Expected savings to Medicaid from substituting electronic for tobacco cigarettes

A technical appendix explaining in much greater depth the calculations found in the attached report can be accessed here.

Smoking is well established as the cause of numerous health effects including cancer, coronary heart disease, and respiratory ailments such as chronic obstructive pulmonary disease and emphysema. Vaping poses essentially none of these risks because it involves no products of combustion. For this reason, numerous reports make the case that switching smokers to vaping would greatly reduce or eliminate these health risks.

Most medical care expenditures on smoking-related ailments are made by third-party insurers and are substantially passed through to insureds, though under applicable federal regulations forbidding proper risk-rating, nonsmokers bear a substantial share of these costs. The exceptions are Medicare and (especially) Medicaid, where expenditures are made by taxpayers but are not passed through. This creates a strong incentive for governments to take a more active role to manage the financial consequences of smoking.

Accordingly, the present study provides state-level estimates of the cost savings to Medicaid programs that could be realized if enrollees who smoke switched to e-cigarettes. A stylized example is created in which 1% of smokers within each of eight demographic groups permanently switch. For this standardized cohort, the present value of estimated cost savings to Medicaid programs is about $2.8 billion, with the median state’s present value cost savings exceeding $32 million. For a series of ten annual standardized 1% switch cohorts, the present value of nationwide cost savings is 10 times greater, or $28 billion, with the median state saving about $320 million. These estimates provide a foundation for state-specific estimates based on state-specific circumstances and defined program features or market behaviors. Resulting estimates would be multiples of the estimates from the standardized cohort analysis.