Excessive drinking

Percentage of adults reporting binge or heavy drinking.
The 2019 County Health Rankings used data from 2016 for this measure.

Measure Tabs

About

Reason for Ranking

Excessive drinking is a risk factor for a number of adverse health outcomes, such as alcohol poisoning, hypertension, acute myocardial infarction, sexually transmitted infections, unintended pregnancy, fetal alcohol syndrome, sudden infant death syndrome, suicide, interpersonal violence, and motor vehicle crashes.[1] Approximately 80,000 deaths are attributed annually to excessive drinking, and it is the third leading lifestyle-related cause of death in the United States.[2]

Key Measure Methods

Excessive Drinking is a Percentage

Excessive Drinking measures the percentage of a county’s adult population that reports binge or heavy drinking in the past 30 days.

The Method for Calculating Excessive Drinking Changed

Prior to the 2016 County Health Rankings, the CDC’s BRFSS provided the Rankings with county-level estimates that were constructed from seven years of responses from participants who used a landline phone. Beginning with the 2016 Rankings, the CDC provided single-year modeled county-level estimates that included both landline and cell phone users. These changes were implemented in order to provide users with the most accurate estimates of health in their community as possible.

Excessive Drinking is Created Using Statistical Modeling

Statistical modeling is used to obtain more informed and reliable estimates than survey data alone can provide. Our Excessive Drinking estimates are produced from one year of survey data and are created using complex statistical modeling. Modeling generates more stable estimates for places with small numbers of residents or survey responses. For more technical information on BRFSS modeling please see their methodology.[3]

Caution Should be Used When Comparing these Estimates Across State Lines

The model used to create these estimates includes a state-level factor that limits comparability between neighboring counties of adjacent states.

Measure Limitations

Using self-reported survey data to assess excessive drinking has limitations. First, excessive drinking is often underreported in surveys because of recall bias, social desirability response bias, and non-response bias.[1] Second, BRFSS changed the definition of excessive drinking for women in 2006; this means that there will be a higher prevalence in recent years compared to prior years for women.[1] Third, the measure does not include youth drinking prevalence, and binge drinking accounts for 90% of alcohol consumption for youth ages 12-17. Having a measure that includes youth binge drinking would be beneficial for understanding youth drinking patterns in different counties.[4] Some US states and counties administer a Youth Behavioral Risk Surveillance Survey, but there is not adequate coverage or consistent enough methodology to aggregate the results to represent all counties across the country.[5]

Numerