Epidemiology

Obesity Epidemiology in Alabama (Prevalence Map)

Explore and compare county-level estimates of obesity prevalence across Alabama

Crude prevalence reflects the estimated prevalence in the local population with its actual age structure. Age-adjusted prevalence standardizes for differences in age distribution, making geographic comparisons more appropriate. Both values are CDC PLACES model-based estimates.
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How to read this map: Each polygon represents a U.S. county. Hover over a county for a quick prevalence estimate, or click on it for detailed data. Use the control panel above to filter by state, health measure, and estimate type.

Data source:

CDC PLACES

Geographic level:

U.S. counties

Measure:

Obesity among adults

Estimates:

Crude and age-adjusted prevalence

Data release:

PLACES 2025

BRFSS data:

2023

Alabama Obesity Prevalence

Obesity prevalence varies considerably across Alabama’s 67 counties. In the current dataset, crude obesity prevalence ranges from 31.7% in Shelby County to 52.9% in Perry County, representing a difference of more than 21 percentage points across the state. Greene County (52.7%), Bullock County (51.3%), Lowndes County (51.2%), and Wilcox County (51.2%) also have crude prevalence estimates above 50%.

Overall, the median crude prevalence across Alabama counties is 40.8%. A total of 40 of the 67 counties have crude prevalence estimates of 40% or higher, while 14 counties have estimates of at least 45%. Counties with the highest estimates include Perry, Greene, Bullock, Lowndes, Wilcox, Pickens, Hale, Sumter, Barbour, and Macon. At the lower end of the distribution, Shelby County has the lowest crude estimate, followed by Baldwin (35.2%), Lauderdale (35.6%), Walker (35.8%), and Blount (36.1%).

ScienceCodons_Map_OBESITY_Alabama

Age-adjusted estimates show a broadly similar pattern. Age-adjusted obesity prevalence ranges from 31.6% in Shelby County to 54.0% in Perry County, with a median county estimate of 40.8%. For most counties, crude and age-adjusted estimates are relatively close: in 60 of 67 counties, the difference between the two measures is no greater than one percentage point. Pike County (40.3% to 43.3%), Sumter County (47.2% to 50.0%), Macon County (46.7% to 49.1%), and Tuscaloosa County (41.0% to 43.2%) show larger upward changes after age adjustment.

These estimates highlight substantial county-level variation in obesity prevalence across Alabama. Because each estimate includes a 95% confidence interval, interpret differences between counties alongside the uncertainty around each estimate rather than from point estimates alone.

Obesity Prevalence Data by County

The table below provides a structured view of obesity prevalence estimates for all 67 Alabama counties, allowing users to search, sort, filter, and compare geographic areas. Each county record includes crude and age-adjusted obesity prevalence estimates, along with their corresponding 95% confidence intervals.

Available data include:

