Why mothers still deliver at home: Understanding factors associated with home deliveries and cultural practices in rural coastal Kenya, a cross-section study Global health

BMC Public Health, Volume 16, No. 1, Year 2016

Interpretação

Background: Maternal mortality has declined by 43 % globally between 1990 and 2013, a reduction that was insufficient to achieve the 75 % reduction target by millennium development goal (MDG) five. Kenya recorded a decline of 18 % from 490 deaths in 1990 to 400 deaths per 100,000 live births in 2013. Delivering at home, is associated with higher risk of maternal deaths, therefore reducing number of home deliveries is important to improve maternal health. In this study, we aimed at establishing the proportion of home deliveries and evaluating factors associated with home deliveries in Kilifi County. Methods: The study was conducted among mothers seeking immunization services in selected health facilities within Kilifi County using Semi-structured questionnaires administered through face to face oral interviews to collect both quantitative and qualitative data. Six Focus Group Discussion (FGD) and ten in-depth interviews (IDIs) were used to collect qualitative data. A random sample of 379 mothers was sufficient to answer the study question. Log-binomial regression model was used to identify factors associated with childbirth at home. Results: A total of 103 (26 %) mothers delivered at home. From the univariate analysis, both mother and the partners old age, being in a polygamy marriage, being a mother of at least two children and staying ≥5 Kms radius from the nearest health facility were associated with higher risk of delivering at home (crude P < 0.05). Both mother and partner's higher education level were associated with a protective effect on the risk of delivering at home (RR < 1.0 and P < 0.05). In multivariate regression model, only long distance (≥10Kms) from the nearest health facility was associated with higher risk of delivering at home (adjusted RR 3.86, 95 % CI 2.13 to 7.02). Conclusion: From this population, the major reason why mothers still deliver at home is the long distance from nearest health facility. To reduce maternal mortality, access to health facility by pregnant mothers need to be improved. Kilifi County is one of the 47 counties in Kenya located along the Kenya coastline and covers 12,609.7 km2 of land. In 2012 it had a population of 1,217,892, with more than 68 % of the population living below poverty line and the main economic activities being subsistence farming (maize and cassava farming), fishing in the Indian Ocean and tourism [7]. The entire road network covers about 3000 Kms. Only 30 kms of rural roads are tarmacked, the rest are in poor state and mostly impassable especially in rainy seasons [3]. The county has nine level 4 public hospitals, 20 level 3 public health Centres, 197 level 2 public dispensaries, one mission hospital, two private hospitals, one armed forces hospital, five private nursing homes and 107 private clinics. Level 4 public hospitals are the primary hospitals, level 3 are health centres, maternities or nursing homes and level 2 are Dispensaries or clinics [7]. The study was carried out in three health facilities within Kilifi County; Kilifi County hospital (level 4), Ganze health centre and Bamba sub-district hospital (level 3). The three health facilities were picked because of their geographical locations, high volume facilities and evenly cover the study location. This was a facility based cross sectional study interviewing mothers in study health facilities who had brought their children for routine immunization services and delivered within six months prior to commencement of the study. The outcome of interest was childbirth either at home or at a health facility. The sample size n was calculated using the formula of Fishers et al. [8] Where Z = standard normal distribution curve value for 95 % CI which is 1.96 P = proportion of home deliveries according to KDHS of 2007/08–0.56. d = absolute precision (0.05) Attrition of 10 % = 0.1* 379 = 38 Therefore a sample size of 417 mothers (379 + 38) was enough to answer the study question after adjusting for 10 % of attrition. The study population was women of child bearing age from 18–49 years attending the study health facilities during the study period. Women attending the study health facilities who had given birth in the last six months prior to study period and a resident of Kilifi County were screened and those eligible were asked to provide written consent to participate in the study. Mothers with very sick children were excluded in the study. The population of females in reproductive age (15–49 years) in the County was 257,521 (23 %) in 2009 [7]. In 2008/09, 56.2 % of mothers delivered at home without being attended by skilled birth attendant, maternal mortality rate was 488 per 100,000 live births in the County [1]. Trained research assistants were used to collect data from the mothers using structured questionnaires. Every questionnaire was cross-checked for completeness after the interview. After data collection, double entry was done on a password protected Microsoft Access database and exported to STATA 13.1 (College Station, TX, USA) for statistical analysis. The distance from the household to the nearest health facility was categorized into 4 groups; <5, 5 to 10, ≥10 kms and don’t know. Categorical variables were summarized using proportions and associations tested using chi-square or fisher’s exact test where applicable. Continuous variables were summarized using means and standard deviations for normally distributed data while skewed data were summarized using medians and interquartile range. A two tailed independent t-test was used to test difference of means for normally distributed continuous variables and Mann–Whitney U test for skewed continuous variables. To identify risk factors of delivering at home, we computed relative risks (RR) using log-binomial regression model, retaining all variables with a crude P-value < 0.1 (10 %) in the multivariate model. Statistical significance was evaluated using 95 % confidence interval and a two-tailed p-value of <0.05. The study was approved by Kenya Ethical Review Committee and conducted in accordance to good clinical practices principles. Permission to conduct the study was also granted by Kilifi County Director of Health Services and the study health Facilities in-charges.

