Oral rehydration solution coverage in under 5 children with diarrhea: a tri-country, subnational, cross-sectional comparative analysis of two demographic health surveys cycles

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Study Justification:
– The study aims to examine changes in oral rehydration solution (ORS) coverage in three countries (Zimbabwe, Zambia, and Malawi) to address the preventable and treatable disease of diarrhea in children under 5 years old.
– Diarrhea is a leading cause of death in developing countries, with over 3 million children dying from dehydration due to diarrhea each year.
– The study provides valuable information on the prevalence of ORS use and identifies areas where resource allocation can be targeted to reduce geographic inequalities and improve access to appropriate treatments.
Study Highlights:
– The study analyzed two cycles of Demographic Health Surveys (DHS) to compare ORS coverage in the three countries.
– ORS coverage increased in Zimbabwe, stagnated in Zambia, and declined in Malawi.
– The rates of change in coverage varied among provinces within each country.
– Factors such as mother’s age, education, HIV status, urban vs. rural setting, subnational region, and survey period were considered in the analysis.
Study Recommendations:
– Health policies should be developed and implemented to strengthen access to appropriate treatments for diarrhea, such as vaccines for rotavirus and cholera, and promote the use of ORS.
– Provision of safe drinking-water, adequate sanitation, and hygiene should be prioritized to reduce the causes and incidence of diarrhea.
– Monitoring national and province-level trends of ORS use is crucial for identifying priority areas for resource allocation and targeting interventions.
Key Role Players:
– Ministries of Health in Zimbabwe, Zambia, and Malawi
– Researchers and program managers involved in diarrhea prevention and treatment programs
– Health policymakers and planners
– NGOs and international organizations working in child health and development
Cost Items for Planning Recommendations:
– Development and implementation of health policies: budget for policy formulation, stakeholder consultations, and program implementation
– Provision of safe drinking-water, sanitation, and hygiene: budget for infrastructure development, maintenance, and promotion campaigns
– Access to appropriate treatments: budget for vaccine procurement, distribution, and administration, as well as promotion of ORS use
– Monitoring and evaluation: budget for data collection, analysis, and reporting to track progress and inform decision-making

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study design is robust, using a stratified two-stage cluster sampling design and conducting cross-sectional surveys on a representative sample. The analysis includes weighted proportions and multivariable-adjusted logistic regression. The results show changes in ORS coverage over time and highlight geographic inequalities. The conclusion suggests actionable steps to improve diarrhea prevention and treatment. However, the abstract could be improved by providing more details on the sample size, response rates, and statistical significance of the findings.

Background: More than 3 million children under 5 years in developing countries die from dehydration due to diarrhea, a preventable and treatable disease. We conducted a comparative analysis of two Demographic Health Survey (DHS) cycles to examine changes in ORS coverage in Zimbabwe, Zambia and Malawi. These surveys are cross-sectional conducted on a representative sample of the non-institutionalized individuals. Methods: The sample is drawn using a stratified two-stage cluster sampling design with census enumeration areas, typically, selected first as primary sampling units (PSUs) and then a fixed number of households from each PSU. We examined national and sub-regional prevalence of ORS use during a recent episode of diarrhea (within 2 weeks of survey) using DHSs for 2007–2010 (1st Period), and 2013–2016 (2nd Period). Weighted proportions of ORS were obtained and multivariable- design-adjusted logistic regression analysis was used to obtain Odds Ratios (aORs) and 95% confidence intervals (CIs) and weighted proportions of ORS coverage. Results: Crude ORS coverage increased from 21.0% (95% CI: 17.4–24.9) in 1st Period to 40.5% (36.5–44.6) in 2nd Period in Zimbabwe; increased from 60.8% (56.1–65.3) to 64.7% (61.8–67.5) in Zambia; and decreased from 72.3% (68.4–75.9) to 64.6% (60.9–68.1) in Malawi. The rates of change in coverage among provinces in Zimbabwe ranged from 10.3% over the three cycles (approximately 10 years) in Midlands to 44.2% in Matabeleland South; in Zambia from − 9.5% in Eastern Province to 24.4% in Luapula; and in Malawi from − 16.5% in the Northern Province to − 3.2% in Southern Province. The aORs for ORS use was 3.95(2.66–5.86) for Zimbabwe, 2.83 (2.35–3.40) for Zambia, and, 0.71(0.59–0.87) for Malawi. Conclusion: ORS coverage increased in Zimbabwe, stagnated in Zambia, but declined in Malawi. Monitoring national and province-level trends of ORS use illuminates geographic inequalities and helps identify priority areas for targeting resource allocation. Provision of safe drinking-water, adequate sanitation and hygiene will help reduce the causes and the incidence of diarrhea. Health policies to strengthen access to appropriate treatments such as vaccines for rotavirus and cholera and promoting use of ORS to reduce the burden of diarrhea should be developed and implemented.

