Evidence map›Paper›PMID 39297199›Full record

ArticleRenal failure2024

Epidemiological analysis of chronic kidney disease from 1990 to 2019 and predictions to 2030 by Bayesian age-period-cohort analysis.

Boqing Dong, Yuting Zhao, Jiale Wang, Cuinan Lu, Zuhan Chen, Ruiyang Ma, Huanjing Bi, Jingwen Wang, Ying Wang, Xiaoming Ding and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Renal failure, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
–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

25 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Boqing DongDepartment of Renal Transplantation, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Yuting ZhaoDepartment of Gynecologic Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Jiale WangDepartment of Renal Transplantation, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Cuinan LuDepartment of Renal Transplantation, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Zuhan ChenDepartment of Renal Transplantation, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Ruiyang MaDepartment of Renal Transplantation, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Huanjing BiDepartment of Renal Transplantation, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Jingwen WangDepartment of Renal Transplantation, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Ying WangDepartment of Renal Transplantation, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Xiaoming DingDepartment of Renal Transplantation, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Yang LiDepartment of Renal Transplantation, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChronic Kidney Disease (CKD) has emerged as a significant global health issue. This study aimed to reveal and predict the epidemiological characteristics of CKD.

methodsData from the Global Burden of Disease Study spanning the years 1990 to 2019 were employed to analyze the incidence, prevalence, death, and disability-adjusted life year (DALY) of CKD. Joinpoint analysis assessed epidemiological trends of CKD from 1990 to 2019. An age-period-cohort model evaluated risk variations. Risk factor analysis uncovered their influences on DALYs and deaths of CKD. Decomposition analysis explored the drivers to CKD. Frontier analysis evaluated the correlations between CKD burden and the sociodemographic index (SDI). A Bayesian Age-Period-Cohort model was employed to predict future incidence and death of CKD.

resultsIn 2019, there were 18,986,903 incident cases, 697,294,307 prevalent cases, 1,427,232 deaths, and 41,538,592 DALYs of CKD globally. Joinpoint analysis showed increasing age-standardized rates of CKD incidence, prevalence, mortality, and DALY from 1990 to 2019. High systolic blood pressure significantly contributed to CKD-related deaths and DALYs, particularly in the high SDI region. Decomposition analysis identified population growth as the primary driver of CKD incident cases and DALYs globally. Countries like Nicaragua showed the highest effective differences, indicating room for improvement in CKD management. By 2030, while incident cases of CKD were predicted to rise, the global deaths might decrease.

conclusionsThe study revealed a concerning upward trend in the global burden of CKD, emphasizing the need for targeted management strategies across different causes, regions, age groups, and genders.

Indexed as

Bayes TheoremGlobal Burden of DiseaseRenal Insufficiency, ChronicAdolescentAdultAgedAged, 80 and overChildCohort StudiesDisability-Adjusted Life YearsFemaleForecastingGlobal HealthHumansIncidenceMaleChronic kidney diseaseepidemiologyfrontier analysisglobal burdenprediction

Identifiers

PMID39297199
PMCPMC11413963

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.