10.2 Research Interpretation and Statistics for Evidence-Based Lactation Care

Why the Exam Tests Research Interpretation

The IBCLC Scope of Practice obligates consultants to deliver evidence-based care and to give families accurate, current information. Lactation guidance changes as new studies emerge — on galactagogues, tongue-tie release, mastitis management, donor milk, and more — so an IBCLC must be able to read a study and judge how much weight it deserves.

The exam does not ask you to compute statistics by hand. Instead, items present a finding ("a cohort study reported a relative risk of 1.8, 95% CI 1.2-2.6") and ask what it means, whether it is significant, or how strong the design is. Treating research literacy as a vocabulary-plus-judgment skill, not arithmetic, is the key to these points.

The Evidence Hierarchy

Not all evidence is equal. Study designs are ranked by how well they control bias and support causal claims. Memorize this ladder from strongest to weakest.

A systematic review uses a transparent, reproducible search and appraisal; a meta-analysis statistically combines those studies' results. On the exam, when two findings conflict, the higher-ranked design generally wins.

Core Statistics an IBCLC Must Read

Frequency: Incidence vs Prevalence

Incidence is the rate of new cases over a period (e.g., new mastitis cases per 1,000 breastfeeding parents per year). Prevalence is the proportion of existing cases at a point in time. A common trap is to call a point-in-time count an incidence rate.

Association: Relative Risk vs Odds Ratio

When the outcome is common (event rate above roughly 10%), the OR overstates the RR, so do not read an OR of 2.0 as "twice the risk" in a study with a frequent outcome.

Precision and Significance: Confidence Intervals and p-values

A 95% confidence interval (CI) gives the plausible range for the true value. The decisive rule: for a ratio (RR or OR), if the 95% CI includes 1.0, the result is not statistically significant; for a difference, the null value is 0. A p-value below 0.05 conventionally signals statistical significance but says nothing about clinical importance — a tiny, irrelevant difference can be "significant" in a huge study.

Diagnostic Accuracy: Sensitivity, Specificity, and NNT

• Sensitivity = the proportion of truly affected dyads a test or screen correctly identifies (few false negatives). A highly sensitive screen is good for ruling out.
• Specificity = the proportion of truly unaffected dyads it correctly clears (few false positives). A highly specific test is good for ruling in.
• Number needed to treat (NNT) = the inverse of the absolute risk reduction: how many dyads must receive an intervention to prevent one additional bad outcome. A smaller NNT means a more effective intervention.

Example: A cohort study reports that early skin-to-skin contact is associated with RR 0.70 (95% CI 0.55-0.89) for formula supplementation in the first 48 hours. Interpretation: skin-to-skin was associated with a 30% lower relative risk of supplementation (RR below 1 = protective), and because the entire CI lies below 1.0, the result is statistically significant. Because it is a cohort study, you note possible confounding before treating it as proof of causation.

Critically Appraising Lactation Research and Applying It

When you read a lactation study, ask a short, repeatable set of questions:

1. What is the design, and where does it sit on the hierarchy?
2. Who was studied (sample size, population) — does it match your client?
3. What is the effect size (RR, OR, or difference) and is the CI significant?
4. Is the effect clinically meaningful, not just statistically significant?
5. What are the limitations — confounding, bias, conflicts of interest, industry funding?

Evidence-based practice then integrates that appraised research with clinical expertise and the family's values and goals. The credited exam answer rarely abandons clinical judgment for a single weak study, and never adopts a manufacturer-funded claim uncritically.