Unequal impact of the COVID-19 pandemic on paediatric cancer care: a population-based cohort study in China.

BACKGROUND: The COVID-19 pandemic has had widespread adverse collateral effects on health care delivery for non-COVID-19 disease conditions. Paediatric oncology care is reliant on prompt testing and diagnosis and on timely and coordinated multimodal treatment, all of which have been impacted by the pandemic. This study aimed to quantify the initial and enduring effects of the COVID-19 pandemic on the utilization of paediatric cancer care and to examine whether the pandemic differentially impacted specific demographic groups. METHOD: We performed an interrupted time series analysis using negative binomial regression to estimate the change in the monthly admissions for paediatric cancer patients (Age 0-17) associated with the COVID-19 pandemic and subsequent lockdown policies. We obtained data from deidentified individual electronic medical records of paediatric cancer inpatients admitted between January 1, 2015 and May 31, 2021 to a tertiary hospital that provides general and specialized healthcare services to an estimated population of 8.4 million in Jining China. Relative risk (RR) estimates representing monthly admissions compared with expected admissions had the pandemic not occurred were derived. The number of inpatient admissions lost due to the pandemic were estimated. FINDINGS: The overall denominator for the paediatric population was 1 858 209 individuals in January 2015, which increased to 2 043 803 by May 2021. In total, there were 4 901 admissions for paediatric cancer during the study period, including 1 479 (30%) since February 2020 when the lockdown was implemented. A 33% reduction (95% CI: -43% to -22%) in admissions was observed in February 2020, with the largest relative reduction (-48%, 95% CI: -64% to -24%) among first-time admissions and admissions for patients from rural districts (-46%, 95% CI: -55% to -36%). Admissions quickly rebounded in March 2020 when many government-imposed mobility restrictions were lifted, and continued to resume gradually over time since April 2020, leading to a full recovery as of November 2020. However, the recovery for first-time admissions, and among female patients, younger patients (<5 years) and patients from rural districts was slower over time and incomplete (first-time admissions and rural patients) as of January 2021. INTERPRETATION: The COVID-19 pandemic has had substantial impact on the timely utilization of paediatric oncology services in China, particularly in the early stage of the first wave. Importantly, some population groups were disproportionately affected and the recovery of admissions among those subgroups has been slow and incomplete, warranting targeted approaches to address potentially exacerbated gender and socio-economic inequalities in access to healthcare resources.

Sex, Racial, and Ethnic Representation in COVID-19 Clinical Trials: A Systematic Review and Meta-analysis.

Importance: Since the onset of the COVID-19 pandemic, there have been calls for COVID-19 clinical trials to be fully representative of all demographic groups. However, limited evidence is available about the sex, racial, and ethnic representation among COVID-19 prevention and treatment trials. Objective: To investigate whether female participants and racial and ethnic minority individuals are adequately represented in COVID-19 prevention and treatment trials in the US. Data Sources: Identified studies were registered on ClinicalTrials.gov or published in the PubMed database from October 2019 to February 2022. Study Selection: Included studies must have provided the number of enrolled participants by sex, race, or ethnicity. Only interventional studies conducted in the US for the primary purpose of the diagnosis, prevention, or treatment of (or supportive care for) COVID-19 conditions were included. Data Extraction and Synthesis: Data on counts of enrollments by demographic variables (sex, race, and ethnicity) and location (country and state) were abstracted. Studies were broadly categorized by primary purpose as prevention (including vaccine and diagnosis studies) vs treatment (including supportive care studies). A random effects model for single proportions was used. Trial estimates were compared with corresponding estimates of representation in the US population with COVID-19. Main Outcomes and Measures: Sex, racial, and ethnic representation in COVID-19 clinical trials compared with their representation in the US population with COVID-19. Results: Overall, 122 US-based COVID-19 clinical trials comprising 176 654 participants were analyzed. Studies were predominantly randomized trials (n = 95) for treatment of COVID-19 (n = 103). Sex, race, and ethnicity were reported in 109 (89.3%), 95 (77.9%), and 87 (71.3%) trials, respectively. Estimated representation in prevention and treatment trials vs the US population with COVID-19 was 48.9% and 44.6% vs 52.4% for female participants; 23.0% and 36.6% vs 17.7% for Hispanic or Latino participants; 7.2% and 16.5% vs 14.1% for Black participants; 3.8% and 4.6% vs 3.7% for Asian participants; 0.2% and 0.9% vs 0.2% for Native Hawaiian or Other Pacific Islander participants; and 1.3% and 1.4% vs 1.1% for American Indian or Alaska Native participants. Compared with expected rates in the COVID-19 reference population, female participants were underrepresented in treatment trials (85.1% of expected; P < .001), Black participants (53.7% of expected; P = .003) and Asian participants (64.4% of expected; P = .003) were underrepresented in prevention trials, and Hispanic or Latino participants were overrepresented in treatment trials (206.8% of expected; P < .001). Conclusions and Relevance: In this systematic review and meta-analysis, aggregate differences in representation for several demographic groups in COVID-19 prevention and treatment trials in the US were found. Strategies to better ensure diverse representation in COVID-19 studies are needed, especially for prevention trials.

