Interactive analysis · August 2026
Fifteen problems from my KC burden-of-disease analysis, rank-ordered by the effective altruism framework: impact × tractability × neglectedness. Tractability comes from verified intervention cost-effectiveness and Missouri's actual legal room to act. Neglectedness comes from mapping who currently funds what, in dollars. A 10,000-draw Monte Carlo over the uncertainty turns the ranking into rank intervals, and the explorer below lets you disagree with any score I made and watch the ranking respond.
The impact figures are measured; the T and N scores are structured judgment over verified evidence, which is exactly why they are adjustable. Every claim cites its source at the bottom of the page.
1 · P(top 3) = 100%
Quitlines at $849–2,358/QALY meet the most extreme neglect in the study: Missouri funds tobacco control at 7.4% of the CDC-recommended level.
2 · P(top 3) = 93%
Naloxone at $438/QALY, a 2–5× mortality gap for people in vs out of MOUD treatment, and settlement money sitting undeployed while KC lags the national decline.
3 · P(top 3) = 65%
Cost-saving interventions, zero dedicated local infrastructure, and the least-funded cause of death in America relative to its mortality.
4 · P(top 3) = 28%
$2,800–15,000/QALY levers against the region's single largest burden, deliverable through safety-net clinics with no policy fight.
One number that integrates all three ITN factors: if an additional $10M/yr showed up in Kansas City, how many disability-adjusted life years would it buy against each problem, through the best verified locally-feasible intervention, at current levels of crowding? Whiskers are the 10th–90th percentile of the Monte Carlo.
Each dot is a problem's median rank across 10,000 Monte Carlo draws; the line is its 10th–90th percentile rank range. Tight lines mean the ranking is confident; long lines mean reasonable people can land in different places. Violence has the longest line in the study.
Impact is measured (annual DALYs, fixed here). Tractability and neglectedness are 0–10 scores I assigned from the evidence in each capsule; drag them if you read the evidence differently and the ranking recomputes live using the factor-product score (log10 I + T/2 + N/2). "Evidence" expands each problem's reasoning with citations.
A sketch, not a plan. Splitting $10M across the top four: roughly $3M for a quitline and cessation surge (media plus free nicotine replacement, Missouri-side), $3M for naloxone saturation, low-barrier medication access, and settlement-deployment advocacy, $2M for falls programs (CAPABLE and Otago through senior centers and Medicaid waivers), and $2M for self-measured blood pressure and team-based hypertension care through the safety-net clinics. Central estimate on the model's own numbers: 1,500–2,500 DALYs averted per year at scale, with the falls and cessation lines partly cost-saving to Medicare and Medicaid. Every line is deliverable by organizations that already exist here, without a single preempted policy fight.
Impact carries over from the companion burden analysis: WHO Global Health Estimates 2021 US cause rates, localized with county death certificates and CDC PLACES prevalence, with 80% CIs.
Tractability (0–10) anchors to the best locally-feasible intervention's verified cost-effectiveness on a log scale ($500/DALY ≈ 9, $5K ≈ 7, $50K ≈ 5, nothing proven ≈ 1–2), adjusted for evidence strength (CPSTF- or RCT-verified beats contested beats absent), Missouri's legal room to act, and KC-specific precedent.
Neglectedness (0–10) is scored at the prevention margin: annual local dollars aimed at reducing the problem, not treating it, per DALY of burden, from verified budgets (city, county COMBAT tax, settlement flows, HUD awards) and philanthropy (990-verified), benchmarked against national funding-versus-burden residuals. Treatment spending is reported but does not count as crowding for prevention.
The Monte Carlo draws every factor from a triangular distribution over its range, 10,000 times (fixed seed), and records each problem's rank per draw. The full simulation is a 60-line stdlib Python script that reproduces every number in the table above. Two scoring views run in parallel: marginal DALYs per $10M (the headline) and the factor product (the explorer). Where they disagree, the disagreement is the finding: falls leads the factor product but its modest burden caps absolute gains; musculoskeletal disease scores high on burden-times-neglect but has no deployable intervention, making it a research bet, not a program bet.
Post-vetting update (Aug 15). A follow-on pass vetting KC's actual charities fed back into three neglectedness scores: falls down (delivery infrastructure exists after all, small and deficit-funded), tobacco up (no local giving vehicle exists at all), overdose up slightly (the metro's joint federal harm-reduction grant lapsed in May). Re-running the Monte Carlo: the headline View A ranking is unchanged in every number; View B's only change is falls and tobacco swapping #1 and #2. Everything stays inside the published rank intervals. For a donor, note the asymmetry the scores can't hold: tobacco ranks #1 but has nothing to fund without building the channel, while falls has a starving, fundable vehicle today.
What this is not. Local institutions' own priorities entered only as crowdedness data, never as conclusions. The T and N scores are my judgment over the verified evidence, stated openly and adjustable above. A state-level actor's ranking would differ (alcohol and tobacco taxes jump); so would a researcher's (musculoskeletal jumps).
Numbered as cited in the capsules and text. Flags mark anything that resisted primary verification; those claims are labeled in place.