Determinants of dietary diversity and its relationship with the nutritional status of pregnant women

Journal of Nutritional Science, Year 2021

Interpretation

There is little information regarding factors that determine dietary diversity among pregnant women in Ghana. The present study, therefore, sought to assess the independent predictors of dietary diversity and its relationship with nutritional status of pregnant women in the Northern Region of Ghana. The present study was an analytical cross-sectional survey involving 423 pregnant women in different stages of gestation. The 24-h dietary recall method was used to assess minimum dietary diversity for women (MDD-W), and nutritional status was assessed using mid-upper arm circumference (MUAC) measurements. Binary logistic regression was performed to assess the association between maternal dietary diversity and maternal thinness and a P value of <0 .05 was considered statistically significant. Of the 423 women, 79 .9 % (95 % CI 76 .1, 83 .7) met the MDD-W and the prevalence of undernutrition among the pregnant women was 26 .0 %. The analysis showed that women of low household wealth index were 48 % less likely (AOR 0 .52, CI 0 .31, 0 .88) of meeting the MDD-W, whereas women from households of poor food insecurity were 88 % less likely (AOR 0 .12, CI 0 .05, 0 .27) of achieving the MDD-W. Women of low household size were three times more likely of meeting the MDD-W (AOR 3 .07, CI 1 .13, 8 .39). MDD-W was not associated with maternal underweight during pregnancy. In conclusion, the results of the present study showed that food insecurity and not low MDD-W, associated with mothers' thinness (underweight) during pregnancy in peri-urban setting of Northern Ghana. The present study was conducted in the Sagnarigu Municipality of the Northern Region of Ghana. The municipality, which is largely peri-urban, covers a total land area of 200⋅4 km2 and has a population of 163 513. The present study used a cross-sectional design to collect quantitative data. All women independent of their stage of pregnancy were asked to participate in the study when they attended antenatal care (ANC) in selected health facilities. The present study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects/patients were approved by the Institutional Review Board (IRB) of the Tamale Teaching Hospital, Ghana (Ref no. TTH/10/11/15/01). Written informed consent was obtained from all subjects/patients. The data were collected in all the three subdistricts (Taha-Kamina, Choggu and Sagnerigu) of the Sagnerigu District. The subdistrict health facilities (Choggu, Bagabaga, Garizeigu Clinic and Kalpohini Health Centres), private health facilities (Fulera maternity home and CSSH) and a CHAC facility (St. Louise) at Kpalsi were used as the data collection points. A systematic random sampling procedure was used to select the study participants. The attendance list of the women who sought ANC services served as the sampling frame in each facility. The sample size is determined using the formula for one-point sample estimation: where n is the required sample size, t is the statistical uncertainty chosen = 1⋅96 at a confidence level of 95 %, p is the estimated proportion of pregnant women using diversified diet (Unknown) = 50⋅0 % and m is the margin of error at 5 % (standard value of 0⋅05). A total required sample size large enough to detect a reliable smallest difference and the relationship between the variables tested in the study was, thus, estimated as 384. Allowing for a 10 % non-response rate (i.e. 39 respondents), the overall sample was adjusted to 423 respondents. The data were collected from the respondents using a structured questionnaire which was administered through face-to-face interviews at the household level. Anthropometric equipment includes mid-upper arm circumference (MUAC) tape and Seca electronic adult scale. Maternal height, haemoglobin concentration (Hb) and gestational age records were retrieved from Maternal Health Record Books (ANC cards). Maternal height was measured both as continuous and as a categorical variable with the following cut points: less than 145, 145–149⋅9, 150–154⋅9, 155–159⋅9 and at least 160⋅0 cm. Marital status was classified as married or as unmarried if a woman was divorced, separated, widowed or never married. Maternal occupation was classified according to whether the mother was not working or was working in a manual, non-manual or agricultural profession. The primary dependent variable was the nutritional status of pregnant women as measured by MUAC. The independent explanatory variable was dietary quality as measured by individual dietary diversity scores. The covariate variables included gestational age, maternal age, height, education, occupation, SES, number under-fives in household, parity, birth interval and ANC during the current pregnancy. The minimum dietary diversity for women (MDD-W) was used to assess the overall dietary quality of respondents since it has been shown to indicate adequate nutrient intake(6,19) and can be used as a proxy indicator for measuring nutrient adequacy among pregnant females(20). The MDD-W