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Potential of Swedish healthcare data illustrated by new study

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Potential of Swedish healthcare data illustrated by new study

Dr. Johanna Simin of Medaffcon, who shared last authorship of the comprehensive study of CDI cases in Sweden, says the researchers were surprised to find a higher mortality rate associated with the first than recurrent infections.

Sweden’s abundance of high-quality digital healthcare data has enabled what is surely one of the most exhaustive, if not the most exhaustive, study of mortality associated with Clostridioides difficile infections (CDI) in the world.

Conducted at Karolinska Institutet in Stockholm, the study encompassed 43,150 individuals who had the bacterial infection between 1 January 2006 and 31 December 2019. The individuals had more than 355,170 controls during the 14-year period and were predominantly elderly, three quarters of them being 65 years or older.

“It’s the largest study of its kind at least as far as I know,” says Dr. Johanna Simin, real-world evidence epidemiologist at Medaffcon. Dr. Simin shared last authorship of the study.

CDI is one of the leading cause gastroenteritis-related deaths in the industrialised world, accounting for about 15 per cent of healthcare-associated infections in the US in 2015.

The research cohort was based on data from five nationwide and population-based registers administered by Sweden’s National Board of Health and Welfare. The infections were identified utilising diagnoses in the patient register and the initial control population utilising the prescribed drug register, with supplementary data derived from the cancer register, cause of death register and elderly register.

“The diagnoses are based on laboratory results, but we didn’t have the laboratory results themselves; they weren’t necessary because the diagnoses had the ICD-10 code. The code was enough for us because we were primarily interested in the mortality, recurrence and overall burden of the infections, rather than the strain that caused the infection,” tells Dr. Simin.

The research team filed a very detailed data application, she tells.

“We were able to determine what diseases and co-morbidities the patients had, when they were admitted to and discharged from hospital – including the ward – and for what reason. We also included data on surgeries, all the prescription medications they had used, as well as to where the patients were discharged from hospital.”

“We had so much data that we had to delineate which variables we wanted to examine to be able to open the data on a computer.”

One application can power several studies

Although gaining access to the data can take a while, Dr. Simin points out that, once received, the data can be utilised also for future projects as long as they fall within the parameters defined in the granted ethical permit, they are conducted within five years of receiving the permit and their results are published.

“That’s precisely what we’ve done in several of our larger projects. With a single well-designed ethical application and data application, we’ve been able to maximise data use and answer important research questions in up to 20 publications. In this project, we aim to make further resource-utilisation estimates and examine drug-microbiome interactions, including antibiotics and proton pump inhibitors,” she reveals.

“With a single well-designed ethical application and data application, we’ve been able to maximise data use and answer important research questions in up to 20 publications.”

“It can be a lengthy process, but you can glean so much from the data once you’ve received them.”

Dr. Simin encourages pharmaceutical companies to consider utilising the population-wide registers especially in the early stages of product development and evidence generation to, for example, identify which population groups stand to benefit the most from a new medication.

“If you’re trying to identify these types of populations, assess the risks, benefits or costs to society or answer some other important questions in evidence generation on a national level, population-wide registers are definitely the best choice,” she states. “They’re excellent when you want to identify where you should invest in.”

She also highlights that companies utilise healthcare data readily to compare interventions in terms of their cost or effectiveness but neglect the possibilities data offer in the design phase.

“The data can and should be used already in the design phase to identify where you should invest in and as part of the evidence generation process. These data can also be used in the numerous application forms related to medications.”

Distinguishing between hospital and community-acquired CDI

One of the key findings of the study was that, compared to both the general background population and matched controls, the risk of mortality was three to seven times higher for individuals with CDI.

“That’s true especially for individuals who got infected in hospital. Studies such as this can’t typically distinguish between hospital-acquired and community-acquired infections, but we were able to do that by applying solid methods and using high-quality register data, without any laboratory data,” tells Dr. Simin.

“It’s great to be able to get so much out of a national register.”

Hospital and community-acquired infections also had distinct outlooks. Whereas the mortality rate for hospital-acquired infections was about 31 per cent among under 65-year-olds and 76 per cent among 65 year-olds and older, the corresponding shares for community-acquired infections were 4 and 35 per cent.

Most interestingly, the researchers observed that the highest mortality risk was associated with the first infection rather than recurrent infections. Additionally, most deaths occurred during the first 30 days after diagnosis, yet the risk of death remained elevated for up to a year.

“Mortality is linked most strongly to the first infection, indicating that the first infection is what we should be focused on preventing and treating,” Dr. Simin argues.

The results of the study are to be published in Clinical Microbiology and Infection.

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