AI Digest

Study Justification:

– Maternal mortality rates have declined globally, but the reduction is not sufficient to meet the targets set by the Millennium Development Goals.
– Delivering at home is associated with a higher risk of maternal deaths.
– Understanding the factors associated with home deliveries is important for improving maternal health.

Study Highlights:

– The study was conducted in Kilifi County, Kenya, which has a high poverty rate and limited access to healthcare facilities.
– A total of 103 (26%) mothers delivered at home.
– Factors associated with higher risk of delivering at home include old age of the mother and partner, being in a polygamous marriage, having at least two children, and living a long distance (≥10Kms) from the nearest health facility.
– Higher education levels of both the mother and partner were associated with a lower risk of delivering at home.

Study Recommendations:

– To reduce maternal mortality, access to healthcare facilities for pregnant mothers needs to be improved.
– Efforts should be made to address the long distances between households and the nearest health facility.
– Education programs should be implemented to raise awareness about the risks of delivering at home and the importance of seeking skilled birth attendants.

Key Role Players:

– Kenya Ethical Review Committee
– Kilifi County Director of Health Services
– Study health facilities in-charges

Cost Items for Planning Recommendations:

– Improving road infrastructure to ensure better access to healthcare facilities.
– Establishing more health facilities in rural areas.
– Implementing education programs to raise awareness.
– Training healthcare workers to provide skilled birth attendance services.

Força da prova

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is fairly strong, but there are some areas for improvement. The study used a mixed methods approach, collecting both quantitative and qualitative data, which adds depth to the findings. The sample size of 379 mothers is sufficient to answer the study question. The study also used a regression model to identify factors associated with home deliveries. However, the study could be improved by providing more information on the methodology, such as the selection criteria for the health facilities and the process of data collection. Additionally, the abstract could include more details on the statistical analysis, such as the specific variables included in the regression model. Overall, the evidence in the abstract is solid, but providing more methodological details would strengthen it further.

