Elective surgery system strengthening: development, measurement, and validation of the surgical preparedness index across 1632 hospitals in 119 countries

Abstract

Background
The 2015 Lancet Commission on global surgery identified surgery and anaesthesia as indispensable parts of holistic health-care systems. However, COVID-19 exposed the fragility of planned surgical services around the world, which have also been neglected in pandemic recovery planning. This study aimed to develop and validate a novel index to support local elective surgical system strengthening and address growing backlogs.
Methods
First, we performed an international consultation through a four-stage consensus process to develop a multidomain index for hospital-level assessment (surgical preparedness index; SPI). Second, we measured surgical preparedness across a global network of hospitals in high-income countries (HICs), middle-income countries (MICs), and low-income countries (LICs) to explore the distribution of the SPI at national, subnational, and hospital levels. Finally, using COVID-19 as an example of an external system shock, we compared hospitals' SPI to their planned surgical volume ratio (SVR; ie, operations for which the decision for surgery was made before hospital admission), calculated as the ratio of the observed surgical volume over a 1-month assessment period between June 6 and Aug 5, 2021, against the expected surgical volume based on hospital administrative data from the same period in 2019 (ie, a pre-pandemic baseline). A linear mixed-effects regression model was used to determine the effect of increasing SPI score.
Findings
In the first phase, from a longlist of 103 candidate indicators, 23 were prioritised as core indicators of elective surgical system preparedness by 69 clinicians (23 [33%] women; 46 [67%] men; 41 from HICs, 22 from MICs, and six from LICs) from 32 countries. The multidomain SPI included 11 indicators on facilities and consumables, two on staffing, two on prioritisation, and eight on systems. Hospitals were scored from 23 (least prepared) to 115 points (most prepared). In the second phase, surgical preparedness was measured in 1632 hospitals by 4714 clinicians from 119 countries. 745 (45·6%) of 1632 hospitals were in MICs or LICs. The mean SPI score was 84·5 (95% CI 84·1–84·9), which varied between HIC (88·5 [89·0–88·0]), MIC (81·8 [82·5–81·1]), and LIC (66·8 [64·9–68·7]) settings. In the third phase, 1217 (74·6%) hospitals did not maintain their expected SVR during the COVID-19 pandemic, of which 625 (51·4%) were from HIC, 538 (44·2%) from MIC, and 54 (4·4%) from LIC settings. In the mixed-effects model, a 10-point increase in SPI corresponded to a 3·6% (95% CI 3·0–4·1; p<0·0001) increase in SVR. This was consistent in HIC (4·8% [4·1–5·5]; p<0·0001), MIC (2·8 [2·0–3·7]; p<0·0001), and LIC (3·8 [1·3–6·7%]; p<0·0001) settings.
Interpretation
The SPI contains 23 indicators that are globally applicable, relevant across different system stressors, vary at a subnational level, and are collectable by front-line teams. In the case study of COVID-19, a higher SPI was associated with an increased planned surgical volume ratio independent of country income status, COVID-19 burden, and hospital type. Hospitals should perform annual self-assessment of their surgical preparedness to identify areas that can be improved, create resilience in local surgical systems, and upscale capacity to address elective surgery backlogs.

Full text

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Articles
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Elective surgery system strengthening: development,
measurement, and validation of the surgical preparedness
index across 1632 hospitals in 119 countries
NIHR Global Health Unit on Global Surgery*, COVIDSurg Collaborative*†
Summary
Background The 2015 Lancet Commission on global surgery identified surgery and anaesthesia as indispensable parts
of holistic health-care systems. However, COVID-19 exposed the fragility of planned surgical services around the
world, which have also been neglected in pandemic recovery planning. This study aimed to develop and validate a
novel index to support local elective surgical system strengthening and address growing backlogs.
Methods First, we performed an international consultation through a four-stage consensus process to develop a
multidomain index for hospital-level assessment (surgical preparedness index; SPI). Second, we measured surgical
preparedness across a global network of hospitals in high-income countries (HICs), middle-income countries (MICs),
and low-income countries (LICs) to explore the distribution of the SPI at national, subnational, and hospital levels.
Finally, using COVID-19 as an example of an external system shock, we compared hospitals’ SPI to their planned surgical
volume ratio (SVR; ie, operations for which the decision for surgery was made before hospital admission), calculated as
the ratio of the observed surgical volume over a 1-month assessment period between June 6 and Aug 5, 2021, against the
expected surgical volume based on hospital administrative data from the same period in 2019 (ie, a pre-pandemic
baseline). A linear mixed-eects regression model was used to determine the eect of increasing SPI score.
Findings In the first phase, from a longlist of 103 candidate indicators, 23 were prioritised as core indicators of elective
surgical system preparedness by 69 clinicians (23 [33%] women; 46 [67%] men; 41 from HICs, 22 from MICs, and six
from LICs) from 32 countries. The multidomain SPI included 11 indicators on facilities and consumables, two on
stang, two on prioritisation, and eight on systems. Hospitals were scored from 23 (least prepared) to 115 points
(most prepared). In the second phase, surgical preparedness was measured in 1632 hospitals by 4714 clinicians from
119 countries. 745 (45·6%) of 1632 hospitals were in MICs or LICs. The mean SPI score was 84·5 (95% CI 84·1–84·9),
which varied between HIC (88·5 [89·0–88·0]), MIC (81·8 [82·5–81·1]), and LIC (66·8 [64·9–68·7]) settings. In the
third phase, 1217 (74·6%) hospitals did not maintain their expected SVR during the COVID-19 pandemic, of which
625 (51·4%) were from HIC, 538 (44·2%) from MIC, and 54 (4·4%) from LIC settings. In the mixed-eects model, a
10-point increase in SPI corresponded to a 3·6% (95% CI 3·0–4·1; p<0·0001) increase in SVR. This was consistent in
HIC (4·8% [4·1–5·5]; p<0·0001), MIC (2·8 [2·0–3·7]; p<0·0001), and LIC (3·8 [1·3–6·7%]; p<0·0001) settings.
Interpretation The SPI contains 23 indicators that are globally applicable, relevant across dierent system stressors, vary
at a subnational level, and are collectable by front-line teams. In the case study of COVID-19, a higher SPI was associated
with an increased planned surgical volume ratio independent of country income status, COVID-19 burden, and hospital
type. Hospitals should perform annual self-assessment of their surgical preparedness to identify areas that can be
improved, create resilience in local surgical systems, and upscale capacity to address elective surgery backlogs.
