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.

Electronic health records to capture primary outcome measures: two case studies in HIV prevention research

BACKGROUND

There is increasing interest in the use of electronic health records (EHRs) to improve the efficiency and cost-effectiveness of clinical trials, including the capture of outcome measures.

MAIN TEXT

We describe our experience of using EHRs to capture the primary outcome measure - HIV infection or the diagnosis of HIV infection - in two randomised HIV prevention trials conducted in the UK. PROUD was a clinic-based trial evaluating pre-exposure prophylaxis (PrEP), and SELPHI was an internet-based trial evaluating HIV self-testing kits. The EHR was the national database of HIV diagnoses in the UK, curated by the UK Health Security Agency (UKHSA). In PROUD, linkage to the UKHSA database was performed at the end of the trial and identified five primary outcomes in addition to the 30 outcomes diagnosed by the participating clinics. Linkage also produced an additional 345 person-years follow-up, an increase of 27% over clinic-based follow-up. In SELPHI, new HIV diagnoses were primarily identified via UKHSA linkage, complemented by participant self-report through internet surveys. Rates of survey completion were low, and only 14 of the 33 new diagnoses recorded in the UKHSA database were also self-reported. Thus UKHSA linkage was essential for capturing HIV diagnoses and the successful conduct of the trial.

CONCLUSIONS

Our experience of using the UKHSA database of HIV diagnoses as a source of primary outcomes in two randomised trials in the field of HIV prevention was highly favourable and encourages the use of a similar approach in future trials in this disease area.

Electronic medical records and biobanking

Biosample preservation for future research is a fundamental component of translational medicine. At the same time, the value of stored biosamples is largely determined by the presence of related clinical data and other information. Electronic medical records are a unique source of a large amount of information received over a long period of time. In this regard, genetic and other types of data obtained from the biosample analysis can be associated with phenotypic and other types of information stored in electronic medical records, which pushes the boundaries in large-scale genetic research and improves healthcare. The aim of this review was to analyze the literature on the potential of combining electronic medical records and biobank databases in research and clinical practice.

Challenges and recommendations for high quality research using electronic health records

Harnessing Real World Data is vital to improve health care in the 21st Century. Data from Electronic Health Records (EHRs) are a rich source of patient centred data, including information on the patient's clinical condition, laboratory results, diagnoses and treatments. They thus reflect the true state of health systems. However, access and utilisation of EHR data for research presents specific challenges. We assert that using data from EHRs effectively is dependent on synergy between researchers, clinicians and health informaticians, and only this will allow state of the art methods to be used to answer urgent and vital questions for patient care. We propose that there needs to be a paradigm shift in the way this research is conducted - appreciating that the research process is iterative rather than linear. We also make specific recommendations for organisations, based on our experience of developing and using EHR data in trusted research environments.

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.

Protecting against researcher bias in secondary data analysis: challenges and potential solutions

Analysis of secondary data sources (such as cohort studies, survey data, and administrative records) has the potential to provide answers to science and society's most pressing questions. However, researcher biases can lead to questionable research practices in secondary data analysis, which can distort the evidence base. While pre-registration can help to protect against researcher biases, it presents challenges for secondary data analysis. In this article, we describe these challenges and propose novel solutions and alternative approaches. Proposed solutions include approaches to (1) address bias linked to prior knowledge of the data, (2) enable pre-registration of non-hypothesis-driven research, (3) help ensure that pre-registered analyses will be appropriate for the data, and (4) address difficulties arising from reduced analytic flexibility in pre-registration. For each solution, we provide guidance on implementation for researchers and data guardians. The adoption of these practices can help to protect against researcher bias in secondary data analysis, to improve the robustness of research based on existing data.

The PSYchiatric clinical outcome prediction (PSYCOP) cohort: leveraging the potential of electronic health records in the treatment of mental disorders

BACKGROUND

The quality of life and lifespan are greatly reduced among individuals with mental illness. To improve prognosis, the nascent field of precision psychiatry aims to provide personalised predictions for the course of illness and response to treatment. Unfortunately, the results of precision psychiatry studies are rarely externally validated, almost never implemented in clinical practice, and tend to focus on a few selected outcomes. To overcome these challenges, we have established the PSYchiatric Clinical Outcome Prediction (PSYCOP) cohort, which will form the basis for extensive studies in the upcoming years.

METHODS

PSYCOP is a retrospective cohort study that includes all patients with at least one contact with the psychiatric services of the Central Denmark Region in the period from January 1, 2011, to October 28, 2020 (n = 119 291). All data from the electronic health records (EHR) are included, spanning diagnoses, information on treatments, clinical notes, discharge summaries, laboratory tests, etc. Based on these data, machine learning methods will be used to make prediction models for a range of clinical outcomes, such as diagnostic shifts, treatment response, medical comorbidity, and premature mortality, with an explicit focus on clinical feasibility and implementation.

DISCUSSIONS

We expect that studies based on the PSYCOP cohort will advance the field of precision psychiatry through the use of state-of-the-art machine learning methods on a large and representative data set. Implementation of prediction models in clinical psychiatry will likely improve treatment and, hopefully, increase the quality of life and lifespan of those with mental illness.

Examining the effectiveness of telemonitoring with routinely acquired blood pressure data in primary care: challenges in the statistical analysis

Background

Scale-up BP was a quasi-experimental implementation study, following a successful randomised controlled trial of the roll-out of telemonitoring in primary care across Lothian, Scotland. Our primary objective was to assess the effect of telemonitoring on blood pressure (BP) control using routinely collected data. Telemonitored systolic and diastolic BP were compared with surgery BP measurements from patients not using telemonitoring (comparator patients). The statistical analysis and interpretation of findings was challenging due to the broad range of biases potentially influencing the results, including differences in the frequency of readings, ‘white coat effect’, end digit preference, and missing data.

Methods

Four different statistical methods were employed in order to minimise the impact of these biases on the comparison between telemonitoring and comparator groups. These methods were “standardisation with stratification”, “standardisation with matching”, “regression adjustment for propensity score” and “random coefficient modelling”. The first three methods standardised the groups so that all participants provided exactly two measurements at baseline and 6–12 months follow-up prior to analysis. The fourth analysis used linear mixed modelling based on all available data.

Results

The standardisation with stratification analysis showed a significantly lower systolic BP in telemonitoring patients at 6–12 months follow-up (-4.06, 95% CI -6.30 to -1.82, p < 0.001) for patients with systolic BP below 135 at baseline. For the standardisation with matching and regression adjustment for propensity score analyses, systolic BP was significantly lower overall (− 5.96, 95% CI -8.36 to − 3.55 , p < 0.001) and (− 3.73, 95% CI− 5.34 to − 2.13, p < 0.001) respectively, even after assuming that − 5 of the difference was due to ‘white coat effect’. For the random coefficient modelling, the improvement in systolic BP was estimated to be -3.37 (95% CI -5.41 to -1.33 , p < 0.001) after 1 year.

Conclusions

The four analyses provide additional evidence for the effectiveness of telemonitoring in controlling BP in routine primary care. The random coefficient analysis is particularly recommended due to its ability to utilise all available data. However, adjusting for the complex array of biases was difficult. Researchers should appreciate the potential for bias in implementation studies and seek to acquire a detailed understanding of the study context in order to design appropriate analytical approaches.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01219-8.