Medical Insurance Cost Correlation Analysis
A rigorous correlation study for a London investment firm evaluating BMI-linked pricing tiers for employee medical benefits — separating genuine statistical signal from assumption, and pairing the findings with an explicit ethical and legal limitations review.
The Challenge
BMI is the strongest predictor of insurance costs in this dataset, with a strong, highly significant correlation (r = 0.709, p < 0.001) — a finding a London investment firm used to ground its employee medical benefits decision in evidence rather than assumption.
The firm was weighing changes to its employee medical benefits scheme and wanted evidence-based grounds for any BMI-linked pricing tiers, rather than relying on assumption or industry convention. The brief was to quantify, rigorously, which lifestyle and demographic factors actually correlate with insurance costs, and how confident that relationship is.
The analysis used an anonymised insurance dataset covering demographic and lifestyle variables, with the explicit goal of separating genuine statistical signal from noise before any pricing decision was made.
Approach
Results
BMI emerged as by far the strongest predictor of insurance costs among the variables tested, with a strong, highly significant positive correlation. Age showed a real but much weaker relationship, and number of children showed no meaningful link to cost at all — a useful negative finding that rules out a factor the firm might otherwise have assumed was relevant.
Correlation is not causation: a higher BMI correlating with higher charges does not establish that BMI directly causes those costs, and the dataset is cross-sectional, external, and missing likely confounders such as smoking status, exercise habits, and pre-existing conditions. These limitations were surfaced deliberately, alongside the equality-legislation and employee-wellbeing risks of BMI-linked pricing, so the firm could weigh the statistics against the ethical and legal picture together.
Business Impact
The analysis gave the firm a statistically grounded basis for its benefits decision, plus an honest account of what that basis does and doesn't support. Recommendations included transparent, evidence-based BMI pricing tiers if pursued, paired with employee communication that leads with the data rather than the outcome, complementary wellness programmes offering a genuine route to lower costs, and a formal compliance review with legal and HR before implementation, given the discrimination-risk considerations identified during the analysis itself.