Health systems resilience (HSR) is defined as the ability of a health system to continue providing normal services in response to a crisis, making it a critical concept for analysis of health systems in fragile and conflict-affected settings (FCAS). However, no consensus for this definition exists and even less about how to measure HSR. We examine three current HSR definitions (maintaining function, improving function and achieving health system targets) using real-time data from South Sudan to develop a data-driven understanding of resilience. We used 14 maternal, newborn and child health (MNCH) coverage indicators from household surveys in South Sudan collected at independence (2011) and following 2 years of protracted conflict (2015), to construct a resilience index (RI) for 9 of the former 10 states and nationally. We also assessed health system stress using conflict-related indicators and developed a stress index. We cross tabulated the two indices to assess the relationship of resilience and stress. For maintaining function for 80% of MNCH indicators, seven state health systems were resilient, compared with improving function for 50% of the indicators (two states were resilient). Achieving the health system national target of 50% coverage in half of the MNCH indicators displayed no resilience. MNCH coverage levels were low, with state averages ranging between 15% and 44%. Central Equatoria State displayed high resilience and high system stress. Lakes and Northern Bahr el Ghazal displayed high resilience and low stress. Jonglei and Upper Nile States had low resilience and high stress. This study is the first to investigate HSR definitions using a resilience metric and to simultaneously measure health system stress in FCAS. Improving function is the HSR definition detecting the greatest variation in the RI. HSR and health system stress are not consistently negatively associated. HSR is highly complex warranting more in-depth analyses in FCAS.

Objectives: Two common methods used to measure indicators for health programme monitoring and evaluation are the demographic and health surveys (DHS) and lot quality assurance sampling (LQAS); each one has different strengths. We report on both methods when utilised in comparable situations.Methods: We compared 24 indicators in south‐west Uganda, where data for prevalence estimations were collected independently for the two methods in 2011 (LQAS: n = 8876; DHS: n = 1200). Data were stratified (e.g. gender and age) resulting in 37 comparisons. We used a two‐sample two‐sided Z‐test of proportions to compare both methods.Results: The average difference between LQAS and DHS for 37 estimates was 0.062 (SD = 0.093; median = 0.039). The average difference among the 21 failures to reject equality of proportions was 0.010 (SD = 0.041; median = 0.009); among the 16 rejections, it was 0.130 (SD = 0.010, median = 0.118). Seven of the 16 rejections exhibited absolute differences of 0.10 and 0.20 (mean = 0.261, SD = 0.083).Conclusion: There is 75.7% agreement across the two surveys. Both methods yield regional results, but only LQAS provides information at less granular levels (e.g. the district level) where managerial action is taken. The cost advantage and localisation make LQAS feasible to conduct more frequently, and provides the possibility for real‐time health outcomes monitoring.

Objectives: We adapted a rapid quality of care monitoring method to a fragile state with two aims: to assess the delivery of child health services in South Sudan at the time of independence and to strengthen local capacity to perform regular rapid health facility assessments.Methods: Using a two‐stage lot quality assurance sampling (LQAS) design, we conducted a national cross‐sectional survey among 156 randomly selected health facilities in 10 states. In each of these facilities, we obtained information on a range of access, input, process and performance indicators during structured interviews and observations.Results: Quality of care was poor with all states failing to achieve the 80% target for 14 of 19 indicators. For example, only 12% of facilities were classified as acceptable for their adequate utilisation by the population for sick‐child consultations, 16% for staffing, 3% for having infection control supplies available and 0% for having all child care guidelines. Health worker performance was categorised as acceptable in only 6% of cases related to sick‐child assessments, 38% related to medical treatment for the given diagnosis and 33% related to patient counselling on how to administer the prescribed drugs. Best performance was recorded for availability of in‐service training and supervision, for seven and ten states, respectively.Conclusions: Despite ongoing instability, the Ministry of Health developed capacity to use LQAS for measuring quality of care nationally and state‐by‐state, which will support efficient and equitable resource allocation. Overall, our data revealed a desperate need for improving the quality of care in all states.

