Summary
Background
Paediatric early warning systems (PEWS) aid in the early identification of clinical deterioration events in children admitted to hospital. We aimed to investigate the effect of PEWS implementation on mortality due to clinical deterioration in children with cancer in 32 resource-limited hospitals across Latin America.
Methods
Proyecto Escala de Valoración de Alerta Temprana (Proyecto EVAT) is a quality improvement collaborative to implement PEWS in hospitals providing childhood cancer care. In this prospective, multicentre cohort study, centres joining Proyecto EVAT and completing PEWS implementation between April 1, 2017, and May 31, 2021, prospectively tracked clinical deterioration events and monthly inpatient-days in children admitted to hospital with cancer. De-identified registry data reported between April 17, 2017, and Nov 30, 2021, from all hospitals were included in analyses; children with limitations on escalation of care were excluded. The primary outcome was clinical deterioration event mortality. Incidence rate ratios (IRRs) were used to compare clinical deterioration event mortality before and after PEWS implementation; multivariable analyses assessed the correlation between clinical deterioration event mortality and centre characteristics.
Findings
Between April 1, 2017, and May 31, 2021, 32 paediatric oncology centres from 11 countries in Latin America successfully implemented PEWS through Proyecto EVAT; these centres documented 2020 clinical deterioration events in 1651 patients over 556 400 inpatient-days. Overall clinical deterioration event mortality was 32·9% (664 of 2020 events). The median age of patients with clinical deterioration events was 8·5 years (IQR 3·9–13·2), and 1095 (54·2%) of 2020 clinical deterioration events were reported in male patients; data on race or ethnicity were not collected. Data were reported per centre for a median of 12 months (IQR 10–13) before PEWS implementation and 18 months (16–18) after PEWS implementation. The mortality rate due to a clinical deterioration event was 1·33 events per 1000 patient-days before PEWS implementation and 1·09 events per 1000 patient-days after PEWS implementation (IRR 0·82 [95% CI 0·69–0·97]; p=0·021). In the multivariable analysis of centre characteristics, higher clinical deterioration event mortality rates before PEWS implementation (IRR 1·32 [95% CI 1·22–1·43]; p<0·0001), being a teaching hospital (1·18 [1·09–1·27]; p<0·0001), not having a separate paediatric haematology-oncology unit (1·38 [1·21–1·57]; p<0·0001), and having fewer PEWS omissions (0·95 [0·92–0·99]; p=0·0091) were associated with a greater reduction in clinical deterioration event mortality after PEWS implementation; no association was found with country income level (IRR 0·86 [95% CI 0·68–1·09]; p=0·22) or clinical deterioration event rates before PEWS implementation (1·04 [0·97–1·12]; p=0·29).
Interpretation
PEWS implementation was associated with reduced clinical deterioration event mortality in paediatric patients with cancer across 32 resource-limited hospitals in Latin America. These data support the use of PEWS as an effective evidence-based intervention to reduce disparities in global survival for children with cancer.
Funding
American Lebanese Syrian Associated Charities, US National Institutes of Health, and Conquer Cancer Foundation.
Introduction
The global burden of paediatric cancer is disproportionately higher in low-income and middle-income countries, which bear more than 90% of cases1 and have survival rates as low as 20%.2 Although timely diagnosis and access to cancer care remains a challenge, up to 50% of deaths after initiating treatment are due to the toxicity of cancer-directed therapy.3,4 To reduce disparities in childhood cancer survival, the WHO Global Initiative for Childhood Cancer5 and St Jude Global6 emphasise the need to improve childhood cancer care, including supportive care, globally. Hospitals in low-resource settings, however, frequently lack adequate infrastructure and staffing to deliver the required supportive care during cancer treatment,7-9 resulting in late identification of clinical deterioration events and high rates of preventable mortality.10-12 This limitation illustrates an urgent need for effective, low-cost interventions, including strategies for timely identification of clinical deterioration events, to improve global childhood cancer survival.
To more rapidly identify clinical deterioration events, many hospitals use paediatric early warning systems (PEWS): nursing-administered bedside clinical acuity scoring tools associated with escalation algorithms. PEWS accurately predict the need for intensive care unit (ICU) transfer in paediatric patients with cancer admitted to hospital.13-16 Although robust research exists to support the impact of PEWS on interdisciplinary and family communication, staff empowerment, perceived quality of care, and hospital costs,17-22 data describing the impact on hospital mortality have been mixed.23 Additionally, studies of PEWS in resource-limited settings have largely been single-institution studies, thus limiting assessment of their generalisability to other settings.24-27
Driven by single-centre experiences supporting the positive impact of PEWS on paediatric oncology outcomes in Guatemala,13,19,27 St Jude Children’s Research Hospital worked with regional stakeholders to create Proyecto Escala de Valoración de Alerta Temprana (Proyecto EVAT), a quality improvement collaborative of hospitals providing childhood cancer care in Latin America.28 Proyecto EVAT is designed to improve outcomes for children with cancer who experience clinical deterioration during hospital admission through implementation of PEWS. We aimed to evaluate the impact of PEWS implementation on clinical deterioration event mortality at centres participating in Proyecto EVAT. We hypothesised that PEWS implementation would be associated with a reduction in deterioration-related mortality in the post-PEWS implementation period.
Methods
Study design and setting
We did a prospective, multicentre cohort study of hospitals providing childhood cancer care and participating in Proyecto EVAT, a Spanish-language PEWS adapted for low-resource settings.15 This PEWS comprises a 5-component scoring tool (neurological, cardiovascular, respiratory, and staff and family concerns) based on vital signs, physical examination findings, and treatment requirements.15 Patients admitted to hospital are scored by a bedside nurse during routine vital sign assessments. Higher scores indicate potential clinical deterioration and are addressed following an action algorithm that guides the clinical team towards appropriate escalation of care (appendix 3 pp 3-4).
This study included all Proyecto EVAT centres that joined the collaborative since its inception in April 1, 2017, and completed PEWS implementation by May 31, 2021 (appendix 3 pp 5-6). All participating hospitals self-identify as resource-limited because of a broad range of constraints, including inadequate nursing and physician staffing, insufficient ICU resources, and patients with low socioeconomic, educational, and nutritional indicators.29-32 Data from all centres were included in this analysis.
Registry data on clinical deterioration events (see below) reported from April 17, 2017, to Nov 30, 2021, were included in this study, allowing at least 6 months of post-PEWS implementation data for analysis. Deterioration occurring in children with limitations on escalation of care was not considered as a clinical deterioration event and was excluded from the analysis.
