Factors associated with healthcare seeking for childhood illnesses among mothers of children under five in Chad

PLoS ONE, Volume 16, No. 8 August, Year 2021

Interpretation

Background Poor healthcare-seeking behaviour is a major contributing factor for increased morbidity and mortality among children in low- and middle-income countries. This study assessed the individual and community level factors associated with healthcare-seeking behaviour for childhood illnesses among mothers of children under five in Chad. Methods The study utilized data from the 2014-2015 Chad Demographic and Health Survey. A total of 5,693 mothers who reported that their children under five had either fever accompanied by cough or diarrhea or both within the two weeks preceding the survey were included in this study. The outcome variable for the study was healthcare-seeking behaviour for childhood illnesses. The data were analyzed using Stata version 14.2. Multilevel binary logistic regression model was employed due to the hierarchical nature of the dataset. Results were presented as adjusted odds ratios (aOR) at 95% confidence interval (CI). Results Out of the 5,693 mothers who reported that their children under five had either fever accompanied by cough, diarrhea or both at any time in the 2 weeks preceding the survey, 79.6% recalled having sought treatment for their children’s illnesses. In terms of the individual level factors, mothers who faced financial barriers to healthcare access were less likely to seek healthcare for childhood illnesses, relative to those who faced no financial barrier (aOR = 0.80, 95% CI = 0.65-0.99). Mothers who reported that distance to the health facility was a barrier were less likely to seek healthcare for childhood illnesses, compared to those who faced no geographical barrier to healthcare access (aOR = 79, 95% CI = 0.65-0.95). Mothers who were cohabiting were less likely to seek healthcare for childhood illnesses compared to married mothers (aOR = 0.62 95% CI = 0.47-0.83). Lower odds of healthcare seeking for childhood illnesses was noted among mothers who did not listen to radio at all, relative to those who listened to radio at least once a week (aOR = 0.71, 95% CI = 0.55- 0.91). Mothers who mentioned that their children were larger than average size at birth had a lesser likelihood of seeking childhood healthcare, compared to those whose children were of average size (aOR = 0.79, 95% CI = 0.66-0.95). We further noted that with the community level factors, mothers who lived in communities with medium literacy level were less likely to seek childhood healthcare than those in communities with high literacy (aOR = 0.73, 95% CI = 0.53-0.99). Conclusion The study revealed that both individual (financial barriers to healthcare access, geographical barriers to healthcare access, marital status, frequency of listening to radio and size of children at birth) and community level factors (community level literacy) are associated with healthcare-seeking behaviour for childhood illnesses in Chad. The government of Chad, through multi-sectoral partnership, should strengthen health systems by removing financial and geographical barriers to healthcare access. Moreover, the government should create favourable conditions to improve the status of mothers and foster their overall socio-economic wellbeing and literacy through employment and education. Other interventions should include community sensitization of cohabiting mothers and mothers with children whose size at birth is large to seek healthcare for their children when they are ill. This can be done using radio as means of information dissemination. This study was a cross-sectional study that utilized data from the 2014–15 Chad Demographic and Health Survey (CDHS), which is the most recent DHS conducted in the country. The CDHS is conducted by the National Institute of Statistics, Economic and Demographic Studies (INSEED) and the Inner-City Fund (ICF) International [21]. The CDHS utilized a stratified sampling design to recruit eligible participants. The national territory was demarcated into twenty-one study areas with reference to the 22 regions and the city of N’Djaména. Two strata were created in each field (urban and rural). In all, 626 primary survey units (PSUs) or clusters were systematically selected from the list of enumeration areas that were predefined during the 2009 General Population and Housing Census. Households in each cluster constituted the list from which eligible households were selected, with 25 households per cluster in the urban locations and 30 households per cluster in rural locations at random. A total of 17, 965 households from 4,075 urban areas in 163 clusters and 13,890 rural households nested in 463 clusters were selected. All resident mothers 15–49 years or those present the night preceding the survey were eligible to be interviewed. A total of 5,693 mothers reported that their children under five had either fever accompanied by cough or diarrhea or both within the two weeks preceding the survey. This constituted the sample size for our study. The outcome variable for the study was healthcare seeking behaviour for childhood illnesses. This variable was derived as a composite variable from two questions: “Did [NAME] receive treatment for diarrhea?” and “Did [NAME] receive treatment for fever accompanied by cough?” The responses were “Yes” and “No” in the CDHS. All mothers who responded “Yes” to either of the two questions were considered