Improving rational use of ACTs through diagnosis-dependent subsidies: Evidence from a cluster-randomized controlled trial in western Kenya

Abstract

Background: More than half of artemisinin combination therapies (ACTs) consumed globally are dispensed in the retail sector, where diagnostic testing is uncommon, leading to overconsumption and poor targeting. In many malaria-endemic countries, ACTs sold over the counter are available at heavily subsidized prices, further contributing to their misuse. Inappropriate use of ACTs can have serious implications for the spread of drug resistance and leads to poor outcomes for nonmalaria patients treated with incorrect drugs. We evaluated the public health impact of an innovative strategy that targets ACT subsidies to confirmed malaria cases by coupling free diagnostic testing with a diagnosis-dependent ACT subsidy.

Methods and findings: We conducted a cluster-randomized controlled trial in 32 community clusters in western Kenya (population approximately 160,000). Eligible clusters had retail outlets selling ACTs and existing community health worker (CHW) programs and were randomly assigned 1:1 to control and intervention arms. In intervention areas, CHWs were available in their villages to perform malaria rapid diagnostic tests (RDTs) on demand for any individual >1 year of age experiencing a malaria-like illness. Malaria RDT-positive individuals received a voucher for a discount on a quality-assured ACT, redeemable at a participating retail medicine outlet. In control areas, CHWs offered a standard package of health education, prevention, and referral services. We conducted 4 population-based surveys-at baseline, 6 months, 12 months, and 18 months-of a random sample of households with fever in the last 4 weeks to evaluate predefined, individual-level outcomes. The primary outcome was uptake of malaria diagnostic testing at 12 months. The main secondary outcome was rational ACT use, defined as the proportion of ACTs used by test-positive individuals. Analyses followed the intention-to-treat principle using generalized estimating equations (GEEs) to account for clustering with prespecified adjustment for gender, age, education, and wealth. All descriptive statistics and regressions were weighted to account for sampling design. Between July 2015 and May 2017, 32,404 participants were tested for malaria, and 10,870 vouchers were issued. A total of 7,416 randomly selected participants with recent fever from all 32 clusters were surveyed. The majority of recent fevers were in children under 18 years (62.9%, n = 4,653). The gender of enrolled participants was balanced in children (49.8%, n = 2,318 boys versus 50.2%, n = 2,335 girls), but more adult women were enrolled than men (78.0%, n = 2,139 versus 22.0%, n = 604). At baseline, 67.6% (n = 1,362) of participants took an ACT for their illness, and 40.3% (n = 810) of all participants took an ACT purchased from a retail outlet. At 12 months, 50.5% (n = 454) in the intervention arm and 43.4% (n = 389) in the control arm had a malaria diagnostic test for their recent fever (adjusted risk difference [RD] = 9 percentage points [pp]; 95% CI 2-15 pp; p = 0.015; adjusted risk ratio [RR] = 1.20; 95% CI 1.05-1.38; p = 0.015). By 18 months, the ARR had increased to 1.25 (95% CI 1.09-1.44; p = 0.005). Rational use of ACTs in the intervention area increased from 41.7% (n = 279) at baseline to 59.6% (n = 403) and was 40% higher in the intervention arm at 18 months (ARR 1.40; 95% CI 1.19-1.64; p < 0.001). While intervention effects increased between 12 and 18 months, we were not able to estimate longer-term impact of the intervention and could not independently evaluate the effects of the free testing and the voucher on uptake of testing.

Conclusions: Diagnosis-dependent ACT subsidies and community-based interventions that include the private sector can have an important impact on diagnostic testing and population-wide rational use of ACTs. Targeting of the ACT subsidy itself to those with a positive malaria diagnostic test may also improve sustainability and reduce the cost of retail-sector ACT subsidies.

Trial registration: ClinicalTrials.gov NCT02461628.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1. Map of the study area showing intervention and comparison clusters (i.e., CUs).

Bungoma East was divided into multiple subcounties after the study began. Webuye West and Webuye East were previously divisions within Bungoma East subcounty and are now separate subcounties. In Laktabai and colleagues [10], we refer to Bungoma East and Kiminini. Here, we have used the updated administrative structure. CU, community unit.

Fig 2. CONSORT diagram.

Thirty-two clusters were randomized to 2 arms. All clusters were analyzed at each time point. CHW, community health worker; CONSORT, CONsolidated Standards Of Reporting Trials; IQR, interquartile range.

Fig 3. Number of positive (red portion) and negative (grey portion) RDTs performed by CHWs per month over the study period in 16 intervention CUs.

The first full month of testing for the first 11 CUs was September 2015, and the first full month of testing for the remaining 5 CUs was October 2015. The study continued until the last CU had participated for 19 months (April 2017). Survey periods of 6, 12, and 18 months are indicated. Monthly fluctuations in testing rates are related to fever prevalence and possibly holiday travel (December–January). Seasonal rains in March–June usher in the high malaria season, which typically continues through July. Annual variations in rainfall and overall transmission are common. CHW, community health worker; CU, community unit; RDT, rapid diagnostic test.

Fig 4. Proportion of ACT consumed by those with a febrile illness in the last month according to their testing uptake and result (positive or negative).

Results are reported by intervention arm and survey period. The total number of ACT courses taken is indicated for each bar. ACT, artemisinin combination therapy.

Fig 5. Adjusted modeled RRs and 95% CIs for the primary outcome of uptake of testing and 3 composite outcomes.

Test adherence is defined as those who take ACT with a positive test or do not take ACT with a negative test among those tested. Targeted ACT use is defined as the proportion of all fevers that have a positive test and take ACT or a negative test and do not take ACT. Rational ACT use is defined as the proportion of all ACT courses consumed by individuals with a positive test. ACT, artemisinin combination therapy; RR, risk ratio.