Variation in the quality and out-of-pocket cost of treatment for childhood malaria, diarrhoea, and pneumonia: Community and facility based care in rural Uganda

PLoS ONE, Volume 13, No. 11, Year 2018

Interpretation

Background A key barrier to appropriate treatment for malaria, diarrhoea, and pneumonia (MDP) in children under 5 years of age in low income rural settings is the lack of access to quality health care. The WHO and UNICEF have therefore called for the scale-up of integrated community case management (iCCM) using community health workers (CHWs). The current study assessed access to treatment, out-of-pocket expenditure and the quality of treatment provided in the public and private sectors compared to national guidelines, using data collected in a large representative survey of caregivers of children in 205 villages with iCCM-trained CHWs in mid-Western Uganda. Results The prevalence of suspected malaria, diarrhoea and suspected pneumonia in the preceding two weeks in 6501 children in the study sample were 45%, 11% and 24% respectively. Twenty percent of children were first taken to a CHW, 56% to a health facility, 14% to other providers and no care was sought for 11%. The CHW was more likely to provide appropriate treatment compared to any other provider or to those not seeking care for children with MDP (RR 1.51, 95% CI 1.42–1.61, p<0.001). Seeking care from a CHW had the lowest cost outlay (median $0.00, IQR $0.00-$1.80), whilst seeking care to a private doctor or clinic the highest (median $2.80, IQR $1.20-$6.00). We modelled the expected increase in overall treatment coverage if children currently treated in the private sector or not seeking care were taken to the CHW instead. In this scenario, coverage of appropriate treatment for MDP could increase in total from the current rate of 47% up to 64%. Conclusion Scale-up of iCCM-trained CHW programmes is key to the provision of affordable, high quality treatment for sick children, and can thus significantly contribute to closing the gap in coverage of appropriate treatment. A description of the study context and site is provided elsewhere [20]. In brief, iCCM implementation has been supported in 8 districts in mid-Western Uganda by the Malaria Consortium (a leading not for profit international organization specialising in maternal and child health) through a grant from the Canadian International Development Agency (CIDA) since August 2010. Following this and prior to the roll-out of the inSCALE interventions, a cross sectional survey was undertaken across the 8 participating districts. To be eligible for the inSCALE study, a sub-county had to have VHTs trained in iCCM by 31st January 2011. We excluded sub-counties that contained less than 10 villages and those where other Malaria Consortium projects had operated to avoid respondent fatigue. This yielded an overall total of 41 eligible sub-counties active in iCCM for surveillance. Five villages, with an additional five back-up villages were randomly selected per sub-county for surveillance. A list of all households in each community was supplied by the local parish council and verified by the field supervisor. From this list thirty-two households per community were selected and surveyed at random between May and August 2011 in accordance with the sample size requirements for the evaluation of the inSCALE trial [20]. The analyses presented in this paper are based on data collected in this 2011 inSCALE cross-sectional survey. Data on socioeconomic and demographic characteristics of households, symptoms of the most recent illness episode in children under 5 years of age in the two weeks preceding the survey, care seeking behaviour, treatments received and details of all the self-reported out-of-pocket costs associated with care seeking for the episode of illness, were collected from the primary carer. The survey questionnaires were based on Demographic Health Survey (DHS)/Multiple Indicator Cluster Survey (MICS) child health and heath economics survey instruments used extensively in low and middle income countries [21, 22], and are provided in S1 Questionnaire File. Draft versions of the questionnaire were piloted on site and updated in an iterative process to ensure intended meanings were accurately conveyed. Pictures of locally available drugs for common childhood illnesses were used to increase the accuracy of recall of treatments received for sick children (S1 Fig). The questionnaires were delivered in the local languages of the region (Luganda, Luo and Runyakitara). To ensure consistency, 10% of household interviews were repeated by field supervisors within a week of the original and discrepancies resolved. All questionnaires were double entered into a dedicated database and differences between copies verified and corrected. Range and consistency checks were run on data to ensure that missing/incorrect fields were identified and flagged for resolution. Case definitions of episodes of suspected malaria, diarrhoea and suspected pneumonia, appropriate treatment for each of these diagnoses, care seeking and cost of care seeking were developed in accordance with standard WHO/UNICEF guidelines for treatment of malaria, diarrhoea and pneumonia in the community and at health facilities [10, 23–26]. Detailed definitions are provided in S1A–S1D Table. In brief, cases