Factors associated with health facility utilization during childbirth among 15 to 49-year-old women in Uganda: evidence from the Uganda demographic health survey 2016

BMC Health Services Research, Volume 21, No. 1, Year 2021

Interpretation

Background: Almost all maternal deaths and related morbidities occur in low-income countries. Childbirth supervised by a skilled provider in a health facility is a key intervention to prevent maternal and perinatal morbidity and mortality. Our study aimed to establish the factors associated with health facility utilization during childbirth in Uganda. Methods: We used the Uganda Demographic and Health Survey 2016 data of 10,152 women aged 15 to 49 years. The study focused on their most recent live birth in 5 years preceding the survey. We applied multistage stratified sampling to select study participants and we conducted multivariable logistic regression to establish the factors associated with health facility utilization during childbirth, using SPSS (version 25). Results: The proportion of women who gave birth at a health facility was 76.6% (7780/10,152: (95% confidence interval, CI, 75.8–77.5). The odds of women aged 15–19 years giving birth at health facilities were twice as those of women aged 40 to 49 years (adjusted odds ratio, AOR = 2.29; 95% CI: 1.71–3.07). Residing in urban areas and attending antenatal care (ANC) were associated with health facility use. The odds of women in the northern region of Uganda using health facilities were three times of those of women in the central region (AOR = 3.13; 95% CI: 2.15–4.56). Women with tertiary education (AOR = 4.96; 95% CI: 2.71–9.11) and those in the richest wealth quintile (AOR = 4.55; 95% CI: 3.27–6.32) had higher odds of using a health facility during child birth as compared to those with no education and those in the poorest wealth quintile, respectively. Muslims, Baganda, women exposed to mass media and having no problem with distance to health facility had higher odds of utilizing health facilities during childbirth as compared to Catholic, non Baganda, women not exposed to mass media and those having challenges with distance to access healthcare. Conclusion: Health facility utilization during childbirth was high and it was associated with decreasing age, increasing level of education and wealth index, urban residence, Northern region of Uganda, ANC attendance, exposure to mass media, tribe, religion and distance to the nearby health facility. We recommend that interventions to promote health facility childbirths in Uganda target the poor, less educated, and older women especially those residing in rural areas with less exposure to mass media. We used secondary data of the 2016 nationally representative Uganda Demographic and Health Survey (UDHS) collected from June to December 2016 [8]. The survey was implemented by the Uganda Bureau of Statistics (UBOS) with the technical assistance of Inner City Fund (ICF) International through the USAID-supported MEASURE DHS project [8]. The survey inquired about household members’ and individual characteristics using household questionnaire, women’s questionnaire, men’s questionnaire and biomarker questionnaire [8]. The current study analyzed data that was collected using the women’s questionnaire part of the survey. Women who had given informed consent were asked about the place of delivery for their most recent live birth in the 5 years preceding the survey [8]. Uganda has a tiered health system, from the highest level of national tertiary referral hospitals to the lowest at the community [17, 18]. It is a mixed health system where public and private health providers co-exist [18]. Over 25 years ago, Uganda adopted a decentralized approach to service delivery with local government at districts overseeing, managing and mobilizing resources for service delivery, including healthcare services [18]. The Ugandan government abolished user fees in 2001 in all public health facilities. However, the health service delivery and utilization still face multiple challenges including inadequate staffing, low pay, shortage of medicines and poor infrastructure [18]. UDHS employed two-stage cluster sampling technique where the census enumeration areas were the primary sampling units while households were the second stage of sampling [8]. The enumeration areas were selected from the 2014 population and housing census sample frame [8]. Women aged 15 to 49 years who were either permanent residents or spent the night preceding the survey in the selected household were eligible for inclusion in the Uganda’s demographic health survey 2016 [8]. Of the 18,506 women who consented and filled in the questionnaires, 10,152 responded to the question about place of child birth considering their most recent live birth in the 5 years preceding the survey [8]. Health facility delivery was defined as birth that occurred inside a health facility, whether private or government. Childbirth outside a health facility was defined as birth that occurred outside a health facility including at the home of the