An Analysis of Pregnancy-Related Mortality in the KEMRI/CDC Health and Demographic Surveillance System in Western Kenya

PLoS ONE, Volume 8, No. 7, Year 2013

Interprétation

Background:Pregnancy-related (PR) deaths are often a result of direct obstetric complications occurring at childbirth.Methods and Findings:To estimate the burden of and characterize risk factors for PR mortality, we evaluated deaths that occurred between 2003 and 2008 among women of childbearing age (15 to 49 years) using Health and Demographic Surveillance System data in rural western Kenya. WHO ICD definition of PR mortality was used: “the death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the cause of death”. In addition, symptoms and events at the time of death were examined using the WHO verbal autopsy methodology. Deaths were categorized as either (i) directly PR: main cause of death was ascribed as obstetric, or (ii) indirectly PR: main cause of death was non-obstetric. Of 3,223 deaths in women 15 to 49 years, 249 (7.7%) were PR. One-third (34%) of these were due to direct obstetric causes, predominantly postpartum hemorrhage, abortion complications and puerperal sepsis. Two-thirds were indirect; three-quarters were attributable to human immunodeficiency virus (HIV/AIDS), malaria and tuberculosis. Significantly more women who died in lower socio-economic groups sought care from traditional birth attendants (p = 0.034), while less impoverished women were more likely to seek hospital care (p = 0.001). The PR mortality ratio over the six years was 740 (95% CI 651-838) per 100,000 live births, with no evidence of reduction over time (χ2 linear trend = 1.07; p = 0.3).Conclusions:These data supplement current scanty information on the relationship between infectious diseases and poor maternal outcomes in Africa. They indicate low uptake of maternal health interventions in women dying during pregnancy and postpartum, suggesting improved access to and increased uptake of skilled obstetric care, as well as preventive measures against HIV/AIDS, malaria and tuberculosis among all women of childbearing age may help to reduce pregnancy-related mortality. © 2013. The study site is located in a rural part of Nyanza Province in western Kenya in the areas of Asembo (Rarieda District), Karemo (Siaya District) and Gem (Gem District) in Siaya County [9], [10]. The population comprises approximately 225,000 individuals living in 385 villages spread over 700 km2. It has a typical rural African population age distribution with 44.6% under 15 years of age, and only 5.5% over 65 years of age. By 2008, a total of 94,106 persons were aged 15–49 years, 41.7% of the population, of whom 50,820 (54%) were women of childbearing age. The population is culturally homogeneous; over 95% are members of the Luo ethnic community and live through subsistence farming and local trading. The society is polygynous, with males frequently having more than one wife, each of whom lives in a separate house with young children within a single compound. HDSS residents (defined as those residing in the study area for at least 4 consecutive months or infants born to residents) are visited every four months. Previous studies identified the population to be generally very poor [11]. Malaria is endemic in this area, and transmission occurs throughout the year. The prevalence of malaria among individuals over 15 years of age ranged between 10–20% in the period 2006 to 2008 (KEMRI/CDC, unpublished observations). HIV, tuberculosis (TB) and geohelminth prevalence are also some of the highest in the country. In the period between 2003 and 2008, in the HDSS, the prevalence of HIV among girls between 15–19 years of age was estimated at 8.6% [12], the prevalence of TB in individuals over 15 years of age was 600/100,000 [13], and geohelminth prevalence in pregnant women was recorded to be as high as 76.2% [14]. During this time period, HIV treatment and care centers expanded [15], the coverage of malaria interventions (insecticide-treated bednets and intermittent preventive treatment in pregnancy) increased [16], and training of healthcare workers to provide focused antenatal care was rolled out [17]. There was a gradual shift in Kenyan policy from allowing traditional birth attendants (TBAs) to conduct deliveries to redefining their role as referral agents and birth companions. There are 36 health facilities in the HDSS, including one district hospital, two privately owned hospitals, 11 health centers and 22 dispensaries. The entire population is registered and geo-spatially located within the HDSS [10]. A household census (“round”) is conducted three times per year to capture pregnancies, births, deaths, and internal migration. Socio-economic status (SES), educational and marriage status data are collected every two years from all HDSS residents. Demographic data are used to provide mid-year denominators per 5-year age group, stratified