Causal loop diagrams (CLDs) are a systems thinking method that can be used to visualize and unpack complex health system behaviour. They can be employed prospectively or retrospectively to identify the mechanisms and consequences of policies or interventions designed to strengthen health systems and inform discussion with policymakers and stakeholders on actions that may alleviate sub-optimal outcomes. Whilst the use of CLDs in health systems research has generally increased, there is still limited use in low- and middle-income settings. In addition to their suitability for evaluating complex systems, CLDs can be developed where opportunities for primary data collection may be limited (such as in humanitarian or conflict settings) and instead be formulated using secondary data, published or grey literature, health surveys/reports and policy documents. The purpose of this paper is to provide a step-by-step guide for designing a health system research study that uses CLDs as their chosen research method, with particular attention to issues of relevance to research in low- and middle-income countries (LMICs). The guidance draws on examples from the LMIC literature and authors’ own experience of using CLDs in this research area. This paper guides researchers in addressing the following four questions in the study design process; (1) What is the scope of this research? (2) What data do I need to collect or source? (3) What is my chosen method for CLD development? (4) How will I validate the CLD? In providing supporting information to readers on avenues for addressing these key design questions, authors hope to promote CLDs for wider use by health system researchers working in LMICs.

The estimates of vaccination coverage are measured from administrative data and from population based survey. While both card-based and recall data are collected through population survey, and the recall is when the card is missing, the preferred estimates remain of the card-based due to limited validity of parental recalls. As there is a concern of missing cards in poor settings, the evidence on validity of parental recalls is limited and varied across vaccine types, and therefore timely and needed. We validated the recalls against card-based data based on population survey in Tanzania. We used a cross-sectional survey of about 3000 households with women who delivered in the last 12 months prior to the interview in 2012 from three regions in Tanzania. Data on the vaccination status on four vaccine types were collected using two data sources, card and recall-based. We compared the level of agreement and identified the recall bias between the two data sources. We further computed the sensitivity and specificity of parental recalls, and used a multivariate logit model to identify the determinants of parental recall bias. Most parents (85.4%) were able to present the vaccination cards during the survey, and these were used for analysis. Although the coverage levels were generally similar across data sources, the recall-based data slightly overestimated the coverage estimates. The level of agreement between the two data sources was high above 94%, with minimal recall bias of less than 6%. The recall bias due to over-reporting were slightly higher than that due to under-reporting. The sensitivity of parental recalls was generally high for all vaccine types, while the specificity was generally low across vaccine types except for measles. The minimal recall bias for DPT and measles were associated with the mother’s age, education level, health insurance status, region location and child age. Parental recalls when compared to card-based data are hugely accurate with minimal recall bias in Tanzania. Our findings support the use of parental recall collected through surveys to identify the child vaccination status in the absence of vaccination cards. The use of recall data alongside card-based estimates also ensures more representative coverage estimates.

Objective: To evaluate the effects of payment for performance (P4P) on the availability and stock‐out rate of reproductive, maternal, newborn and child health (RMNCH) medical commodities in Tanzania and assess the distributional effects.Methods: The availability of RMNCH commodities (medicines, supplies and equipment) on the day of the survey, and stock‐outs for at least one day in the 90 days prior to the survey, was measured in 75 intervention and 75 comparison facilities in January 2012 and 13 months later. Composite scores for each subgroup of commodities were generated. A difference‐in‐differences linear regression was used to estimate the effect of P4P on outcomes and differential effects by facility location, level of care, ownership and socio‐economic status of the catchment population.Results: We estimated a significant increase in the availability of medicines by 8.4 percentage points (P = 0.002) and an 8.3 percentage point increase (P = 0.050) in the availability of medical supplies. P4P had no effect on the availability of functioning equipment. Most items with a significant increase in availability also showed a significant reduction in stock‐outs. Effects were generally equally distributed across facilities, with effects on stock‐outs of many medicines being pro‐poor, and greater effects in facilities in rural compared to urban districts.Conclusion: P4P can improve the availability of medicines and medical supplies, especially in poor, rural areas, when these commodities are incentivised at both facility and district levels, making services more acceptable, effective and affordable, enhancing progress towards universal health coverage.

Background: Direct and time costs of accessing and using health care may limit health care access, affect welfare loss, and lead to catastrophic spending especially among poorest households. To date, limited attention has been given to time and transport costs and how these costs are distributed across patients, facility and service types especially in poor settings. We aimed to fill this knowledge gap. Methods: We used data from 1407 patients in 150 facilities in Tanzania. Data were collected in January 2012 through patient exit-interviews. All costs were disaggregated across patients, facility and service types. Data were analysed descriptively by using means, medians and equity measures like equity gap, ratio and concentration index. Results: 71% of patients, especially the poorest and rural patients, accessed care on foot. The average travel time and cost were 30 minutes and 0.41USD respectively. The average waiting time and consultation time were 47 min and 13 min respectively. The average medical cost was 0.23 USD but only18% of patients paid for health care. The poorest and rural patients faced substantial time burden to access health care (travel and waiting) but incurred less transport and medical costs compared to their counterparts. The consultation time was similar across patients. Patients spent more time travelling to public facilities and dispensaries while incurring less transport cost than accessing other facility types, but waiting and consultation time was similar across facility types. Patients paid less amount in public than in private facilities. Postnatal care and vaccination clients spent less waiting and consultation time and paid less medical cost than antenatal care clients. Conclusions: Our findings reinforce the need for a greater investment in primary health care to reduce access barriers and cost burdens especially among the worse-offs. Facility’s construction and renovation and increased supply of healthcare workers and medical commodities are potential initiatives to consider. Other initiatives may need a multi-sectoral collaboration.

The use of supply-side incentives to increase health service utilisation and enhance service quality is gaining momentum in many low- and middle-income countries. However, there is a paucity of evidence on the impact of such schemes, their cost-effectiveness, and the process of implementation and potential unintended consequences in these settings. A pay for performance (P4P) programme was introduced in Pwani region of Tanzania in 2011.

Pay for performance schemes are increasingly being implemented in low income countries to improve health service coverage and quality. This paper describes the context within which a pay for performance programme was introduced in Tanzania and discusses the potential for pay for performance to address health system constraints to meeting targets. 40 in-depth interviews and four focus group discussions were undertaken with health workers, and regional, district and facility managers. Data was collected on work environment characteristics and staff attitudes towards work in the first phase of the implementation of the pilot. A survey of 75 facilities and 101 health workers were carried out to examine facility resourcing, and health worker employment conditions and job satisfaction. Five contextual factors which affect the implementation of P4P were identified by health workers: salary and employment benefits; resource availability, including staff, medicines and functioning equipment; supervision; facility access to utilities; and community preferences. The results suggest that it is important to consider contextual issues when implementing pay for performance schemes in low income settings. It highlights the importance of basic infrastructures being in place, a minimum number of staff with appropriate education and skills as well as sufficient resources before implementing pay for performance. Health professionals working within a pay for performance scheme in Tanzania were concerned about challenges related to shortages of resources, limited supplies and unfavourable community preferences. The P4P scheme may provide the incentive and means to address certain constraints, in so far as they are within the control of providers and managers, however, other constraints will be harder to address.