woman’s, relatives’, or traditional birth attendants’ on the way to the health facility. Birth outside health facility was coded as zero (0) while health facility delivery was coded as one (1). In this study, we conceptualized the factors associated with decision making regarding the place of childbirth by the mother using a conceptual framework heavily influenced by Andersen’s behavioral model of health service use. Borrowing from this theoretical framework, we developed a conceptual framework (See Fig. 1). Additionally, only variables that are collected in the routine DHS were examined in our study. According to our conceptual framework, utilization of health facility during child birth could be a function of three categories of factors, namely: predisposing factors (socio-demographic factors), enabling factors (e.g., wealth index) and healthcare needs [19–21]. The predisposing factors in the conceptual framework are: age, level of education, region of residence, place of residence, religion, marital status, household size, sex of household head and tribe. Wealth index, working status, exposure to mass media, problems seeking permission and distance to the nearest health facility as an indicator of access were considered as enabling factors, while visiting the health facility for antenatal care was included in the model as a proxy for the perceived need, as illustrated in Fig. Fig.11. Conceptual framework for the factors associated with health facility utilization for childbirth Maternal age was categorized as; (15–19 years, 20–29 years, 30–39 years, 40–49 years) [22]. Wealth index is a measure of relative household economic status and was calculated by UDHS from information on household asset ownership using Principal Component Analysis, which was further categorized into poorest, poorer, middle, richer and richest quintiles [8]. Place of Residence was categorized into urban and rural. Region was categorized into four; Northern (Teso, Karamoja, Lango, Acholi, West Nile), Central (Kampala, Central 1 and Central 2), Eastern (Busoga, Bugishu and Bukedi) and Western (Tooro, Ankole, Bunyoro and Kigezi) [23]. Level of Education was categorized into no education, primary education, secondary and tertiary education. Household Size was categorized as less than six members and six and above members (based on the national average and the dataset average of six members per household). Sex of household head was categorized as male or female, working status categorized as: not working and working while marital status as married (this included those in formal and informal unions) and not married. ANC attendance was categorized as yes (for any woman who attended ANC regardless of the frequency) and no (for those who did not attend ANC at all). Religion was categorized as Muslims, Anglican, Catholics, Pentecostal, and others while tribe was categorized as Acholi, Baganda, Bagisu, Bakiga, Banyankole, Basoga, Langi, Itesot and others. Problems with access to care (either seeking permission or distance to health facility) were categorized as big problem and no big problem while exposure to any of the three-mass media avenues (TV, radio, and newspapers) was categorized as yes and no and whether pregnancy was wanted was categorized as no, later and then. In order to account for the multi-stage cluster study design, we used complex sample package of SPSS (version 25.0) statistical software and adjusted the data using sampling weights, primary sampling units, and strata. We carried out the weighted count to account for the unequal probability sampling in different strata and to ensure representativeness of the survey results at the national and regional level. We performed Chi-square tests to determine the association between the independent variables and the outcome variable (place of childbirth). Logistic regression analyses were conducted to determine the strength of associations between independent variables and the outcome variable, after adjusting for extraneous variables. Before multivariable logistic regression, each exposure/predictor (independent variable) was assessed separately for its association with the outcome variable using bivariate logistic regression by reviewing the crude odds ratio (COR), 95% confidence interval (CI) and p-values. The conceptual framework was relied upon to select variables for the multivariable logistic regression. Additionally, independent variables with a p-value ≤0.20 at the bivariate analysis [24] were included in the final multivariable logistic regression model to assess the independent effect of each variable on the outcome variable. All variables in the model were assessed for collinearity. The highest variance inflation factor (VIF) observed was 1.96. Adjusted odds ratios (AOR), 95% confidence intervals (CI) and p-values were calculated with statistical significance level set at p-value < 0.05. Sensitivity analysis was conducted with the multivariable model by examining each mass medium and parameter of access to healthcare for their individual effects on place of childbirth.