by gender and study area. Deaths are captured in two ways. First, village reporters report all deaths to HDSS field supervisors as they occur. Second, community interviewers record any deaths that occurred during the prior 4 months at each routine HDSS round. Field staff then visit the GPS-located coded households at least one month after the reported death to validate deaths and record events surrounding death using VA. VAs [18] are administered to the primary caregiver of the deceased. A standardized questionnaire is used [19], to cover demographic and personal history, pre-mortem illness signs and symptoms, and events surrounding the death. VA is conducted for all deaths. The adult questionnaire is restricted to persons aged 15 years and above. For this analysis, we included women of childbearing ages: 15 to 49 years. Deaths were linked to HDSS data including socio-demographic, educational, marital status, and occupational information. For all deaths, VA information was reviewed independently and conflicts resolved by at least two clinical officers (equivalent to physician assistants in the U.S.A.) and one underlying cause of death assigned. Further details of the VA methodology used in the HDSS have been provided elsewhere [20]. Through the year 2007, VA questionnaires asked for information on miscarriage related to both spontaneous and induced abortions. In 2008, the standardized WHO VA questionnaire which only asked women to report induced abortions was adopted. However, as abortion is illegal in Kenya, we assume that the data gathered in 2008 predominantly capture spontaneous miscarriage. VA data are limited in their ability to differentiate between miscarriage and abortions, thus we do not present data separately by these categories. Following cultural customs, compound heads provide written consent for all compound members to participate in the HDSS activities. Any individual can refuse to participate at any time. The HDSS protocol and consent procedures, including surveillance and VA, are approved by KEMRI and CDC Institutional Review Boards annually. Data analyzed included all deaths in the HDSS occurring between January 1, 2003 and December 31, 2008 among female residents aged 15–49 years at the time of death. Karemo area (Siaya County), the immediate catchment area of the Siaya District Hospital, was included in the HDSS in 2008 only. The following WHO ICD definition of pregnancy-related (PR) mortality was used: “the death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the cause of death”. Deaths were further categorized as either (i) directly PR, where the main cause of death determined by VA was obstetric, or (ii) indirectly PR, where the main cause of death ascribed through VA included any non-obstetric cause: infectious, non-infectious, or external causes. Data analyses were conducted using SPSS for Windows (Release v18.0), and EpiInfo Stat Calc (CDC Atlanta, USA). In the absence of comparative data among survivors, within-death comparisons were made between PR and non-PR deaths to explore differences in characteristics, subdividing analyses into the WHO grouping of died in pregnancy, died after miscarriage/abortion, and died within 42 days of pregnancy. Key social and demographic characteristics included marital status (ever married; divorced or widowed at time of death), education (attended and completed primary school; attended secondary school), SES, and place of death (home, health facility, hospital, on route to/from hospital/health facility). A hospital is a district level or above facility, and a health facility is a local lower level facility. Routinely collected SES indicators such as occupation of household head, primary source of drinking water, use of cooking fuel, in-house assets (e.g. lantern lamp, sofa, bicycle, radio and television) and livestock (poultry, pigs, donkey cattle, sheep and goats) were used to calculate a wealth index as a weighted average using multiple correspondence analysis [21]. This was used to rank households into wealth quintiles with the first quintile representing the poorest and the fifth representing the least poor; for some analyses we collapsed into most (quintiles 1–2) and least (quintiles 3–5) poor. The significance of changes in rates over time was examined using Mantel Haenszel χ2 for linear trend. Differences between groups were determined using Pearson’s χ2 test and Fisher’s Exact test for small numbers, and a p-value of <0.05 was considered statistically significant. The pregnancy-related mortality ratio (PRMR) was calculated as the number of deaths among women of childbearing years (15–49 years) over the total number of live births to women of the same age range per year. The HDSS data are stored securely and, through a formal process of data sharing established at KEMRI/CDC, are available for access to the scientific public two years after the data are cleaned and frozen.