of suspected malaria episodes include all children with reported fever, excluding those with a negative malaria blood-test, suspected pneumonia episodes include all children with reported fast breathing and cough, and those with an episode of diarrhoea will have passed at least three watery stools in a 24hr period during the illness episode. The survey allowed for interviewees to record all the care seeking locations visited for the illness episode in question, and follow-on questions on transport and costs incurred whilst seeking care were asked for the first two locations visited. Definitions of out-of-pocket cost are provided in S1D Table. Analyses were based on caregiver-reported episodes of suspected malaria, diarrhoea or suspected pneumonia (this was the most recent episode in the two weeks prior to the interview). The difference in proportions of appropriate treatment between care seeking locations (by combining all episodes of MDP, and again separated by type of illness episode) was evaluated using logistic regression, with the VHT as the treatment group (or ‘exposure of interest’), and other care providers as the null group. Regression models were calculated using general estimating equations with an exchangeable correlation structure to account for sub-county clusters. Relative risks were derived from the regression models by use of the marginal standardisation technique, and the 95% CIs estimated with the delta method [27]. There are a range of demand-side characteristics that could influence the chances of appropriate treatment for sick children, including household income, parents’ education, rural versus urban location, age of parents and children, gender and others. We made the a priori decision not to force those of the above indicators for which we had data (mothers age, household income, education, religion, occupation, age and gender of child) into the final regression model as there is good evidence to show that these primarily operate via their influence on care seeking behaviour [28, 29]. The objective of this paper is not to determine the root causes of appropriate treatment, rather to ascertain how ultimate care seeking location influences the probability of appropriate treatment, and the implications for future scale-up of community case management programmes for childhood illnesses. We nonetheless provide a comparison of the demographic profiles of households by the care seeking location first visited for reference. In accordance with the age-specific guidelines for classification and treatment of children (S1A and S1B Table) the samples for analysis was restricted to children between the ages of 2 months and 59 months, or 4 months and 59 months in the case of those with suspected or confirmed malaria. These represent the age ranges for community-based treatment of children with MDP. After dropping data outliers, the household financial costs associated with seeking care were converted from Ugandan shillings to US dollars (using 2011 currency exchange rates [30]). Both median and mean out-of-pocket costs of care seeking were calculated to provide an indication of the spread of the data. We assessed the theoretical overall improvement in appropriate treatment rates that could be gained from diverting those sick children for whom no care was sought or who were taken for care to the poorest-performing providers, to instead seeking care to the VHT as the first port of call. For this exercise, the overall appropriate treatment rate observed was described mathematically as the sum of the products of the proportions of MDP episodes taken to each care provider type (first port of call), and the proportions of episodes appropriately treated at the provider type in question (Eq 1). In each scenario, the children not seeking care or taken to poorly performing providers (i.e. βi for each of such providers as below) were instead switched in turn to care seeking to the VHT, and the overall rate of appropriate treatment re-estimated. Pc- Predicted coverage of appropriate treatment β- Proportion of sick children (i.e. with fever, diarrhoea, or pneumonia) taken to provider i ϒ- Proportion of children appropriately treated at provider i n- Total number of provider types (including ‘no care seeking’) in study area As a reference for future evaluations of iCCM programmes we calculated intra-class correlation coefficients (ICCs) to describe the distribution of the variation amongst children within and between sub-counties with respect to their probability of receiving appropriate treatment. These were calculated using the unweighted analysis of variance estimator (‘loneway’ command) in Stata 13.1: loneway calculates an ICC as a function of the F-statistic from a one way analysis of variance of appropriate treatment rate with cluster ID as the only predictor [31, 32]. Data were summarised as tables or Microsoft Excel (Microsoft Corp 2010) graphs, and statistical analysis carried out using Stata version 13.1 (StataCorp Texas USA). Ethical approval was provided by the Higher Degrees, Research and Ethics Committee Uganda (IRB0011353: 2011), the Uganda National Council of Science and Technology (HS958: 2011), and the Ethics Committee of the London School of Hygiene and Tropical Medicine (ref 5762: 2010, 2011). Informed written consent was obtained from all participants.

