Access factors linked to maternal deaths in Lundazi district, Eastern Province of Zambia: A case control study analysing maternal death reviews

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Study Justification:
– Understanding access factors associated with maternal death is crucial for assessing women’s health and healthcare system performance.
– This study aimed to analyze the access risk factors linked to maternal deaths in Lundazi district, Zambia using secondary data from maternal death reviews and delivery registers.
Study Highlights:
– The likelihood of experiencing maternal death was significantly lower among women who completed their scheduled antenatal care visits.
– Delayed referral was associated with maternal deaths and complications.
– Long distances and unskilled deliveries were also identified as factors contributing to maternal deaths.
– Maternal education appeared to influence antenatal healthcare utilization.
Study Recommendations:
– Scale up interventions to motivate women in Lundazi to make at least four scheduled antenatal care visits during pregnancy.
– Bring health services closer to communities to reduce the distances covered by pregnant women.
– Increase awareness and knowledge about the importance of antenatal care.
Key Role Players:
– Lundazi District Health Office
– Obstetricians
– Gynaecologists
– Midwives
– Doctors
Cost Items for Planning Recommendations:
– Training and capacity building for healthcare providers
– Infrastructure development to bring health services closer to communities
– Awareness campaigns and educational materials
– Monitoring and evaluation systems to track progress and outcomes

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is relatively strong, but there are some areas for improvement. The study design is a case-control study, which is a robust method for examining associations. The sample size is also adequate, with 100 cases and 300 controls. The use of odds ratios and confidence intervals adds to the strength of the analysis. However, there are a few suggestions to improve the evidence. First, the abstract does not provide information on the representativeness of the sample, which could affect the generalizability of the findings. Additionally, the abstract does not mention any potential limitations of the study, such as selection bias or confounding variables. Including this information would enhance the transparency and reliability of the evidence. Finally, the abstract could benefit from providing more specific details about the data analysis methods used, such as the specific regression models employed. Overall, the evidence in the abstract is strong, but these suggested improvements would further enhance its quality.

Background: Access factors associated with maternal death are important to understand because they are considered to be an essential measure of women’s health and indicative of the performance of health care systems in any community globally. This study aimed to analyse the access risk factors linked to maternal deaths in Lundazi district of the Eastern Province of Zambia using secondary data obtained from maternal death reviews and delivery registers. Methods: This was a case-control study with cases being recorded maternal deaths for Lundazi district (n=100) while controls were randomly selected Lundazi District Hospital deliveries (n=300) for the period 2010 to 2015. STATA™ (Stata Corporation, Texas, TX, USA) version 12.0 was used to analyse data. Odds ratio and 95% confidence intervals with associated p-values were used to analyse disparities between cases and controls while bivariate and multivariate regression analyses were done to show associations. Results: The likelihood of experiencing maternal death was 94% less among women who completed their scheduled antenatal care visits than those who did not (OR 0.06, 95% CI=0.01-0.27, p=<0.001). Delayed referral associated with maternal deaths and complications were 30% (30) for cases, 12% (37) for controls and 17% (67) for both cases and controls. Long distances, unskilled deliveries were 3%, (15) for both cases and controls with 13% (13) for cases and 1% (2) for controls only. Conclusion: Antenatal care is important in screening for pre-existing risk conditions as well as complications in early stages of pregnancy that could impact adversely during pregnancy and childbirth. Delay in seeking health care during pregnancy could be minimised if health services are brought closer to the communities to reduce on distances covered by pregnant women in Lundazi. Maternal education appears to influence antenatal health care utilisation because greater knowledge and understanding of the importance of antenatal care might increase the ability to select most appropriate service. Therefore, there is need for Lundazi District Health Office to scale up interventions that motivate women to make at least four scheduled antenatal care visits during pregnancy as recommended by the World Health Organization.

