Effect of diagnostic testing on medicines used by febrile children less than five years in 12 malaria-endemic African countries: A mixed-methods study

Background: In 2010, WHO revised guidelines to recommend testing all suspected malaria cases prior to treatment. Yet, evidence to assess programmes is largely derived from limited facility settings in a limited number of countries. National surveys from 12 sub-Saharan African countries were used to examine the effect of diagnostic testing on medicines used by febrile children under five years at the population level, including stratification by malaria risk, transmission season, source of care, symptoms, and age. Methods: Data were compiled from 12 Demographic and Health Surveys in 2010-2012 that reported fever prevalence, diagnostic test and medicine use, and socio-economic covariates (n = 16,323 febrile under-fives taken to care). Mixed-effects logistic regression models quantified the influence of diagnostic testing on three outcomes (artemisinin combination therapy (ACT), any anti-malarial or any antibiotic use) after adjusting for data clustering and confounding covariates. For each outcome, interactions between diagnostic testing and the following covariates were separately tested: malaria risk, season, source of care, symptoms, and age. A multiple case study design was used to understand varying results across selected countries and sub-national groups, which drew on programme documents, published research and expert consultations. A descriptive typology of plausible explanations for quantitative results was derived from a cross-case synthesis. Results: Significant variability was found in the effect of diagnostic testing on ACT use across countries (e.g., Uganda OR: 0.84, 95% CI: 0.66-1.06; Mozambique OR: 3.54, 95% CI: 2.33-5.39). Four main themes emerged to explain results: available diagnostics and medicines; quality of care; care-seeking behaviour; and, malaria epidemiology. Conclusions: Significant country variation was found in the effect of diagnostic testing on paediatric fever treatment at the population level, and qualitative results suggest the impact of diagnostic scale-up on treatment practices may not be straightforward in routine conditions given contextual factors (e.g., access to care, treatment-seeking behaviour or supply stock-outs). Despite limitations, quantitative results could help identify countries (e.g., Mozambique) or issues (e.g., malaria risk) where facility-based research or programme attention may be warranted. The mixed-methods approach triangulates different evidence to potentially provide a standard framework to assess routine programmes across countries or over time to fill critical evidence gaps. This study uses a mixed-methods approach to analyse the effect of diagnostic testing on paediatric fever treatment at the population level across multiple countries, and to plausibly explain findings in select countries. National, population-based, cross-sectional surveys conducted in sub-Saharan Africa between 1 January, 2008 and 1 May, 2014 were systematically reviewed for inclusion in this study (Figure 1). All datasets were included if they measured outcome and explanatory covariates as described below. Twelve Demographic and Health Surveys (DHS) in 2010–2012 met inclusion criteria (Table 1). Survey methods are described elsewhere, including procedures for obtaining ethical approval and written informed consent from participants [28]. Flow chart for inclusion criteria of country datasets. Descriptive statistics for 12 sub-Saharan African countries in 2010-2012 National point estimates were tabulated using sample weights pre-specified in datasets. Standard error estimation accounted for data clustering in survey designs. aChildren under five years old reportedly having fever in the two weeks prior to the interview and taken to any source of care. bChildren under five years old with fever in the previous two weeks taken to any care and reportedly receiving a finger or heel stick for testing. cChildren under five years old with fever in the previous two weeks taken to any care and reportedly receiving any anti-malarial drug of any type. dChildren under five years old with fever in the previous two weeks taken to any care and reportedly receiving ACT. eChildren under five years old with fever in the previous two weeks taken to any care and reportedly receiving any antibiotic drug of any type. f[60] Refers to year national policy changed to recommend parasitological diagnosis in patients of all ages prior to treatment. Paediatric fever treatment was measured by asking caregivers of children under five with reported fever in the previous two weeks if “At any time during the illness did (name) take any drugs, and if so, what drugs did (name) take?” Response categories included anti-malarial drugs (by type), antibiotic drugs (pill/syrup or injection) or other medicines. Multiple responses were allowed and sick children receiving dual treatment were categorized as having positive outcomes for both responses. Anti-malarial medicines reported include ACT, chloroquine, sulphadoxine-pyrimethamine (SP)/Fansidar, quinine and other country-specific brands. Any anti-malarial use included all anti-malarial drugs reportedly used to treat the fever illness while ACT use referred to that treatment alone. Any antibiotic use referred to either pill/syrup or injection antibiotic drugs, and was not further disaggregated by type in response categories. Malaria diagnostic test use was measured by asking caregivers of febrile children if “At any time during the illness did (name) have blood taken from his/her finger or heel for testing?” This question was assumed to refer to either microscopy or RDT. The questionnaire did not explicitly record where testing and treatment occurred, nor if these interventions were received together. 