Background. County-level DALY disease burden estimates are rarely available for rural U.S. counties. This document describes a reproducible Python workflow for computing planning-grade Disability-Adjusted Life Year (DALY) estimates for rural Michigan counties using exclusively open public data. The DALY framework was developed by IHME for the Global Burden of Disease Study — a body of work we deeply respect and do not attempt to replicate. This project applies GBD's openly published disability weights and WHO reference standards for educational and local health planning purposes only. The workflow is designed to scale to all 83 Michigan counties with minimal per-county configuration changes.
Methods. We constructed DALY estimates from five open data sources: CDC WONDER 2020–2022 (mortality), CDC PLACES 2024 (prevalence), IHME GBD 2021 (disability weights), County Health Rankings 2010–2024 (years of potential life lost), and U.S. Census ACS 2022 (population denominators). YLL uses the WHO GHE frontier reference life table (89.1 years) as primary; Michigan observed LE (78.6 years) is reported as a planning sensitivity. Uncertainty was quantified via Monte Carlo simulation (n = 10,000). Analyses were implemented in Python 3.10 using NumPy, Pandas, SciPy, and Matplotlib; working code, configuration files, derived tables, and validation reports can be shared by the author on reasonable request.
Completed counties (June 2026). Isabella County (FIPS 26073): 15,430 DALYs/yr (95% UI 14,459–16,481), frontier LE primary. Clare County (FIPS 26035): 8,198 DALYs/yr (±20% uncertainty), frontier LE primary. Midland County (FIPS 26111): 14,335 DALYs/yr (MI LE) / ~20,400 (frontier LE) — CDC PLACES 2023 observed county-level prevalence. Three counties in pipeline: Gratiot (Q3 2026), Mecosta (Q4 2026), Washtenaw (Q4 2026, urban reference).
Limitations. Mixed-source modeled planning scenario — not a validated GBD-standard county burden estimate. Prevalence inputs are model-based (CDC PLACES MLRP). Mortality uses mean-age-at-death simplification for YLL; rural adjustment factors (×1.2–1.85) proxy suppressed CDC WONDER cells. Results are directional planning approximations for grant writing, CHNA processes, and county health planning.
The analysis was implemented using a Python-based workflow and publicly available source data. The complete working repository is not presented here as a fixed public release while the manuscript and multi-county workflow remain under revision.
Working code, county configuration files, derived tables, validation reports, and replication notes can be shared by the author on reasonable request. Raw source data remain available from CDC WONDER, CDC PLACES, IHME GBD, County Health Rankings, the U.S. Census Bureau, USDA, CMS, and MDHHS under their respective terms of use.
This analysis adopts the standard WHO DALY formula with no age-weighting and no time discounting, consistent with the 2010 GBD revision and subsequent global burden analyses:
YLL = Years of Life Lost (premature mortality); YLD = Years Lived with Disability (prevalent morbidity). The two components capture different dimensions of burden: YLL is sensitive to mortality rates and the reference life expectancy standard; YLD is sensitive to prevalence and disability weights. Conditions with high late-life mortality (CVD, cancer) are YLL-dominated; conditions with high prevalence and low mortality (mental health) are YLD-dominated.
L = reference life expectancy (two standards — see Section 1.5); Ā = mean age at death for the condition (condition-specific constants, see Table 1). Deaths estimated as:
P = prevalent cases (CDC PLACES 2024 prevalence × population denominator); DW = IHME GBD 2021 disability weight (moderate severity). Disability weights do not incorporate severity distributions or comorbidity corrections. For mental health, a remission adjustment is applied to convert lifetime-diagnosed BRFSS prevalence to active burden (see Section 1.6).
Two parallel methods with distinct conceptual frameworks are reported for each county:
Human Capital uses Michigan GDP per capita ($62,000, 2024) and represents indirect productivity losses. VSL uses the HHS ASPE 2026 central estimate ($13.4M per statistical life), income-adjusted per county median household income using income elasticity 0.4 (Viscusi & Aldy 2003):
The ~5–7× difference between Human Capital and VSL central estimates is methodologically expected. For grant applications use VSL (aligned with HHS/OMB cost-benefit guidance); for academic GBD comparison use Human Capital.
