Category Archives: Research

Do Certain Minority Ethnic Groups Receive Poorer Care?

According to research, in the US and UK, certain minority ethnic groups report lower patient experience scores compared to the majority population. For example, analysis of the English General Practice Patient Survey found that South Asian groups report particularly low scores compared to the White British majority.

Fig 1.
Fig 2: Age and gender-specific differences, with 95% confidence intervals, in reported GP–patient communication scores (0–100 scale) between white British patients and responders in Asian and white ethnic groups.

Even though half of the difference in these scores is explained by the concentration of South Asian patients in low-scoring primary care practices, the remaining half has been unexplained. Of course, the open question has been whether South Asian patients receive lower quality care, or whether they receive similar care, but rate this more negatively. Now, a study attempts to understand this disparity and underlying causes and their work shows that the lower scores by minority ethnic groups, at least in the context of GP surveys in England reflects worse experiences of communication compared to the White British majority.




Fig 1: Understanding why some ethnic minority patients evaluate medical care more negatively than white patients: a cross sectional analysis of a routine patient survey in English general practices.

Fig 2: Variations in GP–patient communication by ethnicity, age, and gender: evidence from a national primary care patient survey.

PARR-30: An Example of A Transparent and Open Published Research

In my other post earlier today, I made a plea for researchers to include model details for research done on retrospective data. I thought it will be a good idea to include an example of what it might look like and how it is helpful.

A good example is the research done in England and published on BMJ Open in 2012 that led to development of a predictive model to identify inpatients at risk of re-admission within 30 days of discharge (PARR-30).

The paper lists the coefficients of their logistic regression model



It has a worked example of how a risk score can be calculated



The supplemental website has lots of information to help you implement or validate this model for your own population


Data driven healthcare studies should include model details

The quality and quantity of healthcare data is on an upward trajectory as EMRs become more ubiquitous. One of the many benefits of this data has accrued to researchers who are increasingly using retrospective analysis to publish meaningful research. This is good news because as noted recently by Jeffrey Drazen, MD, Editor-in-Chief of the NEJM

the number of evidence-based recommendations built on randomized controlled trials (RCTs), the current gold standard for data quality, is insufficient to address the majority of clinical decisions.

However, one disappointment that I have encountered fairly consistently with these research studies is that they very rarely, if at all, include details regarding the model.  The most information that they may have regarding the modeling exercise will be the types of models that were considered (for example, logistic regression, naive Bayes, SVM etc.) and measures of their statistical performance. Very rarely will they contain details for those models such as the coefficients, conditional probabilities etc. The exceptions that I have seen are papers published outside of the US, most often from NHS or from Canada. For example, in this post, I discuss a study from NHS that develops a 30-day readmission risk model. The information available in this paper should be par for the course for all such publicly funded research.

Now, I understand that algorithms and models can be a competitive advantage and why someone may opt to not make their secret sauce public. But for research studies that are funded through public grants such as those from NHS, it should be required that not only the model details be made available but the underlying data set (if it can be satisfactorily de-identified) should be publicly shared as well. The idea behind publicly supported research is to advance the knowledge base in a given field and the best way to do so is to share as much as possible regarding the research so that other individuals and organizations can learn from it and carry the field forward.