Résumé de l’IA

Study Justification:

– The study aims to analyze pregnancy-related mortality in a specific region in western Kenya.
– The study provides important information on the burden of pregnancy-related deaths and the risk factors associated with them.
– The study fills a gap in knowledge regarding the relationship between infectious diseases and poor maternal outcomes in Africa.
– The findings of the study can inform policies and interventions to reduce pregnancy-related mortality.

Highlights:

– The study found that 7.7% of deaths among women of childbearing age were pregnancy-related.
– One-third of these deaths were due to direct obstetric causes, while two-thirds were indirect, with HIV/AIDS, malaria, and tuberculosis being the main contributors.
– Women from lower socio-economic groups were more likely to seek care from traditional birth attendants, while less impoverished women were more likely to seek hospital care.
– The pregnancy-related mortality ratio over the six years was 740 per 100,000 live births, with no evidence of reduction over time.

Recommendations:

– Improve access to and increase uptake of skilled obstetric care for women during pregnancy and postpartum.
– Implement preventive measures against HIV/AIDS, malaria, and tuberculosis among all women of childbearing age.
– Increase awareness and education on the importance of seeking hospital care for pregnancy-related complications.
– Strengthen healthcare systems and infrastructure to provide quality maternal health interventions.

Key Role Players:

– Ministry of Health: Responsible for implementing policies and interventions to improve maternal health.
– Healthcare providers: Including doctors, nurses, and midwives, who play a crucial role in providing skilled obstetric care.
– Community health workers: Involved in raising awareness and educating women on maternal health.
– Non-governmental organizations: Engaged in implementing programs and interventions to improve maternal health.

Cost Items for Planning Recommendations:

– Training and capacity building for healthcare providers.
– Infrastructure development and improvement of healthcare facilities.
– Procurement and distribution of essential medical supplies and equipment.
– Awareness campaigns and education materials.
– Monitoring and evaluation of interventions.
– Research and data collection to inform evidence-based interventions.

Force de la preuve

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 provides detailed information on the methods and findings, including the use of the WHO definition of pregnancy-related mortality and the examination of symptoms and events at the time of death. The study also highlights the risk factors for pregnancy-related mortality, such as direct obstetric causes and infectious diseases like HIV/AIDS, malaria, and tuberculosis. The study site and population characteristics are well-described. However, the abstract could be improved by providing more specific information on the sample size and the statistical methods used. Additionally, it would be helpful to include information on the limitations of the study and any potential biases. To improve the evidence, the authors could consider providing more detailed information on the data collection process, including the validation of deaths and the resolution of conflicts in cause of death assignment. They could also discuss any efforts made to ensure the representativeness of the study population. Finally, it would be beneficial to include information on the implications of the findings and potential recommendations for policy and practice.

