Equity in maternal health outcomes in a middle-income urban setting: A cohort study

Reproductive Health, Volume 16, No. 1, Year 2019

Interprétation

Background: Low socioeconomic status (SES) is associated with more adverse perinatal health outcomes, risk factors and lower access to and use of maternal health care services. However, evidence for the association between SES and maternal health outcomes is limited, particularly for middle-income countries like sub-Saharan Ghana. We assessed the association between parental SES and adverse maternal and perinatal outcomes of Ghanaian women during pregnancy, delivery and the postpartum period. Methods: A prospective cohort study of 1010 women of two public hospitals in Accra, Ghana (2012-2014). SES was proxied by maternal and paternal education, wealth and employment status. The association of SES with maternal and perinatal outcomes was analyzed with multivariable logistic and linear regression. Results: The analysis included 790 women with information on pregnancy outcomes. Average age was 28.2 years (standard deviation, SD 5.0). Over a third (n = 292, 37.0%) had low SES, 176 (22.3%) were classified to have high SES using the assets index. Nearly half (n = 374, 47.3%) of women had lower secondary school or vocational training as highest education level. Compared to women with middle assets SES, women with low assets SES were at higher risk for miscarriage (odds ratio, OR 1.61, 95% CI 1.06 to 2.45) and instrumental delivery (OR 1.74, 95% CI 1.03 to 2.94), but this association was not observed for the other SES proxies. For any of the maternal or perinatal outcomes and SES proxies, no other statistically significant differences were found. Conclusion: Women attending public maternal health care services in urban Ghana had overall equitable maternal and perinatal health outcomes, with the exception of a higher risk of miscarriage and instrumental delivery associated with low assets SES. This suggests known associations between SES, risk factors and outcomes could be mitigated with universal and accessible maternal health services. This prospective cohort study was developed to assess factors related to maternal and perinatal outcomes of pregnant Ghanaian women, as described in detail elsewhere [15, 16]. Ghana has maternal mortality of 219 per 100.000 live births in 2015 and is classified as a middle-income country with an above median Human Development Index [17, 18]. The study was conducted at two outpatient departments (OPDs) of public hospitals in Accra, Ghana: the Maamobi General Hospital and Ridge Regional Hospital. The Accra Metropolis is one of the local government districts of the Greater Accra Region of Ghana. The Greater Accra Region is the most densely populated region in Ghana and 90% urban, compared to 50% national urban residence [19]. The population growth in Accra is the highest in the country – primarily through migration for relatively better employment opportunities and the region has the lowest number of children born per woman. The Greater Accra Region has the lowest poverty levels in the country. Pregnant women in Ghana receive universal health insurance through the national health insurance scheme (NHIS) [20]. Data from 1101 adult women were collected in the Accra Metropolis in Ghana from July 2012 to March 2014. Women were eligible for participation if they were over 18 years old, less than 17 weeks pregnant. Women with known pre-existent hypertension were excluded, because the initial aim of the cohort was to assess the incidence of gestational hypertension. Inequities in health outcomes were assessed based on participants socio economic status (SES). Four proxies were used to estimate SES: maternal and paternal education, wealth index and employment status. Level of maternal and paternal education was classified into: (1) no education or primary school, (2) lower secondary school or vocational training, and (3) senior secondary school, professional school or higher tertiary education. This classification was both conceptually and data driven (i.e. sufficiently large categories of women whose education level was considered comparable). An asset (or wealth) index (range of − 10 to 20) was obtained through a principle component analysis (PCA) of various household assets and household characteristics. As such, the index estimates the relative wealth of a household by looking at their living conditions and items the household owns, allowing for differentiation of SES status within this population as described by Vyas and Kumaranayake [21]. The variables included in the PCA were presence and quantity of: irons, refrigerators, televisions, VCDDVD set, radio, landline phone, mobile phone, computer, generator, fan, mattresses or beds, watch/clock, sewing machine, modern stove, bicycle, motorcycle, car or truck and bednets. Household characteristics were also included in the PCA: whether any of the household members owned the house, the number of rooms in the house, materials of floors and roof, kind of toilet facilities, fuel used for cooking and where the household accessed water. The index was both used as a