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Health policy
Original research
Impacts of adjustment of National Reimbursement Drug List on orphan drugs volume and spending in China: an interrupted time series analysis
  1. Hongbin Yi1,
  2. Fenghao Shi2,
  3. Zihao Wang3,
  4. Liping Kuai4,
  5. Dongyan Xu4,
  6. Yu Xie4,
  7. Hua Xing1
  1. 1School of Business Administration, Shenyang Pharmaceutical University, Shenyang, China
  2. 2International Research Center for Medical Administration, Peking University, Beijing, China
  3. 3College of Computer Science and Technology, Civil Aviation University of China, Tianjin, China
  4. 4Science and Technology Development Center, Chinese Pharmaceutical Association, Beijing, China
  1. Correspondence to Dr Hua Xing; gsglxyxh{at}126.com

Abstract

Objective To evaluate the impacts of the 2017 adjustment of National Reimbursement Drug List (NRDL) on orphan drugs hospital procurement volumes and spending in China.

Design We used an interrupted time series design covering the period from 2016 to 2018 to analyse changes in hospital procurement volumes and spending of orphan drugs for which were included in the 2017 NRDL.

Setting and data The study was conducted in China. Orphan drug procurement data of 789 public hospitals (594 tertiary hospitals and 195 secondary hospitals) were derived from the Chinese Medical Economic Information (CMEI).

Outcome measures Monthly orphan drugs hospital procurement volumes and spending.

Results Nine orphan drugs were included in the 2017 NRDL (seven were directly included, and two were included after price negotiation). Comparing to orphan drugs not included in the NRDL, hospital procurement volumes (Embedded Image =43 312, p<0.001) and spending (Embedded Image =6 48 927, p<0.001) of the nine included drugs showed significant upward trends after implementation of the 2017 NRDL adjustment.

Conclusions Our results suggest that the 2017 adjustment of NRDL significantly changed the usage and spending on certain orphan drugs. The increase in orphan drug hospital procurement volumes should improve rare disease patients’ access to these orphan drugs.

  • health policy
  • health economics
  • change management

Data availability statement

Data may be obtained from a third party and are not publicly available.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjopen-2022-064811

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    Strengths and limitations of this study

    • We used an interrupted time series (ITS) design, a quasi-experimental method for evaluating the impacts of interventions, increasing internal validity.

    • Individual orphan drug was evaluated using ITS analysis without the comparison group, so the results of individual orphan drugs may have confounding effects of other interventions.

    • Due to our use of aggregated hospital procurement data from Chinese Medical Economic Information, we could not evaluate policy impacts on the numbers of patients treated at each hospital or appropriateness of drug use at a given level of medication spending or use.

    Introduction

    Over the last few decades, there has been a greater emphasis on rare diseases.1 Rare diseases, although individually rare, collectively affect a significant proportion of the general population.2 Rare diseases often have a lasting impact on individuals, resulting in severe disabilities, and can significantly diminish individuals’ quality of life. Orphan drugs are used to treat rare diseases, many are life-saving, but usually come with unaffordable prices. In China, orphan drugs can be marketed through the regulatory path of priority review, which has greatly incentivised the development of orphan drugs.3 However, a study based in Europe and the USA showed that despite the incentives introduced, the number of medicines for rare diseases is still limited, and this is more evident in certain therapeutic areas, efforts and cooperation seem the only way to accelerate the development and marketing of drugs for rare diseases.4 In addition to priority review, health insurance is an important way to improve access to orphan drugs.

    The government seeks to reduce and control public expenditures on medicines through legislation and economic policy measures,5 and the public expenditures saved can meet the medication needs of more patients. In order to guarantee the changing needs of patients for medications, China has established a dynamic adjustment mechanism for the National Reimbursement Drug List (NRDL), which was adjusted in 2000, 2004, 2007, 2009, 2017, 2018, 2019, 2020 and 2021. The NRDL was introduced in 2000 to ensure accessibility and affordability of essential healthcare and medicines.6 Drugs can be included in the NRDL either directly or after price negotiations. After inclusion of the NRDL, drugs will be partially reimbursed by the government’s health insurance programme. Given the reimbursement mechanism, inclusion into the NRDL is the main approach for patients to access affordable orphan drugs.7 8 In 2017, the Ministry of Human Resources and Social Security of the People’s Republic of China issued the National Basic Medical Insurance, Work Injury Insurance and Maternity Insurance Drug Catalog (2017 Edition).9 A total of nine orphan drugs were added to the new NRDL for the treatment of eight rare diseases (the number of rare diseases is based on the statistics of China’s Catalogue of First Batch of Rare Disease released in May 2018).10 With the inclusion of these nine orphan drugs in the NRDL, provinces must update their Provincial Reimbursement Drug Lists (PRDLs), as all medications in the NRDL are mandated to be included in the PRDLs.7 All public hospitals must purchase these medications via the provincial procurement websites in China. The new NRDL has significantly expanded the coverage of basic medical insurance medication and improved the level of medicine supply.