Abstrait

The study site is located in a rural part of Nyanza Province in western Kenya in the areas of Asembo (Rarieda District), Karemo (Siaya District) and Gem (Gem District) in Siaya County [9], [10]. The population comprises approximately 225,000 individuals living in 385 villages spread over 700 km2. It has a typical rural African population age distribution with 44.6% under 15 years of age, and only 5.5% over 65 years of age. By 2008, a total of 94,106 persons were aged 15–49 years, 41.7% of the population, of whom 50,820 (54%) were women of childbearing age. The population is culturally homogeneous; over 95% are members of the Luo ethnic community and live through subsistence farming and local trading. The society is polygynous, with males frequently having more than one wife, each of whom lives in a separate house with young children within a single compound. HDSS residents (defined as those residing in the study area for at least 4 consecutive months or infants born to residents) are visited every four months. Previous studies identified the population to be generally very poor [11]. Malaria is endemic in this area, and transmission occurs throughout the year. The prevalence of malaria among individuals over 15 years of age ranged between 10–20% in the period 2006 to 2008 (KEMRI/CDC, unpublished observations). HIV, tuberculosis (TB) and geohelminth prevalence are also some of the highest in the country. In the period between 2003 and 2008, in the HDSS, the prevalence of HIV among girls between 15–19 years of age was estimated at 8.6% [12], the prevalence of TB in individuals over 15 years of age was 600/100,000 [13], and geohelminth prevalence in pregnant women was recorded to be as high as 76.2% [14]. During this time period, HIV treatment and care centers expanded [15], the coverage of malaria interventions (insecticide-treated bednets and intermittent preventive treatment in pregnancy) increased [16], and training of healthcare workers to provide focused antenatal care was rolled out [17]. There was a gradual shift in Kenyan policy from allowing traditional birth attendants (TBAs) to conduct deliveries to redefining their role as referral agents and birth companions. There are 36 health facilities in the HDSS, including one district hospital, two privately owned hospitals, 11 health centers and 22 dispensaries. The entire population is registered and geo-spatially located within the HDSS [10]. A household census (“round”) is conducted three times per year to capture pregnancies, births, deaths, and internal migration. Socio-economic status (SES), educational and marriage status data are collected every two years from all HDSS residents. Demographic data are used to provide mid-year denominators per 5-year age group, stratified by gender and study area. Deaths are captured in two ways. First, village reporters report all deaths to HDSS field supervisors as they occur. Second, community interviewers record any deaths that occurred during the prior 4 months at each routine HDSS round. Field staff then visit the GPS-located coded households at least one month after the reported death to validate deaths and record events surrounding death using VA. VAs [18] are administered to the primary caregiver of the deceased. A standardized questionnaire is used [19], to cover demographic and personal history, pre-mortem illness signs and symptoms, and events surrounding the death. VA is conducted for all deaths. The adult questionnaire is restricted to persons aged 15 years and above. For this analysis, we included women of childbearing ages: 15 to 49 years. Deaths were linked to HDSS data including socio-demographic, educational, marital status, and occupational information. For all deaths, VA information was reviewed independently and conflicts resolved by at least two clinical officers (equivalent to physician assistants in the U.S.A.) and one underlying cause of death assigned. Further details of the VA methodology used in the HDSS have been provided elsewhere [20]. Through the year 2007, VA questionnaires asked for information on miscarriage related to both spontaneous and induced abortions. In 2008, the standardized WHO VA questionnaire which only asked women to report induced abortions was adopted. However, as abortion is illegal in Kenya, we assume that the data gathered in 2008 predominantly capture spontaneous miscarriage. VA data are limited in their ability to differentiate between miscarriage and abortions, thus we do not present data separately by these categories. Following cultural customs, compound heads provide written consent for all compound members to participate in the HDSS activities. Any individual can refuse to participate at any time. The HDSS protocol and consent procedures, including surveillance and VA, are approved by KEMRI and CDC Institutional Review Boards annually. Data analyzed included all deaths in the HDSS occurring between January 1, 2003 and December 31, 2008 among female residents aged 15–49 years at the time of death. Karemo area (Siaya County), the immediate catchment area of the Siaya District Hospital, was included in the HDSS in 2008 only. The following WHO ICD definition of pregnancy-related (PR) mortality was used: “the death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the cause of death”. Deaths were further categorized as either (i) directly PR, where the main cause of death determined by VA was obstetric, or (ii) indirectly PR, where the main cause of death ascribed through VA included any non-obstetric cause: infectious, non-infectious, or external causes. Data analyses were conducted using SPSS for Windows (Release v18.0), and EpiInfo Stat Calc (CDC Atlanta, USA). In the absence of comparative data among survivors, within-death comparisons were made between PR and non-PR deaths to explore differences in characteristics, subdividing analyses into the WHO grouping of died in pregnancy, died after miscarriage/abortion, and died within 42 days of pregnancy. Key social and demographic characteristics included marital status (ever married; divorced or widowed at time of death), education (attended and completed primary school; attended secondary school), SES, and place of death (home, health facility, hospital, on route to/from hospital/health facility). A hospital is a district level or above facility, and a health facility is a local lower level facility. Routinely collected SES indicators such as occupation of household head, primary source of drinking water, use of cooking fuel, in-house assets (e.g. lantern lamp, sofa, bicycle, radio and television) and livestock (poultry, pigs, donkey cattle, sheep and goats) were used to calculate a wealth index as a weighted average using multiple correspondence analysis [21]. This was used to rank households into wealth quintiles with the first quintile representing the poorest and the fifth representing the least poor; for some analyses we collapsed into most (quintiles 1–2) and least (quintiles 3–5) poor. The significance of changes in rates over time was examined using Mantel Haenszel χ2 for linear trend. Differences between groups were determined using Pearson’s χ2 test and Fisher’s Exact test for small numbers, and a p-value of <0.05 was considered statistically significant. The pregnancy-related mortality ratio (PRMR) was calculated as the number of deaths among women of childbearing years (15–49 years) over the total number of live births to women of the same age range per year. The HDSS data are stored securely and, through a formal process of data sharing established at KEMRI/CDC, are available for access to the scientific public two years after the data are cleaned and frozen.