continuous variable and categorized according to quintiles: (1) low (lowest two quintiles), (2) middle (third and fourth quintiles), and (3) high (highest quintile), as described elsewhere [15, 16]. Employment was classified into (1) informal sector employment and (2) formal sector employment. Other exposure variables: demographics and anthropometryOther covariates included woman’s age in years; body mass index (BMI) (m/kg2) based on measured weight and height; parity (0–1, 2–3, ≥4); gestational age based on ultrasound: first trimester (< 13 weeks), second trimester (≥13 weeks); area of birth (Ghana urban, Ghana rural, West African country); area of residence (Accra metropolitan area, other urban area, peri-urban and rural area); ethnicity (Akan, Hausa, Ewe, Ga Ga-Dangme, other); religion (Christian, Islam) and marital status (single or widowed, married, engaged or living together). Gestational hypertension (GH) was defined according to the ISSHP definition as “a systolic blood pressure ≥140 mmHg and/or a diastolic blood pressure ≥90 mmHg after 20 weeks gestation, measured twice, with women who previously had normal blood pressure” [22]. Blood pressure was measured according to Korotkov V according to hospital protocols [15, 16]. Pre-eclampsia (PE) was defined as “the combination of pregnancy induced hypertension with proteinuria (≥300 mg/ 24 hours), or minimal 1+ on a dipstick” [22]. Because of the low numbers of women GH and PE in the cohort, these two outcomes were combined and further referred to as hypertensive disorders (yes/no) of pregnancy. Postpartum hemorrhage (PPH) was defined as ‘blood loss more than 500 ml in the first 24 h after delivery [23]. The total blood loss was visually estimated by the midwives of the two hospitals. PPH was categorized into two groups based on the estimated amount of blood loss; < 500 ml and ≥ 500 ml. Maternal mortality was defined as “direct mortality due to complications of pregnancy, delivery, and puerperium”. Mode of delivery was defined as either spontaneous vaginal delivery or instrumental delivery including cesarean section (CS) and assisted delivery (vacuum or forceps). Because of the low numbers of cesarean and assisted delivery, these categories were combined to allow for higher numbers of women per category. WHO definitions were used for miscarriage, perinatal mortality, stillbirth, and preterm birth, as previously described [15, 16]. Apgar score was evaluated at 5 min after birth based on heart rate, respiratory effort, muscle tone, reflex irritability, and skin color. A score of ≥7 was considered normal. Birth weight was analyzed both as continuous and categorical variables (low birth weight (< 2500 g), normal birth weight (≥2500 and ≤ 4000 g), or macrosomia (> 4000 g)). Women were recruited at the their first antenatal care (ANC) visit, where baseline independent variable data was collected by seven trained research assistants. The assistants used a structured questionnaire for socio-demographic characteristics (area of birth, area of residence, ethnical groups, religion and marital status), socio-economic characteristics (level of education, economic activity, assets, and household characteristics), and health status including obstetric history. Pregnancy outcomes, both maternal and neonatal, were obtained from the patient registers available at the two participating hospitals. The information contained in the antenatal record books (which women keep themselves throughout their pregnancy) was also used for prenatal information. Data collection occurred at enrolment, after delivery and at 6 weeks postpartum during the postnatal visit. Prior to the start of the study, questionnaires were validated. Data was entered by trained data clerks using EpiDataEntry software (EpiData Association, Odense, Denmark, 2010). The data was validated by double entry and checked for missing data. Participant characteristics were analyzed descriptively with frequencies (%) and means (standard deviation, SD) where appropriate, by categories of SES. Group (SES) differences were assessed by chi-square test (or Fisher’s exact) and one-way ANOVA for categorical and continuous variables respectively. Depending on the type of outcome variables (binary or continuous), logistic or linear regression analyses were used. Odds ratios (OR) and linear coefficients with corresponding 95% confidence intervals (CI) and two-sided p-values were respectively reported. In adjusted models, regression analysis were controlled for maternal age and body mass index (BMI). For SES estimates with multiple levels, the middle SES group was used as reference. For all analyses, participating women had to have at least one recorded maternal or perinatal outcome. If not, women were considered loss to follow up and not included in the analysis. Missing data was considered missing completely at random (MCAR) and complete-case analysis performed. All analyses were performed using IBM SPSS Statistics version 22 [24]. This study was approved by the Ghana Health Services Ethical Review Committee (GHS-ERC 07/9/11). All participants provided (written or thumb-printed) informed consent.

