Prevalence and risk factors of malaria and anaemia and the impact of preventive methods among pregnant women: A case study at the Akatsi South District in Ghana

PLoS ONE, Volume 17, No. 7 July, Year 2022

Interpretation

Aim This study aimed to ascertain the prevalence and risk factors of malaria and anaemia as well as the impact of preventive methods among pregnant women at the Akatsi South District Hospital of Ghana. Subjects and methods A hospital based cross-sectional study using simple random sampling technique was conducted among 200 pregnant women receiving antenatal care and laboratory services at the Akatsi District Hospital from May 2016 to July 2016. A semi-structured questionnaire was administered to obtain participants’ malaria preventive methods in addition to demographic and gestational details. Participants’ hemoglobin and malaria status were assessed using one milliliter (1 ml) whole blood collected from each participant following standard procedures. Factors that produced a p-value of ≤0.2 from the univariate model were included in the final model. Association between potential covariates and the outcomes was assessed using multivariate logistic regression. The Clopper-Pearson test statistic was used to determine the 95% confidence intervals of the outcome variables of interest. We also estimated the population attributable fraction (PAF) of anaemia due to malaria by substituting the adjusted relative risk estimates (RRi) (using the adjrr command in STATA) of anaemia due to malaria into the category-specific attributable formula. P-values of <0.05 were considered statistically significant. Results Prevalence of anaemia in pregnancy (AiP), malaria in pregnancy (MiP) and AiP/MiP comorbidity was 63.5% (95% CI:56.4–70.2), 11.0% (96% CI:7.0–16.2) and 10.5% (95% CI:6.6–15.6) respectively. Prevalence rates of AiP (66.7%) and MiP (18.5%) predominated among pregnant women aged < 20 years. PAF of AiP due to MiP was 34.5% (95% CI:23.8–43.6). High use of IPTp-SP, 64.0% (95% CI:56.9–70.6) and LLIN, 90.0% (95% CI:85.0–93.8) was observed in this study. Only 42.0% (95% CI:35.1–49.2) used repellent. Not being on the IPTp-SP program posed a 11.70 times risk of MiP (95% CI:2.32–58.96; p = 0.003) compared to pregnant women on the IPTp-SP program. Similarly, not sleeping under LLIN posed an 8.07 times risk of MiP (95% CI:1.98–32.2; p = 0.004) compared to pregnant women who slept under LLIN. Meanwhile, being positive for MiP posed a 12.10 times risk (95% CI:1.35–85.06; p = 0.025) of AiP compared to those negative for malaria whereas failure to attend ANC as scheduled posed 6.34 times risk (95% CI:1.81–22.19; p = 0.004) of AiP among the pregnant women studied. Conclusion The prevalence of MiP and AiP among pregnant women in the Akatsi South District remains a great concern. High utilization of IPTp-SP and LLIN was observed with a resultant positive effect on malaria prevalence among pregnant women. Improved access to IPTp-SP and LLIN is hence encouraged to help further diminish the risk of malaria infection amongst pregnant women in the District. A hospital based cross-sectional study using simple random sampling technique was carried out among 200 pregnant women (Fig 1) receiving antenatal care and laboratory services at the Akatsi South District Hospital, Ghana from May to July, 2016. The Akatsi District hospital is one of the 29 health facilities in the District (Source: Akatsi South District Health Directorate, 2017). The hospital at the time of this study had a staff strength of 140 healthcare workers including two doctors, a medical assistant, a pharmacist, and a biomedical scientist. The hospital is a 70-bed capacity facility, with a maternity unit, medical laboratory unit, family planning unit and an outpatient department. It also serves as a referral center for the surrounding primary health facilities in the district. The choice of Akatsi South District for this study emanates from the fact that malaria consistently remained the topmost disease among the top ten diseases that affect the majority of its population [15]. Pregnant women who were registered with the Akatsi South District Hospital but were not residents of the Akatsi South District were excluded from the study to ensure that reported prevalence rates of malaria and anaemia represent an accurate reflection of the true status of malaria and anaemia prevalence among participants from the study area only. Non-consenting pregnant women were also excluded from the study. Participation of the respondents was voluntary. The minimum sample size for the study was determined using the Raosoft Online Sample Size Calculation formula (http://www.raosoft.com/samplesize.html) with the following parameters; 5% margin of error (E), 95% confidence level, average annual antenatal attendance (N) of approximately 2500, a response rate (r) of 85% (obtained from a prior pilot study), number of ‘positive’ observations (x) of 4898.04 and a critical value (Z(c/100)) of 1.96. The required formulas are: Let Z(c/100) = 1.96 r = 85% N = 7796 Hence x = 1.962 * 85(100−85) = 4898.04 Substituting N = 2500 and x = 4898.04 into (2) to determine the minimum sample size n, gives Hence, the minimum required sample size for this study was 182 pregnant women from the Akatsi South District. Two hundred (200) pregnant women was however sampled to increase statistical power and reliability of the findings. A semi-structured questionnaire was administered to the pregnant women by the researchers. The questionnaire was designed in English and then translated via interview to the respondent in the language best understood by the respondent: Ewe and Twi predominantly. The sociodemographic characteristics of the respondents were maternal age, level of education, occupation, location, residential status, and marital status. Obstetric details included gravidity, parity, the gestational period in weeks (definition: first