Procedures
Proyecto EVAT supports PEWS implementation in participating centres through a mentored implementation strategy that has been previously described.28 Briefly, hospitals are recruited to Proyecto EVAT through collaboration with St Jude Global6 or after learning about the programme from other participating centres. Hospitals apply to an annual cohort, obtain institutional approval to participate, and are assigned to one of 12 regional mentor training centres. Each hospital assembles a local PEWS implementation leadership team, including, at minimum, a ward nurse, ward physician, and critical care physician, adjusting the team size to local needs. Approximately 10–15 hospitals enrol annually.
Proyecto EVAT hospitals are guided through a three-phase process via regular virtual mentorship meetings; the first phase (pre-PEWS) focuses on planning for implementation, the second phase (during PEWS) includes the pilot and implementation of PEWS; and the third phase (post-PEWS) focuses on PEWS sustainability. Throughout these phases, experts from St Jude and the mentor centres teach local teams PEWS implementation strategies through a standardised curriculum.28 As part of the planning phase, local teams formally assess potential barriers to PEWS use, including inadequate stakeholder engagement and resources needed to use PEWS (eg, vital signs equipment), and are mentored to advocate for needed support resources from the hospital and local foundations. Implementation teams then move to the implementation phase and conduct local training with clinicians, pilot PEWS, and assess its effectiveness. From the start of the pilot, local leaders track quality of PEWS use and patient outcomes (as described below), with data sent to St Jude monthly. Implementation is considered complete when a hospital achieves sufficient PEWS quality, defined as less than 15% PEWS errors, for two consecutive months. Finally, centres move to the sustainability phase where they are mentored to develop a PEWS sustainability plan. During this phase, centres are independently sustaining PEWS and continue sending clinical deterioration event registry and PEWS quality data to St Jude for 18 months after implementation.
Participating centres collected data on the quality of PEWS use from the start of the PEWS pilot to 18 months post-PEWS implementation. Quality data included correct use of the PEWS scoring tool and algorithm and a review of all red PEWS (ie, scores ≥5).28 Correct use of PEWS was described by three types of PEWS errors: omissions (not documenting PEWS with each set of routine vital signs), errors calculating the PEWS score, and non-adherence to the PEWS algorithm during escalation of care. Review of red PEWS included assessing for ICU consultation (as indicated by the PEWS algorithm). Data on the use of PEWS were collected by local implementation teams on a weekly basis by reviewing nursing documentation of vital signs and PEWS in all paediatric patients with cancer admitted to hospital. Data were aggregated in monthly summaries submitted to St Jude.
Terminology
Throughout this study, we use the term ICU transfer to describe transfer to a unit proving higher than ward-level care, including an ICU, intermediate care unit, or other hospital area designed to provide a higher level of care to deteriorating patients. ICU-level interventions comprised mechanical ventilation, vasoactive infusion, or cardiopulmonary resuscitation (CPR). Mechanical ventilation includes invasive mechanical ventilation and non-invasive (ie, continuous positive pressure and bi-level positive pressure) ventilation.
A clinical deterioration event was defined as starting at the time of the first ICU-level ward intervention or unplanned ICU transfer and ending at the time of death, ICU discharge, or last ward-based ICU-level intervention. All events were followed up to this timepoint. The primary outcome in this analysis was clinical deterioration event mortality, defined as a clinical deterioration event that resulted in death in the ICU or within 24 h of ICU discharge or end of ward-based ICU interventions.12 For all events, the outcome of hospital admission was also noted, with death in hospital defined as death before hospital discharge.
Event severity of illness and resource utilisation were described by the degree of critical illness at the start of the clinical deterioration event. Critical deterioration events were defined as events requiring an ICU-level intervention or resulting in death on the ward or within 12 h of transfer to the ICU.33,34 Sepsis and organ dysfunction were defined with the criteria proposed by Goldstein and colleagues,35 and the Paediatric Index of Mortality 2 (PIM2) was calculated with standard criteria.36 Ward cardiopulmonary arrest was defined as a clinical deterioration event requiring acute invasive mechanical ventilation or CPR, or resulting in a non-palliative death on the ward. Ward CPR or death was defined as a clinical deterioration event with cardiac arrest on the ward requiring CPR or resulting in a non-palliative ward death.
Registry data
From the start of the planning (pre-PEWS) phase, hospitals implemented a prospective de-identified quality improvement registry of clinical deterioration events in paediatric patients with cancer admitted to hospital, which they continued up to 18 months post-implementation. Clinical deterioration events were defined as unplanned transfer to the ICU, ICU-level interventions (mechanical ventilation, vasoactive infusions, or CPR) on the ward, or a non-palliative ward death.12 Patients with limitations on escalation of care (ie, those with do-not-resuscitate orders or receiving only comfort care) were excluded from the current analysis.
For each clinical deterioration event, a de-identified case report form was completed by site leads (appendix 3 pp 7-8) and entered into a RedCAP database37 by a clinical research associate at St Jude. Data were routinely checked by clinical research associates at St Jude for missing and incorrect values to assure quality. Clinical characteristics of the event, including patient sex, oncological diagnosis, and outcomes, were obtained from the medical record. In addition to data on clinical deterioration events, participating centres reported the monthly volume of paediatric patients with cancer admitted to hospital as the number of inpatient days.
The clinical deterioration event registry was approved by the St Jude Institutional Review Board as quality improvement, and retrospective analysis of de-identified registry data was approved as non-human subjects research. In accordance, patient consent was not required. All collaborating centres obtained formal local approval to participate in Proyecto EVAT and implement PEWS. Additional institutional approvals were obtained, where necessary, per local standards.
Statistical analysis
As a prospective multicentre study evaluating an ongoing quality improvement collaborative, the sample size of included centres was determined by the number of centres completing PEWS implementation 4 years after the start of Proyecto EVAT (by May 31, 2021) and the number of clinical deterioration events was based on the frequency of events occurring at all centres during the study period. Hence sample size calculations were not done a priori.
Clinical deterioration events were used as the unit of analysis. Descriptive statistics were used to summarise event-level and hospital-level characteristics. The incidence rate was calculated as total number of clinical deterioration events per 1000 inpatient days. As a measure of the impact of PEWS on clinical deterioration events, the incidence rate ratio (IRR) was calculated by dividing the post-PEWS incidence event rate by the corresponding pre-PEWS incidence rate. An IRR value greater than one indicated a negative impact of PEWS implementation, an IRR value equal to one indicated no impact, and an IRR value less than one indicated a positive impact of PEWS implementation.
