enrolled in medicaid

Overview

This model predicts whether a voter is enrolled in Medicaid — the joint federal-state program providing health coverage to low-income individuals and families.

Trained on survey data and consuming AHRQ-derived ecological-disaggregation outputs as features, it functions primarily as a ranking tool, enabling organizations to prioritize voters who would materially benefit from Medicaid preservation or expansion.

  • approach: deep learning
  • training rows: 15,541
  • last updated: 2026-08-29 19:49:51

Validation

metric value
area under ROC curve 0.71
area under precision-recall curve 0.31
accuracy 0.56
precision 0.19
recall 0.73
f1 score 0.31
mean log loss 0.84
gini coefficient 0.41
brier skill score 0.07

Lift Chart

Each bar shows how much more common the outcome is within a score decile than in the population overall. Bars taller than the dashed line (lift of 1) mean the model’s top-ranked groups concentrate the outcome; a steady decline from decile 1 to 10 indicates the model is ranking effectively.

Calibration Plot

Bars of similar height within each decile indicate well-calibrated predictions. Systematic differences reveal where the model over- or under-predicts.

Cumulative Gains

The further the model curve bows above the diagonal, the better the model concentrates the outcome in its top-ranked predictions.

Decile Analysis Table

decile count mean actual mean predicted mae lift
10 391 0.39 0.39 0.007 2.92
9 390 0.23 0.18 0.05 1.77
8 390 0.15 0.14 0.01 1.11
7 391 0.12 0.12 0.0009 0.87
6 390 0.08 0.10 0.02 0.64
5 390 0.09 0.09 0.005 0.68
4 391 0.08 0.09 0.01 0.58
3 390 0.05 0.08 0.03 0.41
2 390 0.08 0.07 0.005 0.58
1 391 0.06 0.06 0.002 0.45

Architecture

This model is a deep neural network trained with TensorFlow.

Network structure

layer units activation
↓ dense 96 relu
↓ batch normalization
↓ dropout (0.30 rate)
↓ dense 70 swish
↓ batch normalization
↓ dropout (0.20 rate)
↓ dense 44
↓ batch normalization

Hyperparameter configuration

  • loss: binary_crossentropy
  • optimizer: nadam
  • batch size: 8
  • activation: sigmoid
  • label smoothing: 0.01
  • learning rate: 0.001

Features

Importance

Sourcing

Attom Property Data API

  • bedrooms
  • lot size (acres)

CDC WONDER

  • utility shut-off threat, past 12 mo (adults)

OpenSecrets

  • donations to democrats (%)
  • donations total (count)

Revelio Labs

  • avg salary differential
  • current position salary

State voter files (via TargetSmart)

  • age

TargetSmart

  • child tax credit support score
  • children present score
  • evangelical score
  • high school only score
  • homeowner score
  • ideology score
  • off-year general turnout score
  • race: black
  • working class score

U.S. Census American Community Survey

  • block group % high school diploma
  • block group % hispanic
  • block group % white

Verisk (via TargetSmart)

  • homeowner
  • medical occupation
  • political donations
  • residence year built
  • retired

Implementation

Prediction distribution

statistic value
mean 0.19
std dev 0.16
min 0.03
25th 0.09
median 0.13
75th 0.24
max 0.99

Segments

segment lowest score highest score share of records
Most likely 0.29 0.99 19.6%
Somewhat likely 0.17 0.28 18.6%
Least likely 0.03 0.16 61.9%

External validity

Our model indicates that 19% of people with scored records are enrolled in Medicaid. CBO 2024 Medicaid baseline reports ~34M nonelderly nondisabled adults plus ~10M disabled adults plus ~6M dual-eligibles enrolled in Medicaid (~50M adults total / ~266M U.S. adults ≈ 19%). See also the KFF Medicaid Enrollment and Unwinding Tracker.