AI Digest

Study Justification:

– Maternal mortality and morbidity are significant issues in low-income countries.
– Health facility utilization during childbirth is crucial for preventing maternal and perinatal morbidity and mortality.
– Understanding the factors associated with health facility utilization in Uganda can inform interventions to improve maternal health outcomes.

Highlights:

– The study found that 76.6% of women in Uganda gave birth at a health facility.
– Factors associated with health facility utilization included age, level of education, wealth index, place of residence, region, antenatal care attendance, exposure to mass media, religion, tribe, and distance to the nearest health facility.
– Younger women, those with higher education and wealth, urban residents, and those in the northern region of Uganda were more likely to use health facilities during childbirth.
– Recommendations for interventions include targeting the poor, less educated, and older women in rural areas with limited exposure to mass media.

Recommendations for Lay Reader and Policy Maker:

– Implement targeted interventions to promote health facility childbirths in Uganda.
– Focus on improving access to healthcare for the poor, less educated, and older women in rural areas.
– Increase awareness and education on the benefits of health facility utilization during childbirth.
– Strengthen antenatal care services to encourage women to seek care at health facilities.
– Collaborate with media outlets to disseminate information on maternal health and the importance of health facility utilization.

Key Role Players:

– Ministry of Health in Uganda
– Local government at district level
– Non-governmental organizations (NGOs) working in maternal health
– Healthcare providers and facilities
– Media organizations for awareness campaigns

Cost Items for Planning Recommendations:

– Training and capacity building for healthcare providers
– Infrastructure improvement in health facilities
– Outreach programs to reach rural areas
– Media campaigns and advertisements
– Monitoring and evaluation of interventions
– Research and data collection for evidence-based decision making

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, as it is based on a nationally representative survey with a large sample size. The study used multivariable logistic regression to establish the factors associated with health facility utilization during childbirth. The results show significant associations between various factors such as age, education, wealth index, residence, region, ANC attendance, exposure to mass media, religion, tribe, and distance to the nearest health facility. The study also provides actionable steps to improve health facility childbirths in Uganda, such as targeting interventions towards the poor, less educated, and older women residing in rural areas with less exposure to mass media.

Abstract

We used secondary data of the 2016 nationally representative Uganda Demographic and Health Survey (UDHS) collected from June to December 2016 [8]. The survey was implemented by the Uganda Bureau of Statistics (UBOS) with the technical assistance of Inner City Fund (ICF) International through the USAID-supported MEASURE DHS project [8]. The survey inquired about household members’ and individual characteristics using household questionnaire, women’s questionnaire, men’s questionnaire and biomarker questionnaire [8]. The current study analyzed data that was collected using the women’s questionnaire part of the survey. Women who had given informed consent were asked about the place of delivery for their most recent live birth in the 5 years preceding the survey [8]. Uganda has a tiered health system, from the highest level of national tertiary referral hospitals to the lowest at the community [17, 18]. It is a mixed health system where public and private health providers co-exist [18]. Over 25 years ago, Uganda adopted a decentralized approach to service delivery with local government at districts overseeing, managing and mobilizing resources for service delivery, including healthcare services [18]. The Ugandan government abolished user fees in 2001 in all public health facilities. However, the health service delivery and utilization still face multiple challenges including inadequate staffing, low pay, shortage of medicines and poor infrastructure [18]. UDHS employed two-stage cluster sampling technique where the census enumeration areas were the primary sampling units while households were the second stage of sampling [8]. The enumeration areas were selected from the 2014 population and housing census sample frame [8]. Women aged 15 to 49 years who were either permanent residents or spent the night preceding the survey in the selected household were eligible for inclusion in the Uganda’s demographic health survey 2016 [8]. Of the 18,506 women who consented and filled in the questionnaires, 10,152 responded to the question about place of child birth considering their most recent live birth in the 5 years preceding the survey [8]. Health facility delivery was defined as birth that occurred inside a health facility, whether private or government. Childbirth outside a health facility was defined as birth that occurred outside a health facility including at the home of the woman’s, relatives’, or traditional birth attendants’ on the way to the health facility. Birth outside health facility was coded as zero (0) while health facility delivery was coded as one (1). In this study, we conceptualized the factors associated with decision making regarding the place of childbirth by the mother using a conceptual framework heavily influenced by Andersen’s behavioral model of health service use. Borrowing from this theoretical framework, we developed a conceptual framework (See Fig. 1). Additionally, only variables that are collected in the routine DHS were examined in our study. According to our conceptual framework, utilization of health facility during child birth could be a function of three categories of factors, namely: predisposing factors (socio-demographic factors), enabling factors (e.g., wealth index) and healthcare needs [19–21]. The predisposing factors in the conceptual framework are: age, level of education, region of residence, place of residence, religion, marital status, household size, sex of household head and tribe. Wealth index, working status, exposure to mass media, problems seeking permission and distance to the nearest health facility as an indicator of access were considered as enabling factors, while visiting the health facility for antenatal care was included in the model as a proxy for the perceived need, as illustrated in Fig. Fig.11. Conceptual framework for the factors associated with health facility utilization for childbirth Maternal age was categorized as; (15–19 years, 20–29 years, 30–39 years, 40–49 years) [22]. Wealth index is a measure of relative household economic status and was calculated by UDHS from information on household asset ownership using Principal Component Analysis, which was further categorized into poorest, poorer, middle, richer and richest quintiles [8]. Place of Residence was categorized into urban and rural. Region was categorized into four; Northern (Teso, Karamoja, Lango, Acholi, West Nile), Central (Kampala, Central 1 and Central 2), Eastern (Busoga, Bugishu and Bukedi) and Western (Tooro, Ankole, Bunyoro and Kigezi) [23]. Level of Education was categorized into no education, primary education, secondary and tertiary education. Household Size was categorized as less than six members and six and above members (based on the national average and the dataset average of six members per household). Sex of household head was categorized as male or female, working status categorized as: not working and working while marital status as married (this included those in formal and informal unions) and not married. ANC attendance was categorized as yes (for any woman who attended ANC regardless of the frequency) and no (for those who did not attend ANC at all). Religion was categorized as Muslims, Anglican, Catholics, Pentecostal, and others while tribe was categorized as Acholi, Baganda, Bagisu, Bakiga, Banyankole, Basoga, Langi, Itesot and others. Problems with access to care (either seeking permission or distance to health facility) were categorized as big problem and no big problem while exposure to any of the three-mass media avenues (TV, radio, and newspapers) was categorized as yes and no and whether pregnancy was wanted was categorized as no, later and then. In order to account for the multi-stage cluster study design, we used complex sample package of SPSS (version 25.0) statistical software and adjusted the data using sampling weights, primary sampling units, and strata. We carried out the weighted count to account for the unequal probability sampling in different strata and to ensure representativeness of the survey results at the national and regional level. We performed Chi-square tests to determine the association between the independent variables and the outcome variable (place of childbirth). Logistic regression analyses were conducted to determine the strength of associations between independent variables and the outcome variable, after adjusting for extraneous variables. Before multivariable logistic regression, each exposure/predictor (independent variable) was assessed separately for its association with the outcome variable using bivariate logistic regression by reviewing the crude odds ratio (COR), 95% confidence interval (CI) and p-values. The conceptual framework was relied upon to select variables for the multivariable logistic regression. Additionally, independent variables with a p-value ≤0.20 at the bivariate analysis [24] were included in the final multivariable logistic regression model to assess the independent effect of each variable on the outcome variable. All variables in the model were assessed for collinearity. The highest variance inflation factor (VIF) observed was 1.96. Adjusted odds ratios (AOR), 95% confidence intervals (CI) and p-values were calculated with statistical significance level set at p-value < 0.05. Sensitivity analysis was conducted with the multivariable model by examining each mass medium and parameter of access to healthcare for their individual effects on place of childbirth.

