Determinants of postnatal care use at health facilities in rural Tanzania: Multilevel analysis of a household survey

BMC Pregnancy and Childbirth, Volume 15, No. 1, Year 2015

Interpretation

Background: Postnatal care (PNC) for the mother and infant is a neglected area, even for women who give birth in a health facility. Currently, there is very little evidence on the determinants of use of postnatal care from health facilities in Tanzania. Methods: This study examined the role of individual and community-level variables on the use of postnatal health services, defined as a check up from a heath facility within 42 days of delivery, using multilevel logistic regression analysis. We analyzed data of 1931 women, who had delivered in the preceding 2-14 months, from a two-stage household survey in 4 rural districts of Morogoro region, Tanzania. Individual level explanatory variables included i) Socio-demographic factors: age, birth order, education, and wealth, ii) Factors related to pregnancy: frequency of antenatal visits, history of complications, mode of delivery, place of delivery care, and counseling received. Community level variables included community levels of family planning, health service utilization, trust, poverty and education, and distance to health facility. Results: Less than one in four women in Morogoro reported having visited a health facility for postnatal care. Individual-level attributes positively associated with postnatal care use were women’s education of primary level or higher [Odds Ratio (OR) 1.37, 95 % Confidence Interval (CI) 1.04-1.81], having had a caesarean section or forceps delivery (2.95, 1.8-4.81), and being counseled by a community health worker to go for postnatal care at a health facility (2.3, 1.36-3.89). Other positive associations included those recommended HIV testing for baby (1.94, 1.19-3.15), and whose partners tested for HIV (1.41, 1.07-1.86). High community levels of postpartum family planning usage (2.48, 1.15-5.37) and high level of trust in health system (1.77, 1.12-2.79) were two significant community-level predictors. Lower postnatal care use was associated with having delivered at a hospital (0.5, 0.33-0.76), health center (0.57, 0.38-0.85), or dispensary (0.48, 0.33-0.69), and having had severe swelling of face and legs during pregnancy (0.65, 0.43-0.97). Conclusions: In the context of low postnatal care use in a rural setting, programs should direct efforts towards reaching women who do not avail themselves of postnatal care as identified in our study. The target population for our research was rural women who had a childbirth in the preceding 2–14 months and referred to as “recently delivered women” (RDW). The household survey of women (N = 1968) was carried out from August 2011 to November 2011. The baseline household survey was conducted as part of an evaluation of the Integrated Facility and Community (IFC) program being implemented by the MoHSW with support from Jhpiego since 2008. The principal objective of the survey was to establish baseline household characteristics and MNCH care utilization among RDWs. The study sample for the survey was RDWs living in rural areas of four districts in the Morogoro Region of Tanzania- Morogoro District Council, Mvomero, Kilosa and Ulanga. We performed post-hoc power analysis for our sample size of 1931 women – among the respondents, 37 (2 %) were excluded from the analysis because they had experienced pregnancies that resulted in a miscarriage and did not have reason to access postnatal care. The sample size would have 80 % power to detect a 10 % difference in the proportion of women using postnatal care between two groups with a type 1 error of 0.05, assuming an intra class correlation (ICC) of 0.1. A two-stage sampling strategy was employed to select households from 60 clusters, − a ‘cluster’ being defined as a unit of 1000 population. The first stage was the selection of villages (containing the cluster) through probability proportional to size (PPS) sampling using population estimates from the 2002 Tanzania National Population Census. In each selected village, a list of the population and households was made for the constituent sub-village units (Vitongoji) based on data from the local government authorities which were then grouped to create clusters composed roughly of a population of 1000. The second stage of the sampling process was to choose one cluster in a random manner by lottery. In each cluster, the survey team visited every household to list and interview women who had any pregnancy outcome (live born/stillbirth/abortion) in the preceding 2–14 months. The probability of selection for each household in the sample was equal. If the household had more than one eligible woman, only one was randomly selected for interview. The respondent identified in each household was interviewed using a questionnaire adapted from the model questionnaires developed by the MEASURE Demographic & Health Surveys (DHS) program [18]. The questionnaire was adapted to reflect relevant issues related to the larger ongoing evaluation in the region and collect information on background characteristics, pregnancy history, utilization of health care during pregnancy, delivery, and postnatal period as well as barriers to care seeking. If multiple births were encountered, then it was considered as a single pregnancy event. The adapted questionnaire was translated from English into Kiswahili and pilot tested in a district adjacent to the study site (Pwani Region). The respondents were interviewed after obtaining written informed consent. Two teams of trained interviewers fluent in Swahili and English administered the survey questionnaire. A field editor reviewed questionnaires from all the teams using a checklist for completeness, quality, and consistency at the end of each day while the study investigators made periodic checks to ensure quality of data collection and entry. We framed the use of postnatal care adapting the healthcare-seeking behavior model developed by Anderson and Newman [19]. This model proposes that the use of health care services is a function of three sets of characteristics – predisposing characteristics, enabling characteristics, and need characteristics (Fig. 1). We included (i) predisposing characteristics such as