AI Digest

Study Justification:

– The study aimed to assess access to treatment, out-of-pocket expenditure, and the quality of treatment for childhood malaria, diarrhoea, and pneumonia in rural Uganda.
– The lack of access to quality healthcare is a barrier to appropriate treatment for these illnesses in low-income rural settings.
– The World Health Organization (WHO) and UNICEF have called for the scale-up of integrated community case management (iCCM) using community health workers (CHWs) to address this issue.

Study Highlights:

– The study found that CHWs were more likely to provide appropriate treatment compared to other providers or those not seeking care for children with malaria, diarrhoea, and pneumonia.
– Seeking care from a CHW had the lowest out-of-pocket cost, while seeking care from a private doctor or clinic had the highest cost.
– The study modeled the expected increase in overall treatment coverage if children currently treated in the private sector or not seeking care were taken to CHWs instead. This could increase coverage of appropriate treatment from 47% to 64%.

Study Recommendations:

– The scale-up of iCCM-trained CHW programs is crucial for providing affordable and high-quality treatment for sick children.
– Increasing access to CHWs can help close the gap in coverage of appropriate treatment for childhood malaria, diarrhoea, and pneumonia.

Key Role Players:

– Community health workers (CHWs): Trained individuals who provide healthcare services at the community level.
– Caregivers of children: Responsible for seeking care for sick children and following treatment recommendations.
– Public and private healthcare providers: Play a role in providing treatment for childhood illnesses.
– Malaria Consortium: A leading non-profit international organization specializing in maternal and child health, supporting iCCM implementation in Uganda.
– Canadian International Development Agency (CIDA): Provided funding for the iCCM program in Uganda.

Cost Items for Planning Recommendations:

– Training and capacity building for CHWs: Includes training materials, trainers’ fees, and transportation costs.
– Supplies and equipment for CHWs: Includes diagnostic tools, medications, and other necessary medical supplies.
– Monitoring and evaluation: Includes data collection tools, personnel, and analysis.
– Communication and outreach: Includes community engagement activities, health education materials, and transportation costs.
– Program management and coordination: Includes salaries for program staff, office space, and administrative costs.
Please note that the above cost items are general categories and may vary depending on the specific context and implementation strategy.

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 representative survey and provides specific data on access to treatment, out-of-pocket expenditure, and the quality of treatment provided. The study also includes a comparison of different care providers and their effectiveness in providing appropriate treatment. To improve the evidence, the abstract could include more details on the methodology used, such as the sampling strategy and data collection methods. Additionally, it would be helpful to provide information on the statistical analysis performed and any limitations of the study.

