid multi-collinearity effect on the emerging results from this study. Each household asset for which information is available was assigned a weight generated through PCA. Thereafter, the asset scores were standardized in relation to a standard normal distribution (μ = 0,σ2 = 1). Each household was assigned a standardized score for each asset, and the score differs subject to whether the household owned that particular asset or not. These scores are summed by households, and individuals are ranked according to the total score of the household in which they reside. The sample was divided into terciles of household wealth as poor, middle and rich. Due to the cluster design method used for sampling collection, the data was weighted before use. The weighting of the data becomes important in this situation in order to extrapolate and take into consideration other areas excluded in the clusters during the survey. Weights are adjustment factors applied to each case in tabulations to adjust for differences in probability of selection and interview between cases in a sample, due to cluster design used for the survey. During the survey, the sample was selected with unequal probability to expand the number of cases available (and hence reduce sample variability) for certain areas or subgroups for which statistics are needed. Also, the weighting of the data was done to adjust for the possible differences in response rates across the states of the federation. Sample weights were calculated to six decimals but are presented in the standard recode files without the decimal point. Therefore, for the data set analysed for this study, a weight variable was created by dividing the sampling weight by 1,000,000 before it was used to approximate the number of cases. In addition, because standard errors, confidence intervals and significance testing are required in our outputs, we took into consideration of the complex sample design by using cluster variable, stratification variable, and the weight variable to make adjustment as appropriate [27,29]. The data were analysed using descriptive statistics, Chi-square, and logistic regression model. Frequency distribution was used to present the data and Chi-square test was conducted to determine the association between HHEC and nutritional status. This type of association was also examined for other explanatory variables included in the study. Logistic regression was used due to the dichotomous nature of the dependent variable to identify the predictors of underweight among the subjects. At the level of multivariate analysis, five models were used to define the relationship between underweight and HHEC. SPSS version 20.0 and Microsoft Excel software was used for all analyses. The variables included in each of the five models are as follows: Model 1 is the bivariate model that examines the relationship between HHEC and underweight while model 2 is a multivariate model that involves the dependent variable (BMI), HHEC and demographic variables (parity and age). Model 3 included only the dependent variable, HHEC and economic (household wealth, work status in the past 12 months before the survey) while the variables included in Model 4 are the dependent variable, HHEC, and social (region, residence, education, ethnicity, marital status and sex of the household head) explanatory variables. In the last model, all variables found to be statistically significant at the bivariate level were included in the model to identify the important predictors of underweight (α = .05). The logistic regression model is of the form; Where ϴ is the proportion of women who are underweight and ξi are the regression parameters to be estimated with the exponential of ξ being the odds ratio and xij, are the explanatory variables. The data underlying the results presented in this study is a third-party data owned by the Demographic Health Survey (DHS) Program and is available to registered users from the MEASURE DHS website. Restrictions apply to the availability of the data which were used under license for the current study, and so are not publicly available. Data are however available upon reasonable request through https://dhsprogram.com/data/dataset_admin/login_main.cfm? Prospective users are expected to complete a user registration form in addition to providing a proposed project title and a 300 words (minimum) abstract describing how the user plan to use the DHS data. A link through which the requested data could be downloaded will be sent to the potential user within five days once approval has been granted. We the authors confirm that researchers can access the data set used in this study in the same manner as the authors and that the authors had no special access privileges to the data sets that other researchers would not have.

AI Digest

Study Justification:

– Maternal undernutrition is a significant cause of morbidity and mortality in Nigeria.
– Most intervention programs focus on infant and child nutrition outcomes, neglecting maternal nutrition-related outcomes.
– Integrating household environmental interventions into nutrition actions can help reduce the burden of maternal undernutrition.

Study Highlights:

– The study examined the influence of household environmental conditions (HHEC) on the nutritional status of women of childbearing age in Nigeria.
– Secondary data from the 2013 Nigeria Demographic and Health Survey was used.
– The sample size for analysis was 23,344 women aged 15-49 years.
– The prevalence of undernutrition was 17.2% among women living in houses with unimproved HHEC and 7.2% among those with improved HHEC.
– Women living in houses with unimproved HHEC had significantly higher odds of undernutrition.
– Other predictors of undernutrition included age, wealth status, level of education, marital status, and working status.

Study Recommendations:

– The integration of environmental and nutrition programs can help address the burden of undernutrition in women.
– Policy makers should prioritize interventions that improve household environmental conditions, such as access to clean cooking fuel, improved sanitation facilities, safe drinking water, and better housing materials.
– Programs should target young women and those with lower wealth status, as they are at higher risk of undernutrition.
– Education, marital status, and working status should also be considered in designing interventions.