as seeking healthcare for childhood illnesses (coded as 1) whilst those who did not seek healthcare for any of the two childhood illnesses were coded as 0. There were 21 independent variables made up of 18 individual level variables and three community level variables. None of these variables was selected a prior; instead, the selection was based on conclusions drawn by earlier studies on healthcare seeking for childhood illnesses as well as their conceptual and theoretical bearing on healthcare seeking for childhood illnesses [22, 23]. The individual level variables were difficulty with distance to the facility, difficulty in getting money for treatment, difficulty with getting permission to visit a health facility, and difficulty in not wanting to go for medical help alone (each was coded as big problem and not a big problem). These related to geographical, financial, and partner support barriers faced by mothers when accessing healthcare. Big problem means the respondents considered each of these as a barrier to healthcare access whiles not a big problem means that they were not considered as barriers. Other individual level variables were mothers’ age (15–19, 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49), marital status (married and cohabiting), healthcare decision-making capacity (alone and not alone), parity (one birth, two, three, and four or more), employment status (working or not working), religion (Christianity, Islam, and no religion), frequency of exposure to media (reading newspaper, listening to radio, watching television) which were coded as not at all, less than once a week, and at least once a week, sex of household head (male and female), mother’s subjective perception of the size of child at birth (less than average, average, or smaller than average), birth order (one, two to four, and five and above births), twin status (single or multiple births), and sex of child (male and female). Community literacy level (categorized into low, medium, and high), community socio-economic status (captured as low, medium, and high), and residence (rural and urban) were the community level variables. The categorisation of community literacy level and community socio-economic status into low, medium and high was not directly available in the data but generated from maternal education and household wealth quintile through a method of aggregation at the cluster level. We employed both descriptive and inferential analytical approaches. First, we computed the proportion of mothers who sought healthcare for childhood illnesses across the individual and community level variables. Next, a Chi-square test was carried out to assess the level of significance between the independent variables and healthcare seeking for childhood illnesses (see Table 1). At the bivariate analysis stage, due to multiple-comparisons, we introduced a correction method by using the Bonferroni correction method [24]. This was done by dividing the alpha rate (p = 0.05) by the number of analysis performed (21 explanatory variables) [25, 26], that is, 0.05/21 = 0.002. Therefore, at the bivariate analysis, statistical significance was declared at p≤0.002. Following the hierarchical nature of the dataset, the multilevel logistic regression model (MLRM) was employed after the bivariate analysis to examine the predictors of healthcare seeking for childhood illnesses. This comprises fixed effects and random effects [27]. The fixed effects of the model were gauged with binary logistic regression, which resulted in adjusted odds ratios (aORs) (see Table 2). The random effects, on the other hand, were assessed with intra-cluster correlation (ICC) [28] (see Table 2). The sample weight (v005/1,000,000) was applied in all the analyses to control for over- and under-sampling. All the analyses were carried out using Stata version 14.2. Source: 2014–15 Chad Demographic and Health Survey Source: 2014–15 Chad Demographic and Health Survey PSU = Primary sampling unit; ICC = Intra-Class Correlation; LR Test = Likelihood ratio Test; AIC = Akaike’s Information Criterion; N = Sample size Model 0 is the null model, a baseline model without any independent variable Model 1 is adjusted for individual level variables Model 2 is adjusted for community level variables Model 3 is the final model adjusted for individual and community level variables We assessed the fitness of the models with the likelihood ratio (LR) test. The presence of multicollinearity between the independent variables was checked before fitting the models. The variance inflation factor (VIF) test revealed the absence of high multicollinearity between the variables (Mean VIF = 1.21, Max VIF = 1.43, Minimum = 1.05). In order to develop robust models, only variables that showed statistically significant association in the bivariate analysis were included in the models. This study used publicly available data from DHS. Informed consent was obtained from all participants prior to the survey. The DHS Program adheres to ethical standards for protecting the privacy of respondents. The ICF International also ensures that the survey processes conform to the ethical requirements of the U.S. Department of Health and Human Services. No additional ethical approval was required, as the data is secondary and available to the general public. However, to have access and use the raw data, we sought and obtained permission from MEASURE DHS. Details of the ethical standards are available on http://goo.gl/ny8T6X.