Materials

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 travel to rural and remote areas, providing maternal health services and education to women who may not have easy access to healthcare facilities.

2. Telemedicine: Utilizing telemedicine technology to connect pregnant women in remote areas with healthcare professionals, allowing them to receive prenatal care and consultations 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 to women in their own communities.

4. Financial incentives: Implementing financial incentives or subsidies to encourage pregnant women to seek care at health facilities, such as covering transportation costs or providing cash transfers for attending antenatal care visits.

5. Health education campaigns: Conducting targeted health education campaigns to raise awareness about the importance of skilled birth attendance and the availability of maternal health services, particularly in rural areas.

6. Improving infrastructure: Investing in improving the infrastructure of healthcare facilities, particularly in rural areas, to ensure they have the necessary equipment and resources to provide quality maternal health services.

7. Strengthening referral systems: Developing and strengthening referral systems between lower-level healthcare facilities and higher-level facilities to ensure that pregnant women can access appropriate care when needed.

8. Addressing cultural and social barriers: Implementing interventions that address cultural and social barriers to seeking maternal healthcare, such as community sensitization programs and engaging local leaders and influencers to promote the importance of maternal health.

These innovations, along with targeted interventions that address the specific factors identified in the study, can help improve access to maternal health in Uganda.

AI Innovations Description

Based on the information provided, the following recommendation can be developed into an innovation to improve access to maternal health:

1. Targeted interventions: Develop targeted interventions that specifically focus on improving access to maternal health services for the poor, less educated, and older women residing in rural areas with limited exposure to mass media. These interventions can include community outreach programs, mobile health clinics, and awareness campaigns to educate and empower women about the importance of utilizing health facilities during childbirth.

2. Strengthening healthcare infrastructure: Invest in improving the healthcare infrastructure in rural areas by increasing the number of health facilities and ensuring they are adequately staffed and equipped. This can involve training and deploying skilled healthcare providers, ensuring the availability of essential medical supplies and equipment, and improving the overall quality of care provided at these facilities.

