Determinants of anaemia prevalence in women of reproductive age in Nigeria: A cross-sectional study using secondary data from Nigeria Demographic and Health Survey 2018

Women's Health, Volume 18, Year 2022

Interpretação

Background: Anaemia disproportionately affects women of reproductive age in sub-Saharan Africa including Nigeria. Yet, community-based studies on the prevalence and determinants of anaemia among women of reproductive age are scarce in Nigeria. Design: A cross-sectional community-based survey using a nationally representative sample. Objectives: This study described anaemia prevalence and its associated factors among women of reproductive age, pregnant women, and non-pregnant women in Nigeria. Methods: We analysed data from the 2018 Nigeria Demographic and Health Survey. Pregnant women with a haemoglobin level less than 11 g/dL and non-pregnant women with a haemoglobin level less than 12 g/dL were considered anaemic. Anaemia was also categorized as mild, moderate, and severe. Pearson’s chi-square test was used to evaluate the association between anaemia status and independent variables. All variables with ρ ⩽ 0.25 in bivariate analyses were further analysed using complex sample logistic regression. Results: Anaemia prevalence was 57.8%, 57.4%, and 61.1% for women of reproductive age, non-pregnant women, and pregnant women, respectively. The prevalence of severe anaemia was 1.6%, 1.5%, and 2.3% for overall women of reproductive age, non-pregnant women, and pregnant women, correspondingly. The southern regions, rural residence, low education, unemployment, low wealth index, and non-use of modern contraceptives significantly increased the likelihood of anaemia and severe anaemia among women of reproductive age and non-pregnant women. The likelihood of being anaemic was significantly increased by large family size among women of reproductive age and by being underweight among non-pregnant women. The South-East region, rural residence, low education, and unemployment were significantly associated with anaemia among pregnant women. The South-South region and unemployment increased the likelihood of severe anaemia among pregnant women. Short stature significantly reduced the odds of being anaemic and severely anaemic among pregnant women. Conclusions: Anaemia prevalence among all categories of women of reproductive age is high in Nigeria. Predictors of anaemia prevalence and severity should be considered in policies intended to reduce anaemia among women of reproductive age in Nigeria. Nigeria had an estimated population of 195,874,683 people and annual population growth of 2.62% in 2018.34 Nigeria comprises six geopolitical regions, 36 states, and one Federal Capital territory. Each state consists of local government areas (LGAs). Each LGA is composed of wards. Approximately 50.3% of the 2018 population was urban. WRA constituted around 46% of the population.34 This study used a quantitative, cross-sectional design by analysing data from the Nigeria Demographic and Health Survey (NDHS) 2018. The sampling frame consisted of households listed in Nigeria’s 2006 Population and Housing Census (NPHC). The primary sampling unit (PSU) consisted of a distinct group of enumeration areas (EAs) from the sampling frame referred to as a cluster. An EA is usually a clearly defined geographic area which groups several households together for population and housing census. A two-stage stratified sampling technique was used to select the households. Each of the 36 states and the Federal Capital Territory was stratified into urban and rural areas, creating 74 sampling strata. In the first stage, 1400 (580 urban and 820 rural) EAs were selected from the sampling strata with probability proportional to EA size. In the second stage selection, 30 households were selected from every cluster through equal probability systematic sampling, resulting in a total sample size of about 42,000 households (Figure 1). One-third of the total sample size of households (14,000) were selected for anaemia testing. Using an estimated proportion of WRA that are anaemic (P = 0.578), design effect (Deft = 1.434), relative standard error (α = 0.01), individual response rate (Ri = 97%), household gross response rate (Rh = 95%), and the number of eligible individuals per household (d = 1.032),35 the sample size in terms of the number of households (n) was calculated using the formula36 Flowchart for the sampling procedure. The survey was successfully carried out in 1389 clusters in 36 states and Federal Capital Territory comprising 747 LGAs from August to December 2018. Eleven clusters, with deteriorating law-and-order situations, were dropped during the fieldwork. To prevent bias, no replacements and no changes to the pre-selected households were allowed in the implementing stages. Anaemia testing was conducted for WRA in one-third of sampled households selected through equal probability systematic sampling from the total sample size of 42,000 households. The inclusion criteria were all WRA, either permanent residents or visitors who stayed in the sampled household the night before the survey. Women who did not agree to provide consent and women outside the age of 15–49 years were excluded. A blood sample from a finger prick site was drawn into a microcuvette, and a haemoglobin analysis was carried out on-site with a battery-operated portable HemoCue analyser (HemoCue Hb 301 system, Sweden). Anaemia status at the time of the survey is the dependent variable. Pregnant women with a haemoglobin