trimester, from week 1 to the end of week 12; second trimester, from week 13 to the end of week 26 and third trimester, from week 27 to the end of the pregnancy) and attended ANC as scheduled. Malaria preventive methods were IPTp-SP use, sleeping under LLIN and the use of mosquito repellent. None of the respondents reported using insecticide sprays, mosquito coils, and/or creams to prevent malaria in this study. About one milliliter (1 ml) whole blood was collected from each pregnant woman from the median cubital vein of the antecubital fossa of the arm into an EDTA tube using a sterile syringe and needle following standard blood collection protocols. Samples were collected at the onset of the rains, between May and July 2016 which marks the period of the malaria transmission season. The blood film preparation and observation were done following standard procedures outlined by Monica Chesbrough [16]. Briefly, thick blood films were then prepared on a clean grease-free glass slide using 6 ul of whole blood, spread evenly to cover a dimension of about 15x15 mm of the center of the glass. The smears were air-dried, fixed in absolute methanol, and then left to air-dry in about 1–2 minutes ready for staining. The slides were then stained with 10% Giemsa for 10 minutes and then examined under oil immersion (magnification x100) with a light microscope. Reading of the slides was carried out by two trained microscopists. The absence of malaria parasite after 200 high power fields was examined was considered negative for malaria. A discrepancy in the slide reading outcome between the two initial scientists was resolved by a third opinion from a senior microscopist. Participants’ hemoglobin concentration was determined using the fully automated Sysmex XS-800i hematology analyzer (https://www.sysmex-europe.com/n/products/products-detail/xs-800i.html; Europe). All test procedures were carried out following standard quality-controlled procedures. Anaemia in pregnant women was defined as a hemoglobin concentration less than 11.0 g/dL while hemoglobin concentration greater than or equal to 11.0 g/dL was considered normal. Anaemia was further subclassified as mild (hemoglobin: 10–10.9 g/dL), moderate (hemoglobin: 7–9.9 g/dL) and severe anaemia (hemoglobin <7 g/dL) [17]. Our Sysmex XS-800i hematology analyzer was quality controlled following the manufacturer’s instruction by running manufacturer’s provided quality controlled samples to ensure the accuracy and precision of the instrument before running the samples of the study participants. Known malaria positive and negative slides were used to quality control the 10% Giemsa stain. Two independent and qualified parasitologists examined 15% of both positive and negative well prepared randomly selected slides. A third and final opinion of a third senior parasitologist was sought in the instance of any discrepancy in the two initial examinations. Consistency in microscopic outcomes signaled good working Giemsa stain. Data collected from the questionnaires and results from the laboratory investigations were checked for consistency, completeness and then captured into Microsoft Excel 2016 using fit-for-purpose excel form to avoid as many as possible entry errors. Data were then exported into Statistical Package for Social Sciences (SPSS) for Windows (version 21.0) and STATA (version 15.0) where appropriate for statistical analysis. All data were categorical, therefore, descriptive analysis was done and presented as frequencies and percentages in parenthesis. Chi-square and Fisher’s exact test where appropriate were used to assess statistical associations between explanatory variables and outcome variables. Factors that produced a p-value ≤0.2 from the bivariate model were included in the final model. Association between potential covariates and the outcome variables was assessed using multivariate logistic regression. Explanatory variables having a p-value <0.05 from the multivariable model were considered as having a statistically significant association with the outcome. Measures of association were determined using adjusted odds ratio with a 95% confidence interval (CI) from the multivariate logistics regression. The Clopper-Pearson test statistic was used to determine the 95% confidence intervals of the outcome variables of interest. For the outcome variable malaria, maternal age, educational level, marital status, occupation, gravidity, gestational age, IPTp-SP and LLIN use and ANC as scheduled were included in the multivariate model whereas for the outcome variable anaemia, educational level, location, malaria parasitemia and ANC as scheduled were included in the multivariate model. The model goodness of fit was assessed using the Hosmer and Lemeshow test. The model correctly classified 91.0% of the malaria cases and 66.5% of the anaemia cases. We also determined the population attributable fraction (PAF) to estimate the excess anaemia cases that can be attributed to malaria among the pregnant women studied using the category specific formula; PAF = pd[(RRi-1)/ RRi)] where pd = the proportion of anaemia cases exposed to malaria, and RRi = the adjusted relative risk for exposure to malaria relative to no exposure to malaria [18]. Applying the aforementioned formula, the 95% CI of the PAF was computed using the 95% CI of the corresponding adjusted RRi [19]. Ethical clearance for the study was obtained from the Committee on Human Research, Publications and Ethics (CHRPE) of the School of Medical Sciences (SMS), Kwame Nkrumah University of Science and Technology, Kumasi. Permission to undertake the study at the Akatsi South District Hospital was sought and granted by the Hospital Management and the Head of the laboratory before data collection. In addition, written consent was obtained from all participants who agreed to partake in the study after they were thoroughly informed and sensitized about the study.