The two-sided z test was used to compare overall incidence rates before and after PEWS implementation under the null hypothesis IRR=1. The Wilcoxon rank sum test, χ2 test, and Fisher’s exact test were used to assess the event-level association between continuous and categorical clinical deterioration event characteristics before and after PEWS implementation. Additional post-hoc analyses explored these relationships among subgroups of disease stages (leukaemia in induction and relapsed oncological disease) and oncological diagnoses (acute lymphoblastic leukaemia, haematological malignancy, solid tumours, and CNS tumours).
To control for correlation between events, generalised estimating equations (GEEs) were used to explore the association between patient-level characteristics before and after PEWS implementation (with the patient as the cluster) and the association between centre characteristics and the impact of PEWS on the mortality rate due to clinical deterioration events (with the centre as the cluster). GEE Poisson regression was fitted under the assumption of exchangeable correlation structure and with logarithm link and sandwich variance estimates. In patient-level analyses, a univariable GEE model was fitted for frequency of clinical deterioration events (outcome) and clinical deterioration event characteristics. In centre-level analyses, the univariable GEE model was fitted for number of deaths due to a clinical deterioration event (outcome) and centre characteristics. A logarithm of inpatient days was considered as the offset variable. Predictors that were significant in univariable models were selected for the multivariable model. Model assumptions, including linearity for quantitative predictors, were assessed by reviewing pairwise Pearson correlation coefficients and consideration of clinical relevance. The IRR between centre-level characteristics was obtained by taking the exponential transformation of the regression coefficient. p values less than 0·05 were considered statistically significant. Data were analysed with R (version 4.2.2).
Role of the funding source
The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.
Results
Between April 1, 2017, and May 31, 2021, 32 diverse paediatric oncology centres from 11 countries in Latin America successfully implemented PEWS through Proyecto EVAT (figure). In total, these centres cared for 3731 (median 84 per centre [IQR 48–137]) new paediatric patients with cancer per year and ranged in hospital structure and resources (table 1).
Figure: Map of participating centres.
The map demonstrates the number of participating centres in World-Bank designated lower-middle-income and upper-middle-income countries in Latin America.
Table 1:
Characteristics of participating centres
| Centres (n=32) | |
|---|---|
| Lower-middle-income countries* | |
| El Salvador | 1 (3%) |
| Haiti | 1 (3%) |
| Nicaragua | 1 (3%) |
| Upper-middle-income countries* | |
| Argentina | 1 (3%) |
| Brazil | 1 (3%) |
| Colombia | 1 (3%) |
| Dominican Republic | 2 (6%) |
| Ecuador | 3 (9%) |
| Mexico | 16 (50%) |
| Panama | 2 (6%) |
| Peru | 3 (9%) |
| Hospital type | |
| Paediatric multidisciplinary | 14 (45%) |
| General (adult and paediatric) | 9 (28%) |
| Oncology (adult and paediatric) | 6 (19%) |
| Women’s and children’s | 3 (9%) |
| Financing | |
| Public | 26 (81%) |
| Public–private | 5 (16%) |
| Private | 1 (3%) |
| Teaching hospital | |
| No | 1 (3%) |
| Yes | 31 (97%) |
| Separate PHO unit | |
| No | 2 (6%) |
| Yes | 30 (94%) |
| Type of intensive care unit | |
| Paediatric | 27 (84%) |
| Adult | 3 (9%) |
| None | 2 (6%) |
| Ward nursing ratios | |
| 1 nurse: 3–4 patients | 10 (31%) |
| 1 nurse: 5–6 patients | 12 (38%) |
| 1 nurse: 7–8 patients | 7 (22%) |
| 1 nurse: >8 patients (maximum 18) | 3 (9%) |
| Annual number of new PHO diagnoses, median (IQR) | 84 (48–137) |
Data are n (%), unless otherwise indicated. PHO=paediatric haematology oncology. *By World Bank income level.
Before, during, and after PEWS implementation, centres reported a total of 2020 clinical deterioration events (range 11–199 per centre, appendix 3 pp 5-6) in 1651 patients (range 1–9 events per patient) over 556 400 inpatient-days. The median age of patients with clinical deterioration events was 8·5 years (IQR 3·9–13·2) and 1095 (54·2%) of 2020 clinical deterioration events were reported in male patients. Data on race or ethnicity were not collected in the clinical deterioration event registry. Of the 2020 clinical deterioration events, most were a result of sepsis or septic shock (1317 [65·2%]) or respiratory dysfunction or failure (916 [45·3%]). Overall clinical deterioration event mortality was 32·9% (664 of 2020). Of the 2020 clinical deterioration events, 395 (19·6%) met the criteria for cardiopulmonary arrest, resulting in 242 (61·3%) deaths. 137 (6·8%) events met the criteria for ward cardiac arrest (ward CPR or death), resulting in 124 (90·5%) deaths (appendix 3 p 9).
PEWS implementation data are presented in appendix 3 (pp 10-11). Data were reported per centre for a median of 12 months (IQR 10–13) before PEWS implementation and a median of 18 months (IQR 16–18) after PEWS implementation. All centres maintained high-quality PEWS use, defined as PEWS errors less than 15%, in the post-PEWS implementation period (appendix 3 p 10).
Comparing the periods before and after PEWS implementation, the clinical deterioration event mortality rate was lower after implementation (1·33 events per 1000 inpatient days vs 1·09 events per 1000 inpatient days; IRR 0·82 [95% CI 0·69–0·97]; p=0·021; table 2). The post-implementation period had lower rates of ward cardiopulmonary arrests and of ward CPR or death than the pre-implementation period (table 2). Event rates during the PEWS implementation period were typically between those observed before and after PEWS implementation (table 2).