Abstract

A description of the study context and site is provided elsewhere [20]. In brief, iCCM implementation has been supported in 8 districts in mid-Western Uganda by the Malaria Consortium (a leading not for profit international organization specialising in maternal and child health) through a grant from the Canadian International Development Agency (CIDA) since August 2010. Following this and prior to the roll-out of the inSCALE interventions, a cross sectional survey was undertaken across the 8 participating districts. To be eligible for the inSCALE study, a sub-county had to have VHTs trained in iCCM by 31st January 2011. We excluded sub-counties that contained less than 10 villages and those where other Malaria Consortium projects had operated to avoid respondent fatigue. This yielded an overall total of 41 eligible sub-counties active in iCCM for surveillance. Five villages, with an additional five back-up villages were randomly selected per sub-county for surveillance. A list of all households in each community was supplied by the local parish council and verified by the field supervisor. From this list thirty-two households per community were selected and surveyed at random between May and August 2011 in accordance with the sample size requirements for the evaluation of the inSCALE trial [20]. The analyses presented in this paper are based on data collected in this 2011 inSCALE cross-sectional survey. Data on socioeconomic and demographic characteristics of households, symptoms of the most recent illness episode in children under 5 years of age in the two weeks preceding the survey, care seeking behaviour, treatments received and details of all the self-reported out-of-pocket costs associated with care seeking for the episode of illness, were collected from the primary carer. The survey questionnaires were based on Demographic Health Survey (DHS)/Multiple Indicator Cluster Survey (MICS) child health and heath economics survey instruments used extensively in low and middle income countries [21, 22], and are provided in S1 Questionnaire File. Draft versions of the questionnaire were piloted on site and updated in an iterative process to ensure intended meanings were accurately conveyed. Pictures of locally available drugs for common childhood illnesses were used to increase the accuracy of recall of treatments received for sick children (S1 Fig). The questionnaires were delivered in the local languages of the region (Luganda, Luo and Runyakitara). To ensure consistency, 10% of household interviews were repeated by field supervisors within a week of the original and discrepancies resolved. All questionnaires were double entered into a dedicated database and differences between copies verified and corrected. Range and consistency checks were run on data to ensure that missing/incorrect fields were identified and flagged for resolution. Case definitions of episodes of suspected malaria, diarrhoea and suspected pneumonia, appropriate treatment for each of these diagnoses, care seeking and cost of care seeking were developed in accordance with standard WHO/UNICEF guidelines for treatment of malaria, diarrhoea and pneumonia in the community and at health facilities [10, 23–26]. Detailed definitions are provided in S1A–S1D Table. In brief, cases of suspected malaria episodes include all children with reported fever, excluding those with a negative malaria blood-test, suspected pneumonia episodes include all children with reported fast breathing and cough, and those with an episode of diarrhoea will have passed at least three watery stools in a 24hr period during the illness episode. The survey allowed for interviewees to record all the care seeking locations visited for the illness episode in question, and follow-on questions on transport and costs incurred whilst seeking care were asked for the first two locations visited. Definitions of out-of-pocket cost are provided in S1D Table. Analyses were based on caregiver-reported episodes of suspected malaria, diarrhoea or suspected pneumonia (this was the most recent episode in the two weeks prior to the interview). The difference in proportions of appropriate treatment between care seeking locations (by combining all episodes of MDP, and again separated by type of illness episode) was evaluated using logistic regression, with the VHT as the treatment group (or ‘exposure of interest’), and other care providers as the null group. Regression models were calculated using general estimating equations with an exchangeable correlation structure to account for sub-county clusters. Relative risks were derived from the regression models by use of the marginal standardisation technique, and the 95% CIs estimated with the delta method [27]. There are a range of demand-side characteristics that could influence the chances of appropriate treatment for sick children, including household income, parents’ education, rural versus urban location, age of parents and children, gender and others. We made the a priori decision not to force those of the above indicators for which we had data (mothers age, household income, education, religion, occupation, age and gender of child) into the final regression model as there is good evidence to show that these primarily operate via their influence on care seeking behaviour [28, 29]. The objective of this paper is not to determine the root causes of appropriate treatment, rather to ascertain how ultimate care seeking location influences the probability of appropriate treatment, and the implications for future scale-up of community case management programmes for childhood illnesses. We nonetheless provide a comparison of the demographic profiles of households by the care seeking location first visited for reference. In accordance with the age-specific guidelines for classification and treatment of children (S1A and S1B Table) the samples for analysis was restricted to children between the ages of 2 months and 59 months, or 4 months and 59 months in the case of those with suspected or confirmed malaria. These represent the age ranges for community-based treatment of children with MDP. After dropping data outliers, the household financial costs associated with seeking care were converted from Ugandan shillings to US dollars (using 2011 currency exchange rates [30]). Both median and mean out-of-pocket costs of care seeking were calculated to provide an indication of the spread of the data. We assessed the theoretical overall improvement in appropriate treatment rates that could be gained from diverting those sick children for whom no care was sought or who were taken for care to the poorest-performing providers, to instead seeking care to the VHT as the first port of call. For this exercise, the overall appropriate treatment rate observed was described mathematically as the sum of the products of the proportions of MDP episodes taken to each care provider type (first port of call), and the proportions of episodes appropriately treated at the provider type in question (Eq 1). In each scenario, the children not seeking care or taken to poorly performing providers (i.e. βi for each of such providers as below) were instead switched in turn to care seeking to the VHT, and the overall rate of appropriate treatment re-estimated. Pc- Predicted coverage of appropriate treatment β- Proportion of sick children (i.e. with fever, diarrhoea, or pneumonia) taken to provider i ϒ- Proportion of children appropriately treated at provider i n- Total number of provider types (including ‘no care seeking’) in study area As a reference for future evaluations of iCCM programmes we calculated intra-class correlation coefficients (ICCs) to describe the distribution of the variation amongst children within and between sub-counties with respect to their probability of receiving appropriate treatment. These were calculated using the unweighted analysis of variance estimator (‘loneway’ command) in Stata 13.1: loneway calculates an ICC as a function of the F-statistic from a one way analysis of variance of appropriate treatment rate with cluster ID as the only predictor [31, 32]. Data were summarised as tables or Microsoft Excel (Microsoft Corp 2010) graphs, and statistical analysis carried out using Stata version 13.1 (StataCorp Texas USA). Ethical approval was provided by the Higher Degrees, Research and Ethics Committee Uganda (IRB0011353: 2011), the Uganda National Council of Science and Technology (HS958: 2011), and the Ethics Committee of the London School of Hygiene and Tropical Medicine (ref 5762: 2010, 2011). Informed written consent was obtained from all participants.