A stratified two-stage cluster sampling design was used first to select primary sampling units (PSUs) and secondly to select a fixed number of households from each PSU. The surveys are conducted by interviewers approximately every 5 years to provide data for monitoring and evaluation of indicators for population health and to provide current demographic and health information for use by policymakers, planners, researchers and program managers. The demographic health surveys collect systematic and have comparable data across countries. These surveys are designed to yield representative information for most of the indicators for the country and are designed to cover 100% of the target population in the country. So that exclusions are not encountered during field work, households or dwellings to be excluded are pre-specified and pre-identified to not be included in the final list of the households in the selected EAs. Institutional living arrangements such as army barracks, hospitals, police camps, and boarding schools are excluded from the frame. All these decisions were made at the very beginning of the survey, before the sample is drawn. The survey interviewers then interviewed only the preselected households. No replacements and no changes of the pre-selected households are allowed in the implementation stages [14]. The sample is drawn using a stratified two-stage cluster sampling design. The primary sampling unit (PSU), typically census enumeration areas (EA), are selected with probability proportional to size within each stratum. A fixed number of households is then selected by equal probability systematic sampling in the selected EAs. An eligible woman aged 15–49 in each household is then selected to respond to the Women’s survey [14]. DHS surveys collect data through four main interviewer-administered questionnaires. The Household Questionnaire collects information on the characteristics of the household and a list all household members. The household roster within this questionnaire captures key characteristics of each household member and is used to select women and men eligible for individual interviews. The Woman’s Questionnaire, in addition to questions about the woman, contains a birth history which is then used to list all children (alive or dead) that the respondent has given birth to and the child’s survival status as well as caregiver knowledge of diarrhea care and treatment for diarrhea.. The questionnaires and the survey procedures followed in each country are similar resulting in comparable information, dataset filenames, variable types, names, and coding across countries [14]. The months prior to the survey are devoted to planning, survey logistics, sample design, questionnaire design, household listing, pretest, revision of questionnaires and manuals, training of field personnel, data processing set up, and fieldwork.. HIV testing protocol provides for informed, anonymous, and voluntary testing. Since the testing is anonymous, survey respondents cannot be provided with their results. The DHS Program uses a software package, CSPro (see www.census.gov/data/software/cspro.html), to process its surveys. CSPro is developed by the US Bureau of the Census, ICF, and SerPro SA. CSPro is used in The DHS Program in all steps of data processing with no need for another package or computer language. All steps, from entering/capturing the data to the production of statistics and tables published in DHS final reports, are performed with CSPro. The data is downloadable from https://dhsprogram.com/. We downloaded the data in SAS and Stata format for statistical analysis [14]. Our main outcome of interest was the proportion of U5 children with diarrhea who received ORS. The operational definition of ORS, therefore, was “a pre-packaged electrolyte solution containing glucose or another form of sugar or starch, as well as sodium, chloride, potassium, and bicarbonate” [10]. Diarrhea was defined as three or more abnormally loose or watery stools within a 24-h period. The following questions on the DHS Maternal questionnaire were used to determine whether ORS was administered or not during the most recent episode of diarrhea in children under 5: Now I would like to ask some questions about your children born in the last five years. Has (NAME) had diarrhea in the last 2 weeks? Was (NAME) given any of the following at any time since (NAME) started having the diarrhea? Besides the answers to the above questions, no additional scoring was needed to determine whether a child was treated with ORS or not. The binary variable for ORS use in the last 2 weeks was used as the outcome variable in our statistical analysis. The DHS uses Principal Component Analysis to construct the household wealth index using a composite measure of a household’s cumulative living standard [17]. With inputs comprising of ownership of selected assets, such as televisions and bicycles; materials used for housing construction; and types of water access and sanitation facilities. The resulting asset scores are standardized with a mean of zero and a standard deviation of one. These standardized scores are then used to create the break points that define wealth quintiles as: Lowest, Second, Middle, Fourth, and Highest [17]. .Demographic data, i.e., the mother, age, education, HIV + status, geographic location (urban vs. rural) were obtained from the Woman’s Questionnaire. We obtained prevalence estimates along with 95% confidence intervals (CIs) at the two time periods-overall and subnational. We used the Chi-squared test to compare the prevalence at the two time periods. Furthermore, we computed prevalence estimates stratified by urban vs. rural, mother’s age (< 25 vs. ≥25 years), education (<high school vs. ≥ high school), HIV + status, and quintiles of household wealth index. For each Period, logistic regression analysis were conducted to obtain crude and multivariable-adjusted Odds Ratios (OR) and 95% CIs. The multivariable model adjusted for mother’s age, education, HIV status, urban vs. rural setting, subnational region and the period of survey. All statistical analyses were conducted using SAS/STAT v9.4 (SAS Institute Inc., Cary, North Carolina, USA) and Stata Software, Version 14.2 (StataCorp, College Station, Texas, USA) using sampling weights and accounted for the complex sampling design A two-sided p-value 15 years. SDI contains an interpretable scale: zero represents the lowest income per capita, lowest educational attainment, and highest TFR observed across all GBD geographies from 1980 to 2015, and one represents the highest income per capita, highest educational attainment, and lowest TFR [16]. The SDI for Zambia 0.47 (classified as low middle), for Zimbabwe 0.46, (low middle), and for Malawi 0.35, (low).

