Factors associated with delivery outside a health facility: Cross-sectional study in rural Malawi

Tropical Medicine and International Health, Volume 20, No. 5, Year 2015

Interpretação

Objective: To identify factors associated with delivery outside a health facility in rural Malawi. Method: A cross-sectional survey was conducted in Balaka, Dedza, Mchinji and Ntcheu districts in Malawi in 2013 among women who had completed a pregnancy 12 months prior to the day of the survey. Multilevel logistic regression was used to assess factors associated with delivery outside a facility. Results: Of the 1812 study respondents, 9% (n = 159) reported to have delivered outside a facility. Unmarried women were significantly more likely [OR = 1.88; 95% CI (1.086-3.173)] to deliver outside a facility, while women from households with higher socio-economic status [third-quartile OR = 0.51; 95% CI (0.28-0.95) and fourth-quartile OR = 0.48; 95% CI (0.29-0.79)] and in urban areas [OR = 0.39; 95%-CI (0.23-0.67)] were significantly less likely to deliver outside a facility. Women without formal education [OR 1.43; 95% CI (0.96-2.14)] and multigravidae [OR = 1.14; 95% CI (0.98-1.73)] were more likely to deliver outside a health facility at 10% level of significance. Conclusion: About 9% of women deliver outside a facility. Policies to encourage facility delivery should not only focus on health systems but also be multisectoral to address women’s vulnerability and inequality. Facility-based delivery can contribute to curbing the high maternal illness burden if authorities provide incentives to those not delivering at the facility without losing existing users. The study was conducted in 2013 in four districts in Malawi: Balaka in the southern region and Dedza, Mchinji and Ntcheu in the central region. These districts have a total population of about 2 million, of which 52% are women. The average population growth rate is 3.48% [33] and the total fertility rate for Malawi as a nation is 5.7 [11]. The four districts count a total of 33 facilities officially offering BEmOC and CEmOC services. Our study focused on these four districts given that the first results-based financing (RBF) initiative in the country is being piloted there. Data were collected through a cross-sectional household survey conducted between April and May 2013, which served as the baseline survey for a larger impact evaluation targeting the RBF initiative mentioned above [34]. The survey sample was selected using a three-stage cluster sampling procedure. First, 33 clusters were defined as the catchment areas of the 33 healthcare facilities that are authorised to provide EmOC services. Second, two enumeration areas (EAs) and four EAs were randomly sampled within each BEmOC and each CEmOC catchment area (i.e. cluster), respectively. The enumeration areas used in this study are the administrative data collection units demarcated by the National Statistics Office [11] and count roughly 500 households with between 1000 and 2000 people [33,35]. Twice as many EAs were selected from the CEmOC as compared to the BEmOC clusters to account for a larger catchment population and potential urban–rural differences. Third, in each EA, we aimed to reach a total of 26 women who had completed a pregnancy (either through miscarriage, abortion, stillbirth or delivery of a live baby) in the previous 12 months. We identified the women to be interviewed using a random route approach [36], purposely independent of any support from village leaders or healthcare providers. After randomly identifying one point in each EA (not the central point), our interviewers randomly selected a path (random route), followed it and stopped at every household on that path to enquire whether any woman in the household had completed a pregnancy in the previous 12 months. Every time such a woman was found, the interviewers explained the aim of the study and asked for consent to proceed with the interview. The process of data collection was continued until at least 26 eligible women were identified and interviewed in each EA. Data were collected by trained interviewers using a structured questionnaire that was digitally programmed and administered using tablet computers. The questionnaire was administered in Chichewa, the local language, and prompted women to recall the type of healthcare services sought during the most recently ended pregnancy, including antenatal care (ANC), delivery and postnatal care (PNC), as well as the relevant out-of-pocket expenditure. In addition, questions were asked to define the women’s socio-demographic and socio-economic profile. The information reported on health service utilisation was systematically validated using the information recorded in the mothers’ health booklet (i.e. health passport) [18]. All data reported in this study were collected after the woman was duly and thoroughly informed of the study’s purpose and signed a written consent was obtained. The study protocol was approved by the College of Medicine Review and Ethics Committee, Malawi (protocol number P.08/13/1438) and the Ethics Committee of the Faculty of Medicine of the University of Heidelberg (protocol number S-256/2012). Access to and utilisation of facility-based delivery represent multidimensional concepts as they depend on the interaction between the individual, her household, and the surrounding community and healthcare system [37]. The utilisation of any health service, including labour and delivery services, can be explained by Andersen’s behavioural model [38–41], which recognises healthcare utilisation as the result of the interaction between predisposing factors (such as age, income, parity and health beliefs), enabling resources (community and family resources), need (perceived and actual) and supply-side characteristics (organization of health system) [38]. We collected data on predisposing, enabling, and need factors and not on supply-side characteristics because this was a household survey. The choice of variables used in our study is based on the different dimensions outlined by Andersen’s model. In addition to other data, Table Table11 lists all the variables included in our analysis. Most of the variables included in the analysis are self-explanatory. We defined the outcome variable as binary, distinguishing women who delivered at a facility (coded as 0) from women who delivered elsewhere, most frequently at home (coded as 1). A woman was classified as having had a facility-based-delivery if she delivered in a regional hospital, district hospital or health centre. A woman was classified as having had a delivery outside a health facility if she delivered at home, at the premises of a TBA or on the way to a health facility. Thus, a facility in the study was defined as an institution, whether public or private, where delivery and birth took place in the presence of a skilled attendant, usually a trained midwife. Socio-economic status was defined by a relative index of household wealth computed by aggregating a household assets profile using principal components analysis [42,43]. The components of the household profile included in the index were as follows: house ownership; characteristics of house of residence such as number of rooms, type of wall, roofing material, floor material, dominant source of lighting and water, and type of toilet owned by household; household assets ownership such as radio, television, phone and bicycle; and ownership of agricultural assets such as farmland, goats, sheep, pigs and poultry. Distance to healthcare facilities was measured in kilometres and calculated as a straight line from the household compound to the relevant referral healthcare facility using global position system (GPS) coordinates [44]. Variable distribution and Unadjusted Odds Ratios (n = 1812) Data analysis was conducted using Stata IC 13 (StataCorp LP, Texas, USA). Descriptive statistics were used to assess the general distribution of the variables in the sample and to provide an initial comparison between women delivering at a facility and women delivering elsewhere. Frequency distributions and chi-square tests of independence were computed for categorical variables, while means, standard deviations and t-tests were computed for continuous variables [45,46]. Given the binary nature of the outcome variables, a multilevel logistic regression model was used to identify factors that were associated with non-facility-based delivery. Multilevel modelling was used to account for clustering at the level of the facility catchment area. The statistical significance of the fixed parameters was tested using a Wald 95% confidence interval [47]. Model identification of the regression was conducted using a step-up approach by means of a likelihood ratio test of goodness of fit [48]. At first, a simple logistic model with only the intercept was run. Then, one explanatory variable was added to the model. The models were tested to assess whether the model with the intercept only is nested within the model with the additional variable using the likelihood ratio test. Thus, the model with the additional variable was tested to assess whether it had a superior explanatory power than the model without the additional variable. If the test indicated that the model was not nested, another variable was added to the model with the intercept and the test was repeated. If the model with the intercept was found to be nested in the model with the additional variable, then this new model was taken to be superior to the one with only the intercept. This procedure was repeated until all the variables were entered into the model and tested to examine whether they improved the explanatory power of the model.