Matériaux

N/A

Innovations

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

1. Mobile health clinics: Implementing mobile health clinics that can travel to rural areas, such as the study site in western Kenya, to provide maternal health services. This would increase access to care for women who may not have easy access to healthcare facilities.

2. Telemedicine: Utilizing telemedicine technology to connect healthcare providers in urban areas with pregnant women in rural areas. This would allow for remote consultations, monitoring, and guidance, reducing the need for women to travel long distances for prenatal care.

3. Community health workers: Training and deploying community health workers who can provide basic prenatal care, education, and support to pregnant women in their communities. This would help bridge the gap between healthcare facilities and remote areas.

4. Maternal health education programs: Implementing educational programs that focus on maternal health, including family planning, prenatal care, nutrition, and childbirth education. These programs can be conducted in community settings and help empower women to make informed decisions about their health.

5. Improved transportation infrastructure: Investing in transportation infrastructure, such as roads and transportation services, to improve access to healthcare facilities for pregnant women in rural areas. This would reduce barriers to accessing timely and emergency care.

6. Strengthening healthcare facilities: Investing in healthcare facilities in rural areas, including staffing, equipment, and supplies, to ensure that they can provide quality maternal health services. This would increase the capacity of these facilities to handle pregnancy-related complications.

7. Integration of maternal health services: Integrating maternal health services with other healthcare services, such as HIV/AIDS, malaria, and tuberculosis prevention and treatment. This would ensure that pregnant women receive comprehensive care that addresses their specific needs.

It’s important to note that these recommendations are based on the information provided and may need to be tailored to the specific context and needs of the study site in western Kenya.

AI Innovations Description

Based on the information provided, the recommendation to improve access to maternal health in the study area would be to focus on the following strategies:

1. Increase access to skilled obstetric care: The study found that a significant number of pregnancy-related deaths were due to direct obstetric causes, such as postpartum hemorrhage, abortion complications, and puerperal sepsis. Improving access to skilled obstetric care, including trained healthcare professionals and well-equipped facilities, is crucial in preventing and managing these complications.