indicator is based on a 10-food group women dietary diversity score (WDDS-10). These food groups are starch staples (grains, white roots and tubers, and plantains); vitamin A-rich vegetables and fruits; dark green leafy vegetables; other vegetables; other fruits; flesh foods (meat, fish, poultry and liver/organ meats); eggs; pulses/legumes; nuts and seeds; and dairy products. WDDS, which is based on a 24-h dietary recall period(13), was applied to characterise the average usual dietary intake of pregnant women in the study area. The women were asked to recall all foods consumed from the above food groups on the previous day. Responses were recorded as ‘yes’ or ‘no’. A ‘yes’ response was scored as ‘1’, and a ‘no’ response was scored as ‘0’. The scores were summed up to create the women DD score. Available evidence suggests that WDDS is a good measure of household macronutrient adequacy and household nutrition insecurity. The dietary scores were classified into low and high diversity based on the MDD-W. Women having a diversity score of less than 5 were classified as having low dietary diversity and scores of 5–10 are classified in the high dietary diversity scores(21). Additionally, the FAO validated 11-item food groups frequency questionnaire (FFQ) was used to quantify maternal dietary intake based on 7-d dietary diversity score(13). This was derived based on the number of food groups consumed from a 7-d food frequency questionnaire and included 11 food groups. The food group frequency of consumption (past 7 d) was measured for each food group by assigning a score of 0 if not consumed during the previous week, 1 if consumed on 1–3 d and 2 if consumed for at least 4 d. This composite index of dietary diversity which took into account the weekly food frequency varied from a minimum of 0 to a maximum of 22. The eleven food groups were flesh meats (i.e. beef, pork, lamb, goat, poultry, etc.), fish, eggs, milk and milk products, organ meat (e.g. liver, kidney, etc.), legumes, cereals, roots and tubers, dark green leafy vegetables, vitamin A-rich fruits and fats and oils. Household food access was measured using the food consumption score (FCS), and it was calculated as per the World Food Programme (WFP)(22). The FCS as an index is expected to provide a more accurate measure of the quality of the household diet because it accounts for the nutritional value of food in addition to the number of different types of food consumed. The FCS is a proxy indicator of household caloric availability. MUAC is often used as a measure of fat-free mass, and in the present study, MUAC was used to assess the nutritional status of pregnant women. MUAC was used as a proxy for body weight, since it is not affected by gestational age(23). MUAC was also measured using a non-stretchable MUAC tape(24). MUAC was measured to the nearest 0⋅1 cm, and values below 25⋅0 cm were classified in the analyses as an indicator of low body weight. There is presently no internationally agreed MUAC cut-offs(25). A household wealth index based on household assets and housing quality was used as a proxy indicator for SES of households. Principal component analysis (PCA) was used to determine a household wealth index from information collected on housing quality (floor, walls and roof material), source of drinking water, type of toilet facility, the presence of electricity, type of cooking fuel and ownership of modern household durable goods and livestock (e.g. bicycle, television, radio, motorcycle, sewing machine, telephone, cars, refrigerator, mattress, bed, computer and mobile phone)(26–29). These facilities or durable goods are often regarded as modern goods that have been shown to reflect household wealth. A household of zero-index score for example means that household had not a single modern good. The scores were, thus, added up to give the proxy household wealth index. The main aim of creating the index was to categorise households into SES groupings in order that we could compare the difference in the prevalence of maternal thinness between the groups of lowest and highest SES. Data were analysed using SPSS version 21 (SSPS Inc. Chicago, IL, USA) statistical software. Both (bivariate and multivariable analysis) were performed to identify risk factors of maternal underweight during pregnancy. Only variables that showed significant association (P < 0⋅05) with each dependent variable in the bivariate analysis were selected and adjusted for in the multivariable binary logistic regression analysis. Multicollinearity among the predictor variables was checked before their inclusion in the final regression model. We assessed multicollinearity among independent variables by using the variance inflation factor (VIF), which assesses increment in regression coefficients if the independent variables are correlated. VIF > 5 is an indication that multicollinearity may be present, while VIF > 10 is certainly multicollinearity among the variables. We did not have any VIF exceeding 5, indicating no collinearity. Results were presented as adjusted odds ratio (AOR) with 95 % confidence intervals (CIs) to measure the strength of association.