AI Digest

Study Justification:

– The study aimed to determine the prevalence and risk factors of malaria and anaemia among pregnant women in the Akatsi South District of Ghana.
– The study also aimed to assess the impact of preventive methods, such as IPTp-SP and LLIN, on malaria prevalence among pregnant women.
– The study is important because malaria and anaemia are significant health concerns for pregnant women, and understanding their prevalence and risk factors can inform effective prevention and treatment strategies.

Study Highlights:

– The study found that the prevalence of anaemia in pregnancy (AiP) was 63.5%, malaria in pregnancy (MiP) was 11.0%, and the comorbidity of AiP and MiP was 10.5%.
– Pregnant women aged

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study design (hospital-based cross-sectional study) and sample size (200 pregnant women) provide a good foundation for the research. The data collection methods (questionnaire and laboratory tests) are well-described. The statistical analysis (multivariate logistic regression) is appropriate for the research questions. However, there are a few areas that could be improved. First, the abstract does not mention any limitations of the study, such as potential biases or confounding factors. Second, the abstract does not provide any information about the generalizability of the findings beyond the study population. Finally, the abstract could benefit from a clearer statement of the implications of the findings and potential recommendations for future research or interventions. To improve the evidence, the authors could address these areas by discussing limitations, generalizability, and implications in the abstract.