  • County name
  • County FIPS code
  • Crude obesity prevalence
  • Crude 95% confidence interval
  • Age-adjusted obesity prevalence
  • Age-adjusted 95% confidence interval
State Name County FIPS Crude Prevalence (%) Crude 95% CI Adj Prevalence (%) Adj 95% CI
Alabama Autauga 01001 39.8 (32.3, 47.6) 39.5 (32.0, 47.2)
Alabama Baldwin 01003 35.2 (29.9, 40.9) 35 (29.6, 40.5)
Alabama Barbour 01005 46.8 (38.5, 55.2) 47.1 (38.8, 55.4)
Alabama Bibb 01007 43.9 (35.3, 52.7) 43.7 (35.2, 52.4)
Alabama Blount 01009 36.1 (28.4, 44.5) 36 (28.2, 44.3)
Alabama Bullock 01011 51.3 (42.5, 60.4) 51 (42.2, 60.1)
Alabama Butler 01013 40.4 (32.2, 48.6) 40.7 (32.5, 48.9)
Alabama Calhoun 01015 38.6 (31.8, 45.8) 39.1 (32.3, 46.4)
Alabama Chambers 01017 41.5 (33.4, 50.0) 41.6 (33.5, 50.0)
Alabama Cherokee 01019 39.5 (31.3, 47.9) 39.3 (31.1, 47.9)
Alabama Chilton 01021 40 (32.8, 47.4) 39.8 (32.6, 47.4)
Alabama Choctaw 01023 44.4 (36.2, 53.0) 44.5 (36.3, 52.9)
Alabama Clarke 01025 43.9 (35.6, 52.5) 44.2 (35.8, 52.8)
Alabama Clay 01027 42.5 (33.5, 51.4) 42.4 (33.5, 51.3)
Alabama Cleburne 01029 37.4 (28.8, 46.1) 37.1 (28.6, 45.7)
Alabama Coffee 01031 41 (33.4, 49.0) 40.8 (33.2, 48.7)
Alabama Colbert 01033 37.5 (29.9, 46.0) 37.4 (29.8, 46.0)
Alabama Conecuh 01035 46.4 (37.6, 55.1) 46.8 (38.1, 55.5)
Alabama Coosa 01037 41.9 (33.0, 51.5) 41.6 (32.8, 51.1)
Alabama Covington 01039 37.9 (30.5, 45.9) 38.1 (30.8, 46.1)
Alabama Crenshaw 01041 40.5 (32.7, 48.7) 40.3 (32.5, 48.5)
Alabama Cullman 01043 36.4 (28.9, 44.4) 36.2 (28.7, 44.3)
Alabama Dale 01045 41.3 (33.8, 48.9) 41.5 (34.0, 49.0)
Alabama Dallas 01047 46.3 (38.4, 54.4) 46.5 (38.7, 54.6)
Alabama DeKalb 01049 38.6 (30.9, 47.1) 38.6 (30.9, 47.1)
Alabama Elmore 01051 38.9 (32.0, 46.5) 38.7 (31.8, 46.2)
Alabama Escambia 01053 43.9 (35.9, 52.1) 43.9 (36.0, 52.3)
Alabama Etowah 01055 43.8 (36.3, 51.7) 43.6 (36.2, 51.3)
Alabama Fayette 01057 40.6 (32.3, 49.6) 40.6 (32.4, 49.6)
Alabama Franklin 01059 42.2 (33.7, 50.9) 42.2 (33.7, 51.0)
Alabama Geneva 01061 42.3 (34.4, 50.7) 42.2 (34.3, 50.6)
Alabama Greene 01063 52.7 (43.8, 61.4) 53.4 (44.5, 61.9)
Alabama Hale 01065 47.6 (38.9, 56.2) 47.8 (39.2, 56.4)
Alabama Henry 01067 41.3 (32.9, 50.1) 41.2 (32.9, 50.0)
Alabama Houston 01069 40.5 (34.1, 47.1) 40.5 (34.1, 47.1)
Alabama Jackson 01071 39.3 (31.3, 47.7) 39.1 (31.1, 47.6)
Alabama Jefferson 01073 37.4 (33.2, 42.2) 37.6 (33.3, 42.4)
Alabama Lamar 01075 40.8 (32.1, 50.2) 40.8 (32.1, 50.2)
Alabama Lauderdale 01077 35.6 (28.4, 43.5) 36.7 (29.4, 44.9)
Alabama Lawrence 01079 39.5 (31.2, 48.5) 39.2 (30.9, 48.3)
Alabama Lee 01081 38.4 (32.1, 45.1) 39.9 (33.5, 46.8)
Alabama Limestone 01083 39.6 (31.9, 47.5) 39 (31.4, 46.8)
Alabama Lowndes 01085 51.2 (41.4, 59.7) 51.3 (41.5, 59.9)
Alabama Macon 01087 46.7 (38.0, 55.7) 49.1 (40.1, 58.0)
Alabama Madison 01089 39.9 (33.9, 46.1) 40.1 (34.0, 46.1)
Alabama Marengo 01091 43.1 (35.1, 51.4) 43.2 (35.1, 51.5)
Alabama Marion 01093 42.4 (34.0, 51.4) 42.5 (34.1, 51.4)
Alabama Marshall 01095 37.6 (30.1, 45.6) 37.6 (30.0, 45.4)
Alabama Mobile 01097 39 (34.2, 44.0) 39 (34.2, 44.0)
Alabama Monroe 01099 43.8 (35.3, 52.8) 43.9 (35.5, 52.9)
Alabama Montgomery 01101 46.1 (40.1, 52.3) 46.3 (40.4, 52.6)
Alabama Morgan 01103 38.6 (31.3, 46.2) 38.5 (31.1, 46.1)
Alabama Perry 01105 52.9 (44.5, 61.6) 54 (45.5, 62.7)
Alabama Pickens 01107 48 (39.6, 56.3) 48 (39.7, 56.3)
Alabama Pike 01109 40.3 (33.0, 48.3) 43.3 (35.9, 51.5)
Alabama Randolph 01111 37.8 (29.5, 47.0) 38 (29.7, 47.1)
Alabama Russell 01113 43 (35.5, 51.1) 42.7 (35.2, 50.7)
Alabama Shelby 01117 31.7 (25.9, 37.8) 31.6 (25.7, 37.6)
Alabama St. Clair 01115 39.6 (32.4, 47.3) 39.2 (31.9, 46.8)
Alabama Sumter 01119 47.2 (38.5, 55.9) 50 (41.1, 58.7)
Alabama Talladega 01121 45.6 (38.1, 53.0) 45.4 (38.0, 52.7)
Alabama Tallapoosa 01123 39.5 (31.8, 47.6) 39.6 (32.0, 47.7)
Alabama Tuscaloosa 01125 41 (35.5, 46.9) 43.2 (37.5, 49.2)
Alabama Walker 01127 35.8 (28.7, 43.4) 35.7 (28.8, 43.4)
Alabama Washington 01129 41.1 (32.9, 49.6) 41 (32.7, 49.5)
Alabama Wilcox 01131 51.2 (42.3, 60.0) 51.9 (42.8, 60.7)
Alabama Winston 01133 39.1 (30.9, 47.6) 39 (30.8, 47.5)

References & Data Sources

  • Centers for Disease Control and Prevention (CDC). PLACES: Local Data for Better Health.
  • Centers for Disease Control and Prevention (CDC). PLACES Measure Definitions: Health Outcomes — Obesity Among Adults.
  • Centers for Disease Control and Prevention (CDC). PLACES Methodology.
  • Centers for Disease Control and Prevention (CDC). PLACES Current Release Notes.

This article was reviewed for accuracy by Dr. Bahman Akbari. The content is based on current scientific evidence and is intended for educational purposes only. It does not constitute medical advice and should not be used as a substitute for consultation with a qualified health professional.

Mahdi Morshedi Yekta

I'm a medical biotechnologist, researcher, scientific tool developer, and scientific visual designer with interests in bioinformatics, computational biology, data visualization, and scientific communication. I founded Science Codons in 2022 to make practical scientific resources more accessible to researchers and students. Alongside research and tool development, I also create scientific visualizations and graphical abstracts, turning complex biological concepts, workflows, and research findings into clear and engaging graphics.

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