    Effects of NRDL adjustments or other approaches on medications use have been well described in China.6 Previous studies have also showed that the use of medicines under the health insurance without price negotiation has led to an increase in medication use volumes as well as in spending.11 However, the number of patients with rare diseases is relatively small, so the actual changes and impacts of orphan drugs after inclusion in the NRDL need to be further studied. In this study, we aim to use an interrupted time series (ITS) design and segmented regression analyses to assess the impact of the 2017 NRDL adjustment on hospital procurement volumes and spending for orphan drugs in China.

    Methods

    Study design

    We used an ITS design covering the period from January 2016 to December 2018 to analyse changes in hospital procurement volumes and spending on the orphan drugs included in the NRDL and PRDLs in 2017.12–15 The sample includes nine orphan drugs adjusted into the NRDL in 2017.9 Provinces in mainland China were required to incorporate 2017 price negotiation drugs into the PRDLs before 31 July 2017 and implement them before 31 August 2017.16 Meanwhile, considering the possible lag in policy effects, we chose September 2017 as the intervention point.

    Data and outcome measures

    Chinese Medical Economic Information (CMEI) database is one of the largest medical economic information and drug information service platforms in China. It collects drug procurement data from hundreds of hospitals, but it is not a public database. We used the public hospital procurement data in the CMEI database to collect data before the intervention (January 2016 to August 2017) and after the intervention (September 2017 to December 2018) on a monthly basis. The CMEI captures monthly medicines purchases reported by 594 tertiary and 195 secondary public sector hospitals,17 which respectively accounted for 28.1% and 3.2% of tertiary and secondary public hospitals in China in 2017.

    In this study, we assessed two outcome measures: orphan drugs hospital procurement volumes and spending. The defined daily doses (DDDs) recommended by WHO for drug usage monitoring and research constitutes a measure of purchase volume.18 DDDs were the number of daily doses of each orphan drug based on dosage regimens recommended in the manufacturers’ product labels and as approved by the National Medical Products Administration (NMPA). We prepared data for ITS analysis by summing up monthly hospital procurement spending and volume (in DDDs) of medications procured between January 2016 and December 2018.19

    Statistical analysis

    Statistical model

    We used segmented regression models to assess whether the 2017 NRDL affected hospital procurement volumes and consumption of intervention and comparison group orphan drugs across provinces in China (no data for Tibet, Hong Kong, Macau and Taiwan).20 21 The regression model assumes the following form.

    Embedded Image

    Yt is the dependent variable measured at each monthly time point, Tt is the time since the start of the study, Xt is a dummy variable representing the intervention (preintervention periods 0, otherwise 1) and XtTt is an interaction term of the time and intervention.22 Z is a dummy variable to denote the cohort assignment (treatment or comparison group). When a comparison group is available, Z for the intervention group is set as 1. Embedded Image is the error term, indicating some randomness that cannot be explained by the above parameters in the model error.

    β0 to β3, represent the coefficients of the control group. β4 to β7, represent coefficients of the intervention group. More specifically, β4 represents the difference in the level of the outcome variable between intervention and controls prior to the intervention, β5 represents the difference in the trend of the outcome variable between intervention and controls prior to the intervention, β6 indicates the difference between intervention and control groups in the level of the outcome variable immediately following introduction of the intervention and β7 represents the difference between intervention and comparison groups in the trend of the outcome variable after initiation of the intervention compared with preintervention.23

    We used the Durbin-Watson test to estimate residual autocorrelations (the value of this statistic is between 0 and 4, and the closer it is to 2, the more the model tends to have no autocorrelation),24 and in the case of serial autocorrelation, the generalised linear regression model of Prais-Winsten was used.12 23 The results of the segmented regression models are presented as changes in the levels and trends of orphan drugs hospital procurement volumes (in DDDs) and spending after the implementation of 2017 NRDL policy.

