13
views
0
recommends
+1 Recommend
2 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          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).

          Related collections

          Most cited references53

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak

          Motivated by the rapid spread of COVID-19 in Mainland China, we use a global metapopulation disease transmission model to project the impact of travel limitations on the national and international spread of the epidemic. The model is calibrated based on internationally reported cases, and shows that at the start of the travel ban from Wuhan on 23 January 2020, most Chinese cities had already received many infected travelers. The travel quarantine of Wuhan delayed the overall epidemic progression by only 3 to 5 days in Mainland China, but has a more marked effect at the international scale, where case importations were reduced by nearly 80% until mid February. Modeling results also indicate that sustained 90% travel restrictions to and from Mainland China only modestly affect the epidemic trajectory unless combined with a 50% or higher reduction of transmission in the community.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            The effect of human mobility and control measures on the COVID-19 epidemic in China

            The ongoing COVID-19 outbreak expanded rapidly throughout China. Major behavioral, clinical, and state interventions have been undertaken to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, affected COVID-19 spread in China. We use real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation on transmission in cities across China and ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was explained well by human mobility data. Following the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases were still indicative of local chains of transmission outside Wuhan. This study shows that the drastic control measures implemented in China substantially mitigated the spread of COVID-19.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Interrupted time series regression for the evaluation of public health interventions: a tutorial

              Abstract Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design.
                Bookmark

                Author and article information

                Contributors
                Journal
                Lancet Reg Health West Pac
                Lancet Reg Health West Pac
                The Lancet Regional Health: Western Pacific
                Elsevier
                2666-6065
                24 March 2021
                April 2021
                24 March 2021
                : 9
                : 100122
                Affiliations
                [a ]Public Health Sciences Division, Fred Hutchison Cancer Research Center, Seattle, WA, United States
                [b ]School of Public Health, Zhejiang University, Hangzhou, China
                [c ]Department of Health Metrics Sciences, University of Washington, Seattle, WA, United States
                [d ]Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, United States
                [e ]Department of Global Health, University of Washington, Seattle, WA, United States
                [f ]Department of Epidemiology, University of Washington, Seattle, WA, United States
                [g ]Chinese Center for Disease Control and Prevention, Beijing, China
                [h ]Universidade Eduardo Mondlane, Maputo, Mozambique
                [i ]Department of Global Health, School of Public Health, Peking University, Beijing 100191, China
                Author notes
                Article
                S2666-6065(21)00031-6 100122
                10.1016/j.lanwpc.2021.100122
                8315657
                34327438
                14c5dd91-e227-4f32-93b8-200c18c26ba6
                © 2021 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 17 December 2020
                : 6 February 2021
                : 21 February 2021
                Categories
                Research Paper

                Comments

                Comment on this article