AI Digest

Study Justification:

This study aimed to investigate the factors associated with healthcare-seeking behavior for childhood illnesses among mothers of children under five in Chad. The justification for this study is based on the understanding that poor healthcare-seeking behavior contributes to increased morbidity and mortality among children in low- and middle-income countries. By identifying the individual and community level factors that influence healthcare-seeking behavior, this study provides valuable insights for policymakers and healthcare providers to improve access to healthcare for children in Chad.

Study Highlights:

– The study utilized data from the 2014-2015 Chad Demographic and Health Survey.
– A total of 5,693 mothers were included in the study, and the outcome variable was healthcare-seeking behavior for childhood illnesses.
– The study found that financial barriers, geographical barriers, marital status, frequency of listening to radio, and perceived size of children at birth were associated with healthcare-seeking behavior.
– Community-level factors, such as community literacy level, also influenced healthcare-seeking behavior.
– The study recommends that the government of Chad, through multi-sectoral partnership, should strengthen health systems by removing financial and geographical barriers to healthcare access.
– Additionally, the government should focus on improving the status of mothers and their overall socio-economic wellbeing and literacy through employment and education.
– Other interventions should include community sensitization of cohabiting mothers and mothers with larger-sized children at birth to seek healthcare for their children when they are ill.

Recommendations for Lay Readers:

– The government of Chad should work with different sectors to improve healthcare access for children by addressing financial and geographical barriers.
– It is important to support mothers by providing employment opportunities and education to improve their socio-economic wellbeing and literacy.
– Community awareness campaigns should be conducted to encourage cohabiting mothers and mothers with larger-sized children at birth to seek healthcare for their children when they are ill.
– Listening to the radio can provide valuable information about healthcare, so it is important for mothers to listen to the radio regularly.

Recommendations for Policy Makers:

– Strengthen health systems by removing financial and geographical barriers to healthcare access.
– Implement multi-sectoral partnerships to address the factors influencing healthcare-seeking behavior.
– Focus on improving the status of mothers through employment and education to enhance their ability to seek healthcare for their children.
– Conduct community sensitization programs to promote healthcare-seeking behavior among specific groups, such as cohabiting mothers and mothers with larger-sized children at birth.

Key Role Players:

– Government of Chad
– Ministry of Health
– Non-governmental organizations (NGOs)
– Community leaders and influencers
– Healthcare providers
– Educators and schools
– Media organizations (radio stations, television channels, newspapers)

Cost Items for Planning Recommendations:

– Development and implementation of healthcare access programs
– Training and capacity building for healthcare providers
– Community sensitization campaigns
– Employment and education initiatives for mothers
– Information dissemination through radio and other media channels
– Monitoring and evaluation of interventions
– Research and data collection on healthcare-seeking behavior
Please note that the cost items provided are general suggestions and may vary depending on the specific context and resources available in Chad.

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on a large sample size (5,693 mothers) and utilizes data from the 2014-2015 Chad Demographic and Health Survey. The study employs a multilevel binary logistic regression model to analyze the data and presents adjusted odds ratios (aOR) at a 95% confidence interval. The study identifies individual and community level factors associated with healthcare-seeking behavior for childhood illnesses in Chad. The conclusions drawn from the study provide actionable steps for the government of Chad to strengthen health systems and improve healthcare access, such as removing financial and geographical barriers, improving the status of mothers, and promoting community sensitization through radio. To improve the evidence, it would be beneficial to provide more information on the methodology, such as the specific variables used and the statistical significance of the findings.

