Accounting for complexity: An examination of methodologies for complex intervention research in global health

Accounting for complexity is now a feature of health interventions research, but it is unclear how this might best be accomplished. As the number of methodologies to account for complexity expands, developing a coherent approach to intervention research has become more urgent and yet more difficult. This thesis aimed to address this challenge by examining methodologies used to design and evaluate complex interventions in global health. Four areas considered central to complex interventions research were explored – intervention design, evaluation of outcomes, assessment of causal mechanisms, and evaluation of context. In each of these areas, a different mixed method, statistical, or qualitative methodological approach was employed following available guidance. Data were drawn from the design and evaluation of the PRIME intervention, a complex health service intervention to improve care for malaria at health centres in rural Uganda. Conceptual and methodological challenges were encountered in each area of investigation. Opportunities for improving each methodological application are suggested alongside an overall recommendation for greater reflection on, and reporting of, the processes and investments necessary for conducting complex interventions research. Additionally, the evidence produced in each area of investigation revealed different, partial and incommensurable accounts of the intervention and its effects. This draws attention to the challenges that can arise when seeking to combine evidence of ‘what works’ with evidence from methodologies that employ different approaches to understanding how interventions are taken up and produce effects. Approaches to accounting for complexity in intervention research need to evolve from focusing on the narrow question of ‘what works’ towards emphasising a more dynamic and multi-perspective question of ‘what happens’. Such an approach may be particularly useful for understanding the multiple and varied effects of complex interventions and their role in improving health and wellbeing

The impact of an intervention to introduce malaria rapid diagnostic tests on fever case management in a high transmission setting in Uganda: A mixed-methods cluster-randomized trial (PRIME).

Rapid diagnostic tests for malaria (mRDTs) have been scaled-up widely across Africa. The PRIME study evaluated an intervention aiming to improve fever case management using mRDTs at public health centers in Uganda. A cluster-randomized trial was conducted from 2010-13 in Tororo, a high malaria transmission setting. Twenty public health centers were randomized in a 1:1 ratio to intervention or control. The intervention included training in health center management, fever case management with mRDTs, and patient-centered services; plus provision of mRDTs and artemether-lumefantrine (AL) when stocks ran low. Three rounds of Interviews were conducted with caregivers of children under five years of age as they exited health centers (N = 1400); reference mRDTs were done in children with fever (N = 1336). Health worker perspectives on mRDTs were elicited through semi-structured questionnaires (N = 49) and in-depth interviews (N = 10). The primary outcome was inappropriate treatment of malaria, defined as the proportion of febrile children who were not treated according to guidelines based on the reference mRDT. There was no difference in inappropriate treatment of malaria between the intervention and control arms (24.0% versus 29.7%, adjusted risk ratio 0.81 95\% CI: 0.56, 1.17 p = 0.24). Most children (76.0\%) tested positive by reference mRDT, but many were not prescribed AL (22.5\% intervention versus 25.9\% control, p = 0.53). Inappropriate treatment of children testing negative by reference mRDT with AL was also common (31.3\% invention vs 42.4\% control, p = 0.29). Health workers appreciated mRDTs but felt that integrating testing into practice was challenging given constraints on time and infrastructure. The PRIME intervention did not have the desired impact on inappropriate treatment of malaria for children under five. In this high transmission setting, use of mRDTs did not lead to the reductions in antimalarial prescribing seen elsewhere. Broader investment in health systems, including infrastructure and staffing, will be required to improve fever case management

Behind the scenes of the PRIME intervention: designing a complex intervention to improve malaria care at public health centres in Uganda.

In Uganda, health system challenges limit access to good quality healthcare and contribute to slow progress on malaria control. We developed a complex intervention (PRIME), which was designed to improve quality of care for malaria at public health centres. Responding to calls for increased transparency, we describe the PRIME intervention's design process, rationale, and final content and reflect on the choices and challenges encountered during the design of this complex intervention. To develop the intervention, we followed a multistep approach, including the following: 1) formative research to identify intervention target areas and objectives; 2) prioritization of intervention components; 3) review of relevant evidence; 4) development of intervention components; 5) piloting and refinement of workshop modules; and 6) consolidation of the PRIME intervention theories of change to articulate why and how the intervention was hypothesized to produce desired outcomes. We aimed to develop an intervention that was evidence-based, grounded in theory, and appropriate for the study context; could be evaluated within a randomized controlled trial; and had the potential to be scaled up sustainably. The process of developing the PRIME intervention package was lengthy and dynamic. The final intervention package consisted of four components: 1) training in fever case management and use of rapid diagnostic tests for malaria (mRDTs); 2) workshops in health centre management; 3) workshops in patient-centred services; and 4) provision of mRDTs and antimalarials when stocks ran low. The slow and iterative process of intervention design contrasted with the continually shifting study context. We highlight the considerations and choices made at each design stage, discussing elements we included and why, as well as those that were ultimately excluded. Reflection on and reporting of 'behind the scenes' accounts of intervention design may improve the design, assessment, and generalizability of complex interventions and their evaluations

Treatment-seeking behaviour in low- and middle-income countries estimated using a Bayesian model

Background: Seeking treatment in formal healthcare for uncomplicated infections is vital to combating disease in low- and middle-income countries (LMICs). Healthcare treatment-seeking behaviour varies within and between communities and is modified by socio-economic, demographic, and physical factors. As a result, it remains a challenge to quantify healthcare treatment-seeking behaviour using a metric that is comparable across communities. Here, we present an application for transforming individual categorical responses (actions related to fever) to a continuous probabilistic estimate of fever treatment for one country in Sub-Saharan Africa (SSA).Methods: Using nationally representative household survey data from the 2013 Demographic and Health Survey (DHS) in Namibia, individual-level responses (n = 1138) were linked to theoretical estimates of travel time to the nearest public or private health facility. Bayesian Item Response Theory (IRT) models were fitted via Markov Chain Monte Carlo (MCMC) simulation to estimate parameters related to fever treatment and estimate probability of treatment for children under five years. Different models were implemented to evaluate computational needs and the effect of including predictor variables such as rurality. The mean treatment rates were then estimated at regional level.Results: Modelling results suggested probability of fever treatment was highest in regions with relatively high incidence of malaria historically. The minimum predicted threshold probability of seeking treatment was 0.3 (model 1: 0.340; 95% CI 0.155–0.597), suggesting that even in populations at large distances from facilities, there was still a 30% chance of an individual seeking treatment for fever. The agreement between correctly predicted probability of treatment at individual level based on a subset of data (n = 247) was high (AUC = 0.978), with a sensitivity of 96.7% and a specificity of 75.3%.Conclusion: We have shown how individual responses in national surveys can be transformed to probabilistic measures comparable at population level. Our analysis of household survey data on fever suggested a 30% baseline threshold for fever treatment in Namibia. However, this threshold level is likely to vary by country or endemicity. Although our focus was on fever treatment, the methodology outlined can be extended to multiple health seeking behaviours captured in routine national survey data and to other infectious diseases.<br/