    To analyse the impact of the 2017 NRDL policy on hospital procurement volumes and spending of different orphan drugs, we conducted the ITS regression analyses separately for each orphan drug.

    Statistical analysis software

    All models were run using the statistical software RStudio (V.1.4.1717), and all figures were made using ggplot2 in RStudio.

    Patient and public involvement

    In this study, we only included the orphan drugs information of patients and all the information was anonymous. Neither patients nor the public were involved in this study.

    Results

    Intervention group of this study included the nine orphan drugs in the 2017 NRDL (seven of which were included directly and two were included after price negotiations). The comparison group consisted of four orphan drugs included in the CMEI database for which prices were not negotiated and which were not included in the NRDL from January 2016 to December 2018 (table 1). In the intervention group, drugs such as pirfenidone, compound carbidopa, riluzole, ropinirole, droxidopa, ezetimibe and coagulation factor IX human recombinant were directly included in the NRDL by the government. Everolimus and coagulation factor VIIa human recombinant were listed in the NRDL after price negotiations. The orphan drugs in the intervention group are mainly used to treat eight rare diseases, and the orphan drugs in the control group are mainly used to treat four rare diseases (the number of rare diseases is based on the statistics of China’s Catalogue of First Batch of Rare Disease released in May 2018).10

    View this table:
    Table 1

    Characteristics of orphan drug in the intervention group and comparison group

    Impacts on volume and spending

    The aggregated controlled ITS analysis showed that the procurement volumes and spending of medications in the intervention group increased significantly in September 2017, while there was no increase in purchases and expenditures for drugs in the comparison group (figure 1A,B).

    Figure 1
    Figure 1

    (A) Observed and predicted hospital procurement volume (in DDDs) of intervention group and comparison group orphan drugs. (B) Observed and predicted hospital procurement spending (CNY) on intervention group and comparison group orphan drugs. CNY, Chinese yuan; DDDs, defined daily doses.

    Table 2 shows the results of the ITS model, with no significant change in all the results for the comparison group of procurement volume (β0 =4630, β1 =−324, β2 =2178, β3 =317, p>0.05) and procurement spending (β0 =143 675, β1 =56 939, β2 =360 797, β3 =−35 701, p>0.05), which indicates that our selected comparison group is not affected by the policy, and undoubtedly proves the robustness of the statistical results for our intervention group. Prior to policy implementation, procurement volumes were higher in the intervention group than in the comparison group (β4 =760 886, p<0.001), and there was a significant upward trend in the intervention group (β5 =16 104, p<0.001). Like the change in procurement volume, procurement spending was also higher in the intervention group than in the comparison group (β4 =11 297 943, p<0.001), and there was a significant upward trend in procurement spending in the intervention group(β5 =213 068, p<0.01). After the implementation of the policy, there were no immediate significant changes in procurement volume (β6 =−45 345, p>0.05) and spending (β6 =−28 806, p>0.05), but long-term trend was significantly affected by the policy. With the implementation of the 2017 NRDL adjustment policy, procurement volume(β7 =43 312, p<0.001) and spending (β7=648 927, p<0.001) increased significantly in the intervention group, while there was no significant change in the comparison group (table 2).

    View this table:
    Table 2

    Changes in levels and trends in medication procurement volume and spending, for intervention group and comparison group orphan drugs

    Impacts on individual orphan drugs volume and spending

    Before the implementation of the policy, the hospital procurement volume of droxidopa (β1 =32, p<0.01) and ezetimibe (β1 =15 552, p<0.001) showed an upward trend, while the hospital procurement volume of other orphan drugs did not change significantly. After the implementation of the policy, there was no instantaneous level change in the hospital procurement volume of all orphan drugs in the intervention group (the p value of β2 was not significant). The hospital procurement volume of pirfenidone (β3 =532, p<0.01), riluzole (β3 =859, p<0.001), ezetimibe (β3 =39 489, p<0.001) and coagulation factor IX human recombinant (β3 =30 839, p<0.001) showed a significant upward trend, while the trend of other orphan drugs hospital procurement volume did not change significantly (table 3, online supplemental figure 1).

