Evidence map›Paper›PMID 41848900›Full record

ArticleDiabetologia2026

Differential healthcare costs in individuals with type 2 diabetes and incident chronic kidney disease in Hong Kong: a latent class trajectory analysis.

Yanrong Du, Minglu Zhang, Abby Q Y Li, Eric S H Lau, Hongjiang Wu, Alice P S Kong, Andrea O Y Luk, Ronald C W Ma, Chun-Kwan O, Lee-Ling Lim and 6 more

Abstract read
In one paragraph

Article in Diabetologia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

16 authors.

Yanrong DuDepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.ORCID http://orcid.org/0009-0001-0515-7272
Minglu ZhangDepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.ORCID http://orcid.org/0009-0008-3026-357X
Abby Q Y LiDepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.ORCID http://orcid.org/0009-0004-8589-8522
Eric S H LauDepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.ORCID http://orcid.org/0000-0003-1581-5643
Hongjiang WuDepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.ORCID http://orcid.org/0000-0003-2193-1114
Alice P S KongDepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.ORCID http://orcid.org/0000-0001-8927-6764
Andrea O Y LukDepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.ORCID http://orcid.org/0000-0002-5244-6069
Ronald C W MaDepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.ORCID http://orcid.org/0000-0002-1227-803X
Chun-Kwan ODepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.ORCID http://orcid.org/0009-0009-3904-8056
Lee-Ling LimDepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.ORCID http://orcid.org/0000-0002-6214-6924
Jenny Y Z ZhangNational Center for Mental Health, Beijing, China.ORCID http://orcid.org/0000-0001-9383-7448
Wai Kit MingDepartment of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong SAR, China.ORCID http://orcid.org/0000-0002-8846-7515
Weijian KeThe First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.ORCID http://orcid.org/0000-0002-5158-2245
Yanbing LiThe First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.ORCID http://orcid.org/0000-0003-3782-9210
Juliana C N ChanDepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China. jchan@cuhk.edu.hk.ORCID http://orcid.org/0000-0003-1325-1194
Juliana N M LuiDepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China. julianalui@cuhk.edu.hk.ORCID http://orcid.org/0000-0002-4034-1286

Funding

Research Grants Council, University Grants Committee 14121723
6 · The paper itself

Abstract

aims/hypothesisChronic kidney disease (CKD) represents a major and costly comorbidity in type 2 diabetes management. Identifying individuals with high healthcare costs due to CKD will support decision-making for early intervention. We used latent class analysis (LCA) to classify Chinese individuals with type 2 diabetes and incident CKD based on their demographic and clinical profiles.

methodsFor this study, 2886 individuals with type 2 diabetes and incident CKD and complete data for 42 attributes were selected from the prospective Hong Kong Diabetes Register cohort (2007-2019). We used LCA to select 14 variables to classify participants, followed by a hierarchical generalised linear mixed model to evaluate longitudinal healthcare costs among class memberships.

resultsDuring 109,784 person-years of follow-up, the incidence of CKD was 26.29 per 1000 person-years with a per-patient-per-year (PPPY) cost of US$4395 ± 11,947 (mean ± standard deviation). The four distinct classes used in the LCA based on baseline profiles were as follows: Class 1 (18.3%; PPPY: US$6087 ± 15,519), namely those who were young at onset (44.4 ± 10.3 years), had moderate comorbidities (25.6% had a moderate or high score on the Elixhauser Comorbidity Index [ECI]) and used multiple medications (90.2% used at least three medications); Class 2 (21.2%; PPPY: US$3822 ± 9816), namely those who had old-age onset (66.9 ± 6.9 years), had moderate comorbidities (27.8% had a moderate or high ECI score) and used multiple medications (70.7% used at least three medications); Class 3 (33.9%; PPPY: US$4260 ± 11,725), namely those who were middle-aged at onset (54.2 ± 10.0 years), had few comorbidities (14.0% had a moderate or high ECI score) and used few medications (15.6% used at least three medications); and Class 4 (26.5%; PPPY: US$3923 ± 10,957), namely those who were middle-aged at onset (54.1 ± 7.6 years), had moderate comorbidities (25.3% had a moderate or high ECI score) and used multiple medications (98.9% used at least three medications). Class 1 (young onset) and Class 3 (middle-aged onset) incurred the highest cost during the year of CKD onset, with those in Class 1 having more comorbidities than those in Class 3 at baseline. Multiple healthcare services contributed to the high healthcare costs in Class 1, with costs in Class 3 attributed mainly to post-CKD outpatient and psychiatric care. CONCLUSIONS/

interpretationThose with young-onset type 2 diabetes incurred the highest cost during the year of CKD onset. Individuals with middle-aged onset type 2 diabetes with fewer comorbidities and less intensified treatment at baseline also had subsequent increased healthcare costs.

Indexed as

Diabetes Mellitus, Type 2Health Care CostsRenal Insufficiency, ChronicAdultAgedComorbidityFemaleHong KongHumansIncidenceLatent Class AnalysisMaleMiddle AgedProspective StudiesChronic kidney diseaseElixhauser Comorbidity IndexEQ-5D-3LHealthcare costsLatent class trajectoryType 2 diabetesYoung-onset diabetes

Identifiers

PMID41848900
PMCPMC13236821

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.