2. Strengthen preventive measures against HIV/AIDS, malaria, and tuberculosis: The study revealed that two-thirds of pregnancy-related deaths were indirectly related to infectious diseases like HIV/AIDS, malaria, and tuberculosis. Implementing preventive measures, such as HIV testing and counseling, provision of insecticide-treated bed nets, and access to antiretroviral therapy, can help reduce the impact of these diseases on maternal health.

3. Promote utilization of healthcare facilities: The study found that women from lower socio-economic groups were more likely to seek care from traditional birth attendants, while less impoverished women were more likely to seek hospital care. Encouraging all women of childbearing age to utilize healthcare facilities for antenatal care, delivery, and postpartum care can help ensure access to skilled care and timely interventions.

4. Improve health education and awareness: Enhancing health education and awareness programs can empower women and their families with knowledge about maternal health, including the importance of seeking timely care, recognizing danger signs, and practicing healthy behaviors during pregnancy and childbirth.

5. Address socio-economic factors: The study identified poverty as a contributing factor to poor maternal outcomes. Addressing socio-economic factors, such as poverty alleviation programs, income generation opportunities, and social support systems, can help improve access to maternal health services for vulnerable populations.

It is important to note that these recommendations are based on the specific findings of the study conducted in the rural part of Nyanza Province in western Kenya. Implementing these recommendations would require collaboration between healthcare providers, policymakers, community leaders, and other stakeholders to ensure comprehensive and sustainable improvements in maternal health access.

AI Innovations Methodology

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

1. Increase access to skilled obstetric care: Implement strategies to ensure that pregnant women have access to skilled healthcare professionals during pregnancy, childbirth, and the postpartum period. This could involve training and deploying more midwives, nurses, and doctors in rural areas, as well as improving transportation infrastructure to facilitate access to healthcare facilities.

2. Strengthen preventive measures against HIV/AIDS, malaria, and tuberculosis: Develop and implement comprehensive programs to prevent and treat these infectious diseases among women of childbearing age. This could include increasing access to HIV testing and counseling, providing antiretroviral therapy for pregnant women living with HIV, distributing insecticide-treated bed nets to prevent malaria, and improving access to tuberculosis screening and treatment.

3. Promote education and awareness: Implement community-based education programs to raise awareness about the importance of maternal health and the available healthcare services. This could involve conducting health education sessions in schools, community centers, and through mass media channels to inform women and their families about the benefits of seeking skilled obstetric care.

4. Address socio-economic barriers: Develop strategies to address socio-economic barriers that prevent women from accessing maternal healthcare services. This could involve providing financial assistance or subsidies for healthcare services, improving the availability and affordability of transportation to healthcare facilities, and addressing cultural and social norms that discourage women from seeking skilled obstetric care.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define the indicators: Identify key indicators that measure access to maternal health, such as the number of women receiving antenatal care, the number of facility-based deliveries, and the maternal mortality ratio.

2. Collect baseline data: Gather data on the current status of these indicators in the target population. This could involve conducting surveys, reviewing existing health records, and analyzing data from health facilities and community health workers.

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

4. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to estimate the potential impact of the recommendations. This could involve varying parameters such as the coverage of healthcare services, the effectiveness of preventive measures, and the level of community engagement.

5. Analyze results: Analyze the results of the simulations to assess the potential impact of the recommendations on improving access to maternal health. This could involve comparing different scenarios, identifying key drivers of change, and estimating the magnitude of the expected improvements.

6. Validate and refine the model: Validate the simulation model by comparing the simulated results with real-world data. Refine the model based on feedback from experts and stakeholders, and incorporate additional data or factors that may have been overlooked.

7. Communicate findings and make recommendations: Present the findings of the simulation study in a clear and concise manner, highlighting the potential benefits of the recommendations. Use the results to inform policy decisions, program planning, and resource allocation to improve access to maternal health.

Auteur

Dima Health

Statistics:

Citations: 39

Identifiers:

DOI: 10.1371/journal.pone.0068733

Research Areas:

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

Study Countries:

Kenya

Study Design:

Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female

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