    View this table:
    Table 3

    Changes in levels and trends of intervention group individual orphan drugs procurement volume and spending

    Before the implementation of the policy, the hospital procurement spending of droxidopa (β1 =1725.1, p<0.01) and ezetimibe (β1 =106 868, p<0.001) showed an upward trend, while the trend of the hospital procurement spending of other orphan drugs did not change. After implementation of the policy, there was no instantaneous level change in the hospital procurement spending of all orphan drugs in the intervention group (the p value of β2 was not significant). Pirfenidone (β3 =175 198, p<0.01), riluzole (β3 =104 976, p<0.001), ropinirole (β3 =35 491, p<0.001), everolimus (β3 =143 830, p<0.001), ezetimibe (β3 =258 939, p<0.001) and coagulation factor IX human recombinant (β3 =63 311, p<0.01) showed a significant upward trend in hospital procurement spending, while the trend of hospital procurement spending for other drugs did not change significantly (table 3, online supplemental figure 2).

    Discussions

    Principal findings

    Our results indicate that the adjustment of NRDL led to significant increases in the hospital procurement volumes and spending of orphan drugs in the intervention group, while the level and trend of the comparison group drugs did not change significantly. The results show that we control the missing variables well and evaluate the impact of the policy well.

    Although the procurement volume is not the actual clinical consumption, it indicates to a certain extent that more patients are using these drugs. After the adjustment of NRDL in 2017, the upward trend of the procurement volume and the consumption of drugs in the intervention group increased significantly, indicating that the consumption of drugs in the intervention group increased with the promotion of the policy, which has enabled more rare disease patients to be treated with the appropriate medicines. These changes show that the adjustment of NRDL in 2017 has benefited more patients with rare diseases.

    Controlling medical insurance expenditure and reducing drug prices are the focus of medical reform in China. Controlling medical insurance expenditure can promote the sustainable development of medical insurance, and reducing drug prices can reduce the financial burden of patients and families. After the adjustment of NRDL, although the economic burden of drugs for rare disease patients has been significantly decreased, the procurement spending of drugs for rare diseases has also significantly increased after the adjustment of NRDL in 2017, which will undoubtedly increase the expenditure of the health insurance fund and bring certain challenges to the sustainability of the medical insurance fund. As a universal coverage of the security system, the government need to also fully consider the affordability of the health insurance fund when expanding the coverage of medical insurance drugs, to ensure the safe operation and sustainable development of the health insurance fund.

    Rare diseases are a major healthcare burden worldwide.25 Various approaches have been proposed to mitigate effects of increasing medication prices.26 27 Aiming to improve access to healthcare and medications, the Chinese government released the Opinions on Deepening Health System Reform in 2009, a political commitment to establishing an accessible, equitable, affordable and efficient health system to cover all people by 2020.28 The 2009 Health System Reform focused on expanding insurance coverage and improving drug access.29 In addition, the NMPA has accelerated the marketing of orphan drugs through priority review and approval, but the number of orphan drugs on the market still lags behind that of European Union and USA.4 The 2017 medication price negotiation and mandatory reimbursement policies jointly targeted both policy goals. Prior to 2017, most insured patients needed to pay for expensive orphan drugs entirely out-of-pocket (OOP) except in few provinces or cities which had included the medications in their reimbursement lists. To address this issue and to relieve burdens of patients and families, the Chinese government introduced a number of reforms and policies to improve patient and system affordability and reduce health inequalities, including the adjustment of NRDL.

    We found that after adjustment of NRDL in 2017, procurement volumes of most orphan drugs increased significantly, except for compound carbidopa, droxidopa, everolimus and coagulation factor VIIa human recombinant. Increased hospital procurement volumes of use suggest that patients’ access to these orphan drugs may have improved. A previous study showed that estimated patient OOP spending decreased after a patient assistance programme in Zhejiang province, China.30 However, challenges to equitable access likely remain in China since insurance schemes differ in the amounts of patient copayments.31 Further study, based on individual-level data, including claims data, is needed to evaluate equity in access to and quality of use of orphan drugs following adjustment of NRDL. Our results are consistent with previous studies that have demonstrated price negotiation as a strategy to improve medication affordability in both high-income and low-income/middle-income countries.32 33

    As mentioned, the adjustment of NRDL includes drugs into the NRDL through two approaches (included after price negotiation and directly included). Price negotiation is a main approach to reduce the financial burden of patients in China. The Chinese government conducts price negotiations for exclusive drugs, negotiates and forms medical insurance payment standards on the basis of accurate measurement, controls medical insurance fund expenditures from the source and meets people’s drug needs. In addition, previous studies showed that the adjustment of NRDL can also effectively reduce drug prices.34 Therefore, price negotiation should be taken seriously by the government’s healthcare sector.