AI Digest

Study Justification:

– There is limited information on the factors that determine dietary diversity among pregnant women in Ghana.
– Understanding these factors is crucial for developing effective interventions to improve the nutritional status of pregnant women.
– This study aimed to assess the predictors of dietary diversity and its relationship with the nutritional status of pregnant women in the Northern Region of Ghana.

Study Highlights:

– The study involved 423 pregnant women in different stages of gestation.
– The 24-hour dietary recall method was used to assess minimum dietary diversity for women (MDD-W).
– Nutritional status was assessed using mid-upper arm circumference (MUAC) measurements.
– The study found that 79.9% of women met the MDD-W, indicating a relatively high level of dietary diversity.
– The prevalence of undernutrition among pregnant women was 26.0%.
– Women from low household wealth index and poor food insecurity were less likely to achieve the MDD-W.
– Women from households with low household size were more likely to meet the MDD-W.
– There was no association between MDD-W and maternal underweight during pregnancy.
– Food insecurity, rather than low dietary diversity, was associated with maternal thinness (underweight) during pregnancy.

Recommendations for Lay Reader and Policy Maker:

– Promote interventions to improve household food security, particularly among pregnant women from low-income households.
– Implement programs to increase dietary diversity among pregnant women, focusing on the consumption of nutrient-rich foods.
– Enhance access to nutrition education and counseling for pregnant women, emphasizing the importance of a diverse and balanced diet.
– Strengthen antenatal care services to include regular monitoring of maternal nutritional status and early identification of undernutrition.
– Collaborate with relevant stakeholders, such as government agencies, NGOs, and healthcare providers, to implement and monitor the effectiveness of interventions.

Key Role Players:

– Government agencies responsible for nutrition and health policies and programs.
– Non-governmental organizations (NGOs) working in the field of maternal and child health.
– Healthcare providers, including doctors, nurses, and midwives, involved in antenatal care services.
– Community leaders and local authorities who can support community-based interventions.
– Researchers and academics who can provide expertise and guidance in designing and evaluating interventions.

Cost Items for Planning Recommendations:

– Development and implementation of nutrition education materials and resources.
– Training programs for healthcare providers on nutrition counseling and monitoring.
– Community outreach activities, including awareness campaigns and workshops.
– Monitoring and evaluation of interventions to assess their impact and effectiveness.
– Research studies to further investigate the relationship between dietary diversity, nutritional status, and maternal health outcomes.
– Collaboration and coordination efforts among stakeholders, including meetings and workshops.
– Data collection and analysis to monitor progress and inform decision-making.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because the study design is cross-sectional, which limits the ability to establish causality. However, the study used a large sample size and conducted statistical analysis to assess the association between variables. To improve the evidence, a longitudinal study design could be considered to establish causality and strengthen the findings.