Funding National Institute for Health Research (NIHR) Global Health Research Unit on Global Surgery, NIHR
Academy, Association of Coloproctology of Great Britain and Ireland, Bowel Research UK, British Association of
Surgical Oncology, British Gynaecological Cancer Society, and Medtronic.
Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.
Introduction
The COVID-19 pandemic highlighted the fragility of
elective surgical services around the world, yet global
surgery risks being neglected in pandemic recovery
planning.1–3 At the start of 2022 an estimated 200 million
patients worldwide were awaiting surgery.1,2 For time-
critical conditions, such as cancer, one in seven patients
did not have their planned surgery during SARS-CoV-2
outbreaks and many more had substantial delays to their
care.3 Some patients might never have accessed the
surgery they required, with high associated disability
and millions of years of healthy life lost.4,5 With the
existing challenges in providing accessible and safe
surgical systems in low-income countries (LICs) and
middle-income countries (MICs) identified by the 2015
Lancet Commission on global surgery, health systems
and hospitals with less funding for infrastructure,
stang and equipment were the worst aected, with
Lancet 2022; 400 : 1607–17
Published Online
October 31, 2022
https://doi.org/10.1016/
S0140-6736(22)01846-3
*Members of the NIHR Global
Health Unit on Global Surgery
and the COVIDSurg Collaborative
are listed on appendix
(pp 17–69)
†Members of the writing
committee are listed at the end
of the Article
Correspondence to:
Mr James Glasbey, NIHR Global
Health Research Unit on Global
Surgery, Institute of Applied
Health Research, University of
Birmingham, Birmingham,
B15 2TH, UK
j.glasbey@bham.ac.uk
or
Mr Aneel Bhangu,
NIHR Global Health Research
Unit on Global Surgery, Institute
of Applied Health Research,
University of Birmingham,
Birmingham, B15 2TH, UK
a.a.bhangu@bham.ac.uk
See Online for appendix

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whole-societal health, economic, social, and political
consequences.3,6 The backlog of patients awaiting
planned procedures is now one of the most pressing
challenges to global health for the next 10 years.
The SARS-CoV-2 pandemic presented an unprecedented
stress on global health systems. Many surgery and
anaesthesia services changed their processes for patient
selection and reduced their volume of planned procedures,
reflecting the high risk to patients planned to receive
surgery of perioperative SARS-CoV-2 infection.7 Dierent
models of care have been proposed to support safe
upscaling of planned surgical volume during pandemic
recovery.8,9 However, shared global learning about the best
methods to improve preparedness of surgical systems has
not been done. Surgical capacity urgently needs to be
upscaled to address growing backlogs of patients waiting
for their planned procedures and improve preparedness
for future system shocks. Solutions need to be identified
for infrastructure, stang, and care pathways that can be
applied flexibly across dierent health systems.10 COVID-19
(an airborne pandemic) has been just one form of external
stress on health systems, but provides an important
learning opportunity for surgical providers and policy
makers to strengthen surgical preparedness ahead of
future system shocks.
Although several indices of health system preparedness
and surgical capacity have been proposed, these were not
designed to assess preparedness of surgery and
anaesthesia services nor have they been validated against a
measure of surgical capacity.11–13 Whole-health system
preparedness indicators are often not applicable to
surgery, and surgical capacity indicators (eg, the WHO
Situational Assessment Tool, PIPES Tool, and Ethiopian
Hospital Assessment Tool) are not designed to dynamically
assess the response of services to external system pressure
and are too complex for everyday use. This study describes
Research in context
Evidence before this study
The 2015 Lancet Commission on global surgery identified surgery
and anaesthesia as an indivisible component of holistic health
systems. The COVID-19 pandemic has revealed ongoing fragility
in surgery and anaesthesia systems, with more than 200 million
patients currently awaiting their planned procedures.
We searched PubMed and Embase from the inception of each
database to March 4, 2022, without date limits for indices,
frameworks, or guidelines able to assess a hospital or surgical
system’s ability to deliver planned surgery and anaesthesia
during periods of external system stress. Planned surgery
included all operations done when a decision for surgery was
made before hospital admission, whether this was elective
or expedited. We used search terms related to “preparedness”,
“resilience”, “pandemic”, or “system stress” in combination with
“surgery”, “anaesthesia”, “surgical systems”, “procedures”, or
“non-communicable disease”. External system stressors included
airborne pandemics (eg, SARS-CoV-2), non-airborne disease
(eg, Ebola virus), and other system stressors (eg, natural
disasters, mass trauma, warfare, political instability, and extreme
weather events). We identified 15 indices and six frameworks for
assessing whole-health system preparedness, but none were
specific to surgery nor were they validated against a measure of
planned surgical volume. We also identified three tools to
quantify essential surgical capacity: WHO Situational Assessment
Tool, PIPES Tool, and Ethiopian Hospital Assessment Tool.
However, these were not designed to assess preparedness and
are complex to complete. Together, this shows that the features
of prepared surgery and anaesthesia systems are not yet well
understood or properly implemented.
Added value of this study
The Surgical Preparedness Index (SPI) is the first tool that
specifically assesses elective surgery and anaesthesia system
preparedness. We engaged a diverse, international,
and multidisciplinary community to identify and prioritise
features of prepared surgery and anaesthesia systems that were
relevant across a wide variety of external system shocks. We
prioritised 23 globally relevant indicators of surgical preparedness
across four domains (facilities, staffing, prioritisation, and
processes). The SPI was then measured across a range of hospitals
and settings showing significant variability in preparedness
between hospitals, regions, and countries. During the COVID-19
pandemic, a pressing and globally relevant example of a system
stressor, three-quarters of hospitals reported a reduction in
planned surgical volume. The SPI score was shown to be strongly
associated with a hospital’s ability to continue planned surgery,
validating the concept of preparedness in reducing surgical
cancellations, with a significant and measurable effect. This
relationship was consistent across different types of hospital and
health systems, suggesting that SPI measurement was
generalisable across contexts.