    Our findings are consistent with previous studies that have demonstrated the adjustment of NRDL as a strategy to improve medication affordability in both high-income and low-income/middle-income countries.33 With more bargaining power, centralised national price negotiation seems more effective in constraining medication prices.35 While the effects of the policy changes on access to, quality and outcomes of rare disease treatment need to be studied further, China’s reimbursement-linked medicine price negotiation approach could be useful for healthcare decision-makers in other countries. In general, China’s adjustment of NRDL provides useful Chinese experience and Chinese wisdom for global medical insurance practice.

    Strengths and limitations

    As far as we know, this is the first study to evaluate impacts of national reimbursement-linked adjustment of NRDL on orphan drug hospital procurement volumes and spending in China. We used an ITS design, a quasi-experimental method for evaluating the impacts of interventions, increasing internal validity. In addition, we strengthened the ITS design by adding a comparison group to separate intervention effects from other potential influences on the outcomes that may have occurred at the same time as the adjustment of NRDL.

    This study has several limitations related to our data source. First, our study is based on aggregated medication procurement data of hospitals from CMEI. Therefore, we cannot evaluate access in terms of numbers of rare disease patients treated or affordability in terms of OOP spending. In addition, we cannot explain that neither the hospital procurement volume nor the spending of orphan drugs such as compound carbidopa, droxidopa and coagulation factor VIIa human recombinant are affected by the policy. Third, few orphan drugs were available in China before 2016, and the comparison group is imperfect in that intervention and comparison group orphan drugs have different indications. Different incidences of the rare diseases for which the orphan drugs are indicated may influence changes in use and spending over time. However, the quasi-experimental design we used controls for preintervention levels and trends of orphan drugs use and spending. In addition, there is a certain bias in the selection of orphan drugs. Some orphan drugs also have common indications, but these orphan drugs are still mainly used for rare diseases.

    Further study is needed to assess the actual financial burden of orphan drugs on households and the health system and clinical outcomes among patients after the adjustment of NRDL.

    Conclusion

    Our findings suggest that the 2017 adjustment of NRDL significantly altered the usage and spending on some orphan drugs in China. The increase in orphan drug hospital procurement volumes should improve rare disease patients’ access to these orphan drugs, although this remains to be demonstrated. In the future, we need to further study the impact of increased spending on orphan drugs on health insurance funds.

    Data availability statement

    Data may be obtained from a third party and are not publicly available.

    Ethics statements

    Patient consent for publication

    Ethics approval

    Not required.

    Acknowledgments

    We thank Chinese Medical Economic Information for providing the raw data and related information on the hospital purchase data.