Abstract

This study was a cross-sectional study that utilized data from the 2014–15 Chad Demographic and Health Survey (CDHS), which is the most recent DHS conducted in the country. The CDHS is conducted by the National Institute of Statistics, Economic and Demographic Studies (INSEED) and the Inner-City Fund (ICF) International [21]. The CDHS utilized a stratified sampling design to recruit eligible participants. The national territory was demarcated into twenty-one study areas with reference to the 22 regions and the city of N’Djaména. Two strata were created in each field (urban and rural). In all, 626 primary survey units (PSUs) or clusters were systematically selected from the list of enumeration areas that were predefined during the 2009 General Population and Housing Census. Households in each cluster constituted the list from which eligible households were selected, with 25 households per cluster in the urban locations and 30 households per cluster in rural locations at random. A total of 17, 965 households from 4,075 urban areas in 163 clusters and 13,890 rural households nested in 463 clusters were selected. All resident mothers 15–49 years or those present the night preceding the survey were eligible to be interviewed. A total of 5,693 mothers reported that their children under five had either fever accompanied by cough or diarrhea or both within the two weeks preceding the survey. This constituted the sample size for our study. The outcome variable for the study was healthcare seeking behaviour for childhood illnesses. This variable was derived as a composite variable from two questions: “Did [NAME] receive treatment for diarrhea?” and “Did [NAME] receive treatment for fever accompanied by cough?” The responses were “Yes” and “No” in the CDHS. All mothers who responded “Yes” to either of the two questions were considered as seeking healthcare for childhood illnesses (coded as 1) whilst those who did not seek healthcare for any of the two childhood illnesses were coded as 0. There were 21 independent variables made up of 18 individual level variables and three community level variables. None of these variables was selected a prior; instead, the selection was based on conclusions drawn by earlier studies on healthcare seeking for childhood illnesses as well as their conceptual and theoretical bearing on healthcare seeking for childhood illnesses [22, 23]. The individual level variables were difficulty with distance to the facility, difficulty in getting money for treatment, difficulty with getting permission to visit a health facility, and difficulty in not wanting to go for medical help alone (each was coded as big problem and not a big problem). These related to geographical, financial, and partner support barriers faced by mothers when accessing healthcare. Big problem means the respondents considered each of these as a barrier to healthcare access whiles not a big problem means that they were not considered as barriers. Other individual level variables were mothers’ age (15–19, 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49), marital status (married and cohabiting), healthcare decision-making capacity (alone and not alone), parity (one birth, two, three, and four or more), employment status (working or not working), religion (Christianity, Islam, and no religion), frequency of exposure to media (reading newspaper, listening to radio, watching television) which were coded as not at all, less than once a week, and at least once a week, sex of household head (male and female), mother’s subjective perception of the size of child at birth (less than average, average, or smaller than average), birth order (one, two to four, and five and above births), twin status (single or multiple births), and sex of child (male and female). Community literacy level (categorized into low, medium, and high), community socio-economic status (captured as low, medium, and high), and residence (rural and urban) were the community level variables. The categorisation of community literacy level and community socio-economic status into low, medium and high was not directly available in the data but generated from maternal education and household wealth quintile through a method of aggregation at the cluster level. We employed both descriptive and inferential analytical approaches. First, we computed the proportion of mothers who sought healthcare for childhood illnesses across the individual and community level variables. Next, a Chi-square test was carried out to assess the level of significance between the independent variables and healthcare seeking for childhood illnesses (see Table 1). At the bivariate analysis stage, due to multiple-comparisons, we introduced a correction method by using the Bonferroni correction method [24]. This was done by dividing the alpha rate (p = 0.05) by the number of analysis performed (21 explanatory variables) [25, 26], that is, 0.05/21 = 0.002. Therefore, at the bivariate analysis, statistical significance was declared at p≤0.002. Following the hierarchical nature of the dataset, the multilevel logistic regression model (MLRM) was employed after the bivariate analysis to examine the predictors of healthcare seeking for childhood illnesses. This comprises fixed effects and random effects [27]. The fixed effects of the model were gauged with binary logistic regression, which resulted in adjusted odds ratios (aORs) (see Table 2). The random effects, on the other hand, were assessed with intra-cluster correlation (ICC) [28] (see Table 2). The sample weight (v005/1,000,000) was applied in all the analyses to control for over- and under-sampling. All the analyses were carried out using Stata version 14.2. Source: 2014–15 Chad Demographic and Health Survey Source: 2014–15 Chad Demographic and Health Survey PSU = Primary sampling unit; ICC = Intra-Class Correlation; LR Test = Likelihood ratio Test; AIC = Akaike’s Information Criterion; N = Sample size Model 0 is the null model, a baseline model without any independent variable Model 1 is adjusted for individual level variables Model 2 is adjusted for community level variables Model 3 is the final model adjusted for individual and community level variables We assessed the fitness of the models with the likelihood ratio (LR) test. The presence of multicollinearity between the independent variables was checked before fitting the models. The variance inflation factor (VIF) test revealed the absence of high multicollinearity between the variables (Mean VIF = 1.21, Max VIF = 1.43, Minimum = 1.05). In order to develop robust models, only variables that showed statistically significant association in the bivariate analysis were included in the models. This study used publicly available data from DHS. Informed consent was obtained from all participants prior to the survey. The DHS Program adheres to ethical standards for protecting the privacy of respondents. The ICF International also ensures that the survey processes conform to the ethical requirements of the U.S. Department of Health and Human Services. No additional ethical approval was required, as the data is secondary and available to the general public. However, to have access and use the raw data, we sought and obtained permission from MEASURE DHS. Details of the ethical standards are available on http://goo.gl/ny8T6X.

Materials

N/A

Innovations

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 information and reminders to mothers about the importance of seeking healthcare for childhood illnesses. This could include information on symptoms, treatment options, and nearby healthcare facilities.

2. Financial assistance programs: Implement programs that provide financial support to mothers who face financial barriers to healthcare access. This could involve subsidies or vouchers to cover the cost of healthcare services for childhood illnesses.

3. Telemedicine services: Establish telemedicine services that allow mothers to consult with healthcare professionals remotely. This could be particularly beneficial for mothers who face geographical barriers to accessing healthcare facilities.

