How to Read State Health Statistics

How do you read a state's health statistics?

Reading state mortality data well comes down to four habits: start with the overview, adjust for age, follow trends instead of single years, and put a state in its regional context. Here is the step-by-step.

Step 1: Start with Your State Overview

Every state on PlainHealth has a summary page showing age-adjusted death rates for all leading causes. Start here to get the big picture. Look at three things:

• Which causes rank highest in your state. Heart disease and cancer lead everywhere, but the order of subsequent causes varies significantly, some states have high unintentional injury rates, others high Alzheimer's rates.
• How your state compares to national averages. Rates significantly above the national average (more than 10–15%) flag areas of genuine concern beyond normal variation.
• The trend direction. Is the rate for each cause going up or down over the 19-year period? A declining rate suggests improvements are working; a rising rate suggests a worsening problem.

Browse all state pages to start your analysis.

Step 2: Understand Age Adjustment

Age-adjusted rates are the foundation of fair health comparisons. Here's why they matter with a concrete example:

Florida has a much older population than Utah (median age ~42 vs. ~31). Florida's crude death rate is nearly double Utah's for heart disease simply because older people die of it more often. But when you age-adjust, applying the same hypothetical age distribution (the 2000 US Standard Population) to both states, the gap narrows substantially. The remaining difference after adjustment reflects actual health conditions, behaviors, and healthcare access rather than simply having more elderly residents.

When you see an age-adjusted rate of 163.5 per 100,000 for heart disease in a state, that number is directly comparable to any other state's rate or to the national average. This is the number to use for real comparisons.

Browse the heart disease cause page to see the full state rankings with current data.

Step 3: Read Trends, Not Snapshots

A single year's data can be misleading, especially for smaller states. A disease outbreak, natural disaster, or simply random statistical variation can cause a temporary spike that looks alarming but means nothing long-term. Trends over 5+ years are far more informative.

PlainHealth shows 19 years of data (1999–2017) for every state and cause. When evaluating your state's trajectory:

• Consistent decline: Good news, whatever is being done is working. Most states show this for heart disease and cancer, reflecting decades of public health investment.
• Consistent increase: Concerning, the problem is getting worse. Drug overdose deaths showed this pattern in almost every state from 2000 onward.
• Flat line: Progress has stalled. The cause may need new interventions or a different public health approach.
• Recent reversal: A declining trend that turns upward deserves close attention. This pattern appeared in suicide rates in many states around 2010–2012.

Step 4: Regional Patterns in State Data

Mortality patterns in the US are not random, clear geographic clusters exist and have persisted for decades. Understanding these regional patterns helps contextualize your own state's numbers:

Compare states across a cause using the cause pages, where states are ranked by age-adjusted rate.

Step 5: Drill Into Specific Causes

PlainHealth's cause pages show how each cause of death affects different states. Use these to answer questions like:

• Which states have the highest heart disease mortality? (Southeast states consistently lead.)
• Where are drug overdose deaths highest? (Appalachia, New England, and parts of the Southwest saw early epidemic peaks.)
• Which states have the lowest cancer death rates? (Utah and Colorado consistently rank low, partially linked to lower smoking rates and healthier population behaviors.)
• Why does a neighboring state have such different numbers? (Regional health behaviors, economic conditions, and healthcare access often explain cross-border differences better than state health policy alone.)

What "statistically significant" really means in mortality data

When CDC says a state's rate is "significantly different" from the national average, it generally means a 95% confidence interval, there's less than a 5% chance the difference is due to random sampling variation. For small states this is a high bar to clear, which is why year-to-year fluctuations in places like Wyoming rarely register as significant differences from the national rate.

Crude vs. age-adjusted: a side-by-side example

Florida and Utah illustrate the point cleanly. Florida's crude all-cause death rate runs around 920/100K versus Utah's roughly 580/100K, a 58% gap that mostly reflects Florida having far more retirees. After age adjustment to the 2000 US standard population, the gap shrinks to roughly 745/100K vs 705/100K, a 5.7% gap that reflects actual health-system and behavioral differences.

Why small-state rates bounce around

Statistical noise scales inversely with the square root of population size. A state with 600,000 people will see roughly four times the year-to-year volatility of a state with 9.6 million. Always look at 5+ year trends for small states, never single-year snapshots.

How to combine cause-of-death rates with broader context

Mortality rates alone never tell the full story. To use them well, pair them with: state poverty rate, healthcare-access metrics (uninsured share, hospital density), behavioral indicators (smoking %, obesity %), and geographic context (urban / rural mix). A high heart-disease rate in Mississippi reads very differently when paired with the state's 19.4% poverty rate vs. the national 11.6% baseline.

Worked example: comparing Mississippi vs. Minnesota with full context

Mississippi's age-adjusted heart disease rate runs around 230/100K vs. Minnesota's 114/100K, a 102% gap. Pair that with: Mississippi's adult obesity rate of 39.7% vs. Minnesota's 30.1%, smoking prevalence of 22.2% vs. 13.8%, uninsured share of 13.2% vs. 4.9%, and median household income of $48,716 vs. $77,706.

The picture sharpens fast: the 102% mortality gap correlates tightly with a 32% obesity gap, a 61% smoking gap, a 169% uninsured-share gap, and a 60% income gap. The mortality numbers aren't isolated, they're a downstream signal of a constellation of upstream conditions. State-by-state comparisons that ignore these inputs will keep mistaking effects for causes.

Limitations of State-Level Data

State averages are useful but hide important complexity. Before drawing strong conclusions, consider these limitations:

• It measures deaths, not disease burden. Many people live with chronic conditions (diabetes, COPD, heart failure) that affect quality of life for years before causing death. Mortality data captures only the endpoint, not the burden.
• It does not separate prevention from treatment. A state may have low cancer mortality because fewer people get cancer (prevention success) or because those who do get cancer survive longer (treatment success). The rate alone does not distinguish these very different public health situations.
• It reflects the past. The 2017 data reflects conditions and healthcare from 2017. Improvements since then, new drug treatments, expanded Medicaid, opioid response programs, will not appear in this dataset.
• It masks disparities within states. State averages can hide significant differences between urban and rural areas, different income levels, and population subgroups. A low state average can coexist with pockets of very high mortality in specific communities.
• Small-state instability. States like Wyoming, Vermont, and Alaska have small enough populations that single-year rates can fluctuate dramatically. Use multi-year averages or trend lines for small states.

Frequently Asked Questions