Two reference standards are reported for every county:
| Standard | Value | Source | Use case | Effect on ranking |
|---|---|---|---|---|
| Primary (frontier) | 89.1 yrs (sex-avg.) M: 86.0 · F: 92.0 |
WHO GHE reference life table (global best-observed survival) | Cross-county comparison, academic publication, international GBD alignment | Favors late-death conditions: CVD, Cancer rank higher because more years are lost per late-life death. MH typically ranks lower under this standard. |
| Planning sensitivity (Michigan LE) | 78.6 yrs (sex-avg.) M: 76.2 · F: 80.9 |
MDHHS 2024, Michigan observed LE | Local planning documents, CHNA reports, comparisons with prior county analyses | Favors YLD-dominant conditions: MH typically ranks #1 because late-life YLL is compressed. Preferred by county health departments for local planning. |
CDC PLACES 2024 reports lifetime-diagnosed depressive disorder (BRFSS ADDEPEV3), not current episode. For YLD, a remission factor of 0.50 is applied in the primary estimate to approximate active burden:
Evidence basis: NSDUH 2021 national past-year major depressive episode prevalence ~8.3% vs. PLACES national lifetime-diagnosed ~20.7%, implying ~60% remission at any given time. Published psychiatric epidemiology reports 40–60% 12-month remission for MDD. The central factor of 0.50 is propagated through Monte Carlo with uncertainty Uniform(0.40, 0.60). The planning sensitivity retains raw PLACES prevalence (no remission adjustment) for continuity with prior county documents.
All DALY computations are implemented in Python 3.10+ using a modular agent-based pipeline. The pipeline is designed for reproducibility: every computation step is parameterized by a county FIPS code, all intermediate outputs are serialized to JSON, and the full pipeline can be re-run with a single command.
Collects county data from five open APIs: CDC PLACES 2024 (Socrata API, app.cdc.gov), County Health Rankings 2010–2024 (direct download), CDC WONDER (WONDER API — mortality counts, may require manual download for suppressed cells), U.S. Census ACS 5-year (census.gov API), USDA Food Environment Atlas (static CSV). Output: *_county_harvest.json — all raw collected values with source URLs and retrieval timestamps.
Computes YLL, YLD, DALYs, economic burden, and 95% uncertainty intervals from harvest output. Implements equations (1)–(8) above. Applies rural adjustment factors for suppressed WONDER cells. Runs Monte Carlo simulation (n=10,000 iterations, seed=42) across mortality, prevalence, and disability weight uncertainty. Produces: *_county_config.json with all computed estimates, and data/daly_longitudinal_v2.csv for year-by-year trend analysis.
Extends the cross-sectional estimate backward (2017–2023) and forward (2026 projection) using CDC PLACES multi-year releases and County Health Rankings YPLL trends. YLD varies year-by-year from observed PLACES prevalence (with linear interpolation for 2021–2022 data gaps); YLL is scaled by the CHR 15-year YPLL trend relative to 2018 baseline. Outputs: data/daly_longitudinal_v2.csv (7 conditions × 7 years) and data/daly_2026_projection_v2.csv (OLS trend projection with 95% prediction intervals).
Mathematical, statistical, and epidemiological quality control gatekeeper. Re-derives all DALY totals from config, checks internal consistency of YLL + YLD = DALYs, verifies economic burden formulas, flags stale values in dashboard HTML, checks for overclaiming in manuscript text (e.g., unsupported causal language). Severity levels: HIGH (material error), MEDIUM (inconsistency), LOW (disclosure). Output: *_fact_check_report.md/.json. Pipeline rule: no dashboard may be published if any HIGH issues remain unresolved.
Sends computed estimates to GPT-4o with a structured epidemiological peer-review prompt (based on Naghavi et al. 2010 GBD methodology standards). Reviews for: methodological validity, appropriate uncertainty framing, evidence strength for intervention claims, and cross-county consistency. Requires OPENAI_API_KEY in .env file. Output: *_review_report.md/.json.
Generates county dashboard HTML from config JSON. Will produce home.html (or {county}.html) as a templated output replacing the current manually-maintained HTML files. Uses Chart.js for interactive charts. Planned for Q3 2026.
Commits updated HTML files to the GitHub Pages repository and pushes to production. Will enforce the fact-checker publication rule (no HIGH issues) as a pre-deploy gate. Planned for Q3 2026.