Table 2:
Impact of PEWS implementation on clinical deterioration event rates, outcome, and resource use
| Before PEWS implementation |
During PEWS implementation |
After PEWS implementation |
Incidence rate ratio (95% CI) |
p value* | |
|---|---|---|---|---|---|
| Clinical deterioration event rate | 3·45 | 3·67 | 3·76 | 1·09 (0·99–1·20) | 0·085 |
| Clinical deterioration event mortality | 1·33 | 1·20 | 1·09 | 0·82 (0·69–0·97) | 0·021 |
| Critical deterioration | 2·36 | 2·32 | 2·12 | 0·90 (0·80–1·02) | 0·095 |
| Hospital mortality | 1·54 | 1·49 | 1·33 | 0·86 (0·74–1·00) | 0·056 |
| Ward ICU interventions | 1·52 | 1·66 | 1·41 | 0·93 (0·80–1·08) | 0·35 |
| Ward cardiopulmonary arrest | 0·83 | 0·71 | 0·61 | 0·73 (0·59–0·91) | 0·0048 |
| Ward cardiopulmonary resuscitation or death | 0·31 | 0·28 | 0·18 | 0·58 (0·40–0·85) | 0·0053 |
| ICU transfer | 2·73 | 3·00 | 3·34 | 1·22 (1·10–1·36) | 0·0003 |
| Mechanical ventilation | 1·88 | 1·70 | 1·74 | 0·93 (0·81–1·07) | 0·30 |
| Vasoactive infusions | 2·33 | 2·43 | 2·27 | 0·97 (0·86–1·10) | 0·63 |
PEWS=paediatric early warning system. ICU=intensive care unit. All rates are per 1000 inpatient-days. *Two-sided z-test was used to compare incidence before and after PEWS implementation within each characteristic.
Although events before and after PEWS implementation had a similar oncological disease type and reasons for hospital admission, there was a lower frequency of organ dysfunction at event identification and a lower severity of illness (measured by median PIM2) at ICU transfer after PEWS implementation (table 3). Accordingly, events were less likely to meet the criteria for critical deterioration and less frequently required ward cardiopulmonary resuscitation after PEWS implementation (table 3). As a result, both clinical deterioration event mortality and hospital mortality were lower after PEWS implementation (table 3). Using the post-PEWS clinical deterioration event mortality (28·9%, or 274 of 948 events), PEWS use throughout the study period at all centres could have averted approximately 80 deaths (664 observed minus 584 expected deaths).
Table 3:
Characteristics of clinical deterioration events
| All events (n=2020) |
Before PEWS implementation (n=691) |
During PEWS implementation (n=381) |
After PEWS implementation (n=948) |
p value* | GEE p value† |
|
|---|---|---|---|---|---|---|
| Hospital admission characteristics | ||||||
| Sex | ·· | ·· | ·· | ·· | 0·51 | 0·49 |
| Female | 925 (45·8%) | 313 (45·3%) | 166 (43·6%) | 446 (47·0%) | ·· | ·· |
| Male | 1095 (54·2%) | 378 (54·7%) | 215 (56·4%) | 502 (53·0%) | ·· | ·· |
| Oncological diagnosis category | ·· | ·· | ·· | ·· | 0·65 | 0·49 |
| Haematological malignancy | 1542 (76·3%) | 520 (75·3%) | 295 (77·4%) | 727 (76·7%) | ·· | ·· |
| Solid tumour | 322 (15·9%) | 118 (17·1%) | 59 (15·5%) | 145 (15·3%) | ·· | ·· |
| CNS tumour | 107 (5·3%) | 37 (5·4%) | 15 (3·9%) | 55 (5·8%) | ·· | ·· |
| Non-malignant haematological | 41 (2·0%) | 14 (2·0%) | 12 (3·1%) | 15 (1·6%) | ·· | ·· |
| Other | 8 (0·4%) | 2 (0·3%) | 0 | 6 (0·6%) | ·· | ·· |
| Leukaemia in first induction | 742 (36·7%) | 260 (37·6%) | 132 (34·6%) | 350 (36·9%) | 0·81 | 0·24 |
| Relapsed oncological disease | 371 (18·4%) | 135 (19·5%) | 64 (16·8%) | 172 (18·1%) | 0·52 | 0·87 |
| Reason for hospital admission | ·· | ·· | ·· | ·· | 0·24 | 0·35 |
| New or relapsed cancer | 946 (46·8%) | 311 (45·0%) | 174 (45·7%) | 461 (48·6%) | ·· | ·· |
| Scheduled admission | 327 (16·2%) | 108 (15·6%) | 59 (15·5%) | 160 (16·9%) | ·· | ·· |
| Acute admission | 725 (35·9%) | 264 (38·2%) | 145 (38·1%) | 316 (33·3%) | ·· | ·· |
| Others | 22 (1·1%) | 8 (1·2%) | 3 (0·8%) | 11 (1·2%) | ·· | ·· |
| Deterioration event characteristics | ||||||
| Reason for deterioration | ||||||
| Sepsis or septic shock | 1317 (65·2%) | 434 (62·8%) | 260 (68·2%) | 623 (65·7%) | 0·24 | 0·55 |
| Respiratory distress or dysfunction | 916 (45·3%) | 336 (48·6%) | 181 (47·5%) | 399 (42·1%) | 0·010 | 0·0071 |
| Other cardiovascular dysfunction | 547 (27·1%) | 214 (31·0%) | 111 (29·1%) | 222 (23·4%) | 0·0008 | 0·045 |
| Neurological deterioration | 363 (18·0%) | 142 (20·5%) | 60 (15·7%) | 161 (17·0%) | 0·076 | 0·12 |
| Other | 214 (10·6%) | 79 (11·4%) | 38 (10·0%) | 97 (10·2%) | 0·49 | 0·44 |
| Critical deterioration event | 1249 (61·8%) | 473 (68·5%) | 241 (63·3%) | 535 (56·4%) | <0·0001 | <0·0001 |
| Any organ dysfunction at start | 1713 (84·8%) | 599 (86·7%) | 332 (87·1%) | 782 (82·5%) | 0·025 | 0·036 |
| Median PIM2 at time of transfer‡(IQR) | 7·95 (3·98–20·40) | 9·05 (4·70–26·05) | 8·93 (5·40–21·05) | 7·00 (2·40–17·80) | <0·0001 | <0·0001 |
| Ward cardiopulmonary arrest | 395 (19·6%) | 166 (24·0%) | 74 (19·4%) | 155 (16·4%) | 0·0001 | <0·0001 |
| Ward cardiopulmonary resuscitation or death | 137 (6·8%) | 63 (9·1%) | 29 (7·6%) | 45 (4·7%) | 0·0006 | <0·0001 |
| Resource use | ||||||
| Evaluated by ICU | 1481 (73·3%) | 469 (67·9%) | 288 (75·6%) | 724 (76·4%) | 0·0002 | 0·0031 |
| Any ICU-level interventions on ward | 831 (41·1%) | 304 (44·0%) | 172 (45·1%) | 355 (37·4%) | 0·0088 | 0·0008 |
| Transfer to ICU | 1700 (84·2%) | 547 (79·2%) | 311 (81·6%) | 842 (88·8%) | <0·0001 | 0·013 |
| ICU length of stay (days), median (IQR)‡ | 4·00 (1·95–8·12) | 4·47 (2·11–8·69) | 4·10 (1·92–8·75) | 3·75 (1·91–7·67) | 0·070 | 0·20 |
| Vasoactive infusions | 1292 (64·0%) | 466 (67·4%) | 252 (66·1%) | 574 (60·5%) | 0·0050 | <0·0001 |
| Duration of vasoactive infusions (days)§ | 3·31 (1·81–5·79) | 3·27 (1·65–5·89) | 3·12 (1·81–5·68) | 3·33 (1·94–5·73) | 0·20 | 0·39 |