4. Community health workers: Train and deploy community health workers who can provide education and support to mothers in their communities. These workers can help address barriers to healthcare seeking behavior and provide guidance on when and where to seek healthcare for childhood illnesses.

5. Radio campaigns: Utilize radio as a means of disseminating information about the importance of seeking healthcare for childhood illnesses. Develop targeted campaigns that address specific barriers identified in the study, such as cohabiting mothers and mothers with children who were larger than average size at birth.

6. Literacy programs: Implement literacy programs in communities with medium literacy levels to improve health literacy among mothers. This can help mothers better understand the importance of seeking healthcare for childhood illnesses and empower them to make informed decisions.

7. Multi-sectoral partnerships: Foster partnerships between the government, healthcare providers, non-governmental organizations, and other stakeholders to strengthen health systems and address barriers to healthcare access. This can involve collaboration on policy development, resource allocation, and implementation of interventions.

It is important to note that these recommendations are based on the specific findings and context of the study in Chad. Further research and evaluation would be needed to determine the feasibility and effectiveness of these innovations in improving access to maternal health in other settings.

AI Innovations Description

Based on the information provided, the recommendation to improve access to maternal health in Chad is to strengthen the health system by addressing financial and geographical barriers to healthcare access. This can be achieved through multi-sectoral partnerships and government interventions.

Specifically, the government should focus on the following actions:

1. Remove financial barriers: Implement policies and programs that provide financial support to mothers who face difficulties in accessing healthcare due to financial constraints. This can include subsidies, health insurance schemes, or cash transfer programs targeted at maternal health services.

2. Address geographical barriers: Improve the availability and accessibility of healthcare facilities in remote and underserved areas. This can be done by establishing more health centers, mobile clinics, or outreach programs to reach communities that are far from existing healthcare facilities.

3. Improve maternal socio-economic wellbeing: Create favorable conditions to improve the status of mothers and their overall socio-economic wellbeing. This can be achieved through initiatives that promote employment opportunities and education for women, which can empower them to seek healthcare for themselves and their children.

4. Enhance community sensitization: Conduct community sensitization programs to raise awareness among cohabiting mothers and mothers with children who were larger than average size at birth about the importance of seeking healthcare for childhood illnesses. Utilize radio as a means of information dissemination, as it has been shown to be an effective medium for reaching communities in Chad.

By implementing these recommendations, access to maternal health services can be improved, leading to better health outcomes for mothers and their children in Chad.

AI Innovations Methodology

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

1. Remove financial barriers: The government of Chad should work towards removing financial barriers to healthcare access for mothers. This could include implementing policies that provide financial assistance or subsidies for maternal healthcare services.

2. Improve geographical accessibility: Efforts should be made to address the issue of distance to health facilities as a barrier to healthcare seeking. This could involve establishing more health facilities in remote areas or implementing mobile health clinics to reach underserved communities.

3. Increase health literacy: Community-level interventions should focus on improving health literacy among mothers. This could be done through educational campaigns, community workshops, and the use of local media channels to disseminate information about the importance of seeking healthcare for childhood illnesses.

4. Empower mothers: The government should create favorable conditions to improve the status of mothers and foster their overall socio-economic well-being. This could include providing employment opportunities and promoting education for women, which can empower them to make informed decisions about their children’s healthcare.

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

1. Define the indicators: Identify key indicators that measure access to maternal health, such as the percentage of mothers seeking healthcare for childhood illnesses, distance to the nearest health facility, financial barriers faced by mothers, and community literacy levels.

2. Collect baseline data: Gather data on the current status of these indicators in the target population. This could involve conducting surveys, interviews, or analyzing existing data sources such as the Chad Demographic and Health Survey.

3. Develop a simulation model: Use statistical modeling techniques, such as multilevel logistic regression, to analyze the relationship between the identified factors and healthcare-seeking behavior. This model can help estimate the impact of each recommendation on improving access to maternal health.

4. Simulate scenarios: Apply the simulation model to different scenarios, such as removing financial barriers, improving geographical accessibility, and increasing health literacy. This will allow for the estimation of the potential impact of each recommendation on the indicators of access to maternal health.

5. Evaluate the results: Analyze the simulated results to assess the potential effectiveness of each recommendation in improving access to maternal health. This evaluation can help prioritize and refine the recommendations for implementation.

6. Monitor and adjust: Continuously monitor the indicators of access to maternal health and adjust the recommendations as needed based on the observed impact. This iterative process will help ensure that the interventions are effective and responsive to the changing needs of the population.

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

Author

Dima Health

Identifiers:

DOI: 10.1371/journal.pone.0254885

Research Areas:

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

Study Countries:

Chad

Study Design:

Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Male & Female