Implications of all the available evidence
The COVID-19 pandemic highlighted the fragility of surgical
services around the world, yet surgery risks being neglected in
pandemic recovery planning. Without effective, integrated
surgical and anaesthesia systems, non-communicable diseases
cannot be effectively treated and community health declines,
meaning Sustainable Development Goal 3 cannot be met.
Application of the SPI can identify areas for policy change,
advocacy, and investment at subnational and local levels.
Hospitals should urgently implement annual SPI assessment
and create local action plans to strengthen planned surgical
services, thus supporting whole-health system resilience.
Longitudinal assessment of surgical preparedness can now be
incorporated into national surgical, obstetric, and anaesthesia
planning and considered an essential indicator of surgical
system strength. Future work is required to test the SPI in
low-income countries (4·3% of included hospitals).

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the development and validation of a multinational surgical
preparedness index (SPI) and framework to support
elective surgery and anaesthesia services, strengthening
them against future external system shocks.
Methods
This study was done in three phases (figure 1). First, index
development. An international consultation was done with
a Delphi consensus methodology to develop hospital-level
preparedness indicators to support surgical service
strengthening. For the purposes of this study, preparedness
was defined as the ability of a hospital to maintain capacity
for planned surgery during periods of external system
shock. Planned surgery was defined as any operation for
which the decision for surgery was made before the
hospital admission during which the operation took place.
Second, measurement of surgical preparedness. A cross-
sectional hospital assessment study was done to assess the
distribution of the total SPI score at national, subnational,
and hospital levels. Third, validation of the multidomain
SPI against the observed versus expected elective surgical
volume during the COVID-19 pandemic; this was used as
a contempo raneous example of a globally relevant system
stressor.
Phase 1: development of the SPI
The international consultation was done with a diverse,
multidisciplinary, expert index development group. This
Phase 1: Development of the SPIPhase 2:
Measurement
Phase 3: Validation
Longlisting of candidate indicators through internal consultation
Round 1: online voting with IIDG (rating on a scale of 0 [not important and easy to measure] to 100 [very important and easy to measure])
Round 2: iterative development and discussion in a virtual meeting of the IIDG
Round 3: online voting round with IIDG (overall importance of indicator: essential, desirable, or remove)
Round 4: Final development and discussion in a virtual meeting of the IIDG
Final index of 23 indicators across 4 domains were agreed and entered phase 2 measurement
5375 hospital-level assessment of the SPI (661 ineligible)
65 front-line perioperative clinicians from
44 countries invited to participate in the
longlisting of candidate indicators
110 candidate indicators proposed by the
IIDG
32 recommendations across four domains
were shortlisted after iterative thematic
analysis and combination to reduce
redundancy
15 indicators did not meet predefined
dropping criteria of an ease of reporting
or importance score of ≤70
The ease of measurement score for
17 indicators and the importance score for
4 indicators was ≤70. These indicators
assessed in rounds 2 and 3
6 additional indicators were deemed
essential to ≥50% of respondents and were
accepted
9 indicators were suggested to be removed
by ≥10% of respondents and were dropped
2 additional indicators were not deemed
essential by ≥50% of respondents, nor
were they suggested to be removed by
≥10% of respondents. They entered
round 4 discussion
4714 assessments from 1627 hospitals in 119 countries included in the measurement
Validation of the SPI using COVID-19 as a case study of external system stress
Association of the SPI with observed vs expected planned surgical volume in 1632 hospitals
Figure 1: Overview of study design
IIDG=international guideline development group. SPI=surgical preparedness index.

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included surgeons, anaesthetists, critical-care doctors,
nurses, and hospital managers involved in the delivery of
planned surgical care across high-income country (HIC),
MIC, and LIC settings. The range of people included
across care roles and income setting was designed to
provide breadth of perspectives during consensus rounds
and fulfils typical sample size requirements for Delphi
methodologies.14 A four-stage Delphi process was done
within the development group to prioritise hospital-level
SPIs. Consensus definitions were set a priori, and the
process was done in accordance with best practice
recommendations: (1) the expertise matrix was predefined,
inclusive, and generalisable; (2) dropping rules were
predefined; (3) a limit of two voting and two face-to-face
rounds was prespecified; and (4) frequent reminders were
sent to respondents to maximise the retention rate.15 The
full methodology for the consultation process is described
in the appendix (pp 70–73).
To explore the relevance of the surgical preparedness
indicators across other external system shocks, we used a
consensus ratings exercise with eight international
development group members (two from high-income
countries [HICs], four from MICs, and two from LICs). We
defined five dierent external shocks: airborne pandemic,
non-airborne pandemic, warfare and political instability,
natural disasters, and seasonal pressures. Independent
members were asked to rate the relevance (high, moderate,
or low) of each indicator in their local context. Inter-rater
reliability was estimated using intraclass correlation
coecient (ICC[1k]; one-way random eects, average of
k raters) presented with 95% CIs.
Phase 2: preparedness of global surgery and anaesthesia
systems
A hospital-level assessment of the SPI was done between
June 6 and Aug 5, 2021, and data were recorded by local
assessors on a centralised, encrypted Research Electronic
Data Capture server hosted by the University of
Birmingham, Birmingham, UK.16 COVIDSurg is a net-
work of more than 15 000 front-line clinicians from the
National Institute for Health Research (NIHR) Global
Health Research Unit on Global Surgery focused on
supporting data-driven decision making in perioperative
care.7 The network facilitated distribution of the SPI
assessment was sent to local clinicians and managers to
complete for their hospital. Collaborators were
encouraged to identify other colleagues to complete
multiple assessments of the same hospital to evaluate
inter-rater reliability. Any centre worldwide providing
planned surgery was eligible to participate. Any post-
graduate clinician or manager involved in perioperative
care from any specialty background in these centres was
eligible to participate. Clinicians without a temporary or
permanent contract (ie, locum doctors or equivalent) and
medical students were not eligible to participate.
Features of hospital assessors and hospitals were
summarised overall and by country income group. To
promote application and interpretation of the SPI in clinical
practice, we calculated global, regional, and national
distributions of SPI. We also disaggregated by hospital
type, country income, COVID-19 burden, and country.