This paper reports the first trial of Lot Quality Assurance Sampling (LQAS) assessing associations between access to LQAS data and subsequent improvements in district programming. This trial concerns India's approach to addressing an increase in malaria-attributable deaths by training community health workers to diagnose, treat and prevent malaria, while using LQAS to monitor sub-district performance and make programme improvements. The Ministry of Health introduced LQAS into four matched high malaria burden districts (Annual Parasite Incidence >5) (N > 5 million). In each sub-district, we sampled four populations in three 6-monthly surveys: households, children <5 years, people with fever in the last 2 weeks and community health workers. In three districts, trained local staff collected, analysed and used data for programme management; in one control district, non-local staff collected data and did not disseminate results. For eight indicators, we calculated the change in proportion from survey one to three and used a Difference-in-Differences test to compare the relative change between intervention and control districts. Coverage increased from survey one to three for 24 of 32 comparisons. Difference-in-Differences tests revealed that intervention districts exhibited significantly greater change in four of six vertical strategies (insecticide treated bed-nets and indoor residual spraying), one of six treatment-seeking behaviours and four of 12 health worker capacity indicators. The control district displayed greater improvement than two intervention districts for one health worker capacity indicator. One district with poor management did not improve. In this study, LQAS results appeared to support district managers to increase coverage in underperforming areas, especially for vertical strategies in the presence of diligent managers.

Background: National or regional population-based HIV prevalence surveys have small sample sizes at district or sub-district levels; this leads to wide confidence intervals when estimating HIV prevalence at district level for programme monitoring and decision making. Health facility programme data, collected during service delivery is widely available, but since people self-select for HIV testing, HIV prevalence estimates based on it, is subject to selection bias. We present a statistical annealing technique, Hybrid Prevalence Estimation (HPE), that combines a small population-based survey sample with a facility-based sample to generate district level HIV prevalence estimates with associated confidence intervals. Methods: We apply the HPE methodology to combine the 2011 Uganda AIDS indicator survey with the 2011 health facility HIV testing data to obtain HIV prevalence estimates for districts in Uganda. Multilevel logistic regression was used to obtain the propensity of testing for HIV in a health facility, and the propensity to test was used to combine the population survey and health facility HIV testing data to obtain the HPEs. We assessed comparability of the HPEs and survey-based estimates using Bland Altman analysis. Results: The estimates ranged from 0.012 to 0.178 and had narrower confidence intervals compared to survey-based estimates. The average difference between HPEs and population survey estimates was 0.00 (95% CI: − 0.04, 0.04). The HPE standard errors were 28.9% (95% CI: 23.4–34.4) reduced, compared to survey-based standard errors. Overall reduction in HPE standard errors compared survey-based standard errors ranged from 5.4 to 95%. Conclusions: Facility data can be combined with population survey data to obtain more accurate HIV prevalence estimates for geographical areas with small population survey sample sizes. We recommend use of the methodology by district level managers to obtain more accurate HIV prevalence estimates to guide decision making without incurring additional data collection costs.

Background: Two probability surveys, conducted in the same districts of Bihar, India (Aurangabad and Gopalganj) at approximately the same time in 2016 using identical questionnaires and similar survey methods, produced significantly different responses for 37.2% (58/156) of the indicator comparisons. Interviewers for one survey were men while for the other they were women. Respondents were mothers of children aged 0–59 months living in a traditional rural setting. We examined the influence of interviewer gender on mothers’ survey responses and their implications for interpreting survey results. Methods: We used qualitative methods including 10 focus group discussions (FGDs) and 33 in-depth interviews (IDIs) in the same locations as the 2016 surveys. FGD participants were purposefully selected mothers with children 0–59 months, husbands and other in-law family members. IDIs were carried out with frontline health-workers, enumerators and supervisors from the two previous household surveys. Results: Findings revealed a preference for female interviewers for household surveys in study districts as they facilitated access to mothers and reduced their discomfort as survey participants. However, this gender preference was related to the survey question. Regardless of age, caste and educational level, most mothers were not permitted to communicate with men (aside from husbands) about female-specific health topics, including birth preparedness, delivery, menstrual cycles, contraception, breastfeeding, sexual behaviour, sexually transmitted disease, and domestic violence. Mothers in higher castes perceived these social restrictions more acutely than mothers in lower castes. There was no systematic direction of the resulting error. Mothers were willing to discuss child health issues with interviewers of either gender. Conclusions: Interviewer gender is an important consideration when designing survey protocols for maternal and reproductive health studies and when selecting and training enumerators. Female interviewers are optimal for traditional settings in Bihar as they are more likely to obtain accurate data on sensitive topics and reduce the potential for non-sampling error due to their reduced social distance with maternal respondents.