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. 2020 Jun 12;35(8):1084–1109. doi: 10.1093/heapol/czaa032

Table 4.

Resilience indicators used in quantitative and mixed methods studies (n = 24), by resilience domain and level of data collection

			Level of data collection
		National level	Organizational level	Staff level	Patient/ Population level
Management Capacities	Knowledge	Presence of effective surveillance systems (Ammar et al. 2016; Orru et al. 2018)^a Lancet Countdown Survey Item 2.1 ‘adaptation planning and assessment’ (Watts et al. 2018)^b	General resiliency indicators for health care facilities (Paterson et al. 2014)^a: ‘Builds and enhances climate change knowledge capacity as it relates to hazards of concern for the health care facility’ As well as several knowledge indicators under different emergency scenarios	Short-form version of the Benchmark Resilience Tool Question 1 (Goncalves et al. 2019)^b Resilience Assessment Grid Items relating to the ‘ability to monitor’ (Falegnami et al. 2018)^b
	Uncertainties	Presence of strategic response policies and plans (Ammar et al. 2016; Orru et al. 2018)^a Lancet Countdown Survey Item 2.2 ‘climate information services for health’ (Watts et al. 2018)^b	Structured hospital assessment MA2: ‘Incident Management’, MA 3: ‘Occupant Safety’, MA4: ‘Resiliency and Continuity Operations’, MA5: ‘Medical Surge’ (Dobalian et al. 2016)^a General resiliency indicators for health care facilities: several indicators related to planning and response policies under different emergency scenarios. (Paterson et al. 2014)^a Indicators of hospital resilience (Zhong et al. 2014a, 2015)^b	Short-form version of the Benchmark Resilience Tool, Questions 2, 5 and 9 (Goncalves et al. 2019)^b Resilience Assessment Grid items relating to ‘international guidelines’, ‘internal procedures’, ‘availability of resources in expected and unexpected complications’, ‘participation into update of procedures and protocols’ (Falegnami et al. 2018)^b	Conjoint Community Resilience Assessment Measurement (CCRAM) tool Items 3, 8, 17 and 24 (Cohen et al. 2019)^b
	Interdependence	Changes in funding from (international) donors (Ammar et al. 2016)^a Presence of multi-sectoral health strategies and partnerships (Ammar et al. 2016)^a	Structured hospital assessment MA6: ‘Support to External Requirements’ (Dobalian et al. 2016)^a General resiliency indicators for health care facilities (Paterson et al. 2014)^a: ‘Builds climate change adaptive capacity through partnerships and by securing mutual support’ As well as several indicators related to interdependence under different emergency scenarios Strength of collaboration between organisations or businesses during a disaster (Andrew et al. 2016) ^b	Short-form version of the Benchmark Resilience Tool Question 4 (Goncalves et al. 2019) ^b	Conjoint Community Resilience Assessment Measurement (CCRAM) tool (Cohen et al. 2019)^b
	Legitimacy	N/A	N/A	N/A	Trust in institutional efficacy and issue saliency (Eurobarometer survey; Orru et al. 2018)^a Conjoint Community Resilience Assessment Measurement (CCRAM) tool Items 1, 6, 15, 19, 21 and 23 (Cohen et al. 2019)^b
Levels of Resilience	Absorption	[R] Changes in population-level health indicators (Ammar et al. 2016; Fukuma et al. 2017)^a: Morbidity (Case notification rates, outbreaks of new infections) Mortality (Infant Mortality, Under-5 Mortality, Maternal Mortality Ratio, Cause-specific mortality rates) Change in service utilisation indicators(Ammar et al. 2016; Gizelis et al. 2017; Fukuma et al. 2017; Sochas et al. 2017) ^aChange in service coverage indicators (e.g. vaccination)(Ammar et al. 2016)^a Change in availability of medical supplies and human resources(Simonetti et al. 2018; Fukuma et al. 2017; Ammar et al. 2016)^a Changes in spending on health and contracts with health facilities (Ammar et al. 2016; Fukuma et al. 2017)^a Operationality of health facilities and programmes (i.e. no temporary or permanent health facility or programme closures) (Ammar et al. 2016; Fukuma et al. 2017)^a	Change in service utilisation indicators (Radcliff et al. 2018)^a Change in availability of medical supplies and human resources(Witter et al. 2017; Ray-Bennett et al. 2019) ^a Service quality indicators (Kozuki et al. 2018; Ray-Bennett et al. 2019)^a Models assessing impact of changes in air temperatures on temperatures of health facilities. (Lomas et al. 2012; Iddon et al. 2015; Short et al. 2015)^a Modelling of patient pathways through medical processes under different scenarios (Functional Resonance Analysis Method) (Patriarca et al. 2018)^a Four survey items measuring organizational resiliency during a disaster (Andrew et al. 2016)^b: Ability to carry out routine tasks and help victims and communities cope Resourcefulness in meeting needs of the victims and their communities Ability to overcome operational disruptions Rapidity of providing victims with resources	Short-form version of the Benchmark Resilience Tool Questions 3 and 7 (Goncalves et al. 2019)^b	Health service utilisation during a disaster and reasons for non-utilisation (Ray-Bennett et al. 2019)^b
	Adaptation	Indicators for assessing adaptive financial resilience (Thomas et al. 2013)^a: Reduction of Unit costs (salaries, wages, fees) Increase in system productivity (Average length of stay, proportion of day cases in acute care) Reduction in staffing with no commensurate reduction in service. Protection of services (no loss of entitlements or rationing by volume) Achievement of stated targets. Lancet Countdown Survey Items 2.3 ‘adaptation delivery and implementation’ and 2.4 ‘spending on adaptation for health and health-related activities’ (Watts et al. 2018)^b	Modelling studies effect of different adaptive scenarios health facility temperatures (Lomas et al. 2012; Short et al. 2015)^a	N/A	N/A
	Transformation	N/A	N/A	N/A	N/A

^a

Routine data, document review or observation;.

^b

Survey data.