3. Financial incentives: Implement financial incentives, such as conditional cash transfers or subsidies, to encourage women to give birth at health facilities. These incentives can help alleviate the financial burden associated with accessing maternal health services and make it more affordable for women, especially those from low-income backgrounds.

4. Community engagement: Engage local communities, religious leaders, and traditional birth attendants in promoting the importance of health facility utilization during childbirth. This can involve conducting community awareness sessions, involving traditional birth attendants in referral systems, and fostering partnerships between traditional and modern healthcare providers to ensure a continuum of care.

5. Strengthening antenatal care services: Enhance antenatal care services by ensuring their availability, accessibility, and quality. This can include increasing the number of antenatal care clinics, improving the training and skills of healthcare providers delivering antenatal care, and promoting early and regular attendance of antenatal care visits through community-based initiatives.

6. Data-driven decision making: Utilize data from national surveys, such as the Uganda Demographic and Health Survey, to inform evidence-based decision making and policy formulation. Regularly monitor and evaluate the impact of interventions aimed at improving access to maternal health services to identify areas for improvement and ensure the effectiveness of implemented strategies.

By implementing these recommendations, it is possible to develop innovative solutions that address the factors associated with health facility utilization during childbirth in Uganda and ultimately improve access to maternal health services.

AI Innovations Methodology

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

1. Strengthening education and awareness programs: Implementing comprehensive education and awareness programs that target women and their families can help increase knowledge about the importance of health facility utilization during childbirth. These programs can focus on the benefits of skilled providers, the risks associated with home births, and the availability of free or affordable maternal health services.

2. Improving infrastructure and staffing: Addressing the challenges of inadequate staffing and poor infrastructure in health facilities is crucial. This can be done by increasing the number of skilled healthcare providers, ensuring availability of essential medical supplies and equipment, and improving the physical infrastructure of health facilities to provide a safe and comfortable environment for childbirth.

3. Enhancing antenatal care services: Strengthening antenatal care services can play a significant role in promoting health facility utilization during childbirth. This can be achieved by improving the quality and accessibility of antenatal care, providing comprehensive counseling on the benefits of health facility delivery, and addressing any barriers or challenges that women may face in accessing antenatal care.

4. Targeted interventions for vulnerable populations: Designing and implementing targeted interventions for vulnerable populations, such as women living in rural areas, those with lower education levels, and those from low-income households, can help address the disparities in health facility utilization. These interventions can include providing transportation support, financial incentives, and community-based outreach programs.

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

1. Data collection: Collect data on key indicators related to maternal health, such as the percentage of women giving birth at health facilities, demographic characteristics, education levels, wealth index, and access to antenatal care. This data can be obtained through surveys, interviews, or existing databases.

2. Baseline assessment: Analyze the collected data to establish the current status of health facility utilization during childbirth and identify the factors associated with it. This will serve as the baseline for comparison.

3. Scenario development: Based on the identified recommendations, develop different scenarios that represent the potential impact of each recommendation on improving access to maternal health. For example, scenario 1 could represent the impact of strengthening education and awareness programs, while scenario 2 could represent the impact of improving infrastructure and staffing.

4. Modeling and simulation: Use statistical modeling techniques, such as logistic regression or simulation models, to simulate the impact of each scenario on health facility utilization during childbirth. This involves adjusting the relevant variables in the model based on the recommendations and estimating the resulting changes in health facility utilization rates.

5. Analysis and interpretation: Analyze the simulation results to determine the potential impact of each recommendation on improving access to maternal health. Compare the results of different scenarios to identify the most effective interventions.

6. Policy and program development: Based on the simulation results, develop evidence-based policies and programs that prioritize the most effective interventions. These policies and programs should be tailored to the specific needs and context of the target population.

7. Monitoring and evaluation: Implement the recommended interventions and continuously monitor and evaluate their impact on improving access to maternal health. This will help identify any challenges or areas for improvement and inform future decision-making.

It is important to note that the methodology described above is a general framework and may need to be adapted based on the specific context and available data.

Authors & Co-Authors

Statistics:

Citations: 14

Authors: 5

Identifiers:

DOI: 10.1186/s12913-021-07179-5

Research Areas:

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

Study Countries:

Uganda

Study Design:

Case-Control Study, Cross Sectional Study, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Male & Female