812 (5%) children across 12 countries taken to multiple sources were excluded in order to assume that both interventions were provided at the same source. The model included other covariates associated with both diagnosis and treatment, which were grouped into individual, household and community factors [29-31]. Individual factors included child’s sex and age (0–5, 6–11, 12–23, 24–35, 36–47, 48–59 months), maternal age (15–24, 25–29, 30–34, 35–39, 40–49 years) and education (none, primary or at least secondary), and symptoms (fever alone, fever with cough, and fever with cough and rapid breaths). The latter covariate was also used to proxy illness severity that was not directly measured in surveys, and multiple symptoms were assumed to reflect more severe cases [32]. Household factors included wealth and size, care-seeking behaviour, and access to testing and care. A wealth index was pre-specified in datasets and described elsewhere [33]. Household size was categorized as one to four, five to eight, nine to 12, and 13 or more household members [34]. Care-seeking behaviour was based on caregiver reports of where care was sought for the sick child, and was separately coded by level of care (hospital, non-hospital formal medical, community health worker (CHW), pharmacy, and other) and sector (public, private) [35,36]. Access to testing and care was based on caregivers’ perceptions that money or distance is a “big problem” or “not a big problem” to seeking medical advice or treatment. These two covariates, along with child health card possession, were used to attempt to proxy attendance at a facility stocked with both drugs and diagnostic tests, which is known to influence case management decisions but is not directly measured in surveys. Community factors included residence (urban/rural), malaria risk and transmission season. Malaria Atlas Project malaria endemicity estimates were linked to datasets through geocoded primary sampling units (PSUs) [37]. All individual observations were assigned their PSU-level malaria risk value and categorized as malaria-free, unstable, low (PfPR2–10 < 5%), moderate (PfPR2–10 5%-40%), and high (PfPR2–10 > 40%) stable endemic transmission. Each observation was also classified as occurring during or outside the peak malaria transmission season by comparing each observation’s PSU location and interview date with seasonality maps produced by the Mapping Malaria Risk in Africa (MARA) project [38]. Among 16,323 surveyed febrile children under five taken to care in 12 countries, 17 had missing values for the outcomes, 24 for diagnostic test use, 309 for malaria endemicity and transmission season, seven for health card and one for maternal education. List-wise deletion was used to exclude these observations. Mixed-effects logistic regression models quantified the influence of diagnostic testing on paediatric fever treatment among children taken to care in each country dataset. The binary outcomes analysed were: (1) ACT use; (2) any anti-malarial use; and, (3) any antibiotic use. All covariates were included as categorical fixed effects (first-level) nested within PSUs (second-level), and normal distribution of the random effects was assumed. Crude odds ratios for the main covariate were initially estimated for its effect on each outcome, and were subsequently adjusted for the effect of all covariates. For each outcome, interactions between diagnostic testing and the following covariates were separately tested: malaria risk, season, source of care, age, and symptoms. If there was evidence of an interaction, final models were stratified accordingly to explore results. The level of statistical significance was set to 0.05. National point estimates were tabulated using sample weights to account for unequal probabilities of selection in order to generate nationally representative weighted percentages. Standard error estimation accounted for data clustering in the complex survey design. Stata 12 (STATA Corp, College Station, TX) was used for all analyses. A multiple case study design was employed to help understand results in selected countries and drew on published research, programme documents and expert consultations [27]. Country selection was based on the following criteria: (1) contrasting quantitative results; (2) high ACT coverage; and, (3) available research or programme documents. Benin, Burundi, Malawi, Mozambique, Rwanda and Uganda were selected for case studies. A comprehensive literature review identified published articles on malaria diagnosis and treatment practices in these countries. Benin and Malawi had national facility studies conducted around the same time to help explain results [9,20], while Uganda, Malawi and Mozambique had relevant research to support case studies [12,16,39,40]. National malaria strategic plans for Malawi, Mozambique and Uganda were made available for this study [41-43], and all six countries had US President’s Malaria Initiative operational plans [44]. These materials were reviewed to identify potential explanations for quantitative findings, inform the topic guide used in expert consultations, and cross-reference interview information to confirm conclusions. For expert consultations, seven respondents were purposively selected based on their country programme knowledge and advanced research training. Five informants were identified and contacted by study authors (EWJ, SP) while two others were introduced by initial respondents using snowballing and convenience sampling techniques. Participants included university researchers, paediatricians and epidemiologists with expert knowledge of national malaria control programmes. Prior to involvement, respondents were given detailed information about the study’s objectives, methods and full quantitative results. Respondents were also invited to review case studies as well as the final manuscript. Interviews were based on a semi-structured topic guide that focused on the plausibility of results, programme explanations and perceived value of findings as additional programme evidence. Specific themes included: RDT scale-up status; availability of diagnostics and medicines; stock-outs; case management practices; health system structure; care-seeking behaviour; and, malaria epidemiology. The lead author (EWJ) conducted seven interviews in English via Skype or in person during July-September 2014 (one for each country; two for Benin) each lasting about one hour. Extensive written notes were taken during interviews and transcribed after discussions. Explanation building leading to a cross-case synthesis was the overall analytic strategy [45]. This approach emphasizes defining and testing rival explanations as part of the design, and compiling data from multiple sources to triangulate evidence and evaluate rival interpretations [27]. Thematic analysis identified dominant themes within each case [46]. All transcripts were read multiple times by the lead author to establish preliminary codes and create categories to describe response patterns. Matrices helped to visually examine codes in order to generate within-case themes, and to subsequently compare and revise themes across countries. This led to a typology of plausible explanations for quantitative results for the six countries.