The study was conducted in Lundazi at the District Health Office. It included information obtained from health facilities within Lundazi district that conduct deliveries and recorded maternal deaths during the period 2010 to 2015. Lundazi district is situated about 755 km by road to the eastern side of the Zambian capital city of Lusaka and has a population of 366,432 as projected from 2010 national census [4]. Maternal Death Surveillance and Response (MDSR) reviews are conducted every quarter of the year to examine factors and causes associated with maternal death. This process provides a platform for documenting what obstetricians, gynaecologists, midwives and doctors propose as factors and cause for maternal death [11]. It is from these review reports that maternal death information were extracted for analysis. A case control study design was used where cases were all maternal deaths recorded by Lundazi District Health Office for the period 2010 to 2015. These were extracted from secondary data of MDSR reports and community verbal autopsies. Verbal autopsies are data collection tools used to assign cause of death through interviews with family or community members, where medical certification of cause of death is not available [12]. Data from verbal autopsies assists reviewers in summarising factors associated with a community maternal death and it is from this source that secondary data was extracted. The researchers were not involved in collection of data using the verbal autopsy. However, verbal autopsies were part of the source for the secondary data which was mined. Controls were 300 (n) records of women who survived during the same period and randomly selected from the district hospital delivery register. This sample was obtained from a population of 2794 (N) complicated Lundazi district hospital deliveries conducted in the period 2010–2015 and met the selection criteria. We used a computer to randomly select every 9th record from the delivery register on an Excel spread sheet. Lundazi District Hospital is the main referral health facility for complicated obstetric cases but also ordinary deliveries from the surrounding communities. The ratio of cases to control sampled was 1:3. All recorded maternal deaths in Lundazi district were considered for this study because maternal death is an event which is relatively rare. Lundazi District Hospital is a centrally located referral hospital where women from all corners of the district with obstetric complications deliver. Randomly selected controls had similar characteristics as cases and were drawn from the same population. In Zambia, it is estimated that 30–50% of all child births occur at home and most of the home deliveries are known to take place in rural than urban areas [7]. OpenEpi was used to estimate the sample size for the study with the following assumptions: unmatched case-control study with an estimated exposure rate among the controls of 30%. Comprehensive analysis of Demographic Health Surveys data from the 2000–2010 suggest that about 30% of pregnant women in the Sub-Saharan region do not complete their antenatal care attendance [13]. These specifications were expected to give a power of 80% and to detect odds ratio of 2.0 or greater, at a confidence level of 95%, with the alpha (α) level of 0.05, and case to control ratio of 1:3. The national risk rate is 398 deaths per 100,000 live births. Data extraction checklist was developed to extract relevant information from MDSR reports and verbal autopsies as well as hospital delivery registers. The data extracted was used to identify key characteristics of women who died of maternal cause during the period 2010 to 2015. Data was entered in Excel spread sheet, cleaned before exporting it to STATA version 12.0 (Stata™ Corporation, Texas, TX, USA) for analysis. Thirteen records in the register out of 2807 were not included in the sampling frame because the primary outcome of interest (died or survived) was not known or recorded. Preliminary data analysis involved description of predictor variables to understand their distribution in relation to dependent variable (maternal death being the binary outcome). Antenatal care attendance as an exposure factor for both cases and controls as shown in Table 1 was summarised. Descriptive statistics, inter-quartile ranges, means, medians and standard deviations were analysed for continuous variables such as age, gestation and parity. Test for normality for age distribution of records of women analysed were done using QQ plot and normal distribution curve to determine if age distribution of the case and control populations had a common distribution pattern (Fig. ​(Fig.1).1). Frequency and percentage distributions for discrete variables were computed with cross tabulations to compare cases and controls. Logistic regression was used to analyse the dataset to describe the relationship between the dependent variable ‘maternal deaths’ with the selected predictor variables. Demographic characteristics with ANC attendance as exposure factor aData collection for the year 2015 was only for 9 months compared to full calendar years for the other 5 years QQ plot testing for normality of distribution for participants’ age

Based on the study, the following innovations can be considered to improve access to maternal health in Lundazi district, Eastern Province of Zambia:

1. Scaling up interventions to motivate women to make at least four scheduled antenatal care visits during pregnancy: This can be done through community outreach programs, health education campaigns, and incentives for attending antenatal care visits. By emphasizing the importance of antenatal care in screening for risk conditions and complications, women may be more motivated to seek these services.

