scholarly journals Exploring the differences in ICD and hospital morbidity data collection features across countries: an international survey

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lucia Otero Varela ◽  
Chelsea Doktorchik ◽  
Natalie Wiebe ◽  
Hude Quan ◽  
Catherine Eastwood
Keyword(s):  
Data Collection ◽  
National Level ◽  
National Standards ◽  
World Health ◽  
Online Questionnaire ◽  
Main Condition ◽  
Morbidity Data ◽  
Hospital Morbidity ◽  
Hospital Morbidity Data ◽  
Icd Coding

Abstract Background The International Classification of Diseases (ICD) is the reference standard for reporting diseases and health conditions globally. Variations in ICD use and data collection across countries can hinder meaningful comparisons of morbidity data. Thus, we aimed to characterize ICD and hospital morbidity data collection features worldwide. Methods An online questionnaire was created to poll the World Health Organization (WHO) member countries that were using ICD. The survey included questions focused on ICD meta-features and hospital data collection systems, and was distributed via SurveyMonkey using purposive and snowball sampling. Accordingly, senior representatives from organizations specialized in the topic, such as WHO Collaborating Centers, and other experts in ICD coding were invited to fill out the survey and forward the questionnaire to their peers. Answers were collated by country, analyzed, and presented in a narrative form with descriptive analysis. Results Responses from 47 participants were collected, representing 26 different countries using ICD. Results indicated worldwide disparities in the ICD meta-features regarding the maximum allowable coding fields for diagnosis, the definition of main condition, and the mandatory type of data fields in the hospital morbidity database. Accordingly, the most frequently reported answers were “reason for admission” as main condition definition (n = 14), having 31 or more diagnostic fields available (n = 12), and “Diagnoses” (n = 26) and “Patient demographics” (n = 25) for mandatory data fields. Discrepancies in data collection systems occurred between but also within countries, thereby revealing a lack of standardization both at the international and national level. Additionally, some countries reported specific data collection features, including the use or misuse of ICD coding, the national standards for coding or lack thereof, and the electronic abstracting systems utilized in hospitals. Conclusions Harmonizing ICD coding standards/guidelines should be a common goal to enhance international comparisons of health data. The current international status of ICD data collection highlights the need for the promotion of ICD and the adoption of the newest version, ICD-11. Furthermore, it will encourage further research on how to improve and standardize ICD coding.

scholarly journals Validation of intellectual disability coding through hospital morbidity records using an intellectual disability population-based database in Western Australia

BMJ Open ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. e019113 ◽  
Author(s):  
Jenny Bourke ◽  
Kingsley Wong ◽  
Helen Leonard
Keyword(s):  
Intellectual Disability ◽  
International Classification Of Diseases ◽  
Population Based ◽  
Reliable Source ◽  
Morbidity Data ◽  
Classification Of Diseases ◽  
Data Source ◽  
Hospital Morbidity ◽  
Western Australian ◽  
Hospital Morbidity Data

ObjectivesTo investigate how well intellectual disability (ID) can be ascertained using hospital morbidity data compared with a population-based data source.Design, setting and participantsAll children born in 1983–2010 with a hospital admission in the Western Australian Hospital Morbidity Data System (HMDS) were linked with the Western Australian Intellectual Disability Exploring Answers (IDEA) database. The International Classification of Diseases hospital codes consistent with ID were also identified.Main outcome measuresThe characteristics of those children identified with ID through either or both sources were investigated.ResultsOf the 488 905 individuals in the study, 10 218 (2.1%) were identified with ID in either IDEA or HMDS with 1435 (14.0%) individuals identified in both databases, 8305 (81.3%) unique to the IDEA database and 478 (4.7%) unique to the HMDS dataset only. Of those unique to the HMDS dataset, about a quarter (n=124) had died before 1 year of age and most of these (75%) before 1 month. Children with ID who were also coded as such in the HMDS data were more likely to be aged under 1 year, female, non-Aboriginal and have a severe level of ID, compared with those not coded in the HMDS data. The sensitivity of using HMDS to identify ID was 14.7%, whereas the specificity was much higher at 99.9%.ConclusionHospital morbidity data are not a reliable source for identifying ID within a population, and epidemiological researchers need to take these findings into account in their study design.

scholarly journals Improved methods for estimating incidence from linked hospital morbidity data

2003 ◽  
Vol 32 (4) ◽  
pp. 617-624 ◽  
Author(s):  
Kate J Brameld ◽  
C D’arcy J Holman ◽  
David M Lawrence ◽  
Michael ST Hobbs
Keyword(s):  
Morbidity Data ◽  
Hospital Morbidity ◽  
Improved Methods ◽  
Hospital Morbidity Data

scholarly journals Linking midwives and hospital morbidity data to investigate the effect of interpregnancy interval on gestational diabetes: a 35-year cohort study in Western Australia

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Amanuel Gebremedhin ◽  
Annette Regan ◽  
Gavin Pereira ◽  
Eva Malacova
Keyword(s):  
Cohort Study ◽  
Gestational Diabetes ◽  
Western Australia ◽  
Evidence Base ◽  
High Income ◽  
Notification System ◽  
Increased Risk ◽  
Morbidity Data ◽  
Hospital Morbidity ◽  
Hospital Morbidity Data