Where multiple assessments were made of the same
hospital, the mean was calculated first by indicator, then an
overall mean index score was calculated as an aggregate
mean of these means. Results presented across subgroups
were calculated as the mean of hospitals’ mean SPI scores
in each group and presented with a 95% CI. High fidelity
centre-level SPI data were presented online on a Shiny
(Boston, MA, USA) application hosted on an Argonaut
server at the University of Edinburgh, Edinburgh, UK. The
inter-rater reliability of SPI assessment was estimated
again using the ICC(1k) with 95% CIs.17
We explored the relationship between national mean
SPI scores and four relevant global health indicators using
generalised additive modelling fitted with a penalised
cubic spline (with shrinkage). The four selected indicators
were: (1) the UN’s Human Development Index, which is a
composite index of life expectancy, education, and per
capita income (a higher Human Development Index score
indicates greater development); (2) global health security
index, which is an assessment of global health security
capabilities (ie, a measure of whole health-system
resilience) from the Johns Hopkins Center for Health
Security, Baltimore, MD, USA, the Nuclear Threat
Initiative (Washington DC, USA), and the Economist
Intelligence Unit (London, UK; a high global health
security score indicates a more resilient health system);
(3) the WHO Universal Health Coverage (UHC) service
coverage index, which combines 14 tracer indicators of
service coverage into a single summary measure (a higher
UHC index indicates greater coverage); and (4) Gini
coecient, which is a measure of population wealth
inequality (a Gini coecient of 0 expresses perfect
equality; a coecient of 1 indicates maximal inequality).
Analyses were done with R Studio (version 4.1.1) packages:
tidyverse, finalfit, psych, and ggplot2.
Phase 3: validation of the SPI using planned surgical
volume during COVID-19
To evaluate the criterion validity of the SPI, we compared a
hospital’s self-assessed SPI score with its ability to maintain
planned surgery capacity. This was estimated using the
observed to expected planned surgical volume ratio (SVR),
calculated as the ratio of each hospital’s observed planned
surgical volume over a 1-month assessment period against
the expected planned surgical volume based on data from
the same month in 2019 (the prepandemic baseline) and
expressed as a percentage. Case volume data were
measured from routinely collected hospital administrative
data, such as theatre logbooks and electronic health-care
records. A planned surgery case was defined as any
planned admission for a procedure done by a surgeon in
an operating theatre under general, regional, or local
anaesthesia. This included procedures classified as either

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elective or expedited in the National Confidential Enquiry
into Patient Outcome and Death classification system, but
excluded urgent and immediate surgery.18 Patients
undergoing surgery for any indication were eligible for
inclusion, including benign disease, cancer, trauma, or
obstetrics. This included day-case procedures (ie,
discharged same date as operation).
Analyses were done using R Studio packages tidyverse,
finalfit, lmer, and ggplot2. A complete-case analysis was
preplanned if missing data were both missing at random
and in a low number of samples (<5%).19 In the prestudy
protocol, we planned to impute missing data using multiple
imputation by chained equations based on a missing at
random or missing completely at random assumption if
data missingness was more than 5%. Centres with no
current planned surgery volume estimate were excluded
from analyses. Generalised additive models were fitted
using a penalised cubic spline (with shrinkage). Models
were initially fitted with a basis dimension of 10 (k). Model
fit was checked using residual plots, convergence
confirmed, and basis dimension choice checked. If per
group estimated degrees of freedom approached basis
choice minus one (k–1), then the basis dimension was
increased. The link function was identify. A random-error
distribution was assumed and checked on residual plots as
above. To explore whether this association could be
explained by confounding we created a mixed-eects linear
regression model with country included as a random eect
(normal distribution). We checked assumptions by
exploring normality and homogeneity of variance of
residuals and linearity of quantitative predictors.
Model coecients were adjusted for predefined centre-
level and country-level confounders that were identified
through a scoping review of published literature and
considered a priori by the international development
group as likely to be clinically and causally linked to both
exposure and outcome. A proposed casual model was
presented in a directed acyclic graph. Covariables included
country income—defined according to World Bank 2018
definitions and classified as HIC, MIC (including both
upper-middle and lower-middle classifications), or LIC on
the basis of annual gross domestic product per capita
(US$); hospital funding (public, private, or mixed public
and private); surgical service provision at the facility
(planned only versus planned and unplanned); hospital
location (defined by the assessor as primarily an urban,
rural, or mixed urban and rural area); number of hospital
beds (<50, 50–99, 100–199, 200–499, 500–999, or ≥1000);
and country COVID-19 burden (low, moderate, or high) at
the time of SPI assessment. The Oxford COVID-19
Government Response Tracker (OxCGRT) was used as a
surrogate of the overall COVID-19 burden on a local health
system at the time of the SPI assessment. The OxCGRT is
a composite of 19 indicators, including measures and
behavioural interventions associated with containment
and closure, economic response, and health systems with
an overall score range between 0 (no restrictions) and
100 (most stringent restrictions). It has been validated for
use globally by showing associations with planned surgical
volume,3 population SARS-CoV-2 infection rates, and
Google mobile phone mobility data.20 OxCGRT cutpoint
scores used in previous work based on comparisons of
index scores and national policy sources were used.3 Each
hospital was given a classification based on the country’s
status at the time of assessment: low COVID-19 burden
(index <20), moderate COVID-19 burden (20–60), and
high COVID-19 burden (>60). The OxCGRT was
preferentially used instead of SARS-CoV-2 case notification
rates because of global dierences in access to testing and
Panel: Summary of surgical preparedness index
Hospitals were assessed for each indicator by assessors, scored from 1 (very weak) to
5 (very strong) with an overall summary surgical preparedness index score calculated
between 23 and 115. A full description of each indicator to support hospital assessment is
provided in the appendix (pp 1–2).