level less than 11 g/dL and non-pregnant women with a haemoglobin level less than 12 g/dL were considered anaemic.35,37 Anaemia was categorized as mild (haemoglobin (Hb) of 10.0–10.9 g/dL for pregnant women and 11.0–11.9 g/dL for non-pregnant women), moderate (Hb of 7.0–9.9 g/dL for pregnant women and 8.0–10.9 g/dL for non-pregnant women), and severe (Hb < 7.0 g/dL for pregnant women and <8.0 g/dL for non-pregnant women). The anaemia status of respondents was also recoded into a binary variable as anaemia (mild, moderate, and severe) and no anaemia. Haemoglobin levels were adjusted for cigarette smoking and for the altitude in EAs that are above 1000 metres.38 The adjustment was made with the following formula: ‘adjust = –0.032 × alt + 0.022 × alt2’ and ‘adjHb = Hb – adjust (for adjust > 0)’ where ‘adjust’ is the amount of the adjustment, ‘alt’ is the altitude in 1000 feet (converted from metres by dividing by 1000 and multiplying by 3.3), ‘adjHb’ is the adjusted haemoglobin level, and ‘Hb’ is the measured haemoglobin level in grammes per decilitre. Regarding smoking adjustment, no adjustment for women who smoked less than 10 sticks per day, while the haemoglobin of women who smoked 10–19, 20–39, and 40 or more sticks of cigarette per day were adjusted by –0.3, –0.5, and –0.7 g/dL, correspondingly. The variables were grouped into individual maternal characteristics, socio-economic and household characteristics, and health service–related factors based on the conceptual framework for maternal anaemia determinants.2 The individual characteristics included the age of the respondent, marital status (never in a union, married/living with a partner, and divorced/separated/widowed), family size (<5 and ⩾5), sex of household head (female and male), ever had a termination of pregnancy (yes and no), breastfeeding status (yes and no), body mass index (BMI) (underweight, normal, overweight, and obese), and modern contraceptive use (yes and no). The total children ever born (0, 1, 2–4, and ⩾5) were regrouped into four categories of parity (nulliparity, primiparity, multiparity, and grand multiparity), correspondingly.39 BMI was converted from a numeric to a categorical variable based on the World Health Organization (WHO) BMI.35 As BMI is not appropriate for pregnant women, we used stature (height) for all categories of WRA categorized as short stature (<145 cm) and normal (⩾145 cm).35 The socio-economic and household characteristics included region (North-Central, North-East, North-West, South-East, South-South, and South-West), type of residence (urban and rural), highest education (no education, primary, secondary, and higher), employment (unemployed and employed), wealth index (poorest, poor, moderate, rich, richest), access to sanitation (unimproved and improved), the main source of drinking water (unimproved and improved), ownership of a mosquito bed net for sleeping (yes and no), respondent having slept under a mosquito bed net the night before the survey (yes and no), and media exposure (none and any form). Based on the consumption of 10 food groups in the 24 h preceding the survey, women were categorized into low (<5) and high diversity (⩾5) groups.35 The health service–related factor is the extent to which respondents considered the distance to a health facility as a problem (not a problem, not a big problem). Data were analysed using SPSS 20 (IBM Corp., Armonk, NY). We adjusted the data for sampling weights, stratification, and multistage sampling before analysis to account for the non-proportional allocation of the sample to the different states and provide representative population estimates. The basic characteristics of the respondents were presented using frequencies, population estimates, and percentages (weighted). Pearson’s chi-square test was used to evaluate the association between anaemia prevalence and independent variables. Multicollinearity was assessed using the variable inflation factor (VIF). The independent variables showed no multicollinearity (minimum VIF = 1.00, maximum VIF = 3.80). All variables with a p value ⩽ 0.25 in bivariate analyses were further analysed using multivariable complex samples logistic regression. In addition, we included age, stature, and parity in the model for pregnant women based on clinical significance. The results of regression analysis were presented by crude/unadjusted odds ratio (COR) and adjusted odds ratio (AOR) with 95% confidence intervals (CIs), F statistics, and p values. The McFadden test statistic for overall WRA, non-pregnant women, and pregnant women ranged from 0.02 to 0.04. Since values ranging from 0.2 to 0.4 indicate good model fit and values beyond 0.4 indicate excellent fit, our models might not be the best fit.40 However, McFadden test, a log-likelihood-based pseudo-R2 that represents the improvement in model likelihood over a null model, is influenced by sample size (the smaller the sample size, the higher the value), number of predictor variables, and number of categories of the dependent variable and its distribution asymmetry.40 Statistical significance for the multivariable complex sample logistic regression analyses was set at p < 0.05. The 2018 NDHS protocol was reviewed and approved by the National Health Research Ethics Committee of Nigeria (NHREC) and the ICF Institutional Review Board. Informed consent was obtained from participants before interviews or biomarker tests were conducted. Consequently, our study, being a secondary analysis, did not require further ethical approval.

