| Literature DB >> 27067413 |
Lyndal Bugeja1,2, Joseph E Ibrahim3, Noha Ferrah3, Briony Murphy3, Melissa Willoughby3,4, David Ranson5.
Abstract
BACKGROUND: Medico-legal death investigations are a recognised data source for public health endeavours and its accessibility has increased following the development of electronic data systems. Despite time and cost savings, the strengths and limitations of this method and impact on research findings remain untested. This study examines this issue using the National Coronial Information System (NCIS).Entities:
Keywords: Coroners and medical examiners; Injury prevention; Mortality surveillance; National Coronial Information System Public health research
Mesh:
Year: 2016 PMID: 27067413 PMCID: PMC4828834 DOI: 10.1186/s12961-016-0096-1
Source DB: PubMed Journal: Health Res Policy Syst ISSN: 1478-4505
Fig. 1PRISMA Flow diagram of identification, screening, and inclusion of eligible articles. * The search term NCIS is also an acronym for other organisations and scientific terminology
Description of journals and publications reviewed (n = 106)
| Number of publications | Proportion of publications | |
|---|---|---|
|
| % | |
| Journal country | ||
| Australia | 41 | 38.7 |
| United Kingdom | 34 | 32.1 |
| United States of America/Canada | 14 | 13.2 |
| Germany | 6 | 5.7 |
| Ireland | 5 | 4.7 |
| Switzerland | 3 | 2.8 |
| Netherlands | 2 | 1.9 |
| United Arab Emirates | 1 | 0.9 |
| Journal category | ||
| Occupational Health and Safety | 28 | 26.4 |
| Medicine | 26 | 24.5 |
| Law and Legal Medicine | 13 | 12.3 |
| Psychiatry | 12 | 11.3 |
| Health policy & services | 9 | 8.5 |
| Pharmacology | 7 | 6.6 |
| Engineering | 7 | 6.6 |
| Othera | 4 | 3.8 |
| Journal ranking quartile within category | ||
| Q1 | 33 | 31.1 |
| Q2 | 31 | 29.2 |
| Q3 | 12 | 11.3 |
| Q4 | 10 | 9.4 |
| Not indexed | 20 | 19.0 |
| Design | ||
| Retrospective case series | 94 | 88.7 |
| Retrospective cohort | 6 | 5.7 |
| Ecological | 5 | 4.7 |
| Prospective case series | 1 | 0.9 |
| Number of years of data | ||
| <2 years | 5 | 4.7 |
| 2–5 years | 18 | 17.0 |
| 6–9 years | 62 | 58.5 |
| ≥10 years | 19 | 17.9 |
| Unknown | 2 | 1.9 |
| Intent | ||
| Unintentional | 42 | 39.7 |
| All intent | 35 | 33.0 |
| Intentional self-harm | 27 | 25.5 |
| Assault | 1 | 0.9 |
| Natural | 1 | 0.9 |
| Death type | ||
| Intentional self-harm | 27 | 25.5 |
| Transport | 18 | 17.0 |
| Other reportable deathsb | 13 | 12.3 |
| Toxicology | 12 | 11.3 |
| Work | 10 | 9.4 |
| Special groupsc | 8 | 7.5 |
| Drowning | 7 | 6.6 |
| Recreation | 7 | 6.6 |
| Locationd | 4 | 3.8 |
aIncludes multidisciplinary sciences, sport sciences and social sciences
bIncludes natural deaths, assaults, traumatic brain injuries and other unintentional deaths
cIncludes children, Indigenous and Torres Straight Islanders, and prisoners
dIncludes farms, emergency departments and residential aged care
Strength domains and reported impacts
| Domain – Information was … | Number of publications (proportions)a | Justifications | Reported impacts |
|---|---|---|---|
| COMPREHENSIVE | |||
| Comprehensive coverage | 16 (43%) | Captures all reportable deaths across Australia and New Zealand | Monitor mortality trends at population-level |
| DETAILED | |||
| Detailed data source | 13 (35%) | Richness of information in closed cases | Detailed examination of causes and circumstances of death |
| RELIABLE | |||
| Data consistency | 11 (29%) | Quality assessment by trained staff at the National Coronial Information System (NCIS) and internal quality control | Accurate estimate of mortality |
| Rigorous coding framework | Rigorous and consistent coding scheme | ||
| APPLICABLE | |||
| Utility for death investigation Utility for public health and safety and injury prevention | 9 (24%) | Hazard identification tool | Potential for reduction in preventable deaths |
| OF HIGH QUALITY | |||
| Good quality data | 6 (16%) | Valid information | Contribute to validity of study findings |
| CURRENT | |||
| Most current data source available | 4 (10%) | Contemporary information | Not specified |
aProportions add up to over 100% as studies may report multiple categories of strengths
Limitation domains, remediation and reported impacts
| Domain – Information was not … | Number of publications (proportions)a | Reported impact on findings | Actions taken |
|---|---|---|---|
| AVAILABLE | |||
| Open cases | 23 (34%) | Under-reporting of potentially relevant cases | Exclusion of all open cases |
| Inability to verify information | Used additional data source | ||
| Whole documents/information of interest | 26 (39%) | Unable to conduct detailed analysis | Access paper-based record |
| Under-reporting of potentially relevant cases | Exclusion of cases | ||
| Small dataset | 2 (3%) | Unable to detect trends and evaluate impacts of interventions | Acknowledged limitation |
| COMPLETE | |||
| Missing data on available info of interest | 16 (24%) | Missing/incomplete data for analysis | Access paper-based record |
| ACCURATE | |||
| Potential for human error | 5 (7%) | Errors in information recording during inquest (e.g. spelling of drug names) | Not specified |
| Potential for human error in coding | 7 (10%) | Missing data for some variables | Access paper-based record |
| Misclassification of intent re: intentional self-harm | 5 (7%) | Under-reporting of potentially relevant cases | Used additional data source |
| Discrepancies between the National Coronial Information System (NCIS) and ICD-10 | 3 (4%) | Under-reporting of potentially relevant cases | Used additional data source (ICD-10) |
aProportions add up to over 100% as studies may report multiple categories of limitations