Materials

Innovations

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

1. Mobile health (mHealth) technology: Implementing mobile health applications or text messaging services to provide information and reminders about maternal health care, as well as facilitate communication between health workers and pregnant women.

2. Telemedicine: Using telecommunication technology to provide remote consultations and medical advice to pregnant women in rural areas, allowing them to access specialized care without having to travel long distances.

3. Community health worker training and support: Expanding and strengthening community health worker programs, such as the integrated community case management (iCCM) program mentioned in the description, to improve access to quality maternal health care in rural areas.

4. Transportation solutions: Developing innovative transportation solutions, such as mobile clinics or community ambulance services, to ensure that pregnant women can easily access health facilities for prenatal care, delivery, and postnatal care.

5. Financial incentives: Implementing financial incentives, such as conditional cash transfers or vouchers, to encourage pregnant women to seek and utilize maternal health services.

6. Public-private partnerships: Collaborating with private sector organizations to improve access to maternal health services, such as through the provision of mobile clinics or the establishment of private health facilities in underserved areas.

7. Maternal health education and awareness campaigns: Conducting targeted education and awareness campaigns to increase knowledge and understanding of the importance of maternal health care, as well as to address cultural and social barriers that may prevent women from seeking care.

These are just a few examples of potential innovations that could be used to improve access to maternal health. It is important to consider the specific context and needs of the population in question when designing and implementing these innovations.

AI Innovations Description

The study mentioned in the description highlights the lack of access to quality healthcare for children under 5 years of age in low-income rural settings, specifically for the treatment of malaria, diarrhea, and pneumonia (MDP). The study suggests that the scale-up of integrated community case management (iCCM) using community health workers (CHWs) can improve access to treatment and reduce out-of-pocket costs.

Based on the findings of the study, the following recommendation can be developed into an innovation to improve access to maternal health:

1. Scale-up iCCM-trained CHW programs: The study found that CHWs were more likely to provide appropriate treatment compared to other providers or those not seeking care for children with MDP. Therefore, scaling up iCCM-trained CHW programs can help improve access to maternal health by ensuring that trained CHWs are available in rural areas to provide quality care.

