Background: The Consolidated Framework for Implementation Research (CFIR) is a determinants framework that may require adaptation or contextualization to fit the needs of implementation scientists in low-and middleincome countries (LMICs). The purpose of this review is to characterize how the CFIR has been applied in LMIC contexts, to evaluate the utility of specific constructs to global implementation science research, and to identify opportunities to refine the CFIR to optimize utility in LMIC settings.Methods: A systematic literature review was performed to evaluate the use of the CFIR in LMICs. Citation searches were conducted in Medline, CINAHL, PsycINFO, CINAHL, SCOPUS, and Web of Science. Data abstraction included study location, study design, phase of implementation, manner of implementation (ex., data analysis), domains and constructs used, and justifications for use, among other variables. A standardized questionnaire was sent to the corresponding authors of included studies to determine which CFIR domains and constructs authors found to be compatible with use in LMICs and to solicit feedback regarding ways in which CFIR performance could be improved for use in LMICs.Results: Our database search yielded 504 articles, of which 34 met final inclusion criteria. The studies took place across 21 countries and focused on 18 different health topics. The studies primarily used qualitative study designs (68%). Over half (59%) of the studies applied the CFIR at study endline, primarily to guide data analysis or to contextualize study findings. Nineteen (59%) of the contacted authors participated in the survey. Authors unanimously identified culture and engaging as compatible with use in global implementation research. Only two constructs, patient needs and resources and individual stages of change were commonly identified as incompatible with use. Author feedback centered on team level influences on implementation, as well as systems characteristics, such as health system architecture. We propose a "Characteristics of Systems" domain and eleven novel constructs be added to the CFIR to increase its compatibility for use in LMICs. Conclusions: These additions provide global implementation science practitioners opportunities to account for systemslevel determinants operating independently of the implementing organization. Newly proposed constructs require further reliability and validity assessments. Trial registration: PROSPERO, CRD42018095762

Background The aim of this study is to quantify the effects of the SARS-CoV-2 pandemic on health services utilization in China using over four years of routine health information system data. Methods We conducted a retrospective observational cohort study of health services utilization from health facilities at all levels in all provinces of mainland China. We analyzed monthly all-cause health facility visits and inpatient volume in health facilities before and during the SARS-CoV-2 outbreak using nationwide routine health information system data from January 2016 to June 2020. We used interrupted time series analyses and segmented negative binomial regression to examine changes in healthcare utilization attributable to the pandemic. Stratified analyses by facility type and by provincial Human Development Index (HDI) – an area-level measure of socioeconomic status – were conducted to assess potential heterogeneity in effects. Findings In the months before the SARS-CoV-2 outbreak, a positive secular trend in patterns of healthcare utilization was observed. After the SARS-CoV-2 outbreak, we noted statistically significant decreases in all indicators, with all indicators achieving their nadir in February 2020. The magnitude of decline in February ranged from 63% (95% CI 61–65%; p <0•0001) in all-cause visits at hospitals in regions with high HDI and 71% (95% CI 70–72%; p <0•0001) in all-cause visits at primary care clinics to 33% (95% CI 24–42%; p <0•0001) in inpatient volume and 10% (95% CI 3–17%; p = 0•0076) in all-cause visits at township health centers (THC) in regions with low HDI. The reduction in health facility visits was greater than that in the number of outpatients discharged (51% versus 48%; p <0•0079). The reductions in both health facility visits and inpatient volume were greater in hospitals than in primary health care facilities ( p <0•0001) and greater in developed regions than in underdeveloped regions ( p <0•0001). Following the nadir of health services utilization in February 2020, all indicators showed statistically significant increases. However, even by June 2020, nearly all indicators except outpatient and inpatient volume in regions with low HDI and inpatient volume in private hospitals had not achieved their pre-SARS-COV-2 forecasted levels. In total, we estimated cumulative losses of 1020.5 (95% CI 951.2- 1089.4; P <0.0001) million or 23.9% (95% CI 22.5–25.2%; P <0.0001) health facility visits, and 28.9 (95% CI 26.1–31.6; P <0.0001) million or 21.6% (95% CI 19.7–23.4%; P <0.0001) inpatients as of June 2020. Interpretation Inpatient and outpatient health services utilization in China declined sig...

Routine health information systems (RHISs) are in place in nearly every country and provide routinely collected full-coverage records on all levels of health system service delivery. However, these rich sources of data are regularly overlooked for evaluating causal effects of health programmes due to concerns regarding completeness, timeliness, representativeness and accuracy. Using Mozambique's national RHIS (Módulo Básico) as an illustrative example, we urge renewed attention to the use of RHIS data for health evaluations. Interventions to improve data quality exist and have been tested in low-and middle-income countries (LMICs). Intrinsic features of RHIS data (numerous repeated observations over extended periods of time, full coverage of health facilities, and numerous real-time indicators of service coverage and utilization) provide for very robust quasi-experimental designs, such as controlled interrupted time-series (cITS), which are not possible with intermittent community sample surveys. In addition, cITS analyses are well suited for continuously evolving development contexts in LMICs by: (1) allowing for measurement and controlling for trends and other patterns before, during and after intervention implementation; (2) facilitating the use of numerous simultaneous control groups and non-equivalent dependent variables at multiple nested levels to increase validity and strength of causal inference; and (3) allowing the integration of continuous 'effective dose received' implementation measures. With expanded use of RHIS data for the evaluation of health programmes, investments in data systems, health worker interest in and utilization of RHIS data, as well as data quality will further increase over time. Because RHIS data are ministry-owned and operated, relying upon these data will contribute to sustainable national capacity over time.