| Mechanical ventilation | 922 (45·6%) | 376 (54·4%) | 176 (46·2%) | 440 (46·4%) | 0·0016 | <0·0001 |
| Median duration of mechanical ventilation, days (IQR)¶ | 3·08 (0·82–7·66) | 3·12 (0·72–7·54) | 3·54 (0·71–7·74) | 2·93 (0·91–7·68) | 0·85 | 0·26 |
| Outcome | ||||||
| Clinical deterioration event mortality | 664 (32·9%) | 266 (38·5%) | 124 (32·5%) | 274 (28·9%) | <0·0001 | <0·0001 |
| Hospital mortality | 798 (39·5%) | 308 (44·6%) | 155 (40·7%) | 335 (35·3%) | 0·0002 | <0·0001 |
Data are n (%) or median (IQR). GEE=generalised estimating equations. ICU=intensive care unit. PEWS=paediatric early warning system. PIM2=paediatric index of mortality 2 score. * Wilcoxon rank sum test and χ2 test were used to explore event characteristics before and after PEWS implementation. † GEE Poisson regression was used to access patient-level characteristics before and after PEWS implementation. ‡ Among 1700 events transferred to ICU (higher level of care), data on duration of stay were missing for two patients. § Among 1292 events requiring vasoactive infusions, data on duration of infusion were missing for one patient. ¶ Among 992 events requiring mechanical ventilation.
In post-hoc subgroup analyses, clinical deterioration event mortality and hospital mortality significantly decreased after PEWS implementation in patients with haematological malignancies, specifically acute lymphoblastic leukaemia (appendix 3 p 12).
Although the overall rate of clinical deterioration events did not significantly increase after PEWS implementation, more patients with deterioration were transferred to a higher level of care (tables 2, 3). Most of these transfers were to an ICU, although some centres used an intermediate care unit, the emergency department, and other hospital areas to provide higher-level care (appendix p 13). Similarly, the proportion of clinical deterioration events evaluated by the ICU team increased after PEWS implementation (table 3).
Despite the increased rate of ICU transfer, events after PEWS implementation less frequently required ICU interventions such as vasoactive infusions and mechanical ventilation (table 3). There was no significant increase in the rate (table 2) or duration (table 3) of vasoactive infusions or mechanical ventilation use after PEWS implementation. Additionally, fewer events used ICU-level interventions on the ward after PEWS implementation (table 3). These results were consistent when controlling for multiple sampling with GEE (table 3).
Changes to clinical deterioration event mortality rates after PEWS implementation varied widely across the 32 participating centres (appendix 3 p 14). To explore this variability, we evaluated the association between hospital characteristics and change in clinical deterioration event mortality after PEWS implementation. Three types of hospital factors were evaluated: hospital organisational factors, baseline (pre-PEWS) factors, and PEWS implementation factors (table 4). In the univariable GEE Poisson regression analysis, the country World Bank income group, being a teaching hospital, not having a separate paediatric haematology-oncology (PHO) unit, the pre-PEWS clinical deterioration event rate, the pre-PEWS clinical deterioration event mortality, the pre-PEWS floor cardiopulmonary arrest rate, and postimplementation PEWS omissions were significantly associated with the impact of PEWS on clinical deterioration event mortality. In the multivariable analysis, PEWS implementation was significantly associated with an improved clinical deterioration event mortality in teaching hospitals, centres without a separate PHO unit, centres with a higher pre-PEWS deterioration-related mortality rate, and centres with a lower proportion of PEWS omissions (table 4; appendix 3 p 15). This analysis, however, was limited by the small number of hospitals with certain organisational characteristics (few participating centres did not have dedicated PHO units or ICUs, or were not teaching hospitals).
Table 4:
Results of GEE Poisson regression
| Frequency (%) or median (IQR) |
Univariable analysis |
Multivariable analysis* |
|||
|---|---|---|---|---|---|
| Incidence rate ratio (95% CI) |
p value† | Incidence rate ratio (95% CI) |
p value† | ||
| Hospital organisational factors | |||||
| World bank income group | ·· | 0·48 (0·30–0·77) | 0·0023 | 0·86 (0·68–1·09) | 0·22 |
| Upper middle income (reference) | 29 (90·6%) | ·· | ·· | ·· | ·· |
| Lower middle income | 3 (9·4%) | ·· | ·· | ·· | ·· |
| Hospital type | ·· | 1·00 (0·68–1·48) | 0·98 | ·· | ·· |
| Others (reference) | 23 (71·9%) | ·· | ·· | ·· | ·· |
| General (adult and paediatric) | 9 (28·1%) | ·· | ·· | ·· | ·· |
| Financing | ·· | 1·22 (0·88–1·69) | 0·23 | ·· | ·· |
| Others (reference) | 6 (18·7%) | ·· | ·· | ·· | ·· |
| Public | 26 (81·3%) | ·· | ·· | ·· | ·· |
| Teaching hospital | ·· | 0·73 (0·58–0·92) | 0·0073 | 1·18 (1·09–1·27) | <0·0001 |
| No (reference) | 1 (3·1%) | ·· | ·· | ·· | ·· |
| Yes | 31 (96·9%) | ·· | ·· | ·· | ·· |
| Separate paediatric haematology oncology unit | ·· | 1·76 (1·26–2·46) | 0·0010 | 1·38 (1·21–1·57) | <0·0001 |
| Yes (reference) | 30 (93·7%) | ·· | ·· | ·· | ·· |
| No | 2 (6·3%) | ·· | ·· | ·· | ·· |
| Type of ICU | ·· | 0·83 (0·63–1·11) | 0·21 | ·· | ·· |
| None (reference) | 2 (6·3%) | ·· | ·· | ·· | ·· |
| Any ICU | 30 (93·7%) | ·· | ·· | ·· | ·· |
| Ward nursing ratios | ·· | 0·88 (0·59–1·31) | 0·53 | ·· | ·· |
