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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).
