Using routine health data for research: the devil is in the detail

Model-based disease mapping using primary care registry data

BACKGROUND

Spatial modeling of disease risk using primary care registry data is promising for public health surveillance. However, it remains unclear to which extent challenges such as spatially disproportionate sampling and practice-specific reporting variation affect statistical inference.

METHODS

Using lower respiratory tract infection data from the INTEGO registry, modeled with a logistic model incorporating patient characteristics, a spatially structured random effect at municipality level, and an unstructured random effect at practice level, we conducted a case and simulation study to assess the impact of these challenges on spatial trend estimation.

RESULTS

Even with spatial imbalance and practice-specific reporting variation, the model performed well. Performance improved with increasing spatial sample balance and decreasing practice-specific variation.

CONCLUSION

Our findings indicate that, with correction for reporting efforts, primary care registries are valuable for spatial trend estimation. The diversity of patient locations within practice populations plays an important role.

Deriving a Standardised Recommended Respiratory Disease Codelist Repository for Future Research

Background

Electronic health record (EHR) databases provide rich, longitudinal data on interactions with healthcare providers and can be used to advance research into respiratory conditions. However, since these data are primarily collected to support health care delivery, clinical coding can be inconsistent, resulting in inherent challenges in using these data for research purposes.

Methods

We systematically searched existing international literature and UK code repositories to find respiratory disease codelists for asthma from January 2018, and chronic obstructive pulmonary disease and respiratory tract infections from January 2020, based on prior searches. Medline searches using key terms provided in article lists. Full-text articles, supplementary files, and reference lists were examined for codelists, and codelists repositories were searched. A reproducible methodology for codelists creation was developed with recommended lists for each disease created based on multidisciplinary expert opinion and previously published literature.

Results

Medline searches returned 1126 asthma articles, 70 COPD articles, and 90 respiratory infection articles, with 3%, 22% and 5% including codelists, respectively. Repository searching returned 12 asthma, 23 COPD, and 64 respiratory infection codelists. We have systematically compiled respiratory disease codelists and from these derived recommended lists for use by researchers to find the most up-to-date and relevant respiratory disease codelists that can be tailored to individual research questions.

Conclusion

Few published papers include codelists, and where published diverse codelists were used, even when answering similar research questions. Whilst some advances have been made, greater consistency and transparency across studies using routine data to study respiratory diseases are needed.

Challenges and Pitfalls of Using Repeat Spirometry Recordings in Routine Primary Care Data to Measure FEV1 Decline in a COPD Population

Background

Electronic healthcare records (EHR) are increasingly used in epidemiological studies but are often viewed as lacking quality compared to randomised control trials and prospective cohorts. Studies of patients with chronic obstructive pulmonary disease (COPD) often use the rate of forced expiratory volume in 1 second (FEV₁) decline as an outcome; however, its definition and robustness in EHR have not been investigated. We aimed to investigate how the rate of FEV₁ decline differs by the criteria used in an EHR database.

Methods

Clinical Practice Research Datalink and Hospital Episode Statistics were used. Patient populations were defined using 8 sets of criteria around repeated FEV₁ measurements. At a minimum, patients had a diagnosis of COPD, were ≥35 years old, were current or ex-smokers, and had data recorded from 2004. FEV₁ measurements recorded during follow-up were identified. Thereafter, eight populations were defined based on criteria around: i) the exclusion of patients or individual measurements with potential measurement error; ii) minimum number of FEV₁ measurements; iii) minimum time interval between measurements; iv) specific timing of measurements; v) minimum follow-up time; and vi) the use of linked data. For each population, the rate of FEV₁ decline was estimated using mixed linear regression.

Results

For 7/8 patient populations, rates of FEV₁ decline (age and sex adjusted) were similar and ranged from −18.7mL/year (95% CI −19.2 to −18.2) to −16.5mL/year (95% CI −17.3 to −15.7). Rates of FEV₁ decline in populations that excluded patients with potential measurement error ranged from −79.4mL/year (95% CI −80.7 to −78.2) to −46.8mL/year (95% CI −47.6 to −46.0).

Conclusion

FEV₁ decline remained similar in a COPD population regardless of number of FEV₁ measurements, time intervals between measurements, follow-up period, exclusion of specific FEV₁ measurements, and linkage to HES. However, exclusion of individuals with questionable data led to selection bias and faster rates of decline.