age, parity, marital status, education, wealth index, community poverty and peer usage of services, (ii) enabling characteristics such as distance to facilities, cost of services, trust in health system and community outreach activities, and (iii) need characteristics such as perceived susceptibility, seriousness of complications during antenatal, delivery and postnatal period, mode of delivery and need for contraception, and HIV testing. The outcome variable was ‘use of postnatal care’, which was defined as attending postnatal care for the mother’s care at a dispensary, health center, or hospital (government or private) within 6 weeks of delivery. Individual-level explanatory variables included demographic variables such as woman’s age, birth order, education, marital status, and religion. An index of household wealth, based on household assets was created using principal components analysis (PCA) methods proposed by Filmer and Pritchett and used to group households into wealth quintiles [20]. Frequency of antenatal care visits and location of delivery care were included as measures of health system utilization. The variable ‘HIV testing of baby’ refers to the requirement for HIV counseling and testing of the baby during a postnatal visit and functions as a ‘need characteristic’ variable. The ‘partner test for HIV’ variable is proxy for the involvement of men in the process. If expenses were incurred for a delivery, it was coded as a dichotomous variable ‘money spent on delivery’ which is a proxy barrier to care seeking. To create community-level variables, the individual level responses were averaged at the level of the clusters. The asset score of the households in the cluster was averaged to generate a community poverty score that served as proxy for wealth of the community. The proportion of women in the cluster with primary or higher education was used as proxy for literacy of a community. The proportion of women using contraception and the proportion attending 4 or more antenatal visits were used as proxies for community family planning practices and maternal health service utilization. The community level variables for education, contraceptive prevalence and ANC4+ coverage were generated by assigning each cluster with the value of the prevalence of women for the indicators and dividing them into low, middle and high categories. Communities with more than 80 % of respondents who reported trusting a health provider or CHW for advice on pregnancy related issues were classified as communities with high trust. Survey teams, also, collected information on the presence of the nearest functioning health facility and the distance recorded. The distance variable was categorized as 0 km (facility in the village), less than 5 km and more than 5 km. Frequency distributions of the sample women were explored to describe the characteristics of women included in this study. Bivariate and multivariable analyses were performed for individual-level and community-level variables of interest. Multilevel models take into account the hierarchical structure of the data and clustering of responses at the different levels. The following equation illustrates the multilevel model for utilization of Postnatal Care where i and j are the level 1 (individual) and level 2 (community) units respectively; pij is the probability of the outcome of interest for woman i in the cluster j; the b’s are the fixed coefficients; I and C refer to individual-level and community-level explanatory variables, respectively; and ZjXij is a cross-level interaction term; μ shows the random effects for the jth cluster. The error term, ε, represents unmeasured factors that may influence use of postnatal care at a health facility. A multilevel random intercept logistic regression model without covariates (null model) was used to assess the influence of unobserved community-level characteristics on the overall variation in facility use. Three multilevel random intercept logistic regression models were fitted to estimate associations between the individual and community variables and the likelihood of seeking postnatal care at a health facility. The first model included individual-level characteristics only, the second model included community-level characteristics only and the last model (full) includes individual and community-level variables. The independent variables were retained in each of the models if the the p-value was less than 0.2. Important demographic variables were also retained in the multivariable models, in addition to the variables chosen from the bivariate analysis, based on previous literature on the use of maternal health services. All statistical analyses were carried out with STATA 13.1[21]. The extent of missing data was assessed, and patterns of missingness were explored in order to ensure whether data were missing at random. Approximately 15% of our sample was missing data for at least one variable in the final multilevel model. We used multivariate imputation using chained equations (MICE), which uses a Gibbs-like algorithm to impute multiple variables sequentially using univariate fully conditional specifications. This method is considered more appropriate for the imputation of categorical data because it does not assume normality of imputed variables [22]. Independent variables with more than 2% of values missing were imputed, and 50 concatenated datasets were created to reduce any potential bias caused by rounding. Variables imputed included the outcome variable of use of postnatal care, complications during pregnancy, delivery and postnatal period, counseling from CHW regarding postnatal care, mode of delivery, money spent on delivery, partner test for HIV and HIV testing of baby. The full multilevel model was subjected to sensitivity tests to estimate the impact of missing data using multivariate imputations for the independent variables. The percentage change in the standard error for all independent variables was less than 0.5 % and the estimates derived from the imputed model are used. Ethical and administrative approvals were obtained from the Ethics Review Committee of the Johns Hopkins University and the Muhimbili University of Allied Health Sciences, Dar es Salaam, Tanzania. Written informed consent was obtained from each participant.