Résumé de l’IA

Study Justification:

This study aimed to assess the association between socioeconomic status (SES) and maternal and perinatal outcomes among pregnant women in urban Ghana. The justification for this study is based on the limited evidence available on the relationship between SES and maternal health outcomes in middle-income countries like Ghana. By understanding this association, policymakers and healthcare providers can develop strategies to mitigate the impact of low SES on maternal and perinatal health.

Highlights:

– The study included 790 women attending public maternal health care services in urban Ghana.
– The analysis found that women with low socioeconomic status (SES) had a higher risk of miscarriage and instrumental delivery compared to women with middle SES.
– However, for other SES proxies (maternal and paternal education, wealth index, and employment status), no statistically significant differences were found in maternal and perinatal outcomes.
– Overall, the study suggests that with universal and accessible maternal health services, known associations between SES, risk factors, and outcomes can be mitigated.

Recommendations for Lay Reader:

– The study found that women with low socioeconomic status in urban Ghana had a higher risk of miscarriage and instrumental delivery.
– It is recommended to ensure universal and accessible maternal health services to mitigate the impact of socioeconomic status on maternal and perinatal health outcomes.

Recommendations for Policy Maker:

– Based on the study findings, it is recommended to prioritize the provision of universal and accessible maternal health services in urban Ghana.
– Efforts should be made to address the higher risk of miscarriage and instrumental delivery among women with low socioeconomic status.
– Policymakers should consider implementing strategies to improve socioeconomic conditions and reduce disparities in maternal and perinatal health outcomes.

Key Role Players:

– Healthcare providers: Obstetricians, midwives, nurses, and other healthcare professionals involved in maternal health care services.
– Policy makers: Government officials, health ministry representatives, and other stakeholders responsible for developing and implementing healthcare policies.
– Community leaders: Local leaders and community organizations that can advocate for improved maternal health services and address socioeconomic disparities.
– Researchers: Experts in maternal health and epidemiology who can provide evidence-based recommendations and guidance.

Cost Items for Planning Recommendations:

– Infrastructure: Budget for improving and expanding healthcare facilities, including maternity wards and delivery rooms.
– Human resources: Funds for hiring and training healthcare professionals, including obstetricians, midwives, and nurses.
– Equipment and supplies: Budget for purchasing medical equipment, instruments, medications, and other necessary supplies for maternal health services.
– Outreach and education: Funds for community outreach programs, health education campaigns, and awareness initiatives to promote maternal health and address socioeconomic disparities.
– Monitoring and evaluation: Budget for data collection, analysis, and monitoring of maternal health outcomes to assess the effectiveness of interventions and policies.

Force de la preuve

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong, but there are some areas for improvement. The study design is a prospective cohort study, which is generally considered to provide reliable evidence. The sample size of 790 women is relatively large, which increases the statistical power of the study. The study also includes multivariable logistic and linear regression analyses to assess the association between socioeconomic status (SES) and maternal and perinatal outcomes. However, there are a few limitations that could be addressed to improve the strength of the evidence. First, the abstract does not provide information on the representativeness of the study sample, which could affect the generalizability of the findings. Second, the abstract does not mention any measures taken to minimize bias, such as blinding or randomization. Third, the abstract does not provide information on the validity and reliability of the data collection methods. To improve the strength of the evidence, future studies could consider addressing these limitations by providing information on the representativeness of the sample, implementing measures to minimize bias, and using validated and reliable data collection methods.