AI Digest

Study Justification:

– Anaemia is a significant health issue among women of reproductive age in Nigeria.
– There is a lack of community-based studies on the prevalence and determinants of anaemia in this population.
– Understanding the factors associated with anaemia prevalence can inform policies and interventions to reduce anaemia among women of reproductive age in Nigeria.

Study Highlights:

– Anaemia prevalence was high among women of reproductive age, non-pregnant women, and pregnant women in Nigeria.
– The southern regions, rural residence, low education, unemployment, low wealth index, and non-use of modern contraceptives were associated with increased likelihood of anaemia and severe anaemia.
– Large family size and being underweight were also associated with increased likelihood of anaemia among women of reproductive age and non-pregnant women.
– The South-East region, rural residence, low education, and unemployment were associated with anaemia among pregnant women.
– The South-South region and unemployment increased the likelihood of severe anaemia among pregnant women.
– Short stature reduced the odds of being anaemic and severely anaemic among pregnant women.

Recommendations:

– Policies should prioritize reducing anaemia prevalence among women of reproductive age in Nigeria.
– Interventions should focus on improving education, employment opportunities, and access to modern contraceptives.
– Efforts should be made to address socio-economic disparities and promote healthy nutrition and weight management.
– Targeted interventions should be implemented in regions with high anaemia prevalence, such as the South-East and South-South regions.
– Attention should be given to the specific needs of pregnant women, including access to healthcare services and addressing risk factors for severe anaemia.

Key Role Players:

– Ministry of Health
– National Health Research Ethics Committee of Nigeria
– ICF Institutional Review Board
– Researchers and data analysts
– Healthcare providers and facilities
– Community health workers
– Non-governmental organizations (NGOs) working on women’s health

Cost Items for Planning Recommendations:

– Research and data analysis costs
– Training and capacity building for healthcare providers and community health workers
– Development and implementation of educational programs
– Access to healthcare services, including antenatal care and family planning services
– Distribution of mosquito bed nets
– Nutrition programs and interventions
– Monitoring and evaluation of interventions
– Advocacy and awareness campaigns

Força da prova

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, as it is based on a cross-sectional community-based survey using a nationally representative sample. The study analyzed data from the 2018 Nigeria Demographic and Health Survey, which provides a comprehensive dataset. The prevalence of anaemia among women of reproductive age, pregnant women, and non-pregnant women is described, and the associated factors are identified. The statistical analysis includes chi-square tests and complex sample logistic regression. To improve the evidence, the study could have included more detailed information on the sampling methodology and the specific variables used in the regression analysis.