AI Digest

Study Justification:

– Postnatal care (PNC) for the mother and infant is an overlooked area, even for women who give birth in a health facility.
– There is limited evidence on the determinants of postnatal care use from health facilities in rural Tanzania.
– This study aims to examine the individual and community-level factors that influence the use of postnatal health services in rural Tanzania.

Study Highlights:

– Less than one in four women in the study reported visiting a health facility for postnatal care.
– Women with higher education, those who had a caesarean section or forceps delivery, and those counseled by a community health worker were more likely to use postnatal care.
– Other positive associations included HIV testing for the baby and partner’s HIV testing.
– Community-level factors such as high postpartum family planning usage and trust in the health system were also significant predictors.
– Lower postnatal care use was associated with delivering at a hospital, health center, or dispensary, and having severe swelling during pregnancy.

Recommendations for Lay Reader and Policy Maker:

– Programs should focus on reaching women who do not avail themselves of postnatal care.
– Efforts should be directed towards improving education, counseling, and access to postnatal care services.
– Community-level interventions should promote postpartum family planning and build trust in the health system.

Key Role Players:

– Ministry of Health and Social Welfare
– Jhpiego (organization providing support)
– Community health workers
– Health facility staff
– Local government authorities

Cost Items for Planning Recommendations:

– Education and training programs for health workers and community health workers
– Development and implementation of counseling services
– Infrastructure improvements to health facilities
– Outreach activities to promote postnatal care
– Monitoring and evaluation of postnatal care programs

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study used a multilevel logistic regression analysis to examine the determinants of postnatal care use in rural Tanzania. The sample size of 1931 women is relatively large, and the study accounted for both individual and community-level variables. However, the study did not provide information on the reliability or validity of the data collection methods. To improve the evidence, future studies could include a detailed description of the data collection process and provide information on the reliability and validity of the measures used.