Abstrait

This prospective cohort study was developed to assess factors related to maternal and perinatal outcomes of pregnant Ghanaian women, as described in detail elsewhere [15, 16]. Ghana has maternal mortality of 219 per 100.000 live births in 2015 and is classified as a middle-income country with an above median Human Development Index [17, 18]. The study was conducted at two outpatient departments (OPDs) of public hospitals in Accra, Ghana: the Maamobi General Hospital and Ridge Regional Hospital. The Accra Metropolis is one of the local government districts of the Greater Accra Region of Ghana. The Greater Accra Region is the most densely populated region in Ghana and 90% urban, compared to 50% national urban residence [19]. The population growth in Accra is the highest in the country – primarily through migration for relatively better employment opportunities and the region has the lowest number of children born per woman. The Greater Accra Region has the lowest poverty levels in the country. Pregnant women in Ghana receive universal health insurance through the national health insurance scheme (NHIS) [20]. Data from 1101 adult women were collected in the Accra Metropolis in Ghana from July 2012 to March 2014. Women were eligible for participation if they were over 18 years old, less than 17 weeks pregnant. Women with known pre-existent hypertension were excluded, because the initial aim of the cohort was to assess the incidence of gestational hypertension. Inequities in health outcomes were assessed based on participants socio economic status (SES). Four proxies were used to estimate SES: maternal and paternal education, wealth index and employment status. Level of maternal and paternal education was classified into: (1) no education or primary school, (2) lower secondary school or vocational training, and (3) senior secondary school, professional school or higher tertiary education. This classification was both conceptually and data driven (i.e. sufficiently large categories of women whose education level was considered comparable). An asset (or wealth) index (range of − 10 to 20) was obtained through a principle component analysis (PCA) of various household assets and household characteristics. As such, the index estimates the relative wealth of a household by looking at their living conditions and items the household owns, allowing for differentiation of SES status within this population as described by Vyas and Kumaranayake [21]. The variables included in the PCA were presence and quantity of: irons, refrigerators, televisions, VCDDVD set, radio, landline phone, mobile phone, computer, generator, fan, mattresses or beds, watch/clock, sewing machine, modern stove, bicycle, motorcycle, car or truck and bednets. Household characteristics were also included in the PCA: whether any of the household members owned the house, the number of rooms in the house, materials of floors and roof, kind of toilet facilities, fuel used for cooking and where the household accessed water. The index was both used as a continuous variable and categorized according to quintiles: (1) low (lowest two quintiles), (2) middle (third and fourth quintiles), and (3) high (highest quintile), as described elsewhere [15, 16]. Employment was classified into (1) informal sector employment and (2) formal sector employment. Other exposure variables: demographics and anthropometryOther covariates included woman’s age in years; body mass index (BMI) (m/kg2) based on measured weight and height; parity (0–1, 2–3, ≥4); gestational age based on ultrasound: first trimester (< 13 weeks), second trimester (≥13 weeks); area of birth (Ghana urban, Ghana rural, West African country); area of residence (Accra metropolitan area, other urban area, peri-urban and rural area); ethnicity (Akan, Hausa, Ewe, Ga Ga-Dangme, other); religion (Christian, Islam) and marital status (single or widowed, married, engaged or living together). Gestational hypertension (GH) was defined according to the ISSHP definition as “a systolic blood pressure ≥140 mmHg and/or a diastolic blood pressure ≥90 mmHg after 20 weeks gestation, measured twice, with women who previously had normal blood pressure” [22]. Blood pressure was measured according to Korotkov V according to hospital protocols [15, 16]. Pre-eclampsia (PE) was defined as “the combination of pregnancy induced hypertension with proteinuria (≥300 mg/ 24 hours), or minimal 1+ on a dipstick” [22]. Because of the low numbers of women GH and PE in the cohort, these two outcomes were combined and further referred to as hypertensive disorders (yes/no) of pregnancy. Postpartum hemorrhage (PPH) was defined as ‘blood loss more than 500 ml in the first 24 h after delivery [23]. The total blood loss was visually estimated by the midwives of the two hospitals. PPH was categorized into two groups based on the estimated amount of blood loss; < 500 ml and ≥ 500 ml. Maternal mortality was defined as “direct mortality due to complications of pregnancy, delivery, and puerperium”. Mode of delivery was defined as either spontaneous vaginal delivery or instrumental delivery including cesarean section (CS) and assisted delivery (vacuum or forceps). Because of the low numbers of cesarean and assisted delivery, these categories were combined to allow for higher numbers of women per category. WHO definitions were used for miscarriage, perinatal mortality, stillbirth, and preterm birth, as previously described [15, 16]. Apgar score was evaluated at 5 min after birth based on heart rate, respiratory effort, muscle tone, reflex irritability, and skin color. A score of ≥7 was considered normal. Birth weight was analyzed both as continuous and categorical variables (low birth weight ( 4000 g)). Women were recruited at the their first antenatal care (ANC) visit, where baseline independent variable data was collected by seven trained research assistants. The assistants used a structured questionnaire for socio-demographic characteristics (area of birth, area of residence, ethnical groups, religion and marital status), socio-economic characteristics (level of education, economic activity, assets, and household characteristics), and health status including obstetric history. Pregnancy outcomes, both maternal and neonatal, were obtained from the patient registers available at the two participating hospitals. The information contained in the antenatal record books (which women keep themselves throughout their pregnancy) was also used for prenatal information. Data collection occurred at enrolment, after delivery and at 6 weeks postpartum during the postnatal visit. Prior to the start of the study, questionnaires were validated. Data was entered by trained data clerks using EpiDataEntry software (EpiData Association, Odense, Denmark, 2010). The data was validated by double entry and checked for missing data. Participant characteristics were analyzed descriptively with frequencies (%) and means (standard deviation, SD) where appropriate, by categories of SES. Group (SES) differences were assessed by chi-square test (or Fisher’s exact) and one-way ANOVA for categorical and continuous variables respectively. Depending on the type of outcome variables (binary or continuous), logistic or linear regression analyses were used. Odds ratios (OR) and linear coefficients with corresponding 95% confidence intervals (CI) and two-sided p-values were respectively reported. In adjusted models, regression analysis were controlled for maternal age and body mass index (BMI). For SES estimates with multiple levels, the middle SES group was used as reference. For all analyses, participating women had to have at least one recorded maternal or perinatal outcome. If not, women were considered loss to follow up and not included in the analysis. Missing data was considered missing completely at random (MCAR) and complete-case analysis performed. All analyses were performed using IBM SPSS Statistics version 22 [24]. This study was approved by the Ghana Health Services Ethical Review Committee (GHS-ERC 07/9/11). All participants provided (written or thumb-printed) informed consent.