IntroductionInterpregnancy interval (IPI) is a potentially modifiable risk factor for preganncy outcomes, and short and long IPI may be associated with increased rik of pregnancy complications. Record linkage provides the only practiacble means to investigate IPI effects, which requires large generalisable sample sizes and long follow-up time. Objectives and ApproachThis study examines the effect of IPI on gestational diabetes in Western Australia, with the aim to inform the evidence-base for IPI recommendations in high-income countries. A longitudinal population-based retrospective cohort study was conducted using de-identified, probabilistically-linked records for all births in Western Australia from 1980 to 2015 (inclusive) from the state’s Midwives Notification System and the WA Hospital Morbidity Data Collection. Logistic regression model was used to estimate the odds of gestational diabetes by IPI category. Analyses included all women with at least two consecutive singleton live births at 20-44 weeks of gestation. ResultsA cohort of 320,616 women were included in the study. Of these, 13,680 (4\%) had an IPI > 120 months (AOR:1.53, 95\% CI 1.38-1.70) as compared to 18-23 months. Conclusion/ImplicationsOur findings show that both short and long IPIs may be associated with increased risk of gestational diabetes in a high-income setting. In this study, data linkage improved ascertainment of the outcome measure. Results suggest 18-23 months following a previous livebirth may be optimal for avoiding complications in future pregnancies.

scholarly journals Reporting hospital adverse events using The Alfred Hospital's morbidity data

2003 ◽  
Vol 26 (2) ◽  
pp. 100 ◽  
Author(s):  
Rhonda Carroll ◽  
Jenny McLean ◽  
Michael Walsh
Keyword(s):  
Adverse Events ◽  
Health Care Industry ◽  
Hospital Level ◽  
Reporting Requirements ◽  
Standard Code ◽  
Morbidity Data ◽  
Icd 10 ◽  
Coding Standards ◽  
Hospital Morbidity ◽  
Hospital Morbidity Data

Hospital morbidity data were analysed to determine their usefulness for reporting adverse events. The entire ICD-10-AM classification system was reviewed in conjunction with the Australian Coding Standards to identify external causecodes and code prefixes associated with adverse events.For the 50,712 separations registered at The Alfred from July 2000-June 2001, 4,740 external cause codes wereassociated with adverse events. Place of occurrence code CY92.22 was considered the best indicator of the number ofseparations associated with adverse events. Approximately 4% of all separations were associated with adverse eventsoccurring during an episode of care.Results suggest that hospital morbidity data are useful for monitoring adverse events at hospital level. Reliable reportingacross the health care industry requires consistent reporting requirements at state and national levels and the adoptionof standard code prefixes nationally.

How useful are hospital morbidity data for monitoring conditions occurring in the perinatal period?

2005 ◽  
Vol 45 (1) ◽  
pp. 36-41 ◽  
Author(s):  
Lee K TAYLOR ◽  
Susan TRAVIS ◽  
Margaret PYM ◽  
Emily OLIVE ◽  
David J HENDERSON-SMART
Keyword(s):  
Perinatal Period ◽  
Morbidity Data ◽  
Hospital Morbidity ◽  
Hospital Morbidity Data

scholarly journals ICD-data collection features: an international survey

2019 ◽  
Vol 2 (3) ◽  
pp. 2
Author(s):  
Lucia Otero Varela ◽  
Chelsea Doktorchik ◽  
Natalie Wiebe ◽  
Catherine Eastwood ◽  
Hude Quan
Keyword(s):  
Data Collection ◽  
Online Survey ◽  
International Comparisons ◽  
International Classification Of Diseases ◽  
World Health ◽  
Hospital Data ◽  
Discharge Disposition ◽  
Classification Of Diseases ◽  
Hospital Morbidity ◽  
Health Organization

Background:  The International Classification of Diseases (ICD) is globally used for coding morbidity and mortality statistics, however, its use, as well as the data collection features vary greatly across countries. Objective: To characterize hospital ICD-coded data collection worldwide. Methods: After an in-depth grey and academic literature review, an online survey was created to poll the 194 World Health Organization (WHO) member countries. Questions focused on hospital data collection systems and ICD-coded data features. The survey was distributed, using different methods, to potential participants that met the specific criteria, as well as organizations specialized in the topic, such as WHO Collaborating Centers (WHO-CC) or International Federation of Health Information Management Association (IFHIMA), to be forwarded to their representatives. Answers were analyzed using descriptive statistics. Results: Data from 48 respondents from 26 different countries has been collected. Results reveal worldwide use of ICD, with variations in the maximum allowable coding fields for diagnoses and interventions. For instance, in some countries there is an unlimited number of coding fields (Netherlands, Thailand and Iran), as opposed to others with only 1-6 available (Guatemala or Mauritius). Disparities also exist in the definition of a main condition, as 60% of the countries use “reason for admission” and 40% utilize “resource use”. Additionally, the mandatory type of data fields in the hospital morbidity database (e.g. patient demographics, admission type, discharge disposition, diagnoses, …) differ among countries, with diagnosis timing and physician information being the least frequently required. Conclusion: These survey data will establish the current state of ICD use internationally, which will ultimately be valuable to the WHO for the promotion of ICD and the rollout of ICD-11. Additionally, it will improve international comparisons of health data, and encourage further research on how to improve ICD coding.

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