Resumo

Kilifi County is one of the 47 counties in Kenya located along the Kenya coastline and covers 12,609.7 km2 of land. In 2012 it had a population of 1,217,892, with more than 68 % of the population living below poverty line and the main economic activities being subsistence farming (maize and cassava farming), fishing in the Indian Ocean and tourism [7]. The entire road network covers about 3000 Kms. Only 30 kms of rural roads are tarmacked, the rest are in poor state and mostly impassable especially in rainy seasons [3]. The county has nine level 4 public hospitals, 20 level 3 public health Centres, 197 level 2 public dispensaries, one mission hospital, two private hospitals, one armed forces hospital, five private nursing homes and 107 private clinics. Level 4 public hospitals are the primary hospitals, level 3 are health centres, maternities or nursing homes and level 2 are Dispensaries or clinics [7]. The study was carried out in three health facilities within Kilifi County; Kilifi County hospital (level 4), Ganze health centre and Bamba sub-district hospital (level 3). The three health facilities were picked because of their geographical locations, high volume facilities and evenly cover the study location. This was a facility based cross sectional study interviewing mothers in study health facilities who had brought their children for routine immunization services and delivered within six months prior to commencement of the study. The outcome of interest was childbirth either at home or at a health facility. The sample size n was calculated using the formula of Fishers et al. [8] Where Z = standard normal distribution curve value for 95 % CI which is 1.96 P = proportion of home deliveries according to KDHS of 2007/08–0.56. d = absolute precision (0.05) Attrition of 10 % = 0.1* 379 = 38 Therefore a sample size of 417 mothers (379 + 38) was enough to answer the study question after adjusting for 10 % of attrition. The study population was women of child bearing age from 18–49 years attending the study health facilities during the study period. Women attending the study health facilities who had given birth in the last six months prior to study period and a resident of Kilifi County were screened and those eligible were asked to provide written consent to participate in the study. Mothers with very sick children were excluded in the study. The population of females in reproductive age (15–49 years) in the County was 257,521 (23 %) in 2009 [7]. In 2008/09, 56.2 % of mothers delivered at home without being attended by skilled birth attendant, maternal mortality rate was 488 per 100,000 live births in the County [1]. Trained research assistants were used to collect data from the mothers using structured questionnaires. Every questionnaire was cross-checked for completeness after the interview. After data collection, double entry was done on a password protected Microsoft Access database and exported to STATA 13.1 (College Station, TX, USA) for statistical analysis. The distance from the household to the nearest health facility was categorized into 4 groups; <5, 5 to 10, ≥10 kms and don’t know. Categorical variables were summarized using proportions and associations tested using chi-square or fisher’s exact test where applicable. Continuous variables were summarized using means and standard deviations for normally distributed data while skewed data were summarized using medians and interquartile range. A two tailed independent t-test was used to test difference of means for normally distributed continuous variables and Mann–Whitney U test for skewed continuous variables. To identify risk factors of delivering at home, we computed relative risks (RR) using log-binomial regression model, retaining all variables with a crude P-value < 0.1 (10 %) in the multivariate model. Statistical significance was evaluated using 95 % confidence interval and a two-tailed p-value of <0.05. The study was approved by Kenya Ethical Review Committee and conducted in accordance to good clinical practices principles. Permission to conduct the study was also granted by Kilifi County Director of Health Services and the study health Facilities in-charges.

Materiais

N/A

Inovações

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

1. Mobile health clinics: Implementing mobile health clinics that can travel to remote areas and provide maternal health services, including prenatal care, delivery assistance, and postnatal care. This would help overcome the barrier of long distances to the nearest health facility.

2. Telemedicine: Introducing telemedicine services that allow pregnant women in remote areas to consult with healthcare professionals through video calls or phone calls. This would enable them to receive medical advice and guidance without having to travel long distances.

3. Community health workers: Training and deploying community health workers who can provide basic maternal health services, education, and support in rural areas. These workers can conduct home visits, assist with deliveries, and provide essential care to pregnant women and new mothers.

4. Improving road infrastructure: Investing in improving the road network in rural areas to ensure better access to health facilities. This could involve repairing existing roads, building new roads, or implementing transportation services specifically for pregnant women.

5. Awareness campaigns: Conducting awareness campaigns to educate communities about the importance of delivering at a health facility and the risks associated with home deliveries. This could help change cultural practices and encourage more women to seek professional care during childbirth.

6. Financial incentives: Introducing financial incentives, such as cash transfers or subsidies, to encourage pregnant women to deliver at health facilities. This could help offset the costs associated with transportation and healthcare services, making it more affordable for women to access maternal health care.

7. Partnerships with local organizations: Collaborating with local organizations, such as community-based groups or non-governmental organizations, to provide maternal health services in underserved areas. This could involve setting up temporary clinics or organizing outreach programs to reach women who may not have easy access to health facilities.

It is important to note that the implementation of these innovations should be tailored to the specific needs and context of rural coastal Kenya, taking into account cultural practices, infrastructure limitations, and available resources.

