What big data could achieve in Scotland

Understanding variations in reported epidemiology of major lower extremity amputation in the UK: a systematic review

OBJECTIVE

Estimate the prevalence/incidence/number of major lower extremity amputations (MLEAs) in the UK; identify sources of routinely collected electronic health data used; assess time trends and regional variation; and identify reasons for variation in reported incidence/prevalence of MLEA.

DESIGN

Systematic review and narrative synthesis.

DATA SOURCES

Medline, Embase, EMcare, CINAHL, The Cochrane Library, AMED, Scopus and grey literature sources searched from 1 January 2009 to 1 August 2021.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

Reports that provided population-based statistics, used routinely collected electronic health data, gave a measure of MLEA in adults in the general population or those with diabetes in the UK or constituent countries were included.

DATA EXTRACTION AND SYNTHESIS

Data extraction and quality assessment using the Joanna Briggs Institute Critical Appraisal Instruments were performed by two reviewers independently. Due to considerable differences in study populations and methodology, data pooling was not possible; data were tabulated and narratively synthesised, and study differences were discussed.

RESULTS

Twenty-seven reports were included. Incidence proportion for the general population ranged from 8.2 to 51.1 per 100 000 and from 70 to 291 per 100 000 for the population with diabetes. Evidence for trends over time was mixed, but there was no evidence of increasing incidence. Reports consistently found regional variation in England with incidence higher in the north. No studies reported prevalence. Differences in database use, MLEA definition, calculation methods and multiple procedure inclusion which, together with identified inaccuracies, may account for the variation in incidence.

CONCLUSIONS

UK incidence and trends in MLEA remain unclear; estimates vary widely due to differences in methodology and inaccuracies. Reasons for regional variation also remain unexplained and prevalence uninvestigated. International consensus on the definition of MLEA and medical code list is needed. Future research should recommend standards for the reporting of such outcomes and investigate further the potential to use primary care data in MLEA epidemiology.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42020165592.

CIDACS-RL: a novel indexing search and scoring-based record linkage system for huge datasets with high accuracy and scalability

Background

Record linkage is the process of identifying and combining records about the same individual from two or more different datasets. While there are many open source and commercial data linkage tools, the volume and complexity of currently available datasets for linkage pose a huge challenge; hence, designing an efficient linkage tool with reasonable accuracy and scalability is required.

Methods

We developed CIDACS-RL (Centre for Data and Knowledge Integration for Health – Record Linkage), a novel iterative deterministic record linkage algorithm based on a combination of indexing search and scoring algorithms (provided by Apache Lucene). We described how the algorithm works and compared its performance with four open source linkage tools (AtyImo, Febrl, FRIL and RecLink) in terms of sensitivity and positive predictive value using gold standard dataset. We also evaluated its accuracy and scalability using a case-study and its scalability and execution time using a simulated cohort in serial (single core) and multi-core (eight core) computation settings.

Results

Overall, CIDACS-RL algorithm had a superior performance: positive predictive value (99.93% versus AtyImo 99.30%, RecLink 99.5%, Febrl 98.86%, and FRIL 96.17%) and sensitivity (99.87% versus AtyImo 98.91%, RecLink 73.75%, Febrl 90.58%, and FRIL 74.66%). In the case study, using a ROC curve to choose the most appropriate cut-off value (0.896), the obtained metrics were: sensitivity = 92.5% (95% CI 92.07–92.99), specificity = 93.5% (95% CI 93.08–93.8) and area under the curve (AUC) = 97% (95% CI 96.97–97.35). The multi-core computation was about four times faster (150 seconds) than the serial setting (550 seconds) when using a dataset of 20 million records.

Conclusion

CIDACS-RL algorithm is an innovative linkage tool for huge datasets, with higher accuracy, improved scalability, and substantially shorter execution time compared to other existing linkage tools. In addition, CIDACS-RL can be deployed on standard computers without the need for high-speed processors and distributed infrastructures.

Data Resource Profile: the Scottish Social Care Survey (SCS) and the Scottish Care Home Census (SCHC)

Introduction

Linked health care datasets have been used effectively in Scotland for some time. Use of social care data has been much more limited, partly because responsibility for these services is distributed across multiple local authorities. However, there are substantial interactions between health and social care (also known internationally as long-term care) services, and keen policy interest in better understanding these. We introduce two social care resources that can now be linked to health datasets at a population level across Scotland to study these interdependencies. These data emerge from the Scottish Government’s centralised collation of data from mandatory returns provided by local authorities and care homes.

Methods

Deterministic and Probabilistic methods were used to match the Social Care Survey (SCS) and Scottish Care Home Census (SCHC) to the Community Health Index (CHI) number via the National Records of Scotland (NRS) Research Indexing Spine.

Results

For the years 2010/11 to 2015/16, an overall match rate of 91.2% was achieved for the SCS to CHI from 31 of Scotland’s 32 local authority areas. This rate varied from 76.7% to 98.5% for local authority areas. A match rate of 89.8% to CHI was achieved for the SCHC in years 2012/13 to 2015/16 but only 52.5% for the years 2010/11 to 2011/12.

Conclusion

Indexing of the SCS and SCHC to CHI offers a new and rich resource of data for health and social care research.

Identifying care-home residents in routine healthcare datasets: a diagnostic test accuracy study of five methods

Background

there is no established method to identify care-home residents in routine healthcare datasets. Methods matching patient's addresses to known care-home addresses have been proposed in the UK, but few have been formally evaluated.

Study design

prospective diagnostic test accuracy study.

Methods

four independent samples of 5,000 addresses from Community Health Index (CHI) population registers were sampled for two NHS Scotland Health Boards on 1 April 2017, with one sample of adults aged ≥65 years and one of all residents. To derive the reference standard, all 20,000 addresses were manually adjudicated as 'care-home address' or not. The performance of five methods (NHS Scotland assigned CHI Institution Flag, exact address matching, postcode matching, Phonics and Markov) was evaluated compared to the reference standard.

Results

the CHI Institution Flag had a high PPV 97-99% in all four test sets, but poorer sensitivity 55-89%. Exact address matching failed in every case. Postcode matching had higher sensitivity than the CHI flag 78-90%, but worse PPV 77-85%. Area under the receiver operating curve values for Phonics and Markov scores were 0.86-0.95 and 0.93-0.98, respectively. Phonics score with cut-off ≥13 had PPV 92-97% with sensitivity 72-87%. Markov PPVs were 90-95% with sensitivity 69-90% with cut-off ≥29.6.

Conclusions

more complex address matching methods greatly improve identification compared to the existing NHS Scotland flag or postcode matching, although no method achieved both sensitivity and positive predictive value > 95%. Choice of method and cut-offs will be determined by the specific needs of researchers and practitioners.