Delayed Diagnosis and Treatment of Cancer Patients During the COVID-19 Pandemic in Henan, China: An Interrupted Time Series Analysis

Abstract

ObjectiveTo investigate the possible impact of lockdown policies on the diagnosis and treatment of cancer patients in Henan, China.Design, Setting, and ParticipantsWe collected data from the Henan Cancer Hospital, affiliated with Zhengzhou University. The monthly numbers of inpatient admissions from January 2014 to December 2019 were used to forecast the number of inpatient admissions in 2020, which was then compared to the actual number of patients admitted during the pandemic to evaluate how the actual number diverges from this forecast. We conducted an interrupted time series analysis using the autoregressive integrated moving average (ARIMA) model.Main Outcomes and MeasuresFor specific diagnoses, treatment modalities, and age groups, we compared the changes in monthly admissions after the pandemic with the forecasted changes from the model.ResultsThe observed overall monthly number of inpatient admissions decreased by 20.2% [95% confidence interval (CI), 11.7–27.2%], 78.9% (95% CI, 77.3–80.4%), and 40.9% (95% CI, 35.6–45.5%) in January, February, and March 2020, respectively, as compared with those predicted using the ARIMA model. After the lockdown, visits for all treatment modalities decreased sharply. However, apparent compensation and recovery of the backlog appeared in later surgeries. As a result, the number of patients who underwent surgery in 2020 (30,478) was close to the number forecasted by the ARIMA model (30,185). In the same period, patients who received other treatments or underwent examinations were 106,074 and 36,968, respectively; the respective numbers that were forecasted by ARIMA were 127,775 and 60,025, respectively. These findings depict a decrease of 16.9 and 38.4% in patients who received other treatments or underwent examinations only, respectively. Regarding diagnosis, the reported incidence of various cancers decreased dramatically in February, with varying extent and speed of recovery.Conclusion and RelevanceThe COVID-19 pandemic has significantly delayed the diagnosis and treatment of cancer in Henan, China. Long-term research should be conducted to assess the future effects of lockdown policies.

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“…The pandemic may also lead to treatment delays for cancer patients, such as postponed surgeries due to circumstances beyond their control. [49][50][51] Given the circumstances described above, the impact of encountering the COVID-19 pandemic can be disastrous for cancer patients who are in urgent need of diagnosis and treatment. Research by Ye et al has elucidated that during the COVID-19 outbreak, cancer patients may experience heightened psychological distress due to treatment delays.…”

Dynamic variations in and prediction of COVID-19 with omicron in the four first-tier cities of mainland China, Hong Kong, and Singapore

“…Mathematical models can predict how infectious diseases progress to have the likely outcome of an epidemic and help to provide scientific information for public health interventions (26,46,47). Since the outbreak of the COVID-19 pandemic, mathematical models have become a crucial and important approach to accurately understand the transmission characteristics and mechanisms in controlling COVID-19 (48), which usually include time series models (e.g., generalized additive models, autoregressive integrated moving average models, and artificial neural network models) (49)(50)(51), and dynamic models (e.g., ODE: ordinary differential equation models, PDE: partial differential equation models and statistical equation models) (52)(53)(54)(55)(56)(57).…”