Key Role Players:

– Ministry of Health: Responsible for implementing and coordinating interventions related to maternal and child nutrition.
– Ministry of Environment: Responsible for improving environmental conditions, such as access to clean cooking fuel, sanitation facilities, and safe drinking water.
– Non-governmental organizations (NGOs): Can provide support in implementing and monitoring interventions.
– Community leaders: Play a crucial role in mobilizing communities and promoting behavior change.

Cost Items for Planning Recommendations:

– Research and data collection: Includes costs for surveys, data analysis, and report writing.
– Intervention implementation: Includes costs for training, awareness campaigns, distribution of resources (clean cooking fuel, sanitation facilities, etc.), and monitoring.
– Capacity building: Includes costs for training health workers, community leaders, and volunteers.
– Evaluation and monitoring: Includes costs for assessing the impact and effectiveness of interventions.
– Advocacy and policy development: Includes costs for workshops, meetings, and policy formulation.
Note: The actual cost of implementing the recommendations will depend on various factors and should be determined through a detailed budgeting process.

Força da prova

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 large sample size and uses statistical analysis. However, to improve the evidence, the study could benefit from a randomized controlled trial design to establish a causal relationship between household environmental conditions and undernutrition. Additionally, including a control group and conducting follow-up assessments would provide more robust evidence.