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

1. Mobile health (mHealth) applications: Develop mobile applications that provide information and resources related to maternal health, including access to oral rehydration solutions (ORS) for children with diarrhea. These apps can provide educational materials, reminders for vaccinations and check-ups, and even allow for remote consultations with healthcare professionals.

2. Community health workers: Train and deploy community health workers to provide education and support to mothers in rural areas. These workers can distribute ORS packets, demonstrate proper usage, and provide guidance on when to seek medical attention.

3. Telemedicine: Establish telemedicine services to connect mothers in remote areas with healthcare professionals. This can help address barriers to accessing healthcare, such as long travel distances and limited availability of healthcare facilities.

4. Supply chain management: Improve the supply chain for ORS by implementing systems that ensure consistent availability and distribution of ORS packets to healthcare facilities and communities in need. This can involve using technology to track inventory, forecast demand, and streamline distribution processes.

5. Public awareness campaigns: Launch public awareness campaigns to educate communities about the importance of ORS in treating diarrhea and preventing dehydration. These campaigns can use various media channels, including radio, television, and social media, to reach a wide audience.

6. Integration of maternal health services: Integrate maternal health services with existing healthcare infrastructure, such as primary care clinics and community health centers. This can help ensure that ORS is readily available and accessible to mothers and children in need.

7. Partnerships and collaborations: Foster partnerships and collaborations between government agencies, non-profit organizations, and private sector entities to pool resources and expertise in addressing maternal health challenges. This can lead to innovative solutions and sustainable interventions.

It’s important to note that these recommendations are based on the information provided and may need to be tailored to the specific context and needs of the target population.
AI Innovations Description
The recommendation to improve access to maternal health based on the provided information is to develop and implement health policies that focus on strengthening access to appropriate treatments for diarrhea, such as vaccines for rotavirus and cholera, and promoting the use of oral rehydration solution (ORS).

The comparative analysis of two Demographic Health Survey (DHS) cycles in Zimbabwe, Zambia, and Malawi revealed variations in ORS coverage for children under 5 with diarrhea. While ORS coverage increased in Zimbabwe, it stagnated in Zambia and declined in Malawi. Monitoring national and sub-regional trends of ORS use can help identify geographic inequalities and prioritize resource allocation.

To address this issue, it is recommended to provide safe drinking water, adequate sanitation, and hygiene practices to reduce the causes and incidence of diarrhea. Additionally, developing and implementing health policies that prioritize access to appropriate treatments, such as vaccines and ORS, can help reduce the burden of diarrhea and improve maternal health outcomes.

It is important to note that the implementation of these recommendations should be tailored to the specific context and needs of each country. Collaboration between governments, healthcare providers, and relevant stakeholders is crucial for the successful implementation of these policies and interventions.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Strengthening healthcare infrastructure: Investing in healthcare facilities, equipment, and trained healthcare professionals can improve access to maternal health services. This includes ensuring the availability of skilled birth attendants, emergency obstetric care, and essential medical supplies.

2. Enhancing community-based interventions: Implementing community-based programs that focus on educating and empowering women and families about maternal health can improve access. This can include training community health workers, conducting awareness campaigns, and providing support for prenatal and postnatal care.

3. Improving transportation and logistics: Addressing transportation barriers can significantly improve access to maternal health services, especially in remote or underserved areas. This can involve providing transportation vouchers, establishing emergency referral systems, and improving road infrastructure.

4. Strengthening health information systems: Developing robust health information systems can help identify gaps in maternal health services and monitor progress. This includes collecting accurate and timely data on maternal health indicators, such as antenatal care coverage, skilled birth attendance, and postnatal care.

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

1. Define the indicators: Identify specific indicators that measure access to maternal health, such as the percentage of women receiving antenatal care, the percentage of births attended by skilled birth attendants, or the percentage of women receiving postnatal care.

2. Collect baseline data: Gather data on the current status of the selected indicators in the target population or region. This can be done through surveys, interviews, or existing data sources.

3. Define the intervention scenarios: Develop different scenarios that represent the potential impact of the recommendations. For example, scenario 1 could represent the impact of strengthening healthcare infrastructure, scenario 2 could represent the impact of community-based interventions, and so on.

4. Simulate the impact: Use statistical modeling or simulation techniques to estimate the potential impact of each scenario on the selected indicators. This can involve analyzing the baseline data and applying the relevant assumptions and parameters for each scenario.

5. Compare the results: Compare the simulated results of each scenario to determine which recommendations have the greatest potential for improving access to maternal health. This can help prioritize interventions and allocate resources effectively.

6. Monitor and evaluate: Continuously monitor and evaluate the implemented interventions to assess their actual impact on improving access to maternal health. This can involve collecting data on the selected indicators over time and comparing them to the simulated results.

By following this methodology, policymakers and program managers can make informed decisions on which recommendations to prioritize and implement to improve access to maternal health.

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