Abstract

The present study was conducted in the Sagnarigu Municipality of the Northern Region of Ghana. The municipality, which is largely peri-urban, covers a total land area of 200⋅4 km2 and has a population of 163 513. The present study used a cross-sectional design to collect quantitative data. All women independent of their stage of pregnancy were asked to participate in the study when they attended antenatal care (ANC) in selected health facilities. The present study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects/patients were approved by the Institutional Review Board (IRB) of the Tamale Teaching Hospital, Ghana (Ref no. TTH/10/11/15/01). Written informed consent was obtained from all subjects/patients. The data were collected in all the three subdistricts (Taha-Kamina, Choggu and Sagnerigu) of the Sagnerigu District. The subdistrict health facilities (Choggu, Bagabaga, Garizeigu Clinic and Kalpohini Health Centres), private health facilities (Fulera maternity home and CSSH) and a CHAC facility (St. Louise) at Kpalsi were used as the data collection points. A systematic random sampling procedure was used to select the study participants. The attendance list of the women who sought ANC services served as the sampling frame in each facility. The sample size is determined using the formula for one-point sample estimation: where n is the required sample size, t is the statistical uncertainty chosen = 1⋅96 at a confidence level of 95 %, p is the estimated proportion of pregnant women using diversified diet (Unknown) = 50⋅0 % and m is the margin of error at 5 % (standard value of 0⋅05). A total required sample size large enough to detect a reliable smallest difference and the relationship between the variables tested in the study was, thus, estimated as 384. Allowing for a 10 % non-response rate (i.e. 39 respondents), the overall sample was adjusted to 423 respondents. The data were collected from the respondents using a structured questionnaire which was administered through face-to-face interviews at the household level. Anthropometric equipment includes mid-upper arm circumference (MUAC) tape and Seca electronic adult scale. Maternal height, haemoglobin concentration (Hb) and gestational age records were retrieved from Maternal Health Record Books (ANC cards). Maternal height was measured both as continuous and as a categorical variable with the following cut points: less than 145, 145–149⋅9, 150–154⋅9, 155–159⋅9 and at least 160⋅0 cm. Marital status was classified as married or as unmarried if a woman was divorced, separated, widowed or never married. Maternal occupation was classified according to whether the mother was not working or was working in a manual, non-manual or agricultural profession. The primary dependent variable was the nutritional status of pregnant women as measured by MUAC. The independent explanatory variable was dietary quality as measured by individual dietary diversity scores. The covariate variables included gestational age, maternal age, height, education, occupation, SES, number under-fives in household, parity, birth interval and ANC during the current pregnancy. The minimum dietary diversity for women (MDD-W) was used to assess the overall dietary quality of respondents since it has been shown to indicate adequate nutrient intake(6,19) and can be used as a proxy indicator for measuring nutrient adequacy among pregnant females(20). The MDD-W indicator is based on a 10-food group women dietary diversity score (WDDS-10). These food groups are starch staples (grains, white roots and tubers, and plantains); vitamin A-rich vegetables and fruits; dark green leafy vegetables; other vegetables; other fruits; flesh foods (meat, fish, poultry and liver/organ meats); eggs; pulses/legumes; nuts and seeds; and dairy products. WDDS, which is based on a 24-h dietary recall period(13), was applied to characterise the average usual dietary intake of pregnant women in the study area. The women were asked to recall all foods consumed from the above food groups on the previous day. Responses were recorded as ‘yes’ or ‘no’. A ‘yes’ response was scored as ‘1’, and a ‘no’ response was scored as ‘0’. The scores were summed up to create the women DD score. Available evidence suggests that WDDS is a good measure of household macronutrient adequacy and household nutrition insecurity. The dietary scores were classified into low and high diversity based on the MDD-W. Women having a diversity score of less than 5 were classified as having low dietary diversity and scores of 5–10 are classified in the high dietary diversity scores(21). Additionally, the FAO validated 11-item food groups frequency questionnaire (FFQ) was used to quantify maternal dietary intake based on 7-d dietary diversity score(13). This was derived based on the number of food groups consumed from a 7-d food frequency questionnaire and included 11 food groups. The food group frequency of consumption (past 7 d) was measured for each food group by assigning a score of 0 if not consumed during the previous week, 1 if consumed on 1–3 d and 2 if consumed for at least 4 d. This composite index of dietary diversity which took into account the weekly food frequency varied from a minimum of 0 to a maximum of 22. The eleven food groups were flesh meats (i.e. beef, pork, lamb, goat, poultry, etc.), fish, eggs, milk and milk products, organ meat (e.g. liver, kidney, etc.), legumes, cereals, roots and tubers, dark green leafy vegetables, vitamin A-rich fruits and fats and oils. Household food access was measured using the food consumption score (FCS), and it was calculated as per the World Food Programme (WFP)(22). The FCS as an index is expected to provide a more accurate measure of the quality of the household diet because it accounts for the nutritional value of food in addition to the number of different types of food consumed. The FCS is a proxy indicator of household caloric availability. MUAC is often used as a measure of fat-free mass, and in the present study, MUAC was used to assess the nutritional status of pregnant women. MUAC was used as a proxy for body weight, since it is not affected by gestational age(23). MUAC was also measured using a non-stretchable MUAC tape(24). MUAC was measured to the nearest 0⋅1 cm, and values below 25⋅0 cm were classified in the analyses as an indicator of low body weight. There is presently no internationally agreed MUAC cut-offs(25). A household wealth index based on household assets and housing quality was used as a proxy indicator for SES of households. Principal component analysis (PCA) was used to determine a household wealth index from information collected on housing quality (floor, walls and roof material), source of drinking water, type of toilet facility, the presence of electricity, type of cooking fuel and ownership of modern household durable goods and livestock (e.g. bicycle, television, radio, motorcycle, sewing machine, telephone, cars, refrigerator, mattress, bed, computer and mobile phone)(26–29). These facilities or durable goods are often regarded as modern goods that have been shown to reflect household wealth. A household of zero-index score for example means that household had not a single modern good. The scores were, thus, added up to give the proxy household wealth index. The main aim of creating the index was to categorise households into SES groupings in order that we could compare the difference in the prevalence of maternal thinness between the groups of lowest and highest SES. Data were analysed using SPSS version 21 (SSPS Inc. Chicago, IL, USA) statistical software. Both (bivariate and multivariable analysis) were performed to identify risk factors of maternal underweight during pregnancy. Only variables that showed significant association (P 5 is an indication that multicollinearity may be present, while VIF > 10 is certainly multicollinearity among the variables. We did not have any VIF exceeding 5, indicating no collinearity. Results were presented as adjusted odds ratio (AOR) with 95 % confidence intervals (CIs) to measure the strength of association.