| 1 nurse to ≤5 patients (reference) | 15 (46·9%) | ·· | ·· | ·· | ·· |
| 1 nurse to ≥6 patients | 17 (53·1%) | ·· | ·· | ·· | ·· |
| Number of annual new paediatric cancer diagnoses | 85 (48–137) | 1·00 (0·99–1·00) | 0·63 | ·· | ·· |
| Baseline characteristics | |||||
| Pre-PEWS average monthly inpatient-days | 5047 (3133–7720) | 0·96 (0·92–1·00) | 0·051 | ·· | ·· |
| Pre-PEWS clinical deterioration event‡ | 3·56 (2·42–5·34) | 1·19 (1·09–1·30) | <0·0001 | 1·04 (0·97–1·12) | 0·29 |
| Pre-PEWS clinical deterioration event mortality‡ | 1·27 (1·00–2·12) | 1·42 (1·30–1·56) | <0·0001 | 1·32 (1·22–1·43) | <0·0001 |
| Pre-PEWS ward ICU intervention‡ | 1·50 (0·84–2·50) | 1·10 (0·99–1·23) | 0·071 | ·· | ·· |
| Pre-PEWS floor cardiopulmonary arrest*‡ | 1·00 (0·27–1·33) | 1·26 (1·03–1·54) | 0·027 | ·· | ·· |
| Pre-PEWS floor cardiac arrest‡ | 0·26 (0·00–0·65) | 1·48 (1·00–2·19) | 0·051 | ·· | ·· |
| Pre-PEWS median PIM2 | 8·85 (6·68–21·18) | 1·00 (0·98–1·02) | 0·79 | ·· | ·· |
| PEWS implementation | |||||
| COVID-19 impact | ·· | 1·22 (0·81–1·85) | 0·34 | ·· | ·· |
| Implementation before March, 2020 (reference) | 22 (68·7%) | ·· | ·· | ·· | ·· |
| Implementation after March, 2020 | 10 (32·3%) | ·· | ·· | ·· | ·· |
| Implementation quality | ·· | 0·91 (0·52–1·61) | 0·75 | ·· | ·· |
| Not all clinical deterioration events with PEWS (reference) | 6 (18·7%) | ·· | ·· | ·· | ·· |
| All clinical deterioration events with PEWS | 26 (81·3%) | ·· | ·· | ·· | ·· |
| Planning duration (pre-PEWS implementation) | 11·81 (10·51–13·02) | 0·96 (0·88–1·04) | 0·30 | ·· | ·· |
| Implementation duration (during PEWS) | 6·52 (5·01–7·99) | 1·02 (0·95–1·09) | 0·63 | ·· | ·· |
| Sustainability duration (post-PEWS implementation) | 17·97 (15·75–18·04) | 1·03 (0·97–1·09) | 0·34 | ·· | ·· |
| Proportion of PEWS omissions | 0·45% (0·00–1·08) | 0·89 (0·86–0·93) | <0·0001 | 0·95 (0·92–0·99) | 0·0091 |
| Proportion of PEWS calculation errors | 2·00% (1·11–3·52) | 1·02 (0·94–1·10) | 0·70 | ·· | ·· |
| Proportion of PEWS algorithm non-adherence | 0·08% (0·00–0·71) | 0·89 (0·76–1·04) | 0·15 | ·· | ·· |
| Proportion of red PEWS (scores ≥5) without ICU consults | 37·66% (17·21–59·09) | 1·00 (0·91–1·10) | 0·98 | ·· | ·· |
Analyses were done for 32 paediatric oncology centres. PEWS=paediatric early warning system. ICU=intensive care unit. PIM2=paediatric index of mortality 2 score. *Floor cardiopulmonary arrest rate not included in multivariable analysis because previous work showed that it correlated with mortality rate due to clinical deterioration events12 and has a weaker association with PEWS impact in univariable analysis. † GEE Poisson regression used to evaluate association between centre characteristics and PEWS impact. ‡ Rate per 1000 inpatient-days.
Discussion
This prospective, multicentre cohort study shows that PEWS implementation was associated with reduced clinical deterioration event mortality in paediatric patients with cancer admitted to resource-limited hospitals. This impact was driven by earlier identification of critical illness, timely escalation of care, and prevention of ward cardiopulmonary arrest, as demonstrated by lower severity of illness on ICU transfer, lower rates of ward cardiopulmonary arrests, and reduced use of ICU-level interventions on the ward. Although PEWS implementation resulted in more proactive use of ICU outreach, with more ICU consultations and a higher proportion of events being transferred to a higher level of care, this did not result in increased resource use (use of ICU interventions decreased after implementation). These findings suggest that PEWS allowed for timely identification, action, and triage of critically ill paediatric patients with cancer to an appropriate level of care in hospital, preventing complications associated with late identification, such as organ failure, cardiopulmonary arrest, and death.
These data add to the existing literature supporting the effectiveness of PEWS in improving care for high-risk patients, such as children with cancer admitted to hospital in resource-limited settings with high baseline mortality. Although data from paediatric patients hospitalised in high-resource settings have shown conflicting findings about the impact of PEWS on mortality,23 studies from resource-limited settings have consistently shown improved patient outcomes.25,27 Additionally, PEWS implementation has been shown to improve staff empowerment, interdisciplinary and family communication, and the perceived quality of care, and result in institutional cost savings.17-22 It is also likely that these impacts of PEWS extend beyond the paediatric oncology unit to improve institutional culture and practice for other patient groups. The current study further supports these findings by showing that PEWS implementation was associated with a significant reduction in deterioration-related mortality among children with cancer admitted to hospital—a major cause of treatment-related mortality in resource-limited settings.12 If implemented globally, PEWS could prevent thousands of deterioration-related deaths. These findings support widespread scale-up of PEWS to reduce preventable deaths and improve equity in survival for these high-risk patients. This work must expand beyond Latin America to promote PEWS use worldwide. Future work should also explore the benefits of PEWS not addressed in this study, such as its impact on interdisciplinary communication and institutional cost savings.
