11
views
0
recommends
+1 Recommend
2 collections
    0
    shares

      Submit your digital health research with an established publisher
      - celebrating 25 years of open access

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

      Quantifying Health Policy Uncertainty in China Using Newspapers: Text Mining Study

      research-article
      , BS 1 , 2 , , PhD 1 , 2 ,
      (Reviewer), (Reviewer), (Reviewer)
      Journal of Medical Internet Research
      JMIR Publications
      China, health policy, newspaper, uncertainty, severe acute respiratory syndrome, SARS, COVID-19

      Read this article at

      ScienceOpenPublisherPMC
      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

          From the severe acute respiratory syndrome (SARS) outbreak in 2003 to the COVID-19 pandemic in 2019, a series of health measures and policies have been introduced from the central to the local level in China. However, no study has constructed an uncertainty index that can reflect the volatility, risk, and policy characteristics of the health environment.

          Objective

          We used text mining analysis on mainstream newspapers to quantify the volume of reports about health policy and the total number of news articles and to construct a series of indexes that could reflect the uncertainty of health policy in China.

          Methods

          Using the Wisenews database, 11 of the most influential newspapers in mainland China were selected to obtain the sample articles. The health policy uncertainty (HPU) index for each month from 2003 to 2022 was constructed by searching articles containing the specified keywords and calculating their frequency. Robustness tests were conducted through correlation analysis. The HPU index was plotted using STATA (version 16.0), and a comparative analysis of the China and US HPU indexes was then performed.

          Results

          We retrieved 6482 sample articles from 7.49 million news articles in 11 newspapers. The China HPU index was constructed, and the robustness test showed a correlation coefficient greater than 0.74, which indicates good robustness. Key health events can cause index fluctuations. At the beginning of COVID-19 (May 2020), the HPU index climbed to 502.0. In December 2022, China’s HPU index reached its highest value of 613.8 after the release of the “New Ten Rules” pandemic prevention and control policy. There were significant differences in HPU index fluctuations between China and the United States during SARS and COVID-19, as well as during the Affordable Care Act period.

          Conclusions

          National health policy is a guide for health development, and uncertainty in health policy can affect not only the implementation of policy by managers but also the health-seeking behavior of the people. Here, we conclude that changes in critical health policies, major national or international events, and infectious diseases with widespread impact can create significant uncertainty in China’s health policies. The uncertainty of health policies in China and the United States is quite different due to different political systems and news environments. What is the same is that COVID-19 has brought great policy volatility to both countries. To the best of our knowledge, our work is the first systematic text mining study of HPU in China.

          Related collections

          Most cited references54

          • Record: found
          • Abstract: not found
          • Article: not found

          Measuring Economic Policy Uncertainty

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Economic Uncertainty Before and During the COVID-19 Pandemic

            We consider several economic uncertainty indicators for the US and UK before and during the COVID-19 pandemic: implied stock market volatility, newspaper-based policy uncertainty, twitter chatter about economic uncertainty, subjective uncertainty about business growth, forecaster disagreement about future GDP growth, and a model-based measure of macro uncertainty. Four results emerge. First, all indicators show huge uncertainty jumps in reaction to the pandemic and its economic fallout. Indeed, most indicators reach their highest values on record. Second, peak amplitudes differ greatly – from a 35% rise for the model-based measure of US economic uncertainty (relative to January 2020) to a 20-fold rise in forecaster disagreement about UK growth. Third, time paths also differ: Implied volatility rose rapidly from late February, peaked in mid-March, and fell back by late March as stock prices began to recover. In contrast, broader measures of uncertainty peaked later and then plateaued, as job losses mounted, highlighting differences between Wall Street and Main Street uncertainty measures. Fourth, in Cholesky-identified VAR models fit to monthly U.S. data, a COVID-size uncertainty shock foreshadows peak drops in industrial production of 12-19%.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Political Uncertainty and Corporate Investment Cycles

                Bookmark

                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J Med Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                2023
                14 November 2023
                : 25
                : e46589
                Affiliations
                [1 ] School of Public Health Capital Medical University Beijing China
                [2 ] Research Center for Capital Health Management and Policy Capital Medical University Beijing China
                Author notes
                Corresponding Author: Junli Zhu smallying@ 123456126.com
                Author information
                https://orcid.org/0000-0002-2897-3897
                https://orcid.org/0000-0001-7244-6229
                Article
                v25i1e46589
                10.2196/46589
                10685290
                37962937
                603f10c0-37e9-40e9-a379-4bf034b35627
                ©Chen Chen, Junli Zhu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 14.11.2023.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 17 February 2023
                : 29 June 2023
                : 18 July 2023
                : 15 August 2023
                Categories
                Original Paper
                Original Paper

                Medicine
                china,health policy,newspaper,uncertainty,severe acute respiratory syndrome,sars,covid-19
                Medicine
                china, health policy, newspaper, uncertainty, severe acute respiratory syndrome, sars, covid-19

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content160

                Cited by3

                Most referenced authors325