AI Digest

Study Justification:

The study aimed to identify factors associated with delivery outside a health facility in rural Malawi. This is an important area of research because delivering outside a facility can increase the risk of maternal and neonatal complications. Understanding the factors that contribute to this practice can help inform policies and interventions to promote facility-based delivery and improve maternal and child health outcomes.

Highlights:

– The study found that 9% of women in rural Malawi delivered outside a health facility.
– Unmarried women were more likely to deliver outside a facility, while women from higher socio-economic status and urban areas were less likely to do so.
– Women without formal education and those who had multiple pregnancies were also more likely to deliver outside a health facility.
– The study highlights the need for policies that address women’s vulnerability and inequality, in addition to improving health systems, to encourage facility-based delivery.
– Facility-based delivery can help reduce the burden of maternal illness if incentives are provided to women who do not currently deliver at a facility, without neglecting existing users.

Recommendations:

– Develop and implement policies and interventions that address the socio-economic and educational factors associated with delivering outside a health facility.
– Provide incentives to encourage women to deliver at a health facility, particularly those who are unmarried or have multiple pregnancies.
– Improve access to and quality of healthcare services in rural areas to make facility-based delivery more accessible and attractive to women.
– Promote community awareness and education about the benefits of facility-based delivery and the risks associated with delivering outside a health facility.