    References

    1. ↵
      1. Richter T,
      2. Nestler-Parr S,
      3. Babela R, et al
      . Rare disease terminology and definitions-A systematic global review: report of the ISPOR rare disease special interest group. Value Health 2015;18:906–14. doi:10.1016/j.jval.2015.05.008
      OpenUrlCrossRefPubMed
    2. ↵
      1. Ferreira CR
      . The burden of rare diseases. Am J Med Genet A 2019;179:885–92. doi:10.1002/ajmg.a.61124
      OpenUrlCrossRefPubMed
    3. ↵
      1. Yang Y,
      2. Kang Q,
      3. Hu J, et al
      . Accessibility of drugs for rare diseases in China: policies and current situation. IRDR 2019;8:80–8. doi:10.5582/irdr.2019.01068
      OpenUrl
    4. ↵
      1. Giannuzzi V,
      2. Conte R,
      3. Landi A, et al
      . Orphan medicinal products in Europe and United States to cover needs of patients with rare diseases: an increased common effort is to be foreseen. Orphanet J Rare Dis 2017;12:64. doi:10.1186/s13023-017-0617-1
    5. ↵
      1. Bojnec Š,
      2. Kajdiž R
      . Determinants of public expenditures for medical products in non hospital consumption in Slovenia. E+M 2014;17:27–34. doi:10.15240/tul/001/2014-3-003
      OpenUrl
    6. ↵
      1. Guan X,
      2. Wushouer H,
      3. Yang M, et al
      . Influence of government price regulation and deregulation on the price of antineoplastic medications in China: a controlled interrupted time series study. BMJ Open 2019;9:e031658. doi:10.1136/bmjopen-2019-031658
    7. ↵
      1. Tang M,
      2. Song P,
      3. He J
      . Progress on drug pricing negotiations in China. Biosci Trends 2020;13:464–8. doi:10.5582/bst.2019.01339
      OpenUrlPubMed
    8. ↵
      1. Guan X,
      2. Zhang Y,
      3. Wushouer H, et al
      . Differences in reimbursement listing of anticancer therapies in China: an observational study. BMJ Open 2020;10:e031203. doi:10.1136/bmjopen-2019-031203
    9. ↵
      The Ministry of Human Resources and Social Security issued the National Basic Medical Insurance, Work Injury Insurance and Maternity Insurance Drug Catalog (2017 Edition), . 2017Available: http://www.gov.cn/xinwen/2017-02/23/content_5170392.htm
    10. ↵
      Notice on publishing the Catalogue of the First Batch of Rare Diseases, . 2018Available: http://www.nhc.gov.cn/yzygj/s7659/201806/393a9a37f39c4b458d6e830f40a4bb99.shtml
    11. ↵
      1. Peng Y-L,
      2. Lee C-T,
      3. Tain Y-L, et al
      . The impact of adoption of a new Urate-lowering agent on trends in utilization and cost in practice. PLoS One 2019;14:e0221504. doi:10.1371/journal.pone.0221504
    12. ↵
      1. Wagner AK,
      2. Soumerai SB,
      3. Zhang F, et al
      . Segmented regression analysis of interrupted time series studies in medication use research. J Clin Pharm Ther 2002;27:299–309. doi:10.1046/j.1365-2710.2002.00430.x
      OpenUrlCrossRefPubMedWeb of Science
    13. ↵
      1. Bernal JL,
      2. Cummins S,
      3. Gasparrini A
      . Interrupted time series regression for the evaluation of public health interventions: a Tutorial. Int J Epidemiol 2017;46:348–55. doi:10.1093/ije/dyw098
      OpenUrlCrossRefPubMed
    14. ↵
      1. Jandoc R,
      2. Burden AM,
      3. Mamdani M, et al
      . Interrupted time series analysis in drug utilization research is increasing: systematic review and recommendations. J Clin Epidemiol 2015;68:950–6. doi:10.1016/j.jclinepi.2014.12.018
      OpenUrlCrossRefPubMed
    15. ↵
      1. Zhang Y,
      2. Wushouer H,
      3. Han S, et al
      . The impacts of government reimbursement negotiation on targeted anticancer medication price, volume and spending in China. BMJ Glob Health 2021;6:e006196. doi:10.1136/bmjgh-2021-006196
    16. ↵
      The Ministry of Human Resources and Social Security released the 2017 National Medical Insurance Catalogue and will be officially implemented before August 31, . 2017Available: https://www.sohu.com/a/127316443_436683
    17. ↵
      1. Guan X,
      2. Tian Y,
      3. Ross-Degnan D, et al
      . Interrupted time-series analysis of the impact of generic market entry of antineoplastic products in China. BMJ Open 2018;8:e022328. doi:10.1136/bmjopen-2018-022328
    18. ↵
      ATC/DDD Index 2022, . 2022Available: https://www.whocc.no/atc_ddd_index/
    19. ↵
      1. O’Donnell A,
      2. Anderson P,
      3. Jané-Llopis E, et al
      . Immediate impact of minimum unit pricing on alcohol purchases in Scotland: controlled interrupted time series analysis for 2015-18. BMJ 2019;366:l5274. doi:10.1136/bmj.l5274
    20. ↵
      1. Penfold RB,
      2. Zhang F
      . Use of interrupted time series analysis in evaluating health care quality improvements. Acad Pediatr 2013;13:S38–44. doi:10.1016/j.acap.2013.08.002