Although our pooled analysis shows a significant reduction in clinical deterioration event mortality, the magnitude of impact varied across centres. Our study found that teaching hospitals, centres without a separate PHO unit, and those with higher baseline clinical deterioration event mortality benefited the most from PEWS implementation. Other factors, such as hospital organisation and funding structure, were not significant predictors of PEWS benefit. Importantly, PEWS was found to be effective across a range of institutional resources to care for critically ill patients (eg, ICU availability) and clinical practices (frequency of ICU-level interventions outside of the ICU). These findings should be interpreted with caution, however, given the small sample sizes in some categories (ie, the small number of non-teaching hospitals). However, they are consistent with previous studies suggesting that modifiable factors, such as practices and organisational norms, drive outcomes for critically ill children with cancer.12 Prioritisation of PEWS implementation to centres that are most likely to benefit—such as those with the highest baseline mortality—is integral to reduce disparities in childhood cancer survival. Additionally, our study highlights global structural challenges in access to critical care for children with cancer, with frequent use of ICU interventions outside of formal ICUs, that must be addressed to scale-up effective paediatric oncology care worldwide. These findings emphasise the importance of collecting local outcomes data, through hospital-based quality improvement, to measure baseline outcomes and identify priorities for targeted interventions to improve care.38,39
This analysis also identified the role of implementation factors on PEWS impact. PEWS omissions measure vital signs events without documented PEWS and represent the consistency of PEWS use in patient care during the 18 months after implementation when centres were independently sustaining PEWS without external mentorship from Proyecto EVAT. Our data suggest that reduced PEWS omissions were associated with a higher reduction in clinical deterioration event mortality after PEWS implementation. These findings highlight the importance of implementation and sustainability strategies targeting ongoing, high-quality PEWS use, rather than simply adoption, for maximising PEWS impact. They might also potentially explain why previous findings did not show an impact of PEWS on mortality in studies without rigorous PEWS fidelity monitoring after implementation.23 Future efforts should focus on strategies to support PEWS implementation in all resource-limited hospitals providing childhood cancer care and systematically address barriers to PEWS implementation and sustainability in these settings.28,40-42 Additionally, future studies should evaluate factors promoting long-term sustainability of PEWS more than 18 months after implementation.
This study has several limitations. This was an observational cohort study, not a randomised control trial, and so it cannot definitively evaluate the impact of PEWS implementation compared to no implementation. Additionally, this study design prevents us from controlling for unmeasured confounding, such as specific treatment protocols or additional hospital characteristics. Proyecto EVAT was developed as a quality improvement collaborative to promote PEWS implementation, rather than as an experimental design such as a cluster randomised control trial, due to feasibility concerns about the capacity of centres to implement PEWS on a set timeline. The study design was strengthened, however, by the inclusion of 32 geographically diverse centres with varied levels of institutional resources and by prospective registration of clinical deterioration events over a long period. Since participating centres implemented PEWS at different times, it is unlikely that global changes or individual institutional improvement initiatives could have affected clinical deterioration event mortality across all centres at the same time, thus supporting our findings of the relationship between PEWS implementation and a reduction in mortality. Although this study analysed more than 2000 deterioration events, the small sample size of 32 centres limited our power to evaluate the relationship between some centre characteristics and PEWS impact. Sparse data bias43 was observed for clinical deterioration event mortality, which is defined as an extremely large or small IRR estimate and wide confidence intervals in the univariable centre-level analyses. This problem is due to very few deterioration-related mortality events being observed in some hospitals. Additionally, several hospital characteristics were rare among participating centres, and so the findings of centre-level analyses should be interpreted with caution. The impact of hospital organisational factors on implementation of evidence-based interventions, with a focus on how to overcome such structural barriers, should be explored in future studies.
Reducing disparities in childhood cancer survival requires a comprehensive, contextually adapted approach to addresses the entire continuity of cancer care, from health-systems strengthening to innovative diagnostics and treatment. Although previous studies have focused largely on strategies to augment resources for and access to cancer therapy, improvements in supportive care have lagged, resulting in high rates of preventable, treatment-related deaths in resource-limited settings. This large, multicentre study shows that PEWS implementation can address this challenge by reducing deterioration-related mortality rates without increasing resource utilisation. These findings show that PEWS is an effective, low-cost evidence-based intervention for reducing global disparities in survival for children with cancer and serve as a call to action to promote global scale-up of this life-saving intervention.
Supplementary Material
Research in context.
Evidence before this study
Paediatric early warning systems (PEWS) improve early identification of clinical deterioration in children admitted to hospital; however, there are conflicting data about the impact of PEWS on mortality. We searched PubMed from database inception to May 1, 2023, using the search terms “pediatric”, “children”, “cancer”, “malignancy”, and “early warning system” or “early warning score”. To date, most studies evaluating PEWS have been conducted in high-resource settings and do not specifically focus on high-risk patients, such as children with cancer.
Added value of this study
This prospective, multicentre cohort study describes mortality during clinical deterioration in children with cancer at 32 diverse resource-limited hospitals before and after PEWS implementation. This large multicentre study across 11 countries in Latin America allowed us to evaluate the effect of PEWS implementation on patient outcomes and explore how those impacts varied across different hospital settings. Our study found that after PEWS implementation, centres had a significantly lower rate of deterioration mortality; this impact was more significant in centres with higher pre-PEWS mortality. Events after implementation also had a lower severity of illness on transfer to a higher level of care and experienced fewer ward cardiopulmonary arrests. Importantly, these benefits occurred without an increase in intensive care resource utilisation.
Implications of all the available evidence
This study adds to existing evidence that PEWS are effective quality improvement interventions to reduce deterioration mortality among children with cancer. Importantly, because PEWS are most effective in settings with the highest morality, they are also effective interventions to reduce global disparities in childhood cancer outcomes across settings. These data are a call to action to promote global scale up of this life-saving intervention.
Acknowledgments
This study was funded in part by the American Lebanese Syrian Associated Charities (ALSAC), grant 5P30CA021765 from the US National Institutes of Health, and the Conquer Cancer Foundation (Global Oncology Young Investigator Award). We thank the St Jude Global teams that made this work possible, including contractual, travel, and training support for Proyecto EVAT from the St Jude Global programme of St Jude Children’s Research Hospital. We especially thank the EVAT Study Group and the Proyecto EVAT Steering Committee for their commitment to Proyecto EVAT. We thank Maria Puerto-Torres for her support with figures for this Article. Finally, we acknowledge funding to the corresponding author (AA) from the National Cancer Institute that resulted from the work described in this Article. All members of the EVAT Study Group are listed in appendix 3 (p 2).