2. Strengthen collaboration between public and private sectors: The study also found that seeking care from a CHW had the lowest cost outlay, while seeking care from a private doctor or clinic had the highest cost. Strengthening collaboration between the public and private sectors can help reduce out-of-pocket costs for maternal health services. This can be done through partnerships, subsidies, or other mechanisms to make private healthcare more affordable and accessible.

3. Improve awareness and education: The study highlighted the importance of care-seeking behavior in accessing appropriate treatment. Improving awareness and education among caregivers about the availability and benefits of iCCM-trained CHWs can help increase utilization of these services. This can be done through community outreach programs, health education campaigns, and targeted messaging.

4. Use technology for remote consultations: In rural areas where access to healthcare facilities is limited, leveraging technology such as telemedicine can help improve access to maternal health services. Remote consultations with healthcare providers can provide timely and appropriate care without the need for physical travel. This can be particularly beneficial for emergency situations or when specialized care is required.

Overall, the recommendation is to prioritize the scale-up of iCCM-trained CHW programs, strengthen collaboration between public and private sectors, improve awareness and education, and leverage technology for remote consultations. These innovations can help improve access to maternal health services in low-income rural settings.

AI Innovations Methodology

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

1. Scale-up integrated community case management (iCCM) programs: The study found that seeking care from community health workers (CHWs) resulted in higher rates of appropriate treatment for childhood illnesses. Therefore, expanding and strengthening iCCM programs, which utilize CHWs to provide healthcare services at the community level, can improve access to maternal health.

2. Increase awareness and education: Many caregivers in the study did not seek care for their children or sought care from providers who did not adhere to national guidelines. Increasing awareness and education about the importance of seeking care from trained providers and the availability of iCCM programs can help improve access to maternal health.

3. Reduce out-of-pocket costs: The study found that seeking care from CHWs had the lowest cost outlay compared to other providers. Reducing out-of-pocket costs for maternal health services, such as through subsidies or health insurance schemes, can make healthcare more affordable and accessible for women.

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

1. Define the indicators: Determine the key indicators that will be used to measure access to maternal health, such as the proportion of women receiving antenatal care, the proportion of women delivering in a healthcare facility, or the proportion of women receiving postnatal care.

2. Collect baseline data: Gather data on the current status of access to maternal health services in the target population. This can be done through surveys, interviews, or existing data sources.

3. Develop a simulation model: Create a mathematical model that simulates the impact of the recommendations on access to maternal health. This model should take into account factors such as population size, geographic distribution, healthcare infrastructure, and the potential reach of iCCM programs.

4. Input the recommendations: Incorporate the recommendations into the simulation model. This can be done by adjusting the parameters of the model to reflect the expected changes in access to maternal health based on the implementation of the recommendations.

5. Run the simulation: Use the simulation model to estimate the impact of the recommendations on access to maternal health. This can be done by running multiple iterations of the model with different scenarios, such as varying levels of implementation of the recommendations or different target populations.

6. Analyze the results: Examine the output of the simulation model to assess the potential impact of the recommendations on access to maternal health. This can include comparing the baseline data with the simulated data to determine the magnitude of the improvement.

7. Validate the results: Validate the results of the simulation by comparing them with real-world data, if available. This can help ensure the accuracy and reliability of the simulation model.

By following this methodology, policymakers and healthcare providers can gain insights into the potential impact of different recommendations on improving access to maternal health and make informed decisions about resource allocation and program implementation.

Author

Dima Health

Statistics:

Citations: 10

Identifiers:

DOI: 10.1371/journal.pone.0200543

Research Areas:

Community Interventions, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Quality of Care, Social Determinants, Technology and Innovations, Workforce

Study Countries:

Uganda

Study Design:

Cross Sectional Study

Study Approach:

Quantitative