Resumo

Nigeria had an estimated population of 195,874,683 people and annual population growth of 2.62% in 2018.34 Nigeria comprises six geopolitical regions, 36 states, and one Federal Capital territory. Each state consists of local government areas (LGAs). Each LGA is composed of wards. Approximately 50.3% of the 2018 population was urban. WRA constituted around 46% of the population.34 This study used a quantitative, cross-sectional design by analysing data from the Nigeria Demographic and Health Survey (NDHS) 2018. The sampling frame consisted of households listed in Nigeria’s 2006 Population and Housing Census (NPHC). The primary sampling unit (PSU) consisted of a distinct group of enumeration areas (EAs) from the sampling frame referred to as a cluster. An EA is usually a clearly defined geographic area which groups several households together for population and housing census. A two-stage stratified sampling technique was used to select the households. Each of the 36 states and the Federal Capital Territory was stratified into urban and rural areas, creating 74 sampling strata. In the first stage, 1400 (580 urban and 820 rural) EAs were selected from the sampling strata with probability proportional to EA size. In the second stage selection, 30 households were selected from every cluster through equal probability systematic sampling, resulting in a total sample size of about 42,000 households (Figure 1). One-third of the total sample size of households (14,000) were selected for anaemia testing. Using an estimated proportion of WRA that are anaemic (P = 0.578), design effect (Deft = 1.434), relative standard error (α = 0.01), individual response rate (Ri = 97%), household gross response rate (Rh = 95%), and the number of eligible individuals per household (d = 1.032),35 the sample size in terms of the number of households (n) was calculated using the formula36 Flowchart for the sampling procedure. The survey was successfully carried out in 1389 clusters in 36 states and Federal Capital Territory comprising 747 LGAs from August to December 2018. Eleven clusters, with deteriorating law-and-order situations, were dropped during the fieldwork. To prevent bias, no replacements and no changes to the pre-selected households were allowed in the implementing stages. Anaemia testing was conducted for WRA in one-third of sampled households selected through equal probability systematic sampling from the total sample size of 42,000 households. The inclusion criteria were all WRA, either permanent residents or visitors who stayed in the sampled household the night before the survey. Women who did not agree to provide consent and women outside the age of 15–49 years were excluded. A blood sample from a finger prick site was drawn into a microcuvette, and a haemoglobin analysis was carried out on-site with a battery-operated portable HemoCue analyser (HemoCue Hb 301 system, Sweden). Anaemia status at the time of the survey is the dependent variable. Pregnant women with a haemoglobin level less than 11 g/dL and non-pregnant women with a haemoglobin level less than 12 g/dL were considered anaemic.35,37 Anaemia was categorized as mild (haemoglobin (Hb) of 10.0–10.9 g/dL for pregnant women and 11.0–11.9 g/dL for non-pregnant women), moderate (Hb of 7.0–9.9 g/dL for pregnant women and 8.0–10.9 g/dL for non-pregnant women), and severe (Hb < 7.0 g/dL for pregnant women and 0)’ where ‘adjust’ is the amount of the adjustment, ‘alt’ is the altitude in 1000 feet (converted from metres by dividing by 1000 and multiplying by 3.3), ‘adjHb’ is the adjusted haemoglobin level, and ‘Hb’ is the measured haemoglobin level in grammes per decilitre. Regarding smoking adjustment, no adjustment for women who smoked less than 10 sticks per day, while the haemoglobin of women who smoked 10–19, 20–39, and 40 or more sticks of cigarette per day were adjusted by –0.3, –0.5, and –0.7 g/dL, correspondingly. The variables were grouped into individual maternal characteristics, socio-economic and household characteristics, and health service–related factors based on the conceptual framework for maternal anaemia determinants.2 The individual characteristics included the age of the respondent, marital status (never in a union, married/living with a partner, and divorced/separated/widowed), family size (<5 and ⩾5), sex of household head (female and male), ever had a termination of pregnancy (yes and no), breastfeeding status (yes and no), body mass index (BMI) (underweight, normal, overweight, and obese), and modern contraceptive use (yes and no). The total children ever born (0, 1, 2–4, and ⩾5) were regrouped into four categories of parity (nulliparity, primiparity, multiparity, and grand multiparity), correspondingly.39 BMI was converted from a numeric to a categorical variable based on the World Health Organization (WHO) BMI.35 As BMI is not appropriate for pregnant women, we used stature (height) for all categories of WRA categorized as short stature (<145 cm) and normal (⩾145 cm).35 The socio-economic and household characteristics included region (North-Central, North-East, North-West, South-East, South-South, and South-West), type of residence (urban and rural), highest education (no education, primary, secondary, and higher), employment (unemployed and employed), wealth index (poorest, poor, moderate, rich, richest), access to sanitation (unimproved and improved), the main source of drinking water (unimproved and improved), ownership of a mosquito bed net for sleeping (yes and no), respondent having slept under a mosquito bed net the night before the survey (yes and no), and media exposure (none and any form). Based on the consumption of 10 food groups in the 24 h preceding the survey, women were categorized into low (<5) and high diversity (⩾5) groups.35 The health service–related factor is the extent to which respondents considered the distance to a health facility as a problem (not a problem, not a big problem). Data were analysed using SPSS 20 (IBM Corp., Armonk, NY). We adjusted the data for sampling weights, stratification, and multistage sampling before analysis to account for the non-proportional allocation of the sample to the different states and provide representative population estimates. The basic characteristics of the respondents were presented using frequencies, population estimates, and percentages (weighted). Pearson’s chi-square test was used to evaluate the association between anaemia prevalence and independent variables. Multicollinearity was assessed using the variable inflation factor (VIF). The independent variables showed no multicollinearity (minimum VIF = 1.00, maximum VIF = 3.80). All variables with a p value ⩽ 0.25 in bivariate analyses were further analysed using multivariable complex samples logistic regression. In addition, we included age, stature, and parity in the model for pregnant women based on clinical significance. The results of regression analysis were presented by crude/unadjusted odds ratio (COR) and adjusted odds ratio (AOR) with 95% confidence intervals (CIs), F statistics, and p values. The McFadden test statistic for overall WRA, non-pregnant women, and pregnant women ranged from 0.02 to 0.04. Since values ranging from 0.2 to 0.4 indicate good model fit and values beyond 0.4 indicate excellent fit, our models might not be the best fit.40 However, McFadden test, a log-likelihood-based pseudo-R2 that represents the improvement in model likelihood over a null model, is influenced by sample size (the smaller the sample size, the higher the value), number of predictor variables, and number of categories of the dependent variable and its distribution asymmetry.40 Statistical significance for the multivariable complex sample logistic regression analyses was set at p < 0.05. The 2018 NDHS protocol was reviewed and approved by the National Health Research Ethics Committee of Nigeria (NHREC) and the ICF Institutional Review Board. Informed consent was obtained from participants before interviews or biomarker tests were conducted. Consequently, our study, being a secondary analysis, did not require further ethical approval.