Footnotes
Declaration of interests
We declare no competing interests.
For the Spanish translation of the abstract see Online for appendix 1
For the Portuguese translation of the abstract see Online for appendix 2
EVAT Study Group members are listed in appendix 3 (p 2)
Contributor Information
Asya Agulnik, St Jude Children’s Research Hospital, Memphis, TN, USA.
Hilmarie Muniz-Talavera, St Jude Children’s Research Hospital, Memphis, TN, USA.
Linh T D Pham, St Jude Children’s Research Hospital, Memphis, TN, USA.
Yichen Chen, St Jude Children’s Research Hospital, Memphis, TN, USA.
Angela K Carrillo, St Jude Children’s Research Hospital, Memphis, TN, USA.
Adolfo Cárdenas-Aguirre, St Jude Children’s Research Hospital, Memphis, TN, USA.
Alejandra Gonzalez Ruiz, St Jude Children’s Research Hospital, Memphis, TN, USA.
Marcela Garza, St Jude Children’s Research Hospital, Memphis, TN, USA.
Tania Maria Conde Morelos Zaragoza, Casa de la Amistad, México City, México.
Dora Judith Soberanis Vasquez, Unidad Nacional de Oncología Pediátrica, Guatemala City, Guatemala.
Alejandra Méndez-Aceituno, Unidad Nacional de Oncología Pediátrica, Guatemala City, Guatemala.
Carlos Acuña Aguirre, Hospital Dr. Luis Calvo Mackenna, Santiago, Chile.
Yvania Alfonso Carreras, Hospital Saint Damien, Port-Au-Prince, Haiti.
Shillel Yahamy Alvarez Arellano, Benemérito Hospital General con Especialidades “Juan María de Salvatierra”, La Paz, México.
Leticia Aradi Andrade Sarmiento, Centro Médico Nacional Siglo XXI, México City, México.
Rosario Batista, Hospital Jose Domingo De Obaldia, Chiriquí, Panama.
Erika Esther Blasco Arriaga, SOLCA Guayaquil, Guayaquil, Ecuador.
Patricia Calderon, Hospital Infantil de Nicaragua, Managua, Nicaragua.
Mayra Chavez Rios, Hospital del Niño Poblano, Puebla, México.
María Eugenia Costa, Hospital del Niños de la Santísima Trinidad de Córdoba, Cordoba, Argentina.
Rosdali Díaz-Coronado, Instituto Nacional de Enfermedades Neoplásicas, Lima, Peru.
Ever Amilcar Fing Soto, Hospital General de Celaya, Celaya, Mexico.
Wendy Cristhyna Gómez García, Hospital Infantil Dr. Robert Reid Cabral, Santo Domingo, Dominican Republic.
Martha Herrera Almanza, Hospital Infantil de Especialidades de Chihuahua, Chihuahua, Mexico.
Maria Susana Juares Tobías, Hospital Central Dr. Ignacio Morones Prieto, San Luis Potosí, Mexico.
Esmeralda Mercedes León López, Hospital Guillermo Almenara Irigoyen, Lima, Peru.
Norma Araceli López Facundo, Instituto de Seguridad Social del Estado de México y Municipios Hospital Materno Infantil, Toluca, Mexico.
Ruth Angelica Martinez Soria, Hospital General de Tijuana, Tijuana, Mexico.
Kenia Miller, Hospital del Niño “Jose Renan Esquivel”, Panama, Panama.
Scheybi Teresa Miralda Méndez, Hospital Escuela, Tegucigalpa, Honduras.
Lupe Nataly Mora Robles, Instituto del Cáncer SOLCA Cuenca, Cuenca, Ecuador.
Natalia del Carmen Negroe Ocampo, Hospital General Agustin O’Horan, Mérida, Mexico.
Berenice Noriega Acuña, Hospital de Especialidades Pediátricas, Tuxtla Gutierrez, Mexico.
Alejandra Osuna Garcia, Hospital Pediátrico de Sinaloa, Culiacán, Mexico.
Carlos M Pérez Alvarado, Centro Estatal de Cancerología Dr Miguel Dorantes Mesa, Xalapa, Mexico.
Clara Krystal Pérez Fermin, Hospital Infantil Regional Universitario Dr. Arturo Grullón, Santiago, Dominican Republic.
Estuardo Enrique Pineda Urquilla, Hospital Nacional de Niños Benjamín Bloom, San Salvador, El Salvador.
Carlos Andrés Portilla Figueroa, Centro Médico Imbanaco, Cali, Colombia.
Ligia Estefanía Ríos Lopez, Hospital Nacional Edgardo Rebagliati Martins, Lima, Peru.
Jocelyn Rivera Mijares, Hospital Infantil Teletón de Oncología, Querétaro, Mexico.
Verónica Soto Chávez, Hospital Civil de Guadalajara, Guadalajara, Mexico.
Jorge Iván Suarez Soto, Hospital del Niño. Sistema integral para el Desarrollo de la Familia (DIF), Pachuca, Mexico.
Juliana Teixeira Costa, Hospital Martagão Gesteira, Salvador, Bahía, Brazil.
Isidoro Tejocote Romero, Institutio Materno Infantil del Estado de México-IMIEM, Toluca, Mexico.
Erika Elena Villanueva Hoyos, Hospital Oncológico Solca Núcleo de Quito, Quito, Ecuador.
Marielba Villegas Pacheco, Centro Estatal de Oncología, Campeche, Mexico.
Meenakshi Devidas, St Jude Children’s Research Hospital, Memphis, TN, USA.
Carlos Rodriguez-Galindo, St Jude Children’s Research Hospital, Memphis, TN, USA.
Data sharing
De-identified data (including data dictionaries) that underlie the results reported in this Article, as well as the study protocol, statistical analysis plan, and analytical code will be made available beginning 6 months and ending 5 years following Article publication to researchers who provide a methodologically sound proposal that has been reviewed by the Proyecto EVAT Steering Committee to achieve the aims of the approved proposal. Proposals should be directed to via email to the corresponding author (AA); data requestors will need to sign a data access agreement.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
De-identified data (including data dictionaries) that underlie the results reported in this Article, as well as the study protocol, statistical analysis plan, and analytical code will be made available beginning 6 months and ending 5 years following Article publication to researchers who provide a methodologically sound proposal that has been reviewed by the Proyecto EVAT Steering Committee to achieve the aims of the approved proposal. Proposals should be directed to via email to the corresponding author (AA); data requestors will need to sign a data access agreement.