A Risk Prediction Model to Identify Newborns at Risk for Missing Early Childhood Vaccinations.

BACKGROUND: Approximately 30% of US children aged 24 months have not received all recommended vaccines. This study aimed to develop a prediction model to identify newborns at high risk for missing early childhood vaccines. METHODS: A retrospective cohort included 9080 infants born weighing ≥2000 g at an academic medical center between 2008 and 2013. Electronic medical record data were linked to vaccine data from the Washington State Immunization Information System. Risk models were constructed using derivation and validation samples. K-fold cross-validation identified risk factors for model inclusion based on alpha = 0.01. For each patient in the derivation set, the total number of weighted adverse risk factors was calculated and used to establish groups at low, medium, or high risk for undervaccination. Logistic regression evaluated the likelihood of not completing the 7-vaccine series by age 19 months. The final model was tested using the validation sample. RESULTS: Overall, 53.6% failed to complete the 7-vaccine series by 19 months. Six risk factors were identified: race/ethnicity, maternal language, insurance status, birth hospitalization length of stay, medical service, and hepatitis B vaccine receipt. Likelihood of non-completion was greater in the high (77.1%; adjusted odds ratio [AOR] 5.6; 99% confidence interval [CI]: 4.2, 7.4) and medium (52.7%; AOR 1.9; 99% CI: 1.6, 2.2) vs low (38.7%) risk groups in the derivation sample. Similar results were observed in the validation sample. CONCLUSIONS: Our prediction model using information readily available in birth hospitalization records consistently identified newborns at high risk for undervaccination. Early identification of high-risk families could be useful for initiating timely, tailored vaccine interventions.

Geographic social inequalities in information-seeking response to the COVID-19 pandemic in China: longitudinal analysis of Baidu Index.

The outbreak of the COVID-19 pandemic alarmed the public and initiated the uptake of preventive measures. However, the manner in which the public responded to these announcements, and whether individuals from different provinces responded similarly during the COVID-19 pandemic in China, remains largely unknown. We used an interrupted time-series analysis to examine the change in Baidu Search Index of selected COVID-19 related terms associated with the COVID-19 derived exposure variables. We analyzed the daily search index in Mainland China using segmented log-normal regressions with data from Jan 2017 to Mar 2021. In this longitudinal study of nearly one billion internet users, we found synchronous increases in COVID-19 related searches during the first wave of the COVID-19 pandemic and subsequent local outbreaks, irrespective of the location and severity of each outbreak. The most precipitous increase occurred in the week when most provinces activated their highest level of response to public health emergencies. Search interests increased more as Human Development Index (HDI) -an area level measure of socioeconomic status-increased. Searches on the index began to decline nationwide after the initiation of mass-scale lockdowns, but statistically significant increases continued to occur in conjunction with the report of major sporadic local outbreaks. The intense interest in COVID-19 related information at virtually the same time across different provinces indicates that the Chinese government utilizes multiple channels to keep the public informed of the pandemic. Regional socioeconomic status influenced search patterns.