Key Role Players:

– Ministry of Health: Responsible for developing and implementing policies and interventions to promote facility-based delivery.
– Local healthcare providers: Involved in delivering healthcare services and providing support to women during pregnancy and childbirth.
– Community leaders and organizations: Play a role in raising awareness and promoting the benefits of facility-based delivery.
– Non-governmental organizations (NGOs): Can provide support and resources to implement interventions and programs aimed at promoting facility-based delivery.

Cost Items for Planning Recommendations:

– Training and capacity building for healthcare providers to improve the quality of care during pregnancy and childbirth.
– Infrastructure development and improvement of healthcare facilities in rural areas to make them more attractive and accessible to women.
– Community awareness campaigns and education programs to promote the benefits of facility-based delivery.
– Incentives for women to encourage them to deliver at a health facility, such as transportation vouchers or cash transfers.
– Monitoring and evaluation of interventions to assess their effectiveness and make necessary adjustments.
Please note that the cost items provided are examples and not actual costs. The actual budget for implementing the recommendations would depend on the specific context and resources available.

Força da prova

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study provides quantitative data from a cross-sectional survey conducted in rural Malawi in 2013. The sample size is relatively large (1812 study respondents) and multilevel logistic regression was used to assess factors associated with delivery outside a health facility. However, the study design is cross-sectional, which limits the ability to establish causality. To improve the strength of the evidence, future research could consider using a longitudinal design to examine the factors associated with delivery outside a health facility over time. Additionally, including qualitative data could provide a more comprehensive understanding of the factors influencing facility-based delivery in rural Malawi.