Materials

N/A

Innovations

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

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide pregnant women with information on dietary diversity, nutrition, and maternal health. These apps can include features such as personalized meal plans, reminders for prenatal appointments, and educational resources.

2. Community-based Nutrition Programs: Implement community-based programs that focus on improving dietary diversity among pregnant women. These programs can include cooking demonstrations, nutrition education sessions, and support groups to encourage healthy eating habits.

3. Telemedicine Services: Establish telemedicine services that allow pregnant women in remote areas to access prenatal care and consultations with healthcare providers. This can help overcome geographical barriers and ensure that women receive timely and appropriate care.

4. Maternal Health Vouchers: Introduce voucher programs that provide pregnant women with financial assistance to access maternal health services, including antenatal care, nutrition counseling, and delivery services. These vouchers can be distributed through community health centers or local organizations.

5. Public-Private Partnerships: Foster collaborations between public and private sectors to improve access to maternal health services. This can involve partnering with private healthcare providers to offer affordable and quality prenatal care, as well as leveraging private sector resources for health education campaigns.

6. Maternal Health Hotlines: Establish toll-free hotlines that pregnant women can call to receive information and support related to maternal health. Trained healthcare professionals can provide guidance on nutrition, prenatal care, and address any concerns or questions.

7. Maternal Health Education Campaigns: Launch targeted education campaigns that raise awareness about the importance of dietary diversity during pregnancy. These campaigns can utilize various media channels, such as radio, television, and social media, to reach a wide audience.

8. Integration of Maternal Health Services: Ensure that maternal health services are integrated into existing healthcare systems, including primary healthcare centers and community health programs. This can help streamline access to care and improve coordination between different healthcare providers.

9. Maternal Health Monitoring Systems: Implement digital health solutions that enable real-time monitoring of maternal health indicators, such as dietary diversity and nutritional status. These systems can provide healthcare providers with timely data to identify areas of improvement and tailor interventions accordingly.

10. Capacity Building for Healthcare Providers: Offer training programs and workshops for healthcare providers to enhance their knowledge and skills in maternal health, nutrition counseling, and dietary diversity. This can help ensure that healthcare providers are equipped to provide comprehensive and evidence-based care to pregnant women.

It is important to note that the implementation of these innovations should be context-specific and tailored to the needs and resources of the specific region or community.

AI Innovations Description

Based on the description provided, the recommendation to improve access to maternal health and address the determinants of dietary diversity among pregnant women in Ghana is to implement targeted interventions that focus on improving household wealth index and food security.

