Healthy sampling A very brief primer on selection bias for students of #epidemiology

Selection bias can occur when choosing people to participate in the study isn’t random. This creates a study sample that is not representative of the entire population you want to know about.  This systematic error leads to error in your results.

Selection bias can occur when choosing people to participate in the study isn’t random. This creates a study sample that is not representative of the entire population you want to know about.  This systematic error leads to error in your results.

What if your population is segmented, perhaps into people who have diabetes and those that don’t? Or people that are exposed to an important cause of disease like cigarette smoking and those that are not exposed? Can you see that if you pick a sample mostly from the upper left portion of the circle you will overestimate the amount of smoking in your population? And if you are studying a disease strongly associated with smoking, you will end up estimating a higher proportion of disease in your population.   *According to the CDC, prevalence of cigarette smoking among U.S. adults is highest among people living in the Midwest (25.4%), where TheEvidenceDoc is located. https://www.cdc.gov/tobacco/disparities/geographic/index.htm

What if your population is segmented, perhaps into people who have diabetes and those that don’t? Or people that are exposed to an important cause of disease like cigarette smoking and those that are not exposed? Can you see that if you pick a sample mostly from the upper left portion of the circle you will overestimate the amount of smoking in your population? And if you are studying a disease strongly associated with smoking, you will end up estimating a higher proportion of disease in your population.

 

*According to the CDC, prevalence of cigarette smoking among U.S. adults is highest among people living in the Midwest (25.4%), where TheEvidenceDoc is located. https://www.cdc.gov/tobacco/disparities/geographic/index.htm

You can find some examples of biased sampling in the polls on Twitter. Since twitter uses hashtags to group tweets and make it easier to follow certain topics, some pollsters have made use of the hashtag to direct their polls to certain groups of people. If the intent is to accurately measure a population opinion, how will this segmented reach impact the results of their polls and the generalizability of those findings?

You can find some examples of biased sampling in the polls on Twitter. Since twitter uses hashtags to group tweets and make it easier to follow certain topics, some pollsters have made use of the hashtag to direct their polls to certain groups of people. If the intent is to accurately measure a population opinion, how will this segmented reach impact the results of their polls and the generalizability of those findings?

Can't get no #patientsatisfaction while waiting for Godot

I have adult children. It's sometimes difficult staying out of their lives and decisions, especially in matters of health. So instead I ask a lot of questions. The last several weeks I've been asking quite a few of one child who is struggling, as so many patients do, with getting through the referral process into specialty care. She first saw her primary care doc for an unusual acute illness. He was easy enough to get in to see. He evaluated her, took blood for labs and said the office would call with results. After a week of no response I encouraged her to call and get the results. They were then promptly provided along with a scheduled next appointment for the following week. During the appointment, the doc said he would refer her to a specialist to follow up with the lab findings. She waited another week with no contact from the specialist so I prodded her to call the primary care again. They said they sent the referral and to wait for contact from the specialist. She did get the number of the specialist, but waited another week with no call from them. With more urging from me, she then called the specialist office who said yes, she was on the list to be called and would hear in a couple of days. Ten days later with no call, I urged her to call back...

Are you tired of reading this yet? Imagine being the patient, fearful with abnormal labs and wondering what's wrong and whether or not the delay in care will impact her long term health.

How often does this happen in the US? We have surprisingly little data to address. Merritt Hawkins conducted a survey by calling physician offices in 15 metropolitan areas to schedule an appointment with several specialties for non-emergent conditions. https://www.merritthawkins.com/2014-survey/patientwaittime.aspx  They found average wait to schedule a cardiology appointment in DC was 32 days ranging from as short as 4 to as long as 186 days! Average wait to see an ob-gyn in Boston was 46 days ranging from 5 to 103.

How long do patients wait to get to your care or the care of your specialty colleagues? Do you know? We cannot improve what we haven't measured.

  1. Do you know how long your patients wait to get an appointment to see you?
  2. Do you know how long your patients wait to get an appointment when you refer them for specialty care?
  3. Do you know how smooth the process is for patients to navigate?
  4. Do you provide your patients with information they can use while waiting for their next appointment?
  5.  Have you ever thought about what it's like for patients trying to get access to your care?

I know we were trained to provide the best care to our patients when we interact with them. But the patient interaction begins long before you see them in your office.

How can you improve scheduling for your patients? You can start by asking your patients about their wait experience (gather data). Then you can read this free resource from the National Academies of Science, Engineering and Medicine for some ideas on what to do about it. https://www.nap.edu/catalog/20220/transforming-health-care-scheduling-and-access-getting-to-now

Make sure your patients don't compare their wait to see you to Waiting for Godot.