Cancer Clinical Trial Participation at the 1-Year Anniversary of the Outbreak of the COVID-19 Pandemic.

Importance: During the initial outbreak of the COVID-19 pandemic, cancer clinical trial participation decreased precipitously. Given the continued pandemic-especially the severe wave of new cases and deaths in winter 2020 to 2021-a vital question is whether trial enrollments have remained low or even worsened. Objective: To examine the experience of cancer clinical trial enrollment 1 year after the COVID-19 outbreak. Design, Setting, and Participants: This cohort study examines initial enrollments to treatment trials and cancer control and prevention (CCP) trials conducted by the SWOG Cancer Research Network between January 1, 2016, and February 28, 2021. Participants include patients enrolled in the trials. Exposures: Landmark time points reflecting the onset and the apex, respectively, of the initial COVID-19 wave (March 1 to April 25, 2020) and the winter 2020 to 2021 wave (October 4, 2020, to January 23, 2021). Main Outcomes and Measures: This study used interrupted time-series analysis to examine enrollments over time related to the COVID-19-derived exposure variables using negative-binomial regression. Relative risk (RR) estimates representing weekly enrollment changes compared with expected rates (had the pandemic not occurred) were derived. The numbers of enrollments lost during the pandemic were estimated. Results: Overall, 29 398 patients (mean [SD] age, 60.3 [13.2] years) were enrolled (24 034 before the pandemic and 5364 during the pandemic), with 9198 patients (31.3%) aged 65 years or older, 17 199 female patients (58.6%), 3039 Black patients (10.8%), and 2260 Hispanic patients (7.9%). Most enrollments (19 451 [66.2%]) were to treatment trials. During the initial COVID-19 wave, there was a 9.0% model-estimated weekly reduction in enrollments (RR, 0.91; 95% CI, 0.89-0.93; P < .001), with effects compounding each week. Enrollment recovered thereafter, but decreased again during the winter 2020 to 2021 wave, although by only 2.0% each week (RR, 0.98; 95% CI, 0.97-0.99; P < .001). Overall, during the pandemic, actual enrollments were 77.3% of expected enrollments (5364 of 6913 enrollments; 95% CI, 70.5%-85.0%; P < .001). Actual enrollments were 54.0% of expected enrollments for CCP trials (1421 of 2641 enrollments; 95% CI, 43.0%-67.0%; P < .001) and 91.0% of expected enrollments for treatment trials (3922 of 4304 enrollments; 95% CI, 81.0%-102.0%; P = .12). Conclusions and Relevance: In this cohort study, clinical trial enrollments decreased during the full year of the COVID-19 pandemic. Enrollment reductions were primarily to CCP trials, whereas, remarkably, there was not strong evidence of enrollment reductions to treatment trials. This finding suggests that clinical research rapidly adapted to the circumstances of enrolling and treating patients on protocols during the COVID-19 pandemic.

The COVID-19 pandemic and new clinical trial activations.

BACKGROUND: The COVID-19 pandemic has caused severe disruptions in care for many patients. A key question is whether the landscape of clinical research has also changed. METHODS: In a retrospective cohort study, we examined the association of the COVID-19 outbreak with new clinical trial activations. Trial data for all interventional and observational oncology, cardiovascular, and mental health studies from January 2015 through September 2020 were obtained from ClinicalTrials.gov . An interrupted time-series analysis with Poisson regression was used. RESULTS: We examined 62,252 trial activations. During the initial COVID-19 outbreak (February 2020 through May 2020), model-estimated monthly trial activations for US-based studies were only 57% of the expected estimate had the pandemic not occurred (relative risk = 0.57, 95% CI 0.52 to 0.61, p < .001). For non-US-based studies, the impact of the pandemic was less dramatic (relative risk = 0.77, 95% CI 0.73 to 0.82, p < .001), resulting in an overall 27% reduction in the relative risk of new trial activations for US-based trials compared to non-US-based trials (relative risk ratio = 0.73, 95% CI 0.67 to 0.81, p < .001). Although a rebound occurred in the initial reopening phase (June 2020 through September 2020), the rebound was weaker for US-based studies compared to non-US-based studies (relative risk ratio = 0.87, 95% CI 0.80 to 0.95, p < .001). CONCLUSIONS: These findings are consistent with the disproportionate burden of COVID-19 diagnoses and deaths during the initial phase of the pandemic in the USA. Reduced activation of cancer clinical trials will likely slow the pace of clinical research and new drug discovery, with long-term negative consequences for cancer patients. An important question is whether the renewed outbreak period of winter 2020/2021 will have a similarly negative impact on the initiation of new clinical research studies for non-COVID-19 diseases.

The impact of the COVID-19 pandemic on health services utilization in China: Time-series analyses for 2016-2020.

BACKGROUND: The aim of this study is to quantify the effects of the SARS-CoV-2 pandemic on health services utilization in China using over four years of routine health information system data. METHODS: We conducted a retrospective observational cohort study of health services utilization from health facilities at all levels in all provinces of mainland China. We analyzed monthly all-cause health facility visits and inpatient volume in health facilities before and during the SARS-CoV-2 outbreak using nationwide routine health information system data from January 2016 to June 2020. We used interrupted time series analyses and segmented negative binomial regression to examine changes in healthcare utilization attributable to the pandemic. Stratified analyses by facility type and by provincial Human Development Index (HDI) - an area-level measure of socioeconomic status - were conducted to assess potential heterogeneity in effects. FINDINGS: In the months before the SARS-CoV-2 outbreak, a positive secular trend in patterns of healthcare utilization was observed. After the SARS-CoV-2 outbreak, we noted statistically significant decreases in all indicators, with all indicators achieving their nadir in February 2020. The magnitude of decline in February ranged from 63% (95% CI 61-65%; p<0•0001) in all-cause visits at hospitals in regions with high HDI and 71% (95% CI 70-72%; p<0•0001) in all-cause visits at primary care clinics to 33% (95% CI 24-42%; p<0•0001) in inpatient volume and 10% (95% CI 3-17%; p = 0•0076) in all-cause visits at township health centers (THC) in regions with low HDI. The reduction in health facility visits was greater than that in the number of outpatients discharged (51% versus 48%; p<0•0079). The reductions in both health facility visits and inpatient volume were greater in hospitals than in primary health care facilities (p<0•0001) and greater in developed regions than in underdeveloped regions (p<0•0001). Following the nadir of health services utilization in February 2020, all indicators showed statistically significant increases. However, even by June 2020, nearly all indicators except outpatient and inpatient volume in regions with low HDI and inpatient volume in private hospitals had not achieved their pre-SARS-COV-2 forecasted levels. In total, we estimated cumulative losses of 1020.5 (95% CI 951.2- 1089.4; P<0.0001) million or 23.9% (95% CI 22.5-25.2%; P<0.0001) health facility visits, and 28.9 (95% CI 26.1-31.6; P<0.0001) million or 21.6% (95% CI 19.7-23.4%; P<0.0001) inpatients as of June 2020. INTERPRETATION: Inpatient and outpatient health services utilization in China declined significantly after the SARS-CoV-2 outbreak, likely due to changes in patient and provider behaviors, suspension of health facilities or their non-emergency services, massive mobility restrictions, and the potential reduction in the risk of non-SARS-COV-2 diseases. All indicators rebounded beginning in March but most had not recovered to their pre-SARS-COV-2 levels as of June 2020. FUNDING: The National Natural Science Foundation of China (No. 72042014).