Materiais

N/A

Inovações

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

1. Mobile Health (mHealth) Solutions: Develop mobile applications or SMS-based platforms that provide pregnant women and women of reproductive age with information on maternal health, including anaemia prevention and management, nutrition, antenatal care, and family planning.

2. Telemedicine: Implement telemedicine services to enable remote consultations between healthcare providers and pregnant women or women of reproductive age. This can help overcome geographical barriers and improve access to healthcare services, including anaemia screening and treatment.

3. Community Health Workers: Train and deploy community health workers to provide education, screening, and basic treatment for anaemia in rural and underserved areas. These workers can also refer women to higher-level healthcare facilities when necessary.

4. Maternal Health Vouchers: Introduce voucher programs that provide pregnant women and women of reproductive age with subsidized or free access to maternal health services, including anaemia screening, iron supplementation, and antenatal care.

5. Public-Private Partnerships: Foster collaborations between the government, private sector, and non-profit organizations to improve access to maternal health services. This can involve leveraging private healthcare providers and facilities to expand service coverage and reduce costs.

6. Health Education Campaigns: Conduct targeted health education campaigns to raise awareness about anaemia prevention and management among pregnant women, women of reproductive age, and their families. These campaigns can utilize various media channels, including radio, television, and social media.

7. Supply Chain Management: Strengthen the supply chain for essential maternal health commodities, such as iron supplements and diagnostic tools for anaemia screening. This can ensure consistent availability and accessibility of these resources in healthcare facilities.

8. Maternal Health Financing: Advocate for increased investment in maternal health by governments and international donors. This can help improve infrastructure, healthcare workforce, and service delivery for maternal health, including anaemia prevention and treatment.

It is important to note that the implementation of these innovations should be context-specific and tailored to the unique challenges and needs of the Nigerian healthcare system.