Resumo

The study was conducted in 2013 in four districts in Malawi: Balaka in the southern region and Dedza, Mchinji and Ntcheu in the central region. These districts have a total population of about 2 million, of which 52% are women. The average population growth rate is 3.48% [33] and the total fertility rate for Malawi as a nation is 5.7 [11]. The four districts count a total of 33 facilities officially offering BEmOC and CEmOC services. Our study focused on these four districts given that the first results-based financing (RBF) initiative in the country is being piloted there. Data were collected through a cross-sectional household survey conducted between April and May 2013, which served as the baseline survey for a larger impact evaluation targeting the RBF initiative mentioned above [34]. The survey sample was selected using a three-stage cluster sampling procedure. First, 33 clusters were defined as the catchment areas of the 33 healthcare facilities that are authorised to provide EmOC services. Second, two enumeration areas (EAs) and four EAs were randomly sampled within each BEmOC and each CEmOC catchment area (i.e. cluster), respectively. The enumeration areas used in this study are the administrative data collection units demarcated by the National Statistics Office [11] and count roughly 500 households with between 1000 and 2000 people [33,35]. Twice as many EAs were selected from the CEmOC as compared to the BEmOC clusters to account for a larger catchment population and potential urban–rural differences. Third, in each EA, we aimed to reach a total of 26 women who had completed a pregnancy (either through miscarriage, abortion, stillbirth or delivery of a live baby) in the previous 12 months. We identified the women to be interviewed using a random route approach [36], purposely independent of any support from village leaders or healthcare providers. After randomly identifying one point in each EA (not the central point), our interviewers randomly selected a path (random route), followed it and stopped at every household on that path to enquire whether any woman in the household had completed a pregnancy in the previous 12 months. Every time such a woman was found, the interviewers explained the aim of the study and asked for consent to proceed with the interview. The process of data collection was continued until at least 26 eligible women were identified and interviewed in each EA. Data were collected by trained interviewers using a structured questionnaire that was digitally programmed and administered using tablet computers. The questionnaire was administered in Chichewa, the local language, and prompted women to recall the type of healthcare services sought during the most recently ended pregnancy, including antenatal care (ANC), delivery and postnatal care (PNC), as well as the relevant out-of-pocket expenditure. In addition, questions were asked to define the women’s socio-demographic and socio-economic profile. The information reported on health service utilisation was systematically validated using the information recorded in the mothers’ health booklet (i.e. health passport) [18]. All data reported in this study were collected after the woman was duly and thoroughly informed of the study’s purpose and signed a written consent was obtained. The study protocol was approved by the College of Medicine Review and Ethics Committee, Malawi (protocol number P.08/13/1438) and the Ethics Committee of the Faculty of Medicine of the University of Heidelberg (protocol number S-256/2012). Access to and utilisation of facility-based delivery represent multidimensional concepts as they depend on the interaction between the individual, her household, and the surrounding community and healthcare system [37]. The utilisation of any health service, including labour and delivery services, can be explained by Andersen’s behavioural model [38–41], which recognises healthcare utilisation as the result of the interaction between predisposing factors (such as age, income, parity and health beliefs), enabling resources (community and family resources), need (perceived and actual) and supply-side characteristics (organization of health system) [38]. We collected data on predisposing, enabling, and need factors and not on supply-side characteristics because this was a household survey. The choice of variables used in our study is based on the different dimensions outlined by Andersen’s model. In addition to other data, Table Table11 lists all the variables included in our analysis. Most of the variables included in the analysis are self-explanatory. We defined the outcome variable as binary, distinguishing women who delivered at a facility (coded as 0) from women who delivered elsewhere, most frequently at home (coded as 1). A woman was classified as having had a facility-based-delivery if she delivered in a regional hospital, district hospital or health centre. A woman was classified as having had a delivery outside a health facility if she delivered at home, at the premises of a TBA or on the way to a health facility. Thus, a facility in the study was defined as an institution, whether public or private, where delivery and birth took place in the presence of a skilled attendant, usually a trained midwife. Socio-economic status was defined by a relative index of household wealth computed by aggregating a household assets profile using principal components analysis [42,43]. The components of the household profile included in the index were as follows: house ownership; characteristics of house of residence such as number of rooms, type of wall, roofing material, floor material, dominant source of lighting and water, and type of toilet owned by household; household assets ownership such as radio, television, phone and bicycle; and ownership of agricultural assets such as farmland, goats, sheep, pigs and poultry. Distance to healthcare facilities was measured in kilometres and calculated as a straight line from the household compound to the relevant referral healthcare facility using global position system (GPS) coordinates [44]. Variable distribution and Unadjusted Odds Ratios (n = 1812) Data analysis was conducted using Stata IC 13 (StataCorp LP, Texas, USA). Descriptive statistics were used to assess the general distribution of the variables in the sample and to provide an initial comparison between women delivering at a facility and women delivering elsewhere. Frequency distributions and chi-square tests of independence were computed for categorical variables, while means, standard deviations and t-tests were computed for continuous variables [45,46]. Given the binary nature of the outcome variables, a multilevel logistic regression model was used to identify factors that were associated with non-facility-based delivery. Multilevel modelling was used to account for clustering at the level of the facility catchment area. The statistical significance of the fixed parameters was tested using a Wald 95% confidence interval [47]. Model identification of the regression was conducted using a step-up approach by means of a likelihood ratio test of goodness of fit [48]. At first, a simple logistic model with only the intercept was run. Then, one explanatory variable was added to the model. The models were tested to assess whether the model with the intercept only is nested within the model with the additional variable using the likelihood ratio test. Thus, the model with the additional variable was tested to assess whether it had a superior explanatory power than the model without the additional variable. If the test indicated that the model was not nested, another variable was added to the model with the intercept and the test was repeated. If the model with the intercept was found to be nested in the model with the additional variable, then this new model was taken to be superior to the one with only the intercept. This procedure was repeated until all the variables were entered into the model and tested to examine whether they improved the explanatory power of the model.

Materiais

N/A

Inovações

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

1. Mobile health (mHealth) interventions: Develop mobile applications or text messaging services to provide pregnant women with information about prenatal care, delivery, and postnatal care. This can help increase awareness and knowledge about maternal health and encourage women to seek care at health facilities.

2. Community-based interventions: Implement community health worker programs to provide education, support, and referrals for pregnant women. These workers can conduct home visits, organize community meetings, and connect women with local health facilities.

3. Transportation solutions: Improve transportation options for pregnant women in rural areas to overcome geographical barriers. This could involve providing affordable transportation services or partnering with existing transportation providers to ensure women can easily access health facilities for delivery.

4. Financial incentives: Introduce financial incentives, such as cash transfers or vouchers, to encourage pregnant women to deliver at health facilities. This can help offset the costs associated with facility-based delivery and incentivize women to seek care from skilled birth attendants.

5. Quality improvement initiatives: Implement strategies to improve the quality of maternal health services at health facilities. This can include training healthcare providers on best practices for maternal care, ensuring the availability of necessary equipment and supplies, and promoting respectful and compassionate care for pregnant women.

6. Partnerships and collaborations: Foster partnerships between government agencies, non-governmental organizations, and private sector entities to collectively address the barriers to accessing maternal health services. This can involve sharing resources, expertise, and funding to implement comprehensive and sustainable interventions.