2. Bringing health services closer to the communities in Lundazi: This can be achieved by establishing satellite clinics or mobile health units in remote areas, reducing the distance that pregnant women have to travel to access healthcare. By making healthcare more accessible, delays in seeking healthcare during pregnancy can be minimized.

3. Improving maternal education: Greater knowledge and understanding of the importance of antenatal care can increase the likelihood of women selecting and utilizing appropriate services. Health education programs can be implemented to raise awareness about the benefits of antenatal care and address any misconceptions or barriers that women may have.

By implementing these innovations, it is expected that access to maternal health services will be improved in Lundazi district, leading to a reduction in maternal deaths and better health outcomes for women during pregnancy and childbirth.
AI Innovations Description
The recommendation to improve access to maternal health based on the study is to scale up interventions that motivate women in Lundazi district, Eastern Province of Zambia, to make at least four scheduled antenatal care visits during pregnancy. This recommendation is supported by the finding that women who completed their scheduled antenatal care visits had a significantly lower likelihood of experiencing maternal death compared to those who did not. Antenatal care is important in screening for pre-existing risk conditions and complications in early stages of pregnancy, which can have adverse impacts during pregnancy and childbirth. Additionally, the study suggests that bringing health services closer to the communities in Lundazi could minimize delays in seeking healthcare during pregnancy, particularly due to long distances. Maternal education also appears to influence antenatal healthcare utilization, as greater knowledge and understanding of the importance of antenatal care may increase the ability to select the most appropriate services. Therefore, the Lundazi District Health Office should prioritize efforts to encourage women to attend scheduled antenatal care visits and consider strategies to bring healthcare services closer to the communities in order to improve access to maternal health.
AI Innovations Methodology
To simulate the impact of the main recommendations on improving access to maternal health in Lundazi district, Eastern Province of Zambia, the following methodology can be used:

1. Identify the target population: The target population would be pregnant women in Lundazi district who are at risk of experiencing maternal death.

2. Define the intervention: The intervention would involve scaling up interventions to motivate women to make at least four scheduled antenatal care visits during pregnancy. This could include community awareness campaigns, education programs, and incentives to encourage women to attend antenatal care visits.

3. Determine the sample size: The sample size would depend on the resources available and the desired level of precision. A larger sample size would provide more accurate results, but it may not be feasible due to time and budget constraints.

4. Randomly select participants: Randomly select a representative sample of pregnant women from Lundazi district who are at risk of experiencing maternal death. This can be done using a random sampling technique, such as simple random sampling or stratified random sampling.

5. Collect baseline data: Collect baseline data on the participants’ demographic characteristics, knowledge and understanding of the importance of antenatal care, and their access to healthcare services. This can be done through surveys, interviews, or medical records.

6. Implement the intervention: Implement the interventions recommended in the study, such as community awareness campaigns and bringing healthcare services closer to the communities in Lundazi. Monitor the implementation process and ensure that the interventions are reaching the target population.

7. Collect post-intervention data: After implementing the interventions, collect post-intervention data on the participants’ utilization of antenatal care services, including the number of scheduled visits attended. Also, collect data on maternal deaths and any complications experienced during pregnancy and childbirth.

8. Analyze the data: Analyze the data using statistical methods to determine the impact of the interventions on access to maternal health. Compare the pre- and post-intervention data to identify any changes in the utilization of antenatal care services and the occurrence of maternal deaths.

9. Evaluate the results: Evaluate the results to determine the effectiveness of the interventions in improving access to maternal health. Assess whether there was an increase in the number of women attending scheduled antenatal care visits and a decrease in the occurrence of maternal deaths.

10. Make recommendations: Based on the findings, make recommendations for further improvements in access to maternal health in Lundazi district. This could include refining the interventions, expanding the reach of the interventions to more communities, or addressing any barriers that were identified during the study.

By following this methodology, it would be possible to simulate the impact of the main recommendations on improving access to maternal health in Lundazi district, Eastern Province of Zambia. The results of the simulation can provide valuable insights for policymakers and healthcare providers in designing and implementing effective interventions to improve access to maternal health services.

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