Repeated Parkinson Risk Assessments and Cumulative False Alerts: Dependence, Referral Policies, and Patient Expenditure

Abstract An inexpensive assessment can generate substantial downstream burden when it is repeated and every abnormal output prompts further evaluation. This methodological study examines how repeated false alerts accumulate and how within-person dependence changes their distribution. A targeted narrative selection of primary Parkinson prediction, digital measurement, validation, and access research informs a reproducible probability analysis. The hypothetical population consists entirely of people who remain free of the specified target condition throughout the assessment horizon. Each scheduled assessment has a stipulated false-alert probability of 0.05. Independent assessments are compared with an exchangeable beta-binomial model representing persistent differences in alert propensity. Across twelve occasions, the probability of at least one false alert is 45.96% under independence, 33.17% with within-person correlation 0.1, and 21.15% with correlation 0.3. The expected number of alerts remains 0.60 in every scenario. Positive dependence therefore concentrates repeated alerts in fewer people rather than eliminating the expected alert workload. A separate illustrative cost calculation assigns one fictional unit per assessment and fifty per referral. Expected expenditure differs according to whether referral occurs only after the first alert or after every alert. These results are mathematical consequences of chosen assumptions, not estimates of any Parkinson test or NeuralCipher system. The analysis distinguishes person-level cumulative risk, event-level workload, and policy-dependent expenditure, and explains why a confirmatory repeat cannot automatically be treated as independent. Prospective evaluation should define the target, horizon, repeat schedule, abstention handling, referral policy, and patient costs together before claiming that repeated low-cost assessment is affordable. 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