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Private sector engagement in health reform has been suggested to help reduce healthcare inequities in sub-Saharan Africa, where populations with the most need seek the least care. We study the effects of African Health Markets for Equity (AHME), a cluster randomized controlled trial carried out in Kenya from 2012 to 2020 at 199 private health clinics. AHME included four clinic-level interventions: social health insurance, social franchising, SafeCare quality-of-care certification programme and business support. This paper evaluates whether AHME increased the capacity of private health clinics to serve poor clients while maintaining or enhancing the quality of care provided. At endline, clinics that received AHME were 14.5 percentage points (pp) more likely to be empanelled with the National Health Insurance Fund (NHIF), served 51% more NHIF clients and served more clients from the middle three quintiles of the wealth distribution compared to control clinics. Comparing individuals living in households near AHME treatment and control clinics (N  = 8241), AHME led to a 6.7-pp increase in the probability of holding any health insurance on average. We did not find any additional effect of AHME on insurance holding among poor households. We measured quality of care using a standardized patient (SP) experiment (N  = 596 SP–provider interactions) where recruited and trained SPs were randomized to present as either 'not poor', and able to afford all services provided, or 'poor' by telling the provider they could only afford ∼300 Kenyan Shillings (US$3) in fees. We found that poor SPs received lower levels of both correct and unnecessary services, and AHME did not affect this. More work must be done to ensure that clients of all wealth levels receive high-quality care. [ABSTRACT FROM AUTHOR]

BackgroundSocial distancing is a crucial intervention to slow down person-to-person transmission of COVID-19. However, social distancing has negative consequences, including increases in depression and anxiety. Digital interventions, such as text messaging, can provide accessible support on a population-wide scale. We developed text messages in English and Spanish to help individuals manage their depressive mood and anxiety during the COVID-19 pandemic. ObjectiveIn a two-arm randomized controlled trial, we aim to examine the effect of our 60-day text messaging intervention. Additionally, we aim to assess whether the use of machine learning to adapt the messaging frequency and content improves the effectiveness of the intervention. Finally, we will examine the differences in daily mood ratings between the message categories and time windows. MethodsThe messages were designed within two different categories: behavioral activation and coping skills. Participants will be randomized into (1) a random messaging arm, where message category and timing will be chosen with equal probabilities, and (2) a reinforcement learning arm, with a learned decision mechanism for choosing the messages. Participants in both arms will receive one message per day within three different time windows and will be asked to provide their mood rating 3 hours later. We will compare self-reported daily mood ratings; self-reported depression, using the 8-item Patient Health Questionnaire; and self-reported anxiety, using the 7-item Generalized Anxiety Disorder scale at baseline and at intervention completion. ResultsThe Committee for the Protection of Human Subjects at the University of California Berkeley approved this study in April 2020 (No. 2020-04-13162). Data collection began in April 2020 and will run to April 2021. As of August 24, 2020, we have enrolled 229 participants. We plan to submit manuscripts describing the main results of the trial and results from the microrandomized trial for publication in peer-reviewed journals and for presentations at national and international scientific meetings. ConclusionsResults will contribute to our knowledge of effective psychological tools to alleviate the negative effects of social distancing and the benefit of using machine learning to personalize digital mental health interventions. Trial RegistrationClinicalTrials.gov NCT04473599; https://clinicaltrials.gov/ct2/show/NCT04473599 International Registered Report Identifier (IRRID)DERR1-10.2196/23592