Abstract

A hospital based cross-sectional study using simple random sampling technique was carried out among 200 pregnant women (Fig 1) receiving antenatal care and laboratory services at the Akatsi South District Hospital, Ghana from May to July, 2016. The Akatsi District hospital is one of the 29 health facilities in the District (Source: Akatsi South District Health Directorate, 2017). The hospital at the time of this study had a staff strength of 140 healthcare workers including two doctors, a medical assistant, a pharmacist, and a biomedical scientist. The hospital is a 70-bed capacity facility, with a maternity unit, medical laboratory unit, family planning unit and an outpatient department. It also serves as a referral center for the surrounding primary health facilities in the district. The choice of Akatsi South District for this study emanates from the fact that malaria consistently remained the topmost disease among the top ten diseases that affect the majority of its population [15]. Pregnant women who were registered with the Akatsi South District Hospital but were not residents of the Akatsi South District were excluded from the study to ensure that reported prevalence rates of malaria and anaemia represent an accurate reflection of the true status of malaria and anaemia prevalence among participants from the study area only. Non-consenting pregnant women were also excluded from the study. Participation of the respondents was voluntary. The minimum sample size for the study was determined using the Raosoft Online Sample Size Calculation formula (http://www.raosoft.com/samplesize.html) with the following parameters; 5% margin of error (E), 95% confidence level, average annual antenatal attendance (N) of approximately 2500, a response rate (r) of 85% (obtained from a prior pilot study), number of ‘positive’ observations (x) of 4898.04 and a critical value (Z(c/100)) of 1.96. The required formulas are: Let Z(c/100) = 1.96 r = 85% N = 7796 Hence x = 1.962 * 85(100−85) = 4898.04 Substituting N = 2500 and x = 4898.04 into (2) to determine the minimum sample size n, gives Hence, the minimum required sample size for this study was 182 pregnant women from the Akatsi South District. Two hundred (200) pregnant women was however sampled to increase statistical power and reliability of the findings. A semi-structured questionnaire was administered to the pregnant women by the researchers. The questionnaire was designed in English and then translated via interview to the respondent in the language best understood by the respondent: Ewe and Twi predominantly. The sociodemographic characteristics of the respondents were maternal age, level of education, occupation, location, residential status, and marital status. Obstetric details included gravidity, parity, the gestational period in weeks (definition: first trimester, from week 1 to the end of week 12; second trimester, from week 13 to the end of week 26 and third trimester, from week 27 to the end of the pregnancy) and attended ANC as scheduled. Malaria preventive methods were IPTp-SP use, sleeping under LLIN and the use of mosquito repellent. None of the respondents reported using insecticide sprays, mosquito coils, and/or creams to prevent malaria in this study. About one milliliter (1 ml) whole blood was collected from each pregnant woman from the median cubital vein of the antecubital fossa of the arm into an EDTA tube using a sterile syringe and needle following standard blood collection protocols. Samples were collected at the onset of the rains, between May and July 2016 which marks the period of the malaria transmission season. The blood film preparation and observation were done following standard procedures outlined by Monica Chesbrough [16]. Briefly, thick blood films were then prepared on a clean grease-free glass slide using 6 ul of whole blood, spread evenly to cover a dimension of about 15×15 mm of the center of the glass. The smears were air-dried, fixed in absolute methanol, and then left to air-dry in about 1–2 minutes ready for staining. The slides were then stained with 10% Giemsa for 10 minutes and then examined under oil immersion (magnification x100) with a light microscope. Reading of the slides was carried out by two trained microscopists. The absence of malaria parasite after 200 high power fields was examined was considered negative for malaria. A discrepancy in the slide reading outcome between the two initial scientists was resolved by a third opinion from a senior microscopist. Participants’ hemoglobin concentration was determined using the fully automated Sysmex XS-800i hematology analyzer (https://www.sysmex-europe.com/n/products/products-detail/xs-800i.html; Europe). All test procedures were carried out following standard quality-controlled procedures. Anaemia in pregnant women was defined as a hemoglobin concentration less than 11.0 g/dL while hemoglobin concentration greater than or equal to 11.0 g/dL was considered normal. Anaemia was further subclassified as mild (hemoglobin: 10–10.9 g/dL), moderate (hemoglobin: 7–9.9 g/dL) and severe anaemia (hemoglobin <7 g/dL) [17]. Our Sysmex XS-800i hematology analyzer was quality controlled following the manufacturer’s instruction by running manufacturer’s provided quality controlled samples to ensure the accuracy and precision of the instrument before running the samples of the study participants. Known malaria positive and negative slides were used to quality control the 10% Giemsa stain. Two independent and qualified parasitologists examined 15% of both positive and negative well prepared randomly selected slides. A third and final opinion of a third senior parasitologist was sought in the instance of any discrepancy in the two initial examinations. Consistency in microscopic outcomes signaled good working Giemsa stain. Data collected from the questionnaires and results from the laboratory investigations were checked for consistency, completeness and then captured into Microsoft Excel 2016 using fit-for-purpose excel form to avoid as many as possible entry errors. Data were then exported into Statistical Package for Social Sciences (SPSS) for Windows (version 21.0) and STATA (version 15.0) where appropriate for statistical analysis. All data were categorical, therefore, descriptive analysis was done and presented as frequencies and percentages in parenthesis. Chi-square and Fisher’s exact test where appropriate were used to assess statistical associations between explanatory variables and outcome variables. Factors that produced a p-value ≤0.2 from the bivariate model were included in the final model. Association between potential covariates and the outcome variables was assessed using multivariate logistic regression. Explanatory variables having a p-value <0.05 from the multivariable model were considered as having a statistically significant association with the outcome. Measures of association were determined using adjusted odds ratio with a 95% confidence interval (CI) from the multivariate logistics regression. The Clopper-Pearson test statistic was used to determine the 95% confidence intervals of the outcome variables of interest. For the outcome variable malaria, maternal age, educational level, marital status, occupation, gravidity, gestational age, IPTp-SP and LLIN use and ANC as scheduled were included in the multivariate model whereas for the outcome variable anaemia, educational level, location, malaria parasitemia and ANC as scheduled were included in the multivariate model. The model goodness of fit was assessed using the Hosmer and Lemeshow test. The model correctly classified 91.0% of the malaria cases and 66.5% of the anaemia cases. We also determined the population attributable fraction (PAF) to estimate the excess anaemia cases that can be attributed to malaria among the pregnant women studied using the category specific formula; PAF = pd[(RRi-1)/ RRi)] where pd = the proportion of anaemia cases exposed to malaria, and RRi = the adjusted relative risk for exposure to malaria relative to no exposure to malaria [18]. Applying the aforementioned formula, the 95% CI of the PAF was computed using the 95% CI of the corresponding adjusted RRi [19]. Ethical clearance for the study was obtained from the Committee on Human Research, Publications and Ethics (CHRPE) of the School of Medical Sciences (SMS), Kwame Nkrumah University of Science and Technology, Kumasi. Permission to undertake the study at the Akatsi South District Hospital was sought and granted by the Hospital Management and the Head of the laboratory before data collection. In addition, written consent was obtained from all participants who agreed to partake in the study after they were thoroughly informed and sensitized about the study.