      OpenUrlCrossRefPubMed
    21. ↵
      1. Valsamis EM,
      2. Ricketts D,
      3. Husband H, et al
      . Segmented linear regression models for assessing change in retrospective studies in Healthcare. Comput Math Methods Med 2019;2019:9810675. doi:10.1155/2019/9810675
    22. ↵
      1. Zhang F,
      2. Wagner AK,
      3. Soumerai SB, et al
      . Methods for estimating confidence intervals in interrupted time series analyzes of health interventions. J Clin Epidemiol 2009;62:143–8. doi:10.1016/j.jclinepi.2008.08.007
      OpenUrlCrossRefPubMed
    23. ↵
      1. Linden A
      . Conducting interrupted time-series analysis for Single- and multiple-group comparisons. Stata Journal 2015;15:480–500. doi:10.1177/1536867X1501500208
      OpenUrl
    24. ↵
      1. DURBIN J,
      2. WATSON GS
      . Testing for serial correlation in least squares regression. Biometrika 1950;37:409–28.
      OpenUrlCrossRefPubMedWeb of Science
    25. ↵
      1. Spagnolo P,
      2. du Bois RM,
      3. Cottin V
      . Rare lung disease and orphan drug development. Lancet Respir Med 2013;1:479–87. doi:10.1016/S2213-2600(13)70085-7
      OpenUrl
    26. ↵
      1. Kantarjian H,
      2. Rajkumar SV
      . Why are cancer drugs so expensive in the United States, and what are the solutions? Mayo Clin Proc 2015;90:500–4. doi:10.1016/j.mayocp.2015.01.014
      OpenUrlCrossRefPubMed
    27. ↵
      1. Gómez-Dantés O,
      2. Wirtz VJ,
      3. Reich MR, et al
      . A new entity for the negotiation of public procurement prices for patented medicines in Mexico. Bull World Health Organ 2012;90:788–92. doi:10.2471/BLT.12.106633
      OpenUrlPubMed
    28. ↵
      1. Meng Q,
      2. Yin D,
      3. Mills A, et al
      . China’s encouraging commitment to health. BMJ 2019;365:l4178. doi:10.1136/bmj.l4178
    29. ↵
      1. Fang H,
      2. Eggleston K,
      3. Hanson K, et al
      . Enhancing financial protection under China's social health insurance to achieve universal health coverage. BMJ 2019;365:l2378. doi:10.1136/bmj.l2378
    30. ↵
      1. Diao Y,
      2. Qian J,
      3. Liu Y, et al
      . How government insurance coverage changed the utilization and Affordability of expensive targeted anti-cancer medicines in China: an interrupted time-series study. J Glob Health 2019;9:020603. doi:10.7189/jogh.09.020702
    31. ↵
      1. Meng Q,
      2. Fang H,
      3. Liu X, et al
      . Consolidating the social health insurance schemes in China: towards an equitable and efficient health system. Lancet 2015;386:1484–92. doi:10.1016/S0140-6736(15)00342-6
      OpenUrlCrossRefPubMed
    32. ↵
      1. Kwon H-Y,
      2. Kim J
      . Consistency of new drug pricing in Korea: bridging variations among personnel in price negotiations. Health Policy 2020;124:965–70. doi:10.1016/j.healthpol.2020.06.003
      OpenUrlPubMed
    33. ↵
      1. Kim S,
      2. Kim J,
      3. Cho H, et al
      . Trends in the pricing and reimbursement of new anticancer drugs in South Korea: an analysis of listed anticancer drugs during the past three years. Expert Rev Pharmacoecon Outcomes Res 2021;21:479–88. doi:10.1080/14737167.2021.1860023
      OpenUrlPubMed
    34. ↵
      1. Vokinger KN,
      2. Hwang TJ,
      3. Carl DL, et al
      . Price changes and within-class competition of cancer drugs in the USA and Europe: a comparative analysis. Lancet Oncol 2022;23:514–20. doi:10.1016/S1470-2045(22)00073-0
      OpenUrl
    35. ↵
      1. Berkemeier F,
      2. Whaley C,
      3. Robinson JC
      . Increasing divergence in drug prices between the United States and Germany after implementation of comparative effectiveness analysis and collective price negotiations. J Manag Care Spec Pharm 2019;25:1310–7. doi:10.18553/jmcp.2019.25.12.1310
      OpenUrlPubMed

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    Footnotes

    • Contributors HY, YX and HX conceptualised and undertook the analyses, and wrote the first draft of the manuscript. FS and ZW provide support for R code. LK and DX provided data. All authors refined versions of and approved the final manuscript. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. HX accepts full responsibility for the work and/or the conduct of the study as guarantor, has acces to the data and controlled the decision to publish.

    • Funding This study was funded by Department of Bidding and Purchasing of Medicine Price, National Healthcare Security Administration (JCS-ZCHT-2021-003).

    • Disclaimer The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

    • Competing interests None declared.

    • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.