Evidence map›Paper›PMID 35397596›Full record

ArticleBMC public health2022

Transitions between versions of the International Classification of Diseases and chronic disease prevalence estimates from administrative health data: a population-based study.

Ridwan A Sanusi, Lin Yan, Amani F Hamad, Olawale F Ayilara, Viktoriya Vasylkiv, Mohammad Jafari Jozani, Shantanu Banerji, Joseph Delaney, Pingzhao Hu, Elizabeth Wall-Wieler and 1 more

Open access · goldAbstract read
In one paragraph

Article in BMC public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.4field-weighted citation impact, top 9% of its field
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

4 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
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  3. Trend control charts for multiple sclerosis case definitions.International journal of population data science · 2024
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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 at 1 institution in 2 countries.

Ridwan A SanusiDepartment of Community Health Sciences, University of Manitoba, Winnipeg, MB, R3E0T6, Canada. amhigher2010@yahoo.com.
Lin YanDepartment of Community Health Sciences, University of Manitoba, Winnipeg, MB, R3E0T6, Canada.
Amani F HamadDepartment of Community Health Sciences, University of Manitoba, Winnipeg, MB, R3E0T6, Canada.
Olawale F AyilaraDepartment of Community Health Sciences, University of Manitoba, Winnipeg, MB, R3E0T6, Canada.
Viktoriya VasylkivDepartment of Community Health Sciences, University of Manitoba, Winnipeg, MB, R3E0T6, Canada.
Mohammad Jafari JozaniDepartment of Statistics, University of Manitoba, Winnipeg, MB, R3T2N2, Canada.
Shantanu BanerjiCancerCare Manitoba Research Institute, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, R3EOV9, Canada.
Joseph DelaneyCollege of Pharmacy, University of Manitoba, Winnipeg, MB, R3E0T5, Canada.
Pingzhao HuDepartment of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, R3E0J9, Canada.
Elizabeth Wall-WielerDepartment of Community Health Sciences, University of Manitoba, Winnipeg, MB, R3E0T6, Canada.
Lisa M LixDepartment of Community Health Sciences, University of Manitoba, Winnipeg, MB, R3E0T6, Canada.
University of Manitoba · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiagnosis codes in administrative health data are routinely used to monitor trends in disease prevalence and incidence. The International Classification of Diseases (ICD), which is used to record these diagnoses, have been updated multiple times to reflect advances in health and medical research. Our objective was to examine the impact of transitions between ICD versions on the prevalence of chronic health conditions estimated from administrative health data.

methodsStudy data (i.e., physician billing claims, hospital records) were from the province of Manitoba, Canada, which has a universal healthcare system. ICDA-8 (with adaptations), ICD-9-CM (clinical modification), and ICD-10-CA (Canadian adaptation; hospital records only) codes are captured in the data. Annual study cohorts included all individuals 18 + years of age for 45 years from 1974 to 2018. Negative binomial regression was used to estimate annual age- and sex-adjusted prevalence and model parameters (i.e., slopes and intercepts) for 16 chronic health conditions. Statistical control charts were used to assess the impact of changes in ICD version on model parameter estimates. Hotelling's T

resultsThe annual cohort sizes ranged from 360,341 to 824,816. Hypertension and skin cancer were among the most and least diagnosed health conditions, respectively; their prevalence per 1,000 population increased from 40.5 to 223.6 and from 0.3 to 2.1, respectively, within the study period. The average annual rate of change in prevalence ranged from -1.6% (95% confidence interval [CI]: -1.8, -1.4) for acute myocardial infarction to 14.6% (95% CI: 13.9, 15.2) for hypertension. The control chart indicated out-of-control observations when transitioning from ICDA-8 to ICD-9-CM for 75% of the investigated chronic health conditions but no out-of-control observations when transitioning from ICD-9-CM to ICD-10-CA.

conclusionsThe prevalence of most of the investigated chronic health conditions changed significantly in the transition from ICDA-8 to ICD-9-CM. These results point to the importance of considering changes in ICD coding as a factor that may influence the interpretation of trend estimates for chronic health conditions derived from administrative health data.

Indexed as

HypertensionInternational Classification of DiseasesCanadaChronic DiseaseDatabases, FactualHumansMiddle AgedPrevalenceDiagnosis codesHotelling’s T2Multivariate control chartNegative binomialTrend analysis

Identifiers

PMID35397596
PMCPMC8994899
OpenAlexW4223552928

What OpenQuestion holds

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LicenceCC BY
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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.