Matériaux

https://efashare.b-cdn.net/materials/dfa9f5d2d48e9f3f7fa508c3fcb02f47647178e557d39fbe5837d056ec40e89a/12978_2019_736_MOESM1_ESM.docx

Innovations

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

1. Mobile health (mHealth) interventions: Develop mobile applications or text messaging services to provide pregnant women with information on prenatal care, nutrition, and healthy behaviors. These interventions can also be used to send reminders for appointments and medication adherence.

2. Telemedicine: Implement telemedicine programs to provide remote consultations and monitoring for pregnant women in rural or underserved areas. This can help overcome geographical barriers and improve access to specialized care.

3. Community health workers: Train and deploy community health workers to provide maternal health education, antenatal care, and postnatal support in remote or marginalized communities. These workers can bridge the gap between healthcare facilities and the community, ensuring that women receive the necessary care and support.

4. Financial incentives: Introduce financial incentives, such as conditional cash transfers or vouchers, to encourage pregnant women to seek and utilize maternal health services. This can help address financial barriers and increase access to quality care.

5. Maternal waiting homes: Establish maternal waiting homes near healthcare facilities to accommodate pregnant women who live far away and need to travel for delivery. These homes provide a safe and comfortable environment for women to stay during the final weeks of pregnancy, ensuring timely access to skilled birth attendants.

6. Task-shifting: Train and empower non-physician healthcare providers, such as midwives and nurses, to perform certain tasks traditionally done by doctors. This can help alleviate the shortage of skilled healthcare professionals and improve access to maternal health services.

7. Quality improvement initiatives: Implement quality improvement initiatives in healthcare facilities to ensure that maternal health services are provided in a safe and effective manner. This can involve training healthcare providers, improving infrastructure and equipment, and implementing standardized protocols and guidelines.

It is important to note that the specific innovations to be implemented should be based on the local context and needs of the community.