Resumo

Ethical approval was obtained from the National Ethical Review Board of the Federal Ministry of Health before conducting this survey by the data originators (NHREC/2008/07). Written informed consent was obtained from women of reproductive age (15–49 years) at the point of data collection and were assured of the confidentiality and anonymity of the information provided. This cross-sectional design study involved analysis of secondary data collected on women during the 2013 Nigeria Demographic and Health Survey (NDHS). The survey was carried out to provide reliable information about maternal and child health and family planning services in the urban and rural areas, across the country’s six geographical zones, and each of the 36 states and the Federal Capital Territory. For this nationally representative sample survey that covered the entire population residing in non-institutional dwelling units in Nigeria, a multi-stage probability sampling approach was used to select the eligible respondents, who were women of reproductive age (15–49 years). The sampling frame used was the list of enumeration areas (EAs). The primary sampling unit (PSU), referred to as a cluster in the survey, was defined based on EAs and the sample was selected using a stratified three-stage cluster design consisting of 904 clusters, 372 in urban areas and 532 in rural areas. A representative sample of 40,680 households was selected for the survey [20]. The eligible women that were interviewed were either permanent residents of selected households or visitors that were present in the households on the night before the survey. The household and eligible women’s response rates for the 2013 NDHS were 99.0% and 97.6% respectively [20]. The Global Positioning System (GPS) receivers were used in calculating the coordinates of the sampled clusters. A fixed sample take of 45 households was selected in every urban and rural cluster through equal probability systematic sampling. The overall sample size was 38,948 and this number reduced to 23,344 when women with incomplete information on the variables that were used to generate the main independent variable (Household Environmental Conditions) and those who were either ‘breastfeeding’ or ‘pregnant’ at the period of the survey were excluded from the sample. Exclusion of these women was to reduce bias that may be introduced as a result of loss of weight due to breastfeeding, and weight gain or weight loss because of the fetus for pregnant women. In this survey, 12.1% (4710) and 25.4% (9909) were currently pregnant and currently breast-feeding respectively. Only 1.5% (600) did not have information on either their weight or height, thus making the computation of body mass index impossible and 0.9% (384) have missing information in one or more of the variables used for the generation of household environmental conditions. The characteristics of women excluded did not differ significantly from the remaining sample. We therefore regard the resulting sample as being representative of the general population. Anthropometry is a universally acceptable, inexpensive and non-invasive technique that is useful for the evaluation of physical growth and the nutritional status of individuals. The dependent variable was the Body Mass Index (BMI). It was used as a measure of nutritional status following the WHO stipulated guidelines [21]. This variable was created from the information on the weight and height of women. Weight was measured in kilogrammes by trained field workers using a calibrated weighing balance with a capacity of 200 kg to the nearest 0.1 kg. Height was measured in meter to the nearest 1 cm using a tape rule. Thus, the BMI was determined using the formula; BMI = Weight/(Height)2 [21]. The nutritional status was obtained as follows; Underweight if BMI<18.5kg/m2, Normal if 18.5kg/m2≤BMI<25.0kg/m2, Overweight if 25.0kg/m2≤BMI<30.0kg/m2 and Obesity if BMI≥30.0kg/m2 [21]. The analysis reported in this study was based on the underweight category. The main independent variable was the Household Environmental Conditions (HHEC). This variable was generated from information on; cooking fuel, toilet type, source of drinking water, and housing materials. The classification of the variable HHEC was based on the information found in the literature on the association between cooking fuel, toilet type, source of drinking water, housing materials, and undernutrition [9,15,22,23]. In the original questionnaire used for the study, respondents were asked to select the main type of; cooking fuel, toilet type, source of drinking water, and main floor material, main roof material, main wall material from the list of options in Table 1. The categories of environmental factors shown in Table 1 were adapted from the groupings of variables from the year 2013 Nigeria National Demographic Health Survey and the year 2010 WHO and UNICEF report on sanitation and drinking water [20,24]. Each woman was assessed based on the re-categorized score in each of HHEC, thus, the maximum score attainable is 6. Consequently, this overall score was disaggregated into 4 categories as: Other independent variables include; Age, place of residence, region of residence, level of education, household wealth index, ethnicity, work status, parity, marital status and sex of household head. The variable household wealth was created as a composite measure of a household's cumulative living standard using Principal Component Analysis (PCA). It was generated using data on a household’s ownership of selected assets as presented in the DHS guide [25–28]. However, some variables used in the creation of the original wealth index in DHS included the variables used in the calculation of household environmental conditions (source of drinking water, toilet facilities, flooring materials, wall materials, and roof materials). Therefore, such variables were excluded from the newly generated household wealth to avoid multi-collinearity effect on the emerging results from this study. Each household asset for which information is available was assigned a weight generated through PCA. Thereafter, the asset scores were standardized in relation to a standard normal distribution (μ = 0,σ2 = 1). Each household was assigned a standardized score for each asset, and the score differs subject to whether the household owned that particular asset or not. These scores are summed by households, and individuals are ranked according to the total score of the household in which they reside. The sample was divided into terciles of household wealth as poor, middle and rich. Due to the cluster design method used for sampling collection, the data was weighted before use. The weighting of the data becomes important in this situation in order to extrapolate and take into consideration other areas excluded in the clusters during the survey. Weights are adjustment factors applied to each case in tabulations to adjust for differences in probability of selection and interview between cases in a sample, due to cluster design used for the survey. During the survey, the sample was selected with unequal probability to expand the number of cases available (and hence reduce sample variability) for certain areas or subgroups for which statistics are needed. Also, the weighting of the data was done to adjust for the possible differences in response rates across the states of the federation. Sample weights were calculated to six decimals but are presented in the standard recode files without the decimal point. Therefore, for the data set analysed for this study, a weight variable was created by dividing the sampling weight by 1,000,000 before it was used to approximate the number of cases. In addition, because standard errors, confidence intervals and significance testing are required in our outputs, we took into consideration of the complex sample design by using cluster variable, stratification variable, and the weight variable to make adjustment as appropriate [27,29]. The data were analysed using descriptive statistics, Chi-square, and logistic regression model. Frequency distribution was used to present the data and Chi-square test was conducted to determine the association between HHEC and nutritional status. This type of association was also examined for other explanatory variables included in the study. Logistic regression was used due to the dichotomous nature of the dependent variable to identify the predictors of underweight among the subjects. At the level of multivariate analysis, five models were used to define the relationship between underweight and HHEC. SPSS version 20.0 and Microsoft Excel software was used for all analyses. The variables included in each of the five models are as follows: Model 1 is the bivariate model that examines the relationship between HHEC and underweight while model 2 is a multivariate model that involves the dependent variable (BMI), HHEC and demographic variables (parity and age). Model 3 included only the dependent variable, HHEC and economic (household wealth, work status in the past 12 months before the survey) while the variables included in Model 4 are the dependent variable, HHEC, and social (region, residence, education, ethnicity, marital status and sex of the household head) explanatory variables. In the last model, all variables found to be statistically significant at the bivariate level were included in the model to identify the important predictors of underweight (α = .05). The logistic regression model is of the form; Where ϴ is the proportion of women who are underweight and ξi are the regression parameters to be estimated with the exponential of ξ being the odds ratio and xij, are the explanatory variables. The data underlying the results presented in this study is a third-party data owned by the Demographic Health Survey (DHS) Program and is available to registered users from the MEASURE DHS website. Restrictions apply to the availability of the data which were used under license for the current study, and so are not publicly available. Data are however available upon reasonable request through https://dhsprogram.com/data/dataset_admin/login_main.cfm? Prospective users are expected to complete a user registration form in addition to providing a proposed project title and a 300 words (minimum) abstract describing how the user plan to use the DHS data. A link through which the requested data could be downloaded will be sent to the potential user within five days once approval has been granted. We the authors confirm that researchers can access the data set used in this study in the same manner as the authors and that the authors had no special access privileges to the data sets that other researchers would not have.

