## 2 Materials and Methods
### Complete Monte Carlo Simulation Procedure  
Here, we comprehensively summarize the modified version of [PERSON] et al. 's MC simulation procedure that we used in our analysis to perform estimation and uncertainty quantification of the YPLL-based estimands of interest. In summaries of the results of our analysis, we characterize D.C. as a \"state\" for brevity. For each state \(s\), the procedure can be comprehensively summarized as follows. 1. Calculate the difference between the total number of male deaths and the number of male deaths contained in age groups with non-suppressed death counts. This difference is the number of male deaths contained in the union of age groups with suppressed death counts. Construct all possible male mortality datasets, each of which corresponds to a possible male death count combination across the age groups with suppressed death counts. Do the same for female deaths to obtain all possible female mortality datasets. Let \(\mathcal{B}\) denote the total number of MC iterations, and let \(b=1,\ldots,\mathcal{B}\) index the MC iterations. Let \(n_{ms}\) denote the total number of male deaths in state \(s\), and let \(i_{m}=1,\ldots,n_{ms}\) index the individual male deaths. Similarly, let \(n_{fs}\) denote the total number of female deaths in state \(s\), and let \(i_{f}=1,\ldots,n_{fs}\) index the individual female deaths. Let \(J\leq J_{ms}\times J_{fs}\) denote the number of male-female mortality dataset pairs for state \(s\) that remain after omitting those male-female mortality dataset pairs that would yield a maximum or minimum point estimate with probability 0, and let \(j=1,\ldots,J\) index both their male and female mortality dataset constituents. 2. Specify a YPLL upper reference age \(\mathcal{A}\) less than or equal to 85 years. We view the <1 age group as equivalent to the singular age 0, and the remaining numeric NCHS age group endpoints represent integer age at last birthday so that there is a 1-year gap between the endpoints of two chronologically consecutive age groups (e.g., 35-44 and 45-54). We treat age as a continuous variable, and as a consequence, we mathematically interpret the <1 age group (age 0) as the right half-open interval \([0,1)\), the 85+ age group as the half-bounded interval \([85,\infty)\), and the remaining NCHS age groups as right half-open intervals with lower limit equal to the lower endpoint of the corresponding NCHS age group and upper limit equal to the upper endpoint of the corresponding NCHS age group plus one (e.g., age group 15-24 is viewed as \([15,25)\)). Observe that \(\mathcal{A}\) is intentionally and necessarily chosen to be less than or equal to 85 years to obviate the simulation of ages at death for decedents in the 85+ age group because each decedent in that age group contributes zero YPLL. 3. At each MC iteration \(b\), independently simulate an age at death for each male decedent \(i_{m}\) in male mortality dataset \(j\) with reported age at death \(a_{i_{m},js}\in[L_{i_{m},js},U_{i_{m},js})\) from a continuous uniform distribution over the same interval: \[\tilde{a}_{i_{m},js}^{(b)}\overset{ind}{\sim}\mathcal{U}(L_{i_{m},js},U_{i_{m},js}).\] (2)Likewise, independently simulate an age at death for each female \(i_{f}\) in female mortality dataset \(j\) with reported age at death \(a_{i_{f},js}\in[L_{i_{f},js},U_{i_{f},js})\) from a continuous uniform distribution over the same interval: \[\bar{a}^{(b)}_{i_{f},js}\overset{ind}{\sim}\mathcal{U}(L_{i_{f},js},U_{i_{f},js}).\] (3)
4. At each MC iteration \(b\), calculate a point estimate of the estimand of interest from the simulated ages of death corresponding to each of the \(J\) male-female mortality dataset pairs.