The pipeline is orchestrated by director.py, which accepts a county FIPS name and a list of steps to run:
Each county requires a minimal configuration entry in director.py (FIPS code, population, name) and the harvester automatically retrieves all other data from public APIs. Adding a new county to the pipeline is a one-line configuration change.
| Source | Version / Year | DALY Component | Access Method | County-level available? | Quality |
|---|---|---|---|---|---|
| CDC PLACES Multilevel Regression & Poststratification (MLRP) on BRFSS |
2024 release (2022 BRFSS data) |
YLD — prevalence input for all conditions | Socrata APIdata.cdc.gov |
Yes — county FIPS | Model-based |
| CDC WONDER Multiple Cause of Death, 1999–2022 |
2020–2022 (3-yr pooled) |
YLL — cause-specific death counts and rates | WONDER web API or manual download |
Yes, but cells <10 suppressed — rural adjustment required | Observed / Suppressed |
| IHME GBD 2021 Global Burden of Disease Study 2021 |
2021 release | Disability weights (DW) for all conditions; reference LE (frontier standard) | GHDx download ghdx.healthdata.org |
National/state only — applied uniformly to county analyses | Global meta-analysis |
| County Health Rankings Robert Wood Johnson Foundation |
2010–2024 (15-year trend) |
YLL scaling — YPLL trend used for longitudinal YLL adjustment | Direct CSV download countyhealthrankings.org |
Yes — county FIPS | Observed |
| U.S. Census ACS American Community Survey, 5-Year Estimates |
2022 release (2018–2022) |
Population denominators; income for VSL adjustment; poverty, insurance SDOH | Census API api.census.gov |
Yes — county FIPS | Survey sample |
| USDA Food Environment Atlas | 2020 | SDOH context (food access, food insecurity) | Static Excel download ers.usda.gov |
Yes — county FIPS | Administrative |
| CMS Medicare Geographic Variation | 2021 | Healthcare utilization context | CMS data portal data.cms.gov |
Yes — county FIPS | Administrative claims |
| MDHHS PCNA / Michigan vital statistics | 2020 baseline | All-cause mortality comparison; county health rankings context | MDHHS.gov Michigan data portal |
Yes — county FIPS | Administrative |
CDC WONDER suppresses death count cells with fewer than 10 events in a 3-year pooled window. For small rural counties (Clare: pop. 30,013; upcoming Mecosta: pop. ~43,000), most cause-specific death counts are suppressed. Rural adjustment factors are applied to Michigan state-level rates as proxies:
| Condition | Rural adj. factor | Source range | Primary reference |
|---|---|---|---|
| SUD / Drug overdose | ×1.85 | 1.6–2.0× | Hedegaard et al. (2021), Mack et al. (2017) |
| Mental Health | ×1.40 | 1.3–1.5× | Searight (2018); WWAMI Rural Health Research |
| CVD | ×1.30 | 1.2–1.4× | Moy et al. (2017), Garcia et al. (2017) |
| COPD | ×1.35 | 1.2–1.5× | Moy et al. (2017) |
| Stroke | ×1.25 | 1.1–1.4× | Garcia et al. (2017) |
| Diabetes | ×1.30 | 1.2–1.4× | Moy et al. (2017) |
| Cancer | ×1.20 | 1.1–1.3× | Zahnd et al. (2021) |
A structured Monte Carlo simulation (n = 10,000 iterations, NumPy seed=42) propagates uncertainty from three parameter sources. Reported 95% uncertainty intervals represent simulation-based percentile intervals (2.5th to 97.5th percentile across 10,000 iterations). They do not capture systematic biases in underlying data models.