Abstract

The target population for our research was rural women who had a childbirth in the preceding 2–14 months and referred to as “recently delivered women” (RDW). The household survey of women (N = 1968) was carried out from August 2011 to November 2011. The baseline household survey was conducted as part of an evaluation of the Integrated Facility and Community (IFC) program being implemented by the MoHSW with support from Jhpiego since 2008. The principal objective of the survey was to establish baseline household characteristics and MNCH care utilization among RDWs. The study sample for the survey was RDWs living in rural areas of four districts in the Morogoro Region of Tanzania- Morogoro District Council, Mvomero, Kilosa and Ulanga. We performed post-hoc power analysis for our sample size of 1931 women – among the respondents, 37 (2 %) were excluded from the analysis because they had experienced pregnancies that resulted in a miscarriage and did not have reason to access postnatal care. The sample size would have 80 % power to detect a 10 % difference in the proportion of women using postnatal care between two groups with a type 1 error of 0.05, assuming an intra class correlation (ICC) of 0.1. A two-stage sampling strategy was employed to select households from 60 clusters, − a ‘cluster’ being defined as a unit of 1000 population. The first stage was the selection of villages (containing the cluster) through probability proportional to size (PPS) sampling using population estimates from the 2002 Tanzania National Population Census. In each selected village, a list of the population and households was made for the constituent sub-village units (Vitongoji) based on data from the local government authorities which were then grouped to create clusters composed roughly of a population of 1000. The second stage of the sampling process was to choose one cluster in a random manner by lottery. In each cluster, the survey team visited every household to list and interview women who had any pregnancy outcome (live born/stillbirth/abortion) in the preceding 2–14 months. The probability of selection for each household in the sample was equal. If the household had more than one eligible woman, only one was randomly selected for interview. The respondent identified in each household was interviewed using a questionnaire adapted from the model questionnaires developed by the MEASURE Demographic & Health Surveys (DHS) program [18]. The questionnaire was adapted to reflect relevant issues related to the larger ongoing evaluation in the region and collect information on background characteristics, pregnancy history, utilization of health care during pregnancy, delivery, and postnatal period as well as barriers to care seeking. If multiple births were encountered, then it was considered as a single pregnancy event. The adapted questionnaire was translated from English into Kiswahili and pilot tested in a district adjacent to the study site (Pwani Region). The respondents were interviewed after obtaining written informed consent. Two teams of trained interviewers fluent in Swahili and English administered the survey questionnaire. A field editor reviewed questionnaires from all the teams using a checklist for completeness, quality, and consistency at the end of each day while the study investigators made periodic checks to ensure quality of data collection and entry. We framed the use of postnatal care adapting the healthcare-seeking behavior model developed by Anderson and Newman [19]. This model proposes that the use of health care services is a function of three sets of characteristics – predisposing characteristics, enabling characteristics, and need characteristics (Fig. 1). We included (i) predisposing characteristics such as age, parity, marital status, education, wealth index, community poverty and peer usage of services, (ii) enabling characteristics such as distance to facilities, cost of services, trust in health system and community outreach activities, and (iii) need characteristics such as perceived susceptibility, seriousness of complications during antenatal, delivery and postnatal period, mode of delivery and need for contraception, and HIV testing. The outcome variable was ‘use of postnatal care’, which was defined as attending postnatal care for the mother’s care at a dispensary, health center, or hospital (government or private) within 6 weeks of delivery. Individual-level explanatory variables included demographic variables such as woman’s age, birth order, education, marital status, and religion. An index of household wealth, based on household assets was created using principal components analysis (PCA) methods proposed by Filmer and Pritchett and used to group households into wealth quintiles [20]. Frequency of antenatal care visits and location of delivery care were included as measures of health system utilization. The variable ‘HIV testing of baby’ refers to the requirement for HIV counseling and testing of the baby during a postnatal visit and functions as a ‘need characteristic’ variable. The ‘partner test for HIV’ variable is proxy for the involvement of men in the process. If expenses were incurred for a delivery, it was coded as a dichotomous variable ‘money spent on delivery’ which is a proxy barrier to care seeking. To create community-level variables, the individual level responses were averaged at the level of the clusters. The asset score of the households in the cluster was averaged to generate a community poverty score that served as proxy for wealth of the community. The proportion of women in the cluster with primary or higher education was used as proxy for literacy of a community. The proportion of women using contraception and the proportion attending 4 or more antenatal visits were used as proxies for community family planning practices and maternal health service utilization. The community level variables for education, contraceptive prevalence and ANC4+ coverage were generated by assigning each cluster with the value of the prevalence of women for the indicators and dividing them into low, middle and high categories. Communities with more than 80 % of respondents who reported trusting a health provider or CHW for advice on pregnancy related issues were classified as communities with high trust. Survey teams, also, collected information on the presence of the nearest functioning health facility and the distance recorded. The distance variable was categorized as 0 km (facility in the village), less than 5 km and more than 5 km. Frequency distributions of the sample women were explored to describe the characteristics of women included in this study. Bivariate and multivariable analyses were performed for individual-level and community-level variables of interest. Multilevel models take into account the hierarchical structure of the data and clustering of responses at the different levels. The following equation illustrates the multilevel model for utilization of Postnatal Care where i and j are the level 1 (individual) and level 2 (community) units respectively; pij is the probability of the outcome of interest for woman i in the cluster j; the b’s are the fixed coefficients; I and C refer to individual-level and community-level explanatory variables, respectively; and ZjXij is a cross-level interaction term; μ shows the random effects for the jth cluster. The error term, ε, represents unmeasured factors that may influence use of postnatal care at a health facility. A multilevel random intercept logistic regression model without covariates (null model) was used to assess the influence of unobserved community-level characteristics on the overall variation in facility use. Three multilevel random intercept logistic regression models were fitted to estimate associations between the individual and community variables and the likelihood of seeking postnatal care at a health facility. The first model included individual-level characteristics only, the second model included community-level characteristics only and the last model (full) includes individual and community-level variables. The independent variables were retained in each of the models if the the p-value was less than 0.2. Important demographic variables were also retained in the multivariable models, in addition to the variables chosen from the bivariate analysis, based on previous literature on the use of maternal health services. All statistical analyses were carried out with STATA 13.1[21]. The extent of missing data was assessed, and patterns of missingness were explored in order to ensure whether data were missing at random. Approximately 15% of our sample was missing data for at least one variable in the final multilevel model. We used multivariate imputation using chained equations (MICE), which uses a Gibbs-like algorithm to impute multiple variables sequentially using univariate fully conditional specifications. This method is considered more appropriate for the imputation of categorical data because it does not assume normality of imputed variables [22]. Independent variables with more than 2% of values missing were imputed, and 50 concatenated datasets were created to reduce any potential bias caused by rounding. Variables imputed included the outcome variable of use of postnatal care, complications during pregnancy, delivery and postnatal period, counseling from CHW regarding postnatal care, mode of delivery, money spent on delivery, partner test for HIV and HIV testing of baby. The full multilevel model was subjected to sensitivity tests to estimate the impact of missing data using multivariate imputations for the independent variables. The percentage change in the standard error for all independent variables was less than 0.5 % and the estimates derived from the imputed model are used. Ethical and administrative approvals were obtained from the Ethics Review Committee of the Johns Hopkins University and the Muhimbili University of Allied Health Sciences, Dar es Salaam, Tanzania. Written informed consent was obtained from each participant.

Materials

N/A

Innovations

Based on the research described, here are some recommendations for developing innovations to improve access to maternal health:

1. Targeted interventions: Develop targeted interventions and programs specifically aimed at reaching women who do not avail themselves of postnatal care. These interventions should address the barriers and challenges faced by these women in accessing postnatal care services.

2. Community-based education programs: Implement community-based education programs to raise awareness about the importance of postnatal care and the available services. This can be done through the use of community health workers, local leaders, and mass media campaigns.

3. Improved access to health facilities: Address the issue of distance by establishing more health facilities in rural areas and improving transportation options for women to reach these facilities. This can include mobile clinics or transportation vouchers for pregnant women.

4. Enhanced quality of care: Ensure that health facilities are equipped with the necessary resources and trained healthcare providers to provide high-quality postnatal care services. This can include training healthcare providers on postnatal care guidelines and protocols.

5. Involvement of men and partners: Engage men and partners in the postnatal care process by providing education and counseling on the importance of their involvement. This can include encouraging men to accompany their partners to postnatal care visits and promoting joint decision-making regarding postnatal care.

6. Addressing financial barriers: Develop strategies to reduce the financial burden associated with postnatal care, such as providing subsidies or insurance coverage for postnatal care services. This can help to remove financial barriers that prevent women from accessing care.

7. Strengthening community support: Foster community support for postnatal care by engaging community leaders and organizations in promoting and advocating for the importance of postnatal care. This can include establishing support groups for new mothers and providing peer support.

By implementing these recommendations, it is possible to improve access to postnatal care and ultimately improve maternal and infant health outcomes.

AI Innovations Description

The recommendation that can be used to develop an innovation to improve access to maternal health based on the research described is to focus on reaching women who do not avail themselves of postnatal care. This can be achieved through targeted interventions and programs that address the barriers and challenges faced by these women in accessing postnatal care services. Some specific recommendations include:

1. Increasing awareness and education: Implement community-based education programs to raise awareness about the importance of postnatal care and the available services. This can be done through the use of community health workers, local leaders, and mass media campaigns.

2. Improving access to health facilities: Address the issue of distance by establishing more health facilities in rural areas and improving transportation options for women to reach these facilities. This can include mobile clinics or transportation vouchers for pregnant women.

3. Enhancing the quality of care: Ensure that health facilities are equipped with the necessary resources and trained healthcare providers to provide high-quality postnatal care services. This can include training healthcare providers on postnatal care guidelines and protocols.

4. Involving men and partners: Engage men and partners in the postnatal care process by providing education and counseling on the importance of their involvement. This can include encouraging men to accompany their partners to postnatal care visits and promoting joint decision-making regarding postnatal care.

5. Addressing financial barriers: Develop strategies to reduce the financial burden associated with postnatal care, such as providing subsidies or insurance coverage for postnatal care services. This can help to remove financial barriers that prevent women from accessing care.

6. Strengthening community support: Foster community support for postnatal care by engaging community leaders and organizations in promoting and advocating for the importance of postnatal care. This can include establishing support groups for new mothers and providing peer support.

By implementing these recommendations, it is possible to improve access to postnatal care and ultimately improve maternal and infant health outcomes.

AI Innovations Methodology

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

1. Define the target population: The simulation should focus on rural women who have recently given birth and have not accessed postnatal care.

2. Identify the key variables: Based on the research findings, the key variables that should be included in the simulation are individual-level variables such as education, mode of delivery, counseling received, HIV testing for the baby, and partner’s HIV testing. Community-level variables such as family planning usage and trust in the health system should also be included.

3. Collect baseline data: Conduct a survey or use existing data to collect information on the key variables from the target population. This will serve as the baseline data for the simulation.

4. Develop intervention scenarios: Based on the recommendations provided, develop different intervention scenarios that target the barriers and challenges identified in the research. For example, one scenario could focus on increasing awareness and education through community-based programs, while another scenario could focus on improving access to health facilities through the establishment of mobile clinics.

5. Implement the interventions: Simulate the implementation of the different intervention scenarios by applying the recommended strategies to the target population. This could involve providing education and awareness campaigns, establishing new health facilities, training healthcare providers, and engaging community leaders and organizations.

6. Measure the impact: After implementing the interventions, collect data on the key variables from the target population. Compare the post-intervention data with the baseline data to measure the impact of the interventions on improving access to postnatal care.

7. Analyze the results: Use statistical analysis techniques to analyze the data and determine the effectiveness of each intervention scenario in improving access to maternal health. Compare the results of the different scenarios to identify the most effective strategies.

8. Draw conclusions and make recommendations: Based on the analysis of the results, draw conclusions about the impact of the interventions and make recommendations for future interventions and programs to improve access to maternal health.

By following this methodology, it is possible to simulate the impact of the main recommendations on improving access to maternal health and inform decision-making for implementing effective interventions.

Author

Dima Health

Statistics:

Citations: 51

Identifiers:

DOI: 10.1186/s12884-015-0717-7

Research Areas:

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

Study Countries:

Tanzania

Study Design:

Case-Control Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Male & Female