AI Innovations Description

Based on the information provided, the recommendation to improve access to maternal health in middle-income urban settings, such as Accra, Ghana, is to focus on implementing universal and accessible maternal health services. This recommendation is based on the findings of the cohort study, which showed that women attending public maternal health care services in urban Ghana had overall equitable maternal and perinatal health outcomes, except for a higher risk of miscarriage and instrumental delivery associated with low socioeconomic status (SES).

To address this inequity, it is important to ensure that all pregnant women, regardless of their SES, have equal access to quality maternal health services. This can be achieved through the following strategies:

1. Strengthening the public maternal health care system: Invest in improving the infrastructure, staffing, and resources of public hospitals and clinics to ensure that they can provide comprehensive and high-quality maternal health services.

2. Expanding coverage of the national health insurance scheme (NHIS): Ensure that pregnant women have access to affordable and comprehensive health insurance coverage, which includes prenatal care, delivery services, and postpartum care.

3. Enhancing community-based maternal health services: Implement community-based programs that provide prenatal care, education, and support to pregnant women, especially those from low SES backgrounds. This can include mobile clinics, community health workers, and outreach programs.

4. Addressing social determinants of health: Recognize and address the social and economic factors that contribute to inequities in maternal health outcomes, such as poverty, education, and employment. Implement interventions that aim to improve these social determinants, such as providing educational opportunities for women and creating economic empowerment programs.

5. Promoting awareness and education: Conduct public awareness campaigns to educate women and their families about the importance of maternal health care and the available services. This can help reduce barriers to accessing care, such as cultural beliefs and misconceptions.

By implementing these recommendations, it is possible to improve access to maternal health services and reduce inequities in maternal health outcomes in middle-income urban settings like Accra, Ghana.

AI Innovations Methodology

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

1. Strengthening healthcare infrastructure: Invest in improving and expanding healthcare facilities, particularly in urban areas with high population density. This includes increasing the number of hospitals, clinics, and maternity centers, as well as ensuring they have the necessary equipment and trained healthcare professionals.

2. Enhancing transportation services: Improve transportation options for pregnant women, especially in rural and peri-urban areas where access to healthcare facilities may be limited. This can be done through initiatives such as providing subsidized or free transportation vouchers, establishing mobile clinics, or implementing telemedicine services.

3. Increasing awareness and education: Conduct targeted awareness campaigns to educate women and their families about the importance of maternal health and the available healthcare services. This can help reduce cultural and social barriers that may prevent women from seeking timely and appropriate care.

4. Strengthening health insurance coverage: Ensure that all pregnant women have access to affordable and comprehensive health insurance coverage. This can help alleviate financial barriers and enable women to access necessary prenatal, delivery, and postnatal care without incurring significant out-of-pocket expenses.

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 access to maternal health, such as the number of prenatal visits, percentage of deliveries attended by skilled birth attendants, or maternal mortality rate.

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

3. Develop a simulation model: Create a mathematical or statistical model that incorporates the various factors influencing access to maternal health, such as healthcare infrastructure, transportation services, awareness levels, and health insurance coverage. This model should be based on the available data and existing evidence.

4. Introduce the recommendations: Simulate the impact of implementing the recommendations by adjusting the relevant parameters in the model. For example, increase the number of healthcare facilities, improve transportation services, or assume higher levels of awareness and health insurance coverage.

5. Analyze the results: Evaluate the simulated outcomes based on the defined indicators. Compare the results to the baseline data to assess the potential impact of the recommendations on improving access to maternal health.

6. Refine and iterate: Refine the simulation model based on the analysis results and feedback from stakeholders. Repeat the simulation process with updated parameters to further optimize the recommendations and assess their potential long-term impact.

It is important to note that simulation models are simplifications of complex real-world systems, and their accuracy depends on the quality of data and assumptions used. Therefore, it is crucial to continuously validate and update the model as new data and evidence become available.

Auteurs et co-auteurs

Statistics:

Citations: 4

Authors: 9

Identifiers:

DOI: 10.1186/s12978-019-0736-3

Research Areas:

Environmental, Health System and Policy, Maternal Access, Maternal and Child Health, Noncommunicable Diseases, Quality of Care, Sexual and Reproductive Health, Social Determinants, Workforce

Study Countries:

Ghana

Study Design:

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

Study Approach:

Quantitative

Participants Gender:

Female

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