Materiais

N/A

Inovações

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

1. Integrated household environmental interventions: The study suggests that integrating household environmental interventions into nutrition actions can help reduce the burden of maternal undernutrition. This could involve implementing programs that focus on improving cooking fuel, toilet facilities, source of drinking water, and housing materials in households.

2. Targeted interventions for young women: The study found that the odds of undernutrition were higher among young women compared to older women. Implementing targeted interventions specifically designed for young women, such as nutrition education programs and access to nutritious food, can help improve their nutritional status.

3. Addressing socioeconomic factors: The study identified lower wealth status as a predictor of undernutrition. To improve access to maternal health, it is important to address socioeconomic factors that contribute to undernutrition. This could involve implementing income-generating programs, providing financial support for nutritious food, and improving access to healthcare services for women from lower socioeconomic backgrounds.

4. Education and awareness programs: The study found that the level of education was a predictor of undernutrition. Implementing education and awareness programs that focus on the importance of maternal nutrition and provide information on healthy eating habits can help improve the nutritional status of women of childbearing age.

5. Collaboration between environmental and nutrition programs: The study suggests that integrating environmental and nutrition programs could assist in addressing the burden of undernutrition. Collaborative efforts between government agencies, NGOs, and healthcare providers can help ensure a comprehensive approach to improving access to maternal health by addressing both environmental and nutritional factors.

It is important to note that these recommendations are based on the specific findings of the study mentioned and may need to be adapted to the local context and resources available.

AI Innovations Description

The recommendation to improve access to maternal health based on the study is to integrate household environmental interventions into nutrition actions. The study found that living in a house with unimproved environmental conditions is a predictor of undernutrition in women of childbearing age in Nigeria. Therefore, addressing household environmental conditions such as cooking fuel, toilet type, source of drinking water, and housing materials can make a difference in reducing the burden of maternal undernutrition.

By integrating environmental and nutrition programs, healthcare providers and policymakers can work together to improve access to maternal health services and promote better nutrition outcomes for women. This can include initiatives such as providing clean cooking fuel options, improving sanitation facilities, ensuring access to clean drinking water, and promoting better housing conditions.

Additionally, the study identified other predictors of undernutrition in women, such as age, level of education, marital status, and working status. These factors should also be taken into consideration when developing interventions to improve access to maternal health.

Overall, the integration of household environmental interventions into nutrition actions can help address the burden of maternal undernutrition in Nigeria and improve access to maternal health services.

AI Innovations Methodology

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

1. Integration of household environmental interventions: The study suggests that integrating household environmental interventions into nutrition actions can help reduce the burden of maternal undernutrition. This could involve implementing programs that focus on improving cooking fuel, toilet facilities, source of drinking water, and housing materials in households.

2. Education and awareness campaigns: Promoting awareness about the importance of household environmental conditions on maternal health could help improve access to maternal health. This could include educating women and their families about the impact of environmental factors on nutrition and providing information on how to improve these conditions.

3. Collaboration between health and environmental sectors: Strengthening collaboration between the health and environmental sectors can lead to more comprehensive approaches in addressing maternal undernutrition. This could involve joint planning, implementation, and monitoring of interventions that target both health and environmental factors.

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 that reflect access to maternal health, such as maternal mortality rates, prevalence of undernutrition, and utilization of maternal health services.

2. Collect baseline data: Gather data on the current status of the indicators in the target population. This could involve conducting surveys, interviews, or analyzing existing data sources.

3. Develop a simulation model: Create a simulation model that incorporates the identified recommendations and their potential impact on the selected indicators. This model should consider factors such as population size, demographic characteristics, and the specific interventions being implemented.

4. 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 on the selected indicators. This could involve adjusting variables related to the recommendations, such as the coverage of household environmental interventions or the reach of education and awareness campaigns.

5. Analyze results: Analyze the results of the simulations to determine the potential impact of the recommendations on improving access to maternal health. This could involve comparing the simulated outcomes with the baseline data and identifying any significant changes or improvements.

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

7. Communicate findings: Present the findings of the simulation study to relevant stakeholders, such as policymakers, healthcare providers, and community organizations. This will help inform decision-making and guide the implementation of interventions to improve access to maternal health.

It is important to note that the methodology for simulating the impact of recommendations may vary depending on the specific context and available data. The above steps provide a general framework that can be adapted and customized accordingly.

Autor

Dima Health

Statistics:

Citations: 3

Identifiers:

DOI: 10.1371/journal.pone.0243356

Research Areas:

Community Interventions, Environmental, Food Security, Health System and Policy, Maternal Access, Maternal and Child Health, Noncommunicable Diseases, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

Nigeria

Study Design:

Case-Control Study, Cohort Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Female

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