Materials

https://efashare.b-cdn.net/materials/e020b952fd317c2193a22e0b98aecfebcb5a8425c24b7a92d7b3be50caccd64d/pone.0271211.s001.docx

Innovations

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

1. Mobile health (mHealth) applications: Develop mobile applications that provide pregnant women with information on prenatal care, nutrition, and preventive measures for malaria and anaemia. These apps can also send reminders for antenatal care appointments and provide access to telemedicine consultations.

2. Community health workers: Train and deploy community health workers to provide education and support to pregnant women in remote areas. These workers can conduct home visits, provide health counseling, distribute mosquito nets, and ensure adherence to preventive measures.

3. Telemedicine services: Establish telemedicine services that allow pregnant women to consult with healthcare providers remotely. This can help overcome geographical barriers and provide timely access to medical advice and support.

4. Integrated antenatal care: Implement a comprehensive antenatal care program that includes screening and treatment for malaria and anaemia. This can be done by integrating malaria and anaemia prevention and management into routine antenatal care visits.

5. Improved supply chain management: Strengthen the supply chain for essential maternal health commodities, such as insecticide-treated bed nets, antimalarial drugs, and iron supplements. This can ensure consistent availability and accessibility of these commodities in healthcare facilities.

6. Health education campaigns: Conduct targeted health education campaigns to raise awareness about the importance of antenatal care, malaria prevention, and anaemia management among pregnant women and their communities. This can help dispel myths and misconceptions and promote positive health-seeking behaviors.

7. Public-private partnerships: Foster collaborations between the public and private sectors to improve access to maternal health services. This can involve leveraging private healthcare providers, pharmacies, and community-based organizations to expand service delivery and reach underserved populations.

8. Maternal health financing mechanisms: Develop innovative financing mechanisms, such as health insurance schemes or conditional cash transfers, to reduce financial barriers to accessing maternal health services. This can help ensure that pregnant women can afford the necessary care and interventions.

9. Strengthening referral systems: Improve the referral systems between primary healthcare facilities and higher-level facilities to ensure timely access to specialized maternal health services, such as emergency obstetric care. This can involve training healthcare providers, establishing clear referral protocols, and improving transportation options.

10. Continuous monitoring and evaluation: Implement robust monitoring and evaluation systems to track the impact of interventions and identify areas for improvement. This can help ensure that resources are allocated effectively and interventions are tailored to the specific needs of the population.

It is important to note that these recommendations are based on the information provided and may need to be adapted to the specific context and resources available in the Akatsi South District in Ghana.

AI Innovations Description

Based on the information provided, the following recommendation can be developed into an innovation to improve access to maternal health:

1. Strengthening the implementation of Intermittent Preventive Treatment in Pregnancy (IPTp-SP) and Long-Lasting Insecticidal Nets (LLIN): The study found that high utilization of IPTp-SP and LLIN had a positive effect on malaria prevalence among pregnant women. To further improve access to maternal health, it is recommended to enhance the implementation of IPTp-SP and LLIN programs. This can be done by ensuring an adequate supply of IPTp-SP and LLINs in healthcare facilities, training healthcare workers on the proper administration of IPTp-SP, and conducting awareness campaigns to educate pregnant women about the importance of using LLINs.

2. Improving access to antenatal care (ANC): The study found that failure to attend ANC as scheduled posed a significant risk of anaemia in pregnant women. To address this issue, efforts should be made to improve access to ANC services. This can be achieved by increasing the number of healthcare facilities offering ANC services, extending the operating hours of ANC clinics to accommodate women’s schedules, and implementing community-based ANC programs to reach pregnant women in remote areas.

3. Enhancing malaria and anaemia screening and treatment: The study revealed a high prevalence of malaria and anaemia among pregnant women. To improve access to maternal health, it is crucial to strengthen malaria and anaemia screening and treatment services. This can be done by training healthcare workers on the proper diagnosis and treatment of malaria and anaemia, ensuring the availability of diagnostic tools and medications in healthcare facilities, and implementing regular monitoring and evaluation of screening and treatment programs.

4. Promoting health education and awareness: The study highlighted the importance of health education and awareness in preventing malaria and anaemia among pregnant women. To improve access to maternal health, it is essential to conduct health education campaigns to educate pregnant women about the risks of malaria and anaemia, the importance of preventive measures such as IPTp-SP and LLINs, and the benefits of attending ANC regularly. This can be done through community outreach programs, radio broadcasts, and the distribution of educational materials.

Overall, by implementing these recommendations, access to maternal health can be improved, leading to a reduction in the prevalence of malaria and anaemia among pregnant women.

AI Innovations Methodology

Based on the provided information, the study aimed to determine the prevalence and risk factors of malaria and anaemia among pregnant women in the Akatsi South District Hospital in Ghana. The study also assessed the impact of preventive methods, such as IPTp-SP use, sleeping under LLIN, and the use of mosquito repellent, on malaria prevalence among pregnant women.

To simulate the impact of the recommendations on improving access to maternal health, the following methodology can be used:

1. Identify the recommendations: Based on the study findings, the recommendations could include increasing access to IPTp-SP and LLIN, promoting the use of mosquito repellent, and ensuring regular attendance at antenatal care (ANC) appointments.

2. Define the target population: Determine the population that will benefit from the recommendations, such as pregnant women in the Akatsi South District or a specific subgroup based on risk factors.

3. Collect baseline data: Gather data on the current access to maternal health services, including the utilization rates of IPTp-SP, LLIN, and ANC, as well as the prevalence of malaria and anaemia among pregnant women.

4. Develop a simulation model: Create a mathematical model that represents the population and the factors influencing access to maternal health. The model should consider variables such as population size, demographic characteristics, healthcare infrastructure, and the impact of the recommendations.

5. Input data and parameters: Input the baseline data and parameters into the simulation model. This includes information on the prevalence of malaria and anaemia, utilization rates of preventive methods, and the effectiveness of the recommendations.

6. Run simulations: Use the simulation model to simulate different scenarios based on the recommendations. For example, simulate the impact of increasing access to IPTp-SP and LLIN on reducing the prevalence of malaria among pregnant women.

7. Analyze results: Analyze the simulation results to assess the impact of the recommendations on improving access to maternal health. This can include evaluating changes in malaria and anaemia prevalence, utilization rates of preventive methods, and other relevant indicators.

8. Validate the model: Validate the simulation model by comparing the simulated results with real-world data or expert opinions. Adjust the model if necessary to improve its accuracy and reliability.

9. Communicate findings: Present the findings of the simulation study, including the potential impact of the recommendations on improving access to maternal health. This can be done through reports, presentations, or other means of communication to stakeholders and policymakers.

By following this methodology, researchers and policymakers can gain insights into the potential impact of recommendations on improving access to maternal health and make informed decisions on implementing interventions to address maternal health challenges.

Author

Dima Health

Identifiers:

DOI: 10.1371/journal.pone.0271211

Research Areas:

Environmental, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Technology and Innovations, Workforce

Study Countries:

Ghana

Study Design:

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

Study Approach:

Mixed-methods, Qualitative, Quantitative

Participants Gender:

Female