AI Innovations Description

The study mentioned in the description provides valuable insights into the prevalence and determinants of anaemia among women of reproductive age in Nigeria. Based on the findings, here are some recommendations that can be developed into innovations to improve access to maternal health:

1. Increase awareness and education: Implement targeted health education campaigns to raise awareness about anaemia among women of reproductive age, pregnant women, and non-pregnant women. This can include providing information on the causes, symptoms, and consequences of anaemia, as well as promoting the importance of seeking antenatal care and regular health check-ups.

2. Improve access to antenatal care: Strengthen the healthcare system by ensuring that pregnant women have access to quality antenatal care services. This can involve increasing the number of healthcare facilities, especially in rural areas, and training healthcare providers to effectively diagnose and manage anaemia during pregnancy.

3. Enhance nutrition interventions: Develop and implement nutrition interventions that focus on improving the dietary diversity and nutritional status of women of reproductive age. This can include promoting the consumption of iron-rich foods, such as leafy greens, beans, and fortified cereals, as well as providing nutritional supplements, such as iron and folic acid, to pregnant women.

4. Address socio-economic factors: Address the socio-economic factors that contribute to anaemia among women of reproductive age. This can involve implementing poverty alleviation programs, promoting female education and empowerment, and creating employment opportunities for women.

5. Strengthen family planning services: Improve access to and utilization of modern contraceptives to help women space their pregnancies and reduce the risk of anaemia. This can include expanding the availability of contraceptives, providing comprehensive family planning counseling, and addressing cultural and social barriers to contraceptive use.

6. Collaborate with stakeholders: Foster collaboration between government agencies, non-governmental organizations, healthcare providers, and community leaders to develop and implement comprehensive strategies to address anaemia among women of reproductive age. This can involve joint planning, resource sharing, and coordination of efforts to ensure a holistic approach to improving maternal health.

By implementing these recommendations, it is possible to develop innovative solutions that can effectively improve access to maternal health and reduce the prevalence of anaemia among women of reproductive age in Nigeria.

AI Innovations Methodology

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

1. Strengthen healthcare infrastructure: Invest in improving healthcare facilities, especially in rural areas, by providing necessary equipment, supplies, and trained healthcare professionals.

2. Increase awareness and education: Implement comprehensive public health campaigns to raise awareness about maternal health issues, including the importance of antenatal care, skilled birth attendance, and postnatal care.

3. Improve transportation and accessibility: Develop transportation systems and infrastructure to ensure that pregnant women can easily access healthcare facilities, especially in remote areas.

4. Expand community-based healthcare services: Establish and strengthen community-based healthcare programs that provide maternal health services, such as prenatal and postnatal care, close to where women live.

5. Enhance maternal health financing: Increase investment in maternal health by allocating more resources to maternal health programs, including funding for essential services, supplies, and medications.

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

1. Define the indicators: Identify specific indicators that measure access to maternal health, such as the number of antenatal care visits, skilled birth attendance rates, and postnatal care coverage.

2. Collect baseline data: Gather data on the current status of the indicators in the target population. This can be done through surveys, interviews, or existing data sources.

3. Develop a simulation model: Create a mathematical or statistical model that simulates the impact of the recommendations on the selected indicators. This model should take into account factors such as population size, geographical distribution, and existing healthcare infrastructure.

4. Input the recommendations: Incorporate the proposed recommendations into the simulation model by adjusting relevant parameters, such as the availability of healthcare facilities, transportation options, and community-based programs.

5. Run the simulation: Execute the simulation model using the baseline data and the adjusted parameters to estimate the potential impact of the recommendations on the selected indicators. This can be done through computer simulations or statistical analyses.

6. Analyze the results: Evaluate the simulation results to assess the projected changes in access to maternal health. Compare the simulated outcomes with the baseline data to determine the potential effectiveness of the recommendations.

7. Refine and iterate: Based on the simulation results, refine the recommendations and adjust the simulation model as needed. Repeat the simulation process to further refine the projected impact and identify the most effective strategies.

By following this methodology, policymakers and healthcare stakeholders can gain insights into the potential impact of different recommendations on improving access to maternal health and make informed decisions about resource allocation and program implementation.

Autores & Coautores

Statistics:

Authors: 3

Identifiers:

DOI: 10.1177/17455057221142961

ISSN: 17455057

Research Areas:

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

Study Countries:

Nigeria

Study Design:

Case-Control Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Male & Female

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