Dataset Information

Pinocchio testing in the forensic analysis of waiting lists: using public waiting list data from Finland and Spain for testing Newcomb-Benford's Law.

ABSTRACT:

Objective

Newcomb-Benford's Law (NBL) proposes a regular distribution for first digits, second digits and digit combinations applicable to many different naturally occurring sources of data. Testing deviations from NBL is used in many datasets as a screening tool for identifying data trustworthiness problems. This study aims to compare public available waiting lists (WL) data from Finland and Spain for testing NBL as an instrument to flag up potential manipulation in WLs.

Design

Analysis of the frequency of Finnish and Spanish WLs first digits to determine if their distribution is similar to the pattern documented by NBL. Deviations from the expected first digit frequency were analysed using Pearson's ?², mean absolute deviation and Kuiper tests.

Setting/participants

Publicly available WL data from Finland and Spain, two countries with universal health insurance and National Health Systems but characterised by different levels of transparency and good governance standards.

Main outcome measures

Adjustment of the observed distribution of the numbers reported in Finnish and Spanish WL data to the expected distribution according to NBL.

Results

WL data reported by the Finnish health system fits first digit NBL according to all statistical tests used (p=0.6519 in ?² test). For Spanish data, this hypothesis was rejected in all tests (p<0.0001 in ?² test).

Conclusions

Testing deviations from NBL distribution can be a useful tool to identify problems with WL data trustworthiness and signalling the need for further testing.

SUBMITTER: Pinilla J

PROVIDER: S-EPMC5942457 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Publications

Pinocchio testing in the forensic analysis of waiting lists: using public waiting list data from Finland and Spain for testing Newcomb-Benford's Law.

Pinilla Jaime J López-Valcárcel Beatriz G BG González-Martel Christian C Peiro Salvador S

BMJ open 20180509 5

<h4>Objective</h4>Newcomb-Benford's Law (NBL) proposes a regular distribution for first digits, second digits and digit combinations applicable to many different naturally occurring sources of data. Testing deviations from NBL is used in many datasets as a screening tool for identifying data trustworthiness problems. This study aims to compare public available waiting lists (WL) data from Finland and Spain for testing NBL as an instrument to flag up potential manipulation in WLs.<h4>Design</h4>A ...[more]

PMID: 29743333

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Project description:ImportanceWith continuing improvements in medical devices and more than a decade since the 2006 United Network for Organ Sharing (UNOS) allocation policy, it is pertinent to assess survival among patients on the heart transplantation waiting list, especially given the recently approved 2018 UNOS allocation policy.ObjectivesTo assess survival outcomes among patients on the heart transplant waiting list during the past 3 decades and to examine the association of ventricular assist devices (VADs) and the 2006 UNOS allocation policy with survival.Design, setting, and participantsA retrospective cross-sectional used the UNOS database to perform an analysis of 95 323 candidates wait-listed for heart transplantation between January 1, 1987, and December 29, 2017. Candidates for all types of combined transplants were excluded (n = 2087). Patients were followed up from the time of listing to death, transplantation, or removal from the list due to clinical improvement. Competing-risk, Kaplan-Meier, and multivariable Cox proportional hazards regression analyses were used.Main outcomes and measuresThe analysis involved an unadjusted and adjusted survival analysis in which the primary outcome was death on the waiting list. Because of changing waiting list preferences and policies during the study period, the intrinsic risk of death for wait-listed candidates was assessed by individually analyzing, comparing, and adjusting for several candidate risk factors.ResultsIn total, 95 323 candidates (72 915 men [76.5%]; mean [SD] age, 51.9 [12.0] years) were studied. In the setting of changes in listing preferences, 1-year survival on the waiting list increased from 34.1% in 1987-1990 to 67.8% in 2011-2017 (difference in proportions, 0.34%; 95% CI, 0.32%-0.36%; P < .001). The 1-year waiting list survival for candidates with VADs increased from 10.2% in 1996-2000 to 70.0% in 2011-2017 (difference in proportions, 0.60%; 95% CI, 0.58%-0.62%; P < .001). Similarly, in the setting of changing mechanical circulatory support indications, the 1-year waiting list survival for patients without VADs increased from 53.9% in 1996-2000 to 66.5% in 2011-2017 (difference in proportions, 0.13%; 95% CI, 0.12%-0.14%; P < .001). In the decade prior to the 2006 UNOS allocation policy, the 1-year waiting list survival was 51.1%, while in the decade after it was 63.9% (difference in proportions, 0.13%; 95% CI, 0.12%-0.14%; P < .001). In adjusted analysis, each time period after 1987-1990 had a marked decrease in waiting list mortality.Conclusions and relevanceThis study found temporally associated increases in heart transplant waiting list survival for all patient groups (with or without VADs, UNOS status 1 and status 2 candidates, and candidates with poor functional status).

Dataset Information

Pinocchio testing in the forensic analysis of waiting lists: using public waiting list data from Finland and Spain for testing Newcomb-Benford's Law.

Objective

Design

Setting/participants

Main outcome measures

Results

Conclusions

Publications

Pinocchio testing in the forensic analysis of waiting lists: using public waiting list data from Finland and Spain for testing Newcomb-Benford's Law.

Similar Datasets

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