It is important to note that the specific context and needs of the community should be considered when implementing these innovations. Additionally, ongoing monitoring and evaluation should be conducted to assess the effectiveness and impact of these interventions on improving access to maternal health.

AI Innovations Description

Based on the description provided, the recommendation to improve access to maternal health would be to implement a comprehensive approach that addresses the various factors associated with delivering outside a health facility in rural Malawi. This approach should consider the following recommendations:

1. Strengthening health systems: Improve the availability and quality of healthcare facilities in rural areas by increasing the number of skilled healthcare providers, ensuring the availability of essential medical supplies and equipment, and improving the infrastructure of healthcare facilities.

2. Addressing socio-economic disparities: Implement interventions to address socio-economic inequalities that contribute to delivering outside a health facility. This could include providing financial incentives or subsidies for facility-based deliveries, particularly for women from lower socio-economic backgrounds.

3. Promoting education and awareness: Implement educational programs to increase awareness about the importance of facility-based deliveries and the potential risks associated with delivering outside a health facility. This could include community-based education campaigns, antenatal care programs that emphasize the benefits of facility-based deliveries, and engaging community leaders and traditional birth attendants in promoting facility-based deliveries.

4. Improving transportation and accessibility: Address the challenges of transportation and geographical barriers by improving road infrastructure, providing transportation subsidies or vouchers for pregnant women to access healthcare facilities, and establishing mobile health clinics or outreach programs to reach remote areas.

5. Strengthening community engagement: Engage communities in decision-making processes and involve them in the planning and implementation of maternal health programs. This could include establishing community health committees, training community health workers, and promoting community-led initiatives to improve access to maternal health services.

6. Enhancing data collection and monitoring: Improve data collection and monitoring systems to track the utilization of maternal health services and identify areas where interventions are needed. This could include strengthening health information systems, conducting regular surveys and assessments, and using data to inform evidence-based decision-making and program planning.

By implementing these recommendations, it is expected that access to maternal health services will be improved, leading to a reduction in the number of deliveries outside a health facility and ultimately reducing maternal morbidity and mortality rates in rural Malawi.

AI Innovations Methodology

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

1. Strengthening health systems: Investing in infrastructure, equipment, and human resources in health facilities to ensure they are adequately equipped to provide quality maternal health services.

2. Increasing awareness and education: Implementing community-based education programs to raise awareness about the importance of facility-based delivery and the risks associated with delivering outside a health facility.

3. Addressing socio-economic barriers: Implementing policies and programs to address socio-economic factors that contribute to delivering outside a health facility, such as poverty and lack of transportation.

4. Promoting women’s empowerment: Empowering women through education, economic opportunities, and decision-making power to increase their ability to access and utilize maternal health services.

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

1. Define indicators: Identify key indicators to measure access to maternal health, such as the percentage of women delivering in health facilities, maternal mortality rate, and antenatal care coverage.

2. Collect baseline data: Gather data on the current status of access to maternal health services, including the percentage of women delivering outside a health facility and the factors associated with this behavior.

3. Develop a simulation model: Create a simulation model that incorporates the identified recommendations and their potential impact on access to maternal health. This model should consider factors such as population demographics, health system capacity, 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 on access to maternal health. This could involve adjusting variables such as the percentage of women delivering in health facilities, maternal mortality rate, and antenatal care coverage based on the expected effects of the recommendations.

5. Analyze results: Analyze the simulation results to determine the potential impact of the recommendations on improving access to maternal health. This could include assessing changes in key indicators and identifying any potential trade-offs or unintended consequences.

6. Refine recommendations: Based on the simulation results, refine the recommendations to optimize their impact on improving access to maternal health. This could involve adjusting the implementation strategies, targeting specific populations, or addressing any identified barriers or challenges.

7. Monitor and evaluate: Implement the refined recommendations and establish a monitoring and evaluation system to track progress and assess the actual impact on access to maternal health. This could involve collecting data on key indicators over time and comparing them to the simulation results to validate the effectiveness of the recommendations.

By following this methodology, policymakers and stakeholders can gain insights into the potential impact of different recommendations and make informed decisions to improve access to maternal health.

Autor

Dima Health

Statistics:

Citations: 22

Identifiers:

DOI: 10.1111/tmi.12473

ISSN: 13602276

Research Areas:

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

Study Countries:

Malawi

Study Design:

Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Female

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