Facilities and consumables
1. Availability of reserved planned surgery theatres (ring-fenced theatres)
2. Availability of reserved planned surgery beds (ring-fenced beds)
3. Availability of reserved critical care beds for planned surgery (ring-fenced critical care)
4. Flexibility to rearrange hospital areas to provide a segregated pathway for planned
surgery (flexible areas)
5. Access to diagnostics and interventions to identify and treat surgical complications
(managing complications)
6. Reliable supply of electricity (electricity supply)
7. Reliable supply of supplementary oxygen (oxygen supply)
8. Reliable supply and management of essential perioperative drugs (drug supply)
9. Reliable supply and management of devices and implants (device supply)
10. Sufficient surgical instrument and local sterilisation processes (sterilisation)
11. Availability of protective measures for theatre teams (protective equipment)
Staffing
12. Ability to redistribute staff within and between hospitals to maintain capacity (staff
redistribution)
13. Availability of reserved teams to provide planned surgical care (ring-fenced teams)
Prioritisation
14. Cross-specialty patient prioritisation for surgery (patient prioritisation)
15. Ability to identify and cancel procedures of limited clinical value (procedure
prioritisation)
Systems
16. Formal operational plan to continue planned surgery during external system shocks
(formal plan)
17. Ability to do preoperative assessment in the community (preoperative assessment)
18. Access to routine preoperative testing for endemic and epidemic diseases
(preoperative testing)
19. Ability to transfer patients to another hospital with greater capacity (hospital transfer)
20. Ability to facilitate timely discharges (timely discharge)
21. Social support system to facilitate safe discharge (social support)
22. Capacity to use telephone or video calls for outpatient appointments (remote
outpatient appointments)
23. Capacity and capability to communicate with family members (family
communication)

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reporting.21,22 Subgroup analyses were presented by country
income, COVID-19 burden, hospital financing, and
hospital location, presented in cubic spline curves and
with β coecients generated in mixed-eects models.
Role of the funding source
The funders had no role in study design or writing of this
report. The views expressed are those of the authors and
not necessarily those of the National Health Service, the
NIHR or the UK Department of Health and Social Care.
Results
In phase 1, the international consultation to develop the
SPI indicator set involved 69 members (23 [33%]
women; 46 [67%] men; 41 from HICs, 22 from MICs, and
six from LICs) from 32 countries. This included front-line
surgeons, anaesthetists, and critical-care doctors from the
COVIDSurg and NIHR Global Health Unit on Global
Surgery collaborative networks. Of 110 longlisted candidate
indicators, the final index included 23 indicators across
four consensus domains: facilities and consumables,
stang, prioriti sation, and systems (panel). Detailed
descriptions to support hospital assessment are provided in
the appendix (pp 1–3). Each indicator was scored using a
Likert scale between 1 (very weak) and 5 (very strong). The
scores across all 23 indicators were summed to give a total
SPI score for a hospital with a range between 23 (least
prepared) and 115 (most prepared). A summary of the
Delphi voting rounds is presented in figure 1 and full
results are reported in the appendix (pp 4–5). All eight
independent raters considered the 23 indicators to have
high (20 indicators) or moderate (three indicators) relevance
to maintaining volume of planned surgery across all five
examples of external shocks (figure 2) with high agreement
between raters (ICC 0·76 [95% CI 0·59–0·89]).
In phase 2, 5375 hospital-level assessments were
completed, of which 503 did not have an identifiable
hospital or country or both, 118 did not complete
assessment of all indicators, and 40 did not calculate an
SVR. Across included facilities, the level of missingness
was less than 5% for all indicators; we did a preplanned
complete case analysis without imputation. 4714 complete
assessments from 1632 hospitals in 119 countries,
including 887 (54%) hospitals in 52 (44%) HICs, 675 (41%)
hospitals in 56 (47%) MICs, and 70 (4%) hospitals in
11 (9%) LICs, were eligible for analysis in phase 2 and 3.
A summary of included hospitals both overall and by
World Bank income group and the number of hospitals
and assessments by country are reported in the appendix
(pp 6–7). 1217 (74·6%) of 1632 hospitals assessed were
public (government) funded, 196 (12·0%) were private
hospitals, and 219 (13·4%) were mixed public and private.
1570 (96·2%) hospitals delivered both planned and
unplanned surgery. Hospitals in urban, rural, and mixed
settings, with a wide range of hospital bed numbers were
included in the assessment. The median number of
hospitals assessed per country was 6·0 (IQR 2·0–14·5).
There was a median of 2·0 (1·0–3·0) assessments per
hospital, and 764 (46·8%) hospitals had more than one
assessment. In hospitals in which more than one
assessment was completed, inter-rater reliability of the
SPI was moderate (ICC 0·55 [95% CI 0·53–0·57]). A
summary of features of hospital assessors overall and by
World Bank income group are reported in the appendix
(p 9). The hospital assessors were most commonly
Airborne
pandemic
Non-airborne
pandemic
Warfare and
political
instability
Natural
disaster
(eg, flood and
hurricane)
Seasonal
pressures
(eg, winter and
heatwaves)
Facilities and consumables
Ring-fenced theatres
Ring-fenced beds
Ring-fenced critical care
Flexible areas
Managing complications
Electricity supply
Oxygen supply
Drug supply
Device supply
Sterilisation
Protective equipment
Staffing
Staff redistribution
Ring-fenced teams
Prioritisation
Patient prioritisation
Procedure prioritisation
Systems
Formal plan
Preoperative assessment
Preoperative testing
Hospital transfer
Timely discharge
Social support
Remote outpatients
Family communication
+
+
+ + + +
+ + + + +
+ + + + +
+ + –
–
– –
–
+ + + + +
+ + + + +
+ + + + +
+ + + + +
+ + + + +
+ + + + +
+ + + – –
+ + + + +
+ + + + +
+ + + + +
+ + + + +
+ + + + +
+ + + + +
+ + – – –
+ + + + +
+ + + + +
+ + + + +
+ + + + +
+ + + + +
High relevance
Moderate relevance
xLow relevance
Figure 2: Relevance of the surgical preparedness index to different external shocks
Independent development group members were asked to rate the relevance of each surgical preparedness
indicator following five different external health-care system shocks in their local context.

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1613
surgeons (2845 [60·4%] of 4714 assessors), although
assessments were completed by a range of professionals
from across all surgical disciplines. The mean SPI scores
per hospital was 84·5 (95% CI 84·1–84·9) out of 115, and
global distribution of the SPI are reported in figure 3.
Hospital scores ranged from 26 to 115. There was variation
in the mean SPI across subgroups: HICs (88·5 [95% CI
89·0–88·0]), MICs (81·8 [82·5–81·1]), and LICs (66·8
[64·9–68·7]); moderate (81·1 [80·4–81·8) and high (87·1
[86·6–87·6) COVID-19 burden areas; public
(83·0 [82·5–83·5]) and private or mixed (89·8
[88·9–90·7]) hospitals; and urban (86·1 [85·4–86·8]),
rural (77·4 [74·2–80·6]), and mixed (83·7 [83·1–84·3])
settings (appendix p 11).