AI Innovations Description

Based on the information provided, the recommendation to improve access to maternal health in rural coastal Kenya is to focus on improving the transportation infrastructure and services. Specifically, efforts should be made to address the issue of long distances from the nearest health facility, which was identified as a major reason why mothers still deliver at home.

Here are some potential innovations that could be developed based on this recommendation:

1. Mobile health clinics: Implementing mobile health clinics that can travel to remote areas and provide maternal health services, including prenatal care and delivery assistance. This would help overcome the barrier of long distances and improve access to healthcare for pregnant women.

2. Transportation vouchers: Introduce a voucher system that provides pregnant women with transportation assistance to reach the nearest health facility for prenatal care and delivery. This could involve partnering with local transportation providers or utilizing existing public transportation services.

3. Community-based transportation networks: Establish community-based transportation networks, such as volunteer driver programs or community-owned vehicles, to provide transportation services specifically for pregnant women. This would ensure that women have a reliable and accessible means of reaching healthcare facilities.

4. Telemedicine and teleconsultation: Utilize telemedicine technology to enable remote consultations between pregnant women and healthcare providers. This would allow women to receive prenatal care and guidance without needing to travel long distances to a health facility.

5. Improving road infrastructure: Advocate for improvements in the road infrastructure, particularly in rural areas, to make transportation to health facilities easier and more reliable. This could involve collaborating with local government authorities and organizations to prioritize road maintenance and construction projects.

It is important to note that these recommendations should be tailored to the specific context of rural coastal Kenya and take into account the local cultural practices and preferences. Additionally, a comprehensive approach that combines multiple strategies may be most effective in improving access to maternal health.

AI Innovations Methodology

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

1. Improve road infrastructure: Given that the majority of the road network in Kilifi County is in poor condition, it is important to invest in improving the road infrastructure. This will help pregnant women to easily access health facilities, especially during rainy seasons when roads become impassable.

2. Increase the number of health facilities: Kilifi County currently has a limited number of health facilities, particularly in rural areas. Increasing the number of health facilities, especially those providing maternity services, will help to reduce the distance that pregnant women need to travel to access healthcare.

3. Provide transportation services: In areas where the distance to the nearest health facility is significant, providing transportation services, such as ambulances or community transport systems, can help pregnant women reach healthcare facilities in a timely manner.

4. Promote education and awareness: Educating pregnant women and their partners about the importance of delivering in a healthcare facility and the risks associated with home deliveries can help to change cultural practices and increase the demand for skilled birth attendants.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Collect baseline data: Gather information on the current proportion of home deliveries, distance to the nearest health facility, and other relevant factors associated with home deliveries in Kilifi County.

2. Define indicators: Identify key indicators to measure the impact of the recommendations, such as the proportion of home deliveries, distance to the nearest health facility, and maternal mortality rate.

3. Develop a simulation model: Create a simulation model that incorporates the baseline data and the potential impact of the recommendations. This model should consider factors such as the improvement in road infrastructure, the increase in the number of health facilities, and the provision of transportation services.

4. Run simulations: Use the simulation model to run different scenarios based on the potential impact of the recommendations. This can help to estimate the expected changes in the indicators of interest, such as the reduction in the proportion of home deliveries and the decrease in the distance to the nearest health facility.

5. Analyze results: Analyze the results of the simulations to assess the potential impact of the recommendations on improving access to maternal health. This can include comparing the indicators before and after the implementation of the recommendations, as well as evaluating the cost-effectiveness of each recommendation.

6. Refine and iterate: Based on the results of the simulations, refine the recommendations and the simulation model if necessary. Iterate the process to further optimize the impact of the recommendations on improving access to maternal health.

It is important to note that this is a general methodology and the specific details may vary depending on the available data and resources.

Autor

Dima Health

Statistics:

Citations: 34

Identifiers:

DOI: 10.1186/s12889-016-2780-z

Research Areas:

Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

Kenya

Study Design:

Cross Sectional Study, Grounded Theory

Study Approach:

Mixed-methods, Qualitative, Quantitative

Participants Gender:

Female

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