Study design and setting
The DYNAMIC Tanzania study was a pragmatic, open-label, parallel-group, cluster randomized trial conducted in 40 primary health facilities in Tanzania. The health facility was the unit of randomization, since the intervention was targeted at the health facility level.
Study sites were purposefully chosen to represent a variety of health care and epidemiological settings within 5 councils in the Mbeya and Morogoro region, with a total population in those councils of 1,701,717.43 Two councils were semi-urban (Mbeya city and Ifakara Town councils), while the three otherswere rural (Mbeya, Ulanga and Mlimba district councils). 42.8% of the Tanzanian population is less than 15 years old.44 The malaria prevalence in febrile children age 6–59 months is 5.8% in the Morogoro region, and 3.4% in the Mbeya region.45 HIV prevalence among children less than 15 years old is 0.5% in both regions.46 Health care for acute illnesses at government or government designated primary health facilities are free of charge for children under 5 years, including the cost of medications such as antibiotics. For patients above 5 years, health care expenses are at the charge of the patient, unless they have a health insurance plan (around 10% of Tanzanians).47
Inclusion & Ethics
Ethical approval
was obtained in Tanzania from the Ifakara Health Institute (IHI/IRB/No: 11-2020), the Mbeya Medical Research Ethics Committee (SZEC-2439/R.A/V.1/65), the National Institute for Medical Research Ethics Committee (NIMR/HQ/R.8a/Vol. IX/3486 and NIMR/HQ/R.8a/Vol. IX/3583), and in Switzerland from the cantonal ethics review board of Vaud (CER-VD 2020–02800). The study was registered on ClinicalTrials.gov number NCT05144763, where the trial protocol and statistical analysis plan can be found.
The study design and implementation was developed collaboratively between the Ifakara Health Institute, Mbeya Medical Research Centre, Swiss Tropical and Public Health Institute and the Centre for Primary Care and Public Health, University of Lausanne, based on feedback from stakeholders, patients and healthcare providers involved in our similar trials in Tanzania.18,19,29 Over 100 community engagement meetings with over 7 000 participants were conducted before and during the study.
Participants
Primary care health facilities (dispensaries or health centres) were eligible for inclusion if they performed on average 20 or more consultations with children 2 months to 5 years per week, were government or government-designated health facilities, and were located less than 150 km from the research institutions. Acute outpatient care is routinely provided by nurses and clinical officers in primary health facilities, while medical doctors provide care on certain occasions at health centres. Clinical officers, the principal health providers at primary health facilities, are non-physician health professionals with 2–3 years of clinical training following secondary school.48
Infants and children aged between 1 day old and under 15 years of age seeking care for an acute medical or surgical condition at participating health facilities were eligible. Children presenting solely for scheduled consultations for a chronic disease (e.g. HIV, tuberculosis, malnutrition), or for routine preventive care (e.g. growth monitoring, vaccination) were not eligible. Written informed consent was obtained from all parents or guardians of participants when attending the participating health facility during the enrollment period.
Sampling, randomization and masking
The 40 health facilities were randomly selected from all eligible health facilities in the participating councils following a 3:2 ratio between health facilities from the Morogoro and Mbeya region (in order to include more health facilities in the higher malaria transmission area). In addition, to include a representative sample of health centres compared to dispensaries, 4 health centres per region were included.
The sampled health facilities were then randomized (1:1), to ePOCT+ (intervention) or usual care (control). Randomization was stratified by region, council, level of health facility (health center versus dispensary), and attendance rate. An independent statistician in Switzerland was provided the list of all eligible health facilities, and performed the computer-generated sampling and randomization. Intervention allocation by the study team was only shared to study investigators in Tanzania once all council leaders confirmed the participation of their selected health facilities. The nature of the intervention did not allow for masking of the intervention to healthcare providers, patients, or study implementers.
Intervention
The intervention consisted of providing ePOCT + with the supporting IT infrastructure, C-reactive protein (CRP) semi quantitative lateral flow test, hemoglobin point-of-care tests (and hemoglobinometer if not already available), pulse oximeter, training and supportive mentorship (Fig. 4). The development process and details of the ePOCT + CDSA, and the novel medAL-reader Android-based application used to deploy ePOCT + were described in detail previously.26 In summary the clinical algorithm of ePOCT + is based on previous generation CDSAs (ALMANACH and ePOCT),18,27 international and national clinical guidelines, input from national and international expert panels, and was adapted based on piloting and healthcare provider feedback.26 Mentorship by the implementation team included visits to health facilities every 2–3 months and communication by phone call or group messages 3–4 times per month, to resolve issues and provide guidance and feedback on the use of the new tools. Results from quality of care dashboards were shared through group messages to give feedback on the use of ePOCT+, a strategy often described as ‘benchmarking’, allowing healthcare providers to compare their antibiotic prescription, uptake, and other quality of care indicators with other health facilities.49 Control health facilities provided care as usual, with no access to clinical data dashboards.
All participating health facilities were provided with IT infrastructure to support the tablet based ePOCT + CDSA or in the case of control health facilities, to support the use of tablet based electronic case report forms (eCRF). The IT infrastructure included a tablet for each outpatient consultation room, router, local server (Rasberry Pi), internet, and if needed back up power (battery), or solar system. In addition, weighing scales, mid-upper arm circumference (MUAC) bands, and thermometers were provided to health facilities in both arms if not already available. Healthcare providers from both intervention and control health facilities received equivalent clinical refresher training based on the Integrated Management of Childhood Ilness (IMCI) chartbook. In addition specific training was provided on the use of the ePOCT + CDSA in intervention facilities, and the use of the eCRF in control facilities.