The mean scores (out of five) for each individual indicator,
presented overall and by World Bank income group are
reported in figure 4. The highest scored indicators were
electricity supply (4·38 [95% CI 4·34–4·41]), oxygen supply
(4·33 [4·29–4·36]), and perioperative drugs (4·17
[4·34–4·41]). The lowest scored indicators overall were ring-
fenced critical care beds (3·11 [3·07–3·17]), remote
outpatient appointments (3·26 [3·21–3·32]), and formal
operational plan (3·28 [3·23–3·32]). The biggest dierences
by indicator were seen in device supply (standardised mean
dierence between HICs and LICs was –1·80 points),
remote outpatients (–1·63), and drug supply (–1·62).
In the country-level analysis, greater surgical
prepared ness was associated with higher levels of
human develop ment, health security, and UHC, and
lower levels of wealth inequality (appendix p 12).
A suggested framework for assessment of the SPI and
targeted, local systems strengthening initiatives is
reported in the appendix (p 13), and an online application
to support longitudinal evaluation.
In phase 3, at the time of assessment, 1217 (74·6%) of
1632 hospitals had an SVR of less than 1, suggesting that
they were unable to maintain usual planned sur gical
volume during COVID-19. Of these 625 (51·4%)
hospitals were from HICs, 538 (44·2%) from MICs, and
54 (4·4%) from LICs (appendix pp 14–15). The mean
SVR was 79·3% (95% CI 78·1–80·4). This varied
significantly by hospital, ranging from 0·0% (doing no
Mean SPI
index score
100
80
60
40
A
B
25 50 75 100
0
0·01
0·02
0·03
Smoothed density estimate
SPI score
25 50 75 100
SPI score
25 50 75 100
SPI score
High-income countries Middle-income countries Low-income countries
Figure 3: Geographical distribution of SPI score
(A) Distribution displayed is centred around the mean value of SPI total score (84·5). Green indicates better prepared surgical systems; red indicates less prepared
surgical systems. (B) Distribution of the SPI by country income group. The theoretical score range limits of the SPI were 23–115 points. The lowest mean hospital score
was 26 and the highest was 115. These values are displayed at the floor and ceiling values of the x-axis. SPI=surgical preparedness index.
For more on the live hospital
SPI assessment tool see spi.
surgery

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planned surgery) to 200·0% (doing twice as many
planned surgeries than the pre-pandemic baseline). A
histogram of SVR across World Bank income groups is
reported in the appendix (p 14). The proposed causal
model is reported in the appendix (p 16), and figure 5
shows the SPI score against the SVR, overall and across
key subgroups. A linear association was observed between
SPI score and SVR with a ten-point total SPI score increase
associated with a 3·6% (95% CI 3·0 to 4·1; p<0·0001)
increase in SVR in the mixed-eects model. Hospitals in
MICs (–8·37% [95% CI –8·45 to –8·29]; p<0·0001) and
LICs (–10·56% [–14·89 to –6·2]; p<0·0001) versus HICs
were associated with a reduced SVR. Private (3·01%
[0·12 to 5·91]; p<0·0001) and mixed public and private
(3·20% [1·02 to 5·37]; p=0·0002) hospitals were both
associated with increased SVR versus public hospitals. No
significant associations between hospital location (urban
vs rural or mixed) and SVR were observed (appendix p 10).
On subgroup analysis, association between SPI score and
SVR was observed in HICs (4·8% [4·1 to 5·5]; p<0·0001),
MICs (2·8% [2·0 to 3·7]; p<0·0001), and LICs
(3·8 [1·3 to 6·7]; p<0·0001); moderate (3·5 [2·7 to 4·2%];
p<0·0001) and high (4·1 [3·3 to 4·8]; p<0·0001) COVID-19
burden areas; public (3·6% [3·0 to 4·2]; p<0·0001) and
private hospitals (4·1% [3·1 to 5·2]; p<0·0001); and in
urban (4·2% [3·3 to 5·1]; p<0·0001), rural (4·9 [1·6 to 8·2];
p=0·0046), and mixed locations (3·4 [2·7 to 4·1]; p<0·0001).
Discussion
We have developed, measured, and validated a hospital-
level SPI to support strengthening of elective surgery
systems against external shocks. The SPI showed
variability at subnational and hospital levels, identifying
areas that can improve to create resilience in local
surgical systems. Using COVID-19 as an example, a
10-point increase in the SPI was associated with a
3·6% increase in the planned surgical volume ratio. This
relationship was robust across income settings, hospital
types, and COVID-19 burdens. Hospitals with private
versus public financing and in HICs were able to
maintain a higher SVR than those in MICs or LICs,
indicating the importance of hospital resourcing as a
mediator of planned surgical throughput. Our findings
suggest that the under-resourced surgical systems,
identified as at risk by the Lancet Commission on global
surgery,23 will also be at greatest risk of secondary eects
and delayed recovery from COVID-19. Routine SPI
assessment might help to identify actionable targets for
local policy, advocacy, and investment in surgery and
anaesthesia service strengthening that complement
existing frameworks for global health security.24,25 Focused
eorts to address surgical preparedness will be essential
in addressing growing backlogs and mitigating against
harm for patients awaiting surgery.
The 23 surgical preparedness indicators are easy to
measure without additional resources, with moderate
ICC values, and they allow local hospital teams to identify
targets that are relevant to them and are actionable.
There was significant variability in performance across
indicators and across resource settings. For example,
ring-fenced critical care beds was rated as being
challenging in HIC, MIC, and LIC settings, suggesting a
challenge that might be hard to surmount. Conversely,
device supply, drug supply, and remote outpatients
appointments were scored lower in lower-income
settings, perhaps highlighting important areas for
advocacy and service investment. Public hospitals and
those in rural settings had lower SPI scores, highlighting
vulnerable hospital types that warrant future focus.26 The
finding that better resourced surgical services were more
resilient to system stress during SARS-CoV-2, with a
more rapid recovery, aligns with other research in this
area.27 Country-level analysis showed consistency of the
SPI with other measures of health system resilience,
such as the Global Health Security index, and strong
correlation with UHC service coverage and other
measures of wealth equality and development. However,
the SPI has strong clinical use beyond these population-
level measures by allowing hospital benchmarking and
highlighting areas for targeted action.
