What type of bias is most important to address in a retrospective, cross-sectional study evaluating an influenza point-of-care (POC) test-and-treat program, where patients who re-utilized the program were more likely to have positive screening results?

Surveillance Bias is Most Important to Address in the Influenza Point-of-Care Test Study

The most important type of bias to address in this retrospective, cross-sectional study is surveillance bias, as patients who re-utilized the pharmacy screening program were more likely to have positive results.

Understanding Surveillance Bias in This Context

Surveillance bias occurs when the intensity or method of observation affects the likelihood of detecting an outcome. In this pharmacy-based influenza POC test-and-treat program:

• Patients who returned to the pharmacy multiple times for testing were more likely to test positive (p < 0.05)
• This creates a systematic bias where those who seek testing more frequently have increased chances of being diagnosed

Why Surveillance Bias is the Primary Concern

1. Differential detection rates: Patients who repeatedly use the screening service have more opportunities to test positive compared to one-time users, artificially inflating the apparent prevalence of influenza in this group.

2. Self-selection mechanism: Patients who return for repeat testing may do so because:

   • They have ongoing symptoms
   • They have higher risk exposures
   • They have greater health-seeking behaviors
   • They may have had previous positive results

3. Impact on study conclusions: Without addressing this bias, the study may incorrectly conclude that the POC program is detecting more cases in repeat users when this finding is actually an artifact of surveillance intensity.

Distinguishing from Other Types of Bias

Why it's not Immortal Time Bias

Immortal time bias occurs in longitudinal studies when a period exists during which the outcome cannot occur by design 1, 2, 3. This cross-sectional study doesn't involve time-to-event analysis or survival outcomes where patients must survive to receive an intervention.

Why it's not Lead Time Bias

Lead time bias occurs when early detection of disease appears to prolong survival without actually affecting the natural course of disease. This study isn't measuring survival time or disease progression.

Why it's not Loss to Follow-up Bias

Loss to follow-up bias occurs when participants who drop out differ systematically from those who remain. As a retrospective cross-sectional study using chart review, this isn't evaluating outcomes over time with potential dropouts.

Addressing Surveillance Bias in the Study Limitations

To properly address surveillance bias in this study, researchers should:

1. Acknowledge the differential testing frequency: Explicitly state that patients with multiple tests had more opportunities to test positive

2. Stratify analysis by testing frequency: Compare positivity rates for first tests only across all patients

3. Consider symptom severity: Collect and analyze data on symptom severity to determine if repeat testers had more severe or persistent symptoms

4. Adjust for testing frequency: Use statistical methods to account for the number of tests per patient

5. Report per-person and per-test positivity rates: Distinguish between the proportion of people who ever tested positive versus the proportion of tests that were positive

Implications for Practice

Understanding surveillance bias in this context is crucial because:

• Community pharmacies implementing similar programs need to interpret their own data correctly
• Healthcare systems evaluating the effectiveness of pharmacy-based screening need to account for this bias
• Public health officials using this data for surveillance must recognize the potential overestimation of disease burden in frequently tested populations

By properly addressing surveillance bias in the study limitations, researchers can provide more accurate insights into the true effectiveness of the pharmacy-based influenza POC test-and-treat program and avoid misleading conclusions about who benefits most from such services.