1. Improve household wealth index: Implement programs and policies that aim to improve the economic status of households, particularly those with pregnant women. This can include providing income-generating opportunities, vocational training, and access to microfinance services. By improving household wealth, pregnant women will have better access to nutritious food and resources necessary for a diverse diet.

2. Enhance food security: Develop strategies to address food insecurity among pregnant women. This can involve implementing social safety net programs, such as cash transfers or food assistance programs, to ensure that pregnant women have access to an adequate and diverse diet. Additionally, promoting sustainable agriculture practices and improving access to markets can help increase the availability and affordability of nutritious foods.

3. Increase awareness and education: Conduct awareness campaigns and educational programs to promote the importance of dietary diversity during pregnancy. This can include providing information on the benefits of consuming a variety of foods, as well as practical tips on how to achieve a diverse diet within the local context. Health workers, community leaders, and local influencers can play a crucial role in disseminating this information.

4. Strengthen antenatal care services: Enhance the quality and accessibility of antenatal care services, ensuring that pregnant women receive comprehensive nutrition counseling and support. This can include training healthcare providers on the importance of dietary diversity and equipping them with the necessary tools and resources to provide appropriate guidance to pregnant women.

5. Foster community engagement: Engage local communities in the design and implementation of interventions to improve access to maternal health. This can involve establishing community-based support groups, promoting peer-to-peer learning, and involving community leaders in decision-making processes. By involving the community, interventions can be tailored to the specific needs and cultural context of the pregnant women.

By implementing these recommendations, it is expected that access to maternal health will be improved, leading to better nutritional outcomes for pregnant women in Ghana.

AI Innovations Methodology

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

1. Increase awareness and education: Implement programs to educate pregnant women and their families about the importance of proper nutrition during pregnancy and the benefits of a diverse diet. This can be done through community health workers, antenatal care clinics, and mass media campaigns.

2. Improve availability and affordability of nutritious foods: Work with local farmers and food suppliers to ensure a steady supply of diverse and nutritious foods, such as fruits, vegetables, and protein sources. Explore options for subsidies or incentives to make these foods more affordable for pregnant women.

3. Strengthen antenatal care services: Enhance the quality and accessibility of antenatal care services by training healthcare providers on maternal nutrition and integrating nutrition counseling into routine antenatal visits. This can help pregnant women receive personalized advice and support for maintaining a diverse and nutritious diet.

4. Address socio-economic barriers: Develop strategies to address socio-economic factors that contribute to poor dietary diversity, such as household wealth and food insecurity. This may involve implementing income-generating programs, improving social safety nets, and promoting women’s empowerment.

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 reflect improved access to maternal health, such as increased dietary diversity among pregnant women, reduced prevalence of undernutrition, and improved maternal health outcomes.

2. Collect baseline data: Gather data on the current status of maternal health access, including dietary diversity, nutritional status, and socio-economic factors. This can be done through surveys, interviews, and health facility records.

3. Implement interventions: Introduce the recommended innovations and interventions in a targeted area or population. This may involve implementing awareness campaigns, training healthcare providers, improving food availability, and addressing socio-economic barriers.

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

5. Analyze the data: Analyze the collected data to assess the impact of the interventions on the selected indicators. This may involve statistical analysis, such as comparing pre- and post-intervention data, conducting regression analyses, or using other appropriate statistical methods.

6. Interpret the results: Interpret the findings to determine the effectiveness of the interventions in improving access to maternal health. Identify any significant changes in dietary diversity, nutritional status, and other relevant outcomes.

7. Adjust and refine: Based on the results, make any necessary adjustments or refinements to the interventions. This may involve scaling up successful interventions, modifying strategies, or addressing any challenges or limitations identified during the evaluation.

8. Disseminate findings: Share the findings and lessons learned from the simulation with relevant stakeholders, including policymakers, healthcare providers, and community members. This can help inform future interventions and contribute to evidence-based decision-making.

By following this methodology, it is possible to simulate the impact of recommendations on improving access to maternal health and guide the development of effective interventions.

Author

Dima Health

Statistics:

Citations: 5

Identifiers:

DOI: 10.1017/jns.2021.6

ISSN: 20486790

Research Areas:

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

Study Countries:

Ghana

Study Design:

Case-Control Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Female