Study procedures
Children seeking care at included health facilities were screened for eligibility by a research assistant between 8:00 to 16:00 on weekdays. If eligible, demographic information was collected and entered in the eCRF (ePOCT + for intervention health facilities, and eCRF for usual care facilities within the data collection system medAL-reader). Healthcare providers in the control health facilities managed the patients as usual, but documented the main complaints, anthropometrics and test results (if performed), diagnoses, treatments and referral decision in the eCRF. To harmonize data collection across the intervention and control facilities, the eCRF for the control facilities was also programmed into the medAL-reader platform, but no decision support was provided. Research questions were included in the eCRF to capture if an oral or systemic antibiotic was prescribed, and if the patient was referred for inpatient hospitalization or other outpatient investigations. In intervention health facilities, in addition to the same information collected in the eCRF, symptoms and signs of the patients were recorded in the ePOCT + CDSA during the consultation with the patient. The symptoms and signs entered are used by the ePOCT + CDSA to guide the clinical consultation. Healthcare providers who documented the final treatment for a consultation in ePOCT + or the eCRF were categorized as having been managed per-protocol, as recording of the final treatment is required to complete the ePOCT + CDSA.
All patients were called or visited at their home by research assistants to assess clinical outcomes and their care and treatment seeking behaviour at day 7 (range 6–14 days). Research assistants performing the phone calls were blinded to the intervention status, and were not part of the team enrolling patients at health facilities. Home visits rather than phone calls were conducted if the caregiver of patients did not have a phone number or did not know somebody with a phone near their home, or if research assistants were not able to reach the provided phone number after 5 attempts. The home visits were performed by the research assistants enrolling patients from the same health facility, as such they were not blinded to intervention allocation. Patients who were still sick at follow-up were encouraged to return to a health facility for follow-up care. Day 7 data was recorded using RED Cap web for phone calls, and RED Cap mobile application for home visits.
Outcomes
The co-primary outcomes measured at the individual patient level included: 1) antibiotic prescription at the time of the initial consultation as documented by the healthcare provider (superiority analysis); and 2) clinical failure at day 7 defined as “not cured” and “not improved”, or unscheduled hospitalization as reported by caregivers (non-inferiority analysis). Secondary outcomes include unscheduled re-attedance visits at any health facility by day 7, non-referred secondary hospitalization by day 7, death by day 7, and referral for inpatient hospitalization at initial consultation. The intervention was deemed a success if ePOCT + was non-inferior in terms of clinical failure and reduced antibiotic prescription by at least 25%. Pre-specified additional outcomes are outlined in the statistical analysis plan.
Sample size
The sample size was calculated for testing non-inferiority of the clinical failure outcome given that it would require a higher sample size than for the antibiotic prescription co-primary outcome. We assumed a cluster size of 900 patients (average of 150 patients per month x 6 months) based on routine data within the natinoal health management information system, an intraclass correlation coefficient of 0.002, and a clinical failure rate of 3%. To have 80% power to detect an acceptable non-inferiority margin of a relative risk of 1.3, corresponding to 3.9%, we required 19 clusters and 17,100 patients per arm (total patients n = 37,620 assuming 10% loss to follow-up). Given the uncertainty of some of the assumptions, the total number of health facilities was rounded up to 20 clusters per arm.
No interim analysis was planned, however due to lower enrollment than expected, after 8 months of recruitment, we planned an ad-hoc sample size recalculation by an independent statistician to calculate the expected power of the study based on updated parameters. The study team pre-specificied the specifications and approach, documented in an update to the statistical analysis plan.
Statistical analysis
All outcomes were evaluated using random effects logistic regression models using the cluster (health facility) and patient as random effects, with further adjustment using fixed effect terms for randomization stratification factors,50 and baseline characteristics hypothecized to be associated with the outcome, imbalances between arms, and imbalances between characteristics among patients for whom day 7 data was available and not available (lost to follow-up). These included the patient characteristics of age, sex, presenting complaints (fever, respiratory, gastrointestinal, skin) and phone availability, and the health facility characteristics of care provision level (dispensary versus health center), attendance rate per month and council. A partitioning method was used to separate within- and beween- cluster effects to account for confounding by cluster.51,52 In the case of too few events, and small variance among health facilities which did not allow the model to converge, the health facility was incorporated in the model as a fixed effect. Adjusted relative risk (aRR) and absolute differences was estimated based on the computed marginal probabilities of the conditional probabilities.53,54 Formal adjustments were not performed for multiple testing, as adjustments would likely be overly conservative given that the outcomes are not all independent,55 and variable selection was not based on statistical tests of significance.56
Non-inferiority was determined if the upper-limit of the 95% confidence interval (CI) of the adjusted relative risk (aRR) was below 1.3. All analyses based on outcomes from day 0 were performed in the per-protocol population, and outcomes determined at day 7 were performed in the per-protocol and complete case population (only in those for which day 7 outcomes were ascertained) and displayed accordingly unless stated otherwise. The primary analyses were performed on the first visit for an illness, with re-attendance visits (a second visit to a health facility for the same illness) included in exploratory analyses. Prespecified analyses to assess the effect of the intervention in different population groups were performed by sex, age group, and consultation complaint categories (respiratory symptoms, fever, gastrointestinal, skin problem, ear, nose and throat problem). All analyses were performed using Stata v16 and v17.57