Evidence map›Paper›PMID 39162981›Full record

ArticleAdvances in therapy2024

Inside ANEMIA of CKD: Projecting the Future Burden of Anemia of Chronic Kidney Disease and Benefits of Proactive Management: A Microsimulation Model of the Chinese Population.

Lise Retat, Dunming Xiao, Laura Webber, Alexander Martin, Joshua Card-Gowers, Jiaqi Yao, Yuzheng Zhang, Chalet Zhang, Juan Jose Garcia Sanchez, Claudia Cabrera and 5 more

Erratum issuedAbstract read
In one paragraph

Article in Advances in therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Lise RetatHealthLumen Limited, London, UK.ORCID 0000-0002-6689-3599
Dunming XiaoSchool of Public Health, Fudan University, Shanghai, China.
Laura WebberHealthLumen Limited, London, UK.ORCID 0000-0002-8976-1919
Alexander MartinHealthLumen Limited, London, UK.ORCID 0009-0001-4863-3961
Joshua Card-GowersHealthLumen Limited, London, UK.
Jiaqi YaoChina Health Economics, Biopharmaceuticals, AstraZeneca, Beijing, China.
Yuzheng ZhangChina National Health Development Research Center, Beijing, China.
Chalet ZhangChina Health Economics, Biopharmaceuticals, AstraZeneca, Beijing, China.
Juan Jose Garcia SanchezGlobal Health Economics, BioPharmaceuticals, AstraZeneca, Cambridge, UK.ORCID 0000-0001-6546-7769
Claudia CabreraReal World Science and Analytics, Global BioPharmaceuticals Medicine, AstraZeneca, Gothenburg, Sweden.ORCID 0000-0003-2778-1604
Susan GrandyBioPharmaceuticals Medical, AstraZeneca, Wilmington, USA.
Naveen RaoGlobal Health Economics, BioPharmaceuticals, AstraZeneca, Cambridge, UK.
Yiqing WuMedical Affairs, FibroGen China, Shanghai, China.
Zuo LiPeking University People's Hospital, Beijing, China. zuolimd@hotmail.com.
Jianwei XuanSun Yat-Sen University, Guangzhou, China. xuanjw3@mail.sysu.edu.cn.

Funding

AstraZeneca HealthLumen
6 · The paper itself

Abstract

introductionAnemia is a common comorbidity of chronic kidney disease (CKD) that has been associated with increased risk of complications, healthcare expenditure, and reduced quality of life. In China, the treatment of anemia of CKD has been reported to be suboptimal in part because of a lack of awareness of the condition and its management. It is therefore important to raise awareness of the condition by estimating the future health and economic burden of anemia of CKD and also to understand how it may be addressed through proactive policies. This study aims to project the health and economic burden of anemia of CKD, in China, from 2023 to 2027 and to estimate the impact of a hypothetical intervention on related clinical and cost outcomes.

methodsA virtual Chinese population was simulated using demographic, clinical, and economic statistics within a validated CKD microsimulation model. Each individual was assigned a CKD stage, anemia stage, comorbidity status (type 2 diabetes, hypertension), complication status (stroke, heart failure, and/or myocardial infarction), and a probability of receiving treatments and therapies. Annual direct healthcare costs were assigned and based on these factors. The hypothetical intervention reduced the prevalence of moderate and severe anemia by 5% annually. This hypothetical scenario was chosen to highlight the impact of implementing policies that could reduce anemia of CKD, and is aligned with the Healthy China 2030 policy, which aims to reduce mortality from noncommunicable diseases by 30%. Interventions could consist of early screening and intervention to reduce the escalation of anemia from mild to moderate or severe. Results were compared with a baseline "no change" scenario which reflects current trends.

resultsThe number of patients with moderate/severe anemia of CKD was projected to increase from 3.0 to 3.2 million patients, with associated costs increasing from ¥22.0 billion (B) to ¥24.4B between 2023 and 2027, respectively. Compared with the no change scenario, the hypothetical intervention reduced the prevalence of moderate and severe anemia of CKD, saving ¥3.9B in healthcare costs in 2027 (¥24.4B vs ¥20.6B, respectively).

conclusionsConsistent with trends in CKD burden in China, the prevalence of anemia of CKD is projected to increase, leading to greater related healthcare costs. The introduction of healthcare interventions designed to screen for and treat anemia more effectively could therefore reduce its future burden and related costs.

Indexed as

AnemiaRenal Insufficiency, ChronicAdultAgedChinaComorbidityComputer SimulationCost of IllnessEast Asian PeopleFemaleHealth Care CostsHumansMaleMiddle AgedPrevalenceQuality of LifeAnemia of CKDChronic kidney diseaseHealthcare costsMicrosimulation

Identifiers

PMID39162981
PMCPMC11399189

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.