Other indices exist to address both health system
preparedness and surgical capacity separately, but they do
not combine the immediate need to focus on surgery at a
subnational or hospital level and preparedness for external
shocks.28,29 In a review of whole-health system preparedness
indicators, no index was meaningfully associated with
clinical outcomes, and no surgery-specific indices were
found.11 Other frameworks exist to evaluate surgical
Ring-fenced theatres
Ring-fenced beds
Ring-fenced teams
Flexible areas
Social support
Managing complications
Surgical preparedness indicator
Electricity supply
Oxygen supply
Drug supply
Preoperative testing
Sterilisation
Protective equipment
Staff redistribution
Ring-fenced critical care
Patient prioritisation
Procedure prioritisation
Timely discharge
Formal plan
Preoperative assessment
Device supply
Hospital transfer
Remote outpatients
Family communication
2 3 4 5
Mean point score (overall)
Overall
High-income countries
Middle-income countries
Low-income countries
Figure 4: Mean ratings of hospitals across surgical preparedness indicators
Scores are a mean following ratings from 1632 participants. Indicators are ordered from highest to lowest mean
score (out of 5) overall by indicator.

Articles
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1615
capacity in isolation (the PIPES checklist,12 WHO
Situational Assessment tool,13 and Ethiopian Hospital
Assessment tool30). However, these are not designed to
dynamically assess preparedness (ie, the response of
services to external system pressure). They are also
complex, long, and not feasible for regular application.
Our index was validated in the context of the SARS-CoV-2
pandemic, but it is likely to be generalisable beyond this
setting. In a consensus exercise, independent international
raters considered the indicators all to have high or
moderate relevance across five example scenarios.
However, the index might not have full content validity
across every external shock recorded. For example, in the
case of seasonal pressures (heatwaves or winter pressures)
adequate temperature control in clinical areas through air
conditioning or heating might be considered an important
additional indicator. In addition, the relative importance
of SPI indicators might change from system stressor to
stressor and from country to country; for example, during
the COVID-19 recovery period, sta shortages might be
the primary limiting factor for delivery of planned surgery.
This has been compounded over time due to burnout and
sta sickness. Therefore, we consider the SPI to be a
minimum core indicator set to underpin elective surgical
system preparedness with relevance across a variety of
scenarios. However, the SPI requires validation and a
potential need for adaptation for other external stressors
exists, highlighting an important area for ongoing
research.
Surgery has been neglected from planning for pandemic
recovery, despite being a core component of functioning
health-care systems.23,31 The SPI presents a consensus
response by the international community to tackle the
issue of neglect of surgery from planning. However, our
study has limitations. First, this cross-sectional assessment
of preparedness does not account for hospitals at dierent
stages of the readiness–response–recovery cycle.32 Second,
the index does not inform the net benefit of restoring
surgery versus other hospital activities; however, surgical
service preparedness is an essential component of holistic
health system resilience: it strengthens other health-care
processes (eg, readiness to provide oxygen)33 and
transparent priori tisation will be key in competition for
restricted resources.34 Third, 868 (53%) of the 1632 hospitals
had only one assessor. Dierences in preparedness might
exist between specialties or operating theatres that are not
reflected here, and we were unable to assess variability
between subgroups of assessors in more details. Fourth,
there was some imbalance in represen tation between
HICs, MICs, and LICs in both indicator development and
the cross-sectional assessment. However, data were
collected from a large sample of 744 (46·6%) hospitals in
MICs and LICs indicating generalisability. Fifth, data
suggest that some hospitals in countries with lower
COVID-19 burden at the time of assessment (eg, Australia
and China) had a lower SPI score, but still were able to
maintain their planned surgical volume. To address
confounding due to COVID-19 pressures, we adjusted for
this in modelling and did a subgroup analysis in high and
moderate COVID-19 burden areas; no countries were
classified as low COVID-19 burden. Sixth, barriers and
facilitators to implementation of the SPI framework are not
yet fully understood; we include an online assessment tool
to support future imple mentation and evaluation. Seventh,
our surrogate measure of COVID-19 burden (OxCGRT) did
not account for government mandates to pause planned
surgical care, which might have led to unmeasured
25 50 75 100
50
70
90
110
E
Planned SVR
SPI score
40
60
80
100
C
Planned SVR
40
60
80
A
F
D
B
Planned SVR
25 50 75 100
50
70
90
SPI score
40
60
80
100
60
40
80
100
High-income countries
Middle-income countries
Low-income countries
Moderate COVID-19 burden
High COVID-19 burden
Public funded hospitals
Private or mixed funded hospitals
<50 hospital beds
50–99 hospital beds
100–199 hospital beds
200–499 hospital beds
500–999 hospital beds
≥1000 hospital beds
Urban
Rural
Mixed urban and rural
Figure 5: Association between SPI scores and hospitals’ planned surgical volume ratio
Association between SPI scores and hospitals’ planned surgical volume ratio overall (A) and by country income
status (B), COVID-19 burden (C), hospital funding mechanism (D), number of hospital beds (E), and hospital
location (F). The planned surgical volume ratio was calculated as the ratio of each hospital’s observed planned
surgical volume over a 1-month assessment period against the expected planned surgical volume based on data
from the same month in 2019 (the prepandemic baseline) and expressed as a percentage. Shaded areas are
95% CIs. SPI=surgical preparedness index. SVR=surgical volume ratio.
For more on COVID-19 burdens
at the time of our assessment
see https://ourworldindata.org/
coronavirus

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confounding. Eighth, we included several plau sible
confoun ders in mixed-eects modelling support ed by
causal relationships presented in a directed acyclic graph.
However, there might be residual unmeasured
confounding or measure ment error that has been
unaccounted for. Ninth, our primary outcome measure
was data driven and pragmatic, but represented a single
cross-sectional assessment, calculated as a relative measure
of surgical volume. This should be considered when
applying the index to national health policy in which
absolute measures of outcomes and eects might be
important. Tenth, interpretation of composite measures,
such as the SPI, is challenging, and we have only explored
the relationship between the total index score and SVR. We
are unable to judge the relative importance of individual
indicators, which might vary across specialties, hospitals,
and resource contexts. Future exploration of the scaling
and measurement properties of the index and dierential
functioning of the indicators is required during future
development. Future iterations of the SPI could consider
normalising or transforming the scale to make its
minimum and maximum values more intuitive (eg, 0–100),
but this should be balanced with clinical use and
interpretability of the output for local assessors. Eleventh,
unplanned surgery is an important component of surgical
systems, especially in LMICs in which a larger proportion
of patients present to care services requiring emergency or
immediate care.35 The SPI has been developed and validated
in planned surgery only and is not designed to be applied
in emergency systems. Finally, we have not considered the
safety and ecacy of the surgeries. There are likely to be
dierences across resource settings that might be
exacerbated by the whole-health system eect of COVID-19
and should be considered in future work.3,35
As the recovery following the COVID-19 pandemic
continues to gather pace there is a need for urgent and
regular (eg, annual) hospital self-assessment using the SPI.
When possible, this assessment should be integrated with
existing quality and safety programmes and national
surgical, obstetric, and anaesthesia plan ning. SPI imple-
mentation alongside national surgical, obstetric, and
anaesthesia planning will add resilience to national capacity
building that is aleady under way. Improving preparedness
is likely to strengthen planned surgical services against
future external shocks and support upscaling of surgery to
address growing demands. Therefore, the SPI supports a
major priority area for WHO for ongoing progress towards
Sustainable Development Goal 3: Health and Wellbeing.36–38
COVID-19 is just one form of external shock that puts
additional pressure on planned surgical care pathways.
Other epidemics, such as influenzas and Ebola virus, have
had significant eects on surgical services over the past
decade.39 Natural phenomena associated with climate
change, stresses from geopolitical insta bility, and conflict
have also already had a substantial eect and continue
to pose a substantial future threat to surgical system
functioning. Surgical preparedness is a core part of the
response to these stressors in minimising suering and
loss of life.40,41 Although the SPI has been developed during
the COVID-19 pandemic, it has been specifically designed
to be applicable to any context of health system pressure.
Other context-specific modifications (eg, to incorporate
sustainable measures for climate resilience) might become
necessary as use of the index expands into global surgical
practice.42 Best processes for imple mentation of the SPI for
longitudinal assessment of a hospital’s preparedness is an
urgent research area for ongoing development.
Writing group
James C Glasbey, Tom EF Abbott, Adesoji Ademuyiwa, Adewale Adisa,
Ehab AlAmeer, Sattar Alshryda, Alexis P Arnaud,
Brittany Bankhead-Kendall, MK Abou Chaar, Daoud Chaudhry,
Ainhoa Costas-Chavarri, Miguel F Cunha, Justine I Davies, Anant Desai,
Muhammed Elhadi, Marco Fiore, J Edward Fitzgerald,
Maria Fourtounas, Alex James Fowler, Kay Futaba, Gaetano Gallo,
Dhruva Ghosh, Rohan R Gujjuri, Rebecca Hamilton, Parvez Haque,
Ewen M Harrison, Peter Hutchinson, Gabriella Hyman, Arda Isik,
Umesh Jayarajah, Haytham MA Kaafarani, Bryar Kadir, Ismail Lawani,
Hans Lederhuber, Elizabeth Li, Markus W Löer,
Maria Aguilera Lorena, Harvinder Mann, Janet Martin, Dennis Mazingi,
Craig D McClain, Kenneth A McLean, John G Meara,
Antonio Ramos-De La Medina, Mengistu Mengesha, Ana Minaya,
María Marta Modolo, Rachel Moore, Dion Morton, Dmitri Nepogodiev,
Faustin Ntirenganya, Francesco Pata, Rupert Pearse, Maria Picciochi,
Thomas Pinkney, Peter Pockney, Gabrielle H van Ramshorst,
Toby Richards, April Camilla Roslani, Sohei Satoi, Raza Sayyed,
Richard Shaw, Joana Filipa Ferreira Simões, Neil Smart, Richard Sulliva,
Malin Sund, Sudha Sundar, Stephen Tabiri, Elliott H Taylor,
Mary L Venn, Dakshitha Wickramasinghe, Naomi Wright,
Sebastian Bernardo Shu Yip, and Aneel Bhangu.
Contributors
The writing group and the statistical analysis group (JCG, KAM, OO, BK,
EH, AAB) contributed to writing, data interpretation, and critical revision
of the manuscript. The writing group, operations committee, and
dissemination committee contributed to study conception, protocol
development, study delivery, and management. The collaborators
contributed to data collection and study governance across included sites.
All members of the writing group had full access to the data in the study.
JCG, KAM, OO, BK, EH, and AAB verified the underlying data in the
study. JCG, AAB, and the writing committee had final responsibility for
the decision to submit for publication. Detailed role descriptions of all
contributing collaborating authors are shown in the appendix (pp 28–54).
Declaration of interests
RP has received research grants or consultancy fees or both from Edwards
Lifesciences, Intersurgical, and GlaxoSmithKline. JM has consulted for
WHO on projects related to perioperative preparedness. TA has received
consultancy fees from MSD unrelated to this work. All other members of
the writing group declare no competing interests.
Data sharing
Anonymised data are available upon request from the writing group,
and successful completion of a data sharing agreement through an
Application Programming Interface linked to the REDCap data server
hosted at Birmingham Clinical Trials Unit, University of Birmingham,
Birmingham, UK. Summary data and a self-assessment tool are available
online.
Acknowledgments
This work was supported by a National Institute for Health Research
(NIHR) Global Health Research Unit Grant (NIHR 16.136.79), the
Association of Coloproctology of Great Britain and Ireland, Bowel
Research UK, British Association of Surgical Oncology, British
Gynaecological Cancer Society, and Medtronic. JG was supported by an
NIHR Doctoral Research Fellowship (NIHR300175). The views expressed
are those of the authors and not necessarily those of the National Health
Service, the NIHR, or the UK Department of Health and Social Care.

Articles
www.thelancet.com Vol 400 November 5, 2022
1617
We thank the Royal College of Surgeons of England COVID-19 recovery
research group for their support.
Editorial note: The Lancet Group takes a neutral position with respect to
territorial claims in published maps and institutional aliations.
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