| Parameter | Distribution | Notes |
|---|---|---|
| Mortality — observed WONDER cells (cancer, CVD, COPD, stroke) |
Poisson(λ = observed annual deaths) | Sampling variability in small-count observed deaths |
| Mortality — suppressed cells (SUD, MH, diabetes) |
Uniform(lower, upper) for rural adj. factor | Ranges from Table 3; see Section 3.1 |
| Prevalence (CDC PLACES) | Beta(α, β) matching reported 95% CI | Method of moments parameterization from PLACES reported CIs |
| MH remission factor | Uniform(0.40, 0.60) | Active-disease approximation uncertainty; primary estimate only |
| Disability weights (IHME GBD 2021) | Beta(α, β) matching ±15–25% at moderate severity | IHME-reported 95% uncertainty bounds |
For the 2017–2023 longitudinal analysis, additional uncertainty sources apply: (1) CDC PLACES 2021 and 2022 county-level data are unavailable — values for those years are linearly interpolated between 2020 and 2023 observations and flagged with interpolated: True in the output CSV; (2) CHR YPLL scaling introduces year-specific uncertainty in YLL estimates; (3) the 2021 all-cause mortality spike (COVID-19 driven YPLL = 8,100 per Isabella CHR) is preserved in the longitudinal model but flagged as an anomalous year. For 2026 projections, OLS regression is applied where ≥3 observed data points exist; conditions with fewer than 3 points receive flat projections and are flagged reliability: insufficient_data.
This shared framework document covers methodology common to all county analyses. County-specific data quality tables, condition rankings, population characteristics, and analytic considerations are documented in each county's supplement. The supplement also notes where county-specific data availability required modifications to the standard pipeline (e.g., Clare County: WONDER fully suppressed for all causes, requiring full state-rate substitution; Isabella County: University of Michigan CMU student population inflates ACS poverty rate).
| County | FIPS | Population | DALYs/yr (primary) | DALYs/yr (MI LE) | Top condition | Unique feature | Status |
|---|---|---|---|---|---|---|---|
| Isabella | 26073 | 64,565 | 15,430 95% UI 14,459–16,481 |
10,579 | Cancer #1 · CVD #2 · SUD #3 | CMU university pop; MH HPSA 176,938:1 | Complete v5.0 |
| Clare | 26035 | 30,013 | 8,198 ±20% uncertainty |
5,943 (remission-adj) | Cancer #1 · CVD #2 · SUD #3 (frontier) | MUA-designated; opioid ~47/100k; COPD 12.7%; median age 46.8 | Complete v1.0 |
| Midland | 26111 | 82,884 | ~20,400 CDC PLACES 2023 |
14,335 | Cancer #1 · SUD #2 · MH #3 | Dow Chemical HQ; higher income ($62k); industrial SDOH; CDC PLACES 2023 observed prevalence | Complete v1.0 |
| Gratiot | 26057 | ~40,000 | Analysis planned Q3 2026 — agricultural economy, no university anchor, Gratiot County CMH | ||||
| Mecosta | 26107 | ~43,000 | Analysis planned Q4 2026 — Ferris State University, rural health desert designation | ||||
| Washtenaw | 26161 | ~374,000 | Analysis planned Q4 2026 — Urban reference county (U-M Ann Arbor), rural/urban comparison benchmark | ||||
Intended use. These analyses are intended for: county health department planning, Community Health Needs Assessment (CHNA) process support, grant application development (SAMHSA SOR, HRSA Rural Health, MDHHS, NIH R21), academic collaboration, and public health education. They are NOT intended for clinical decision-making, regulatory submissions, or direct policy implementation without additional validation.
Educational use of GBD framework — not a GBD estimate. The IHME Global Burden of Disease Study is the world's most comprehensive disease burden framework, representing decades of rigorous global scholarship by thousands of researchers. We hold this work in deep respect and make no claim to replicate it. This project applies GBD's publicly available disability weights and the WHO GHE frontier life table to open county-level data for educational and rural health planning purposes only. The DALY metric — borrowed from GBD methodology — allows rural planners to express local health burden in the same units used globally, enabling grant writing, community health needs assessments, and program prioritization. A formal GBD estimate requires systematic literature reviews, DisMod-MR incidence/prevalence/duration modeling, Bayesian meta-regression for risk attribution, and international peer validation — none of which this analysis provides or attempts to provide. Results should be cited as "planning-grade DALY estimates derived from open public data, applying GBD disability weights and WHO reference standards" — never as "GBD burden estimates."
Reproducibility caveats. CDC PLACES prevalence estimates are updated annually (new BRFSS data) and may differ between releases. CDC WONDER data are periodically revised. Re-running the pipeline with current-year data will produce updated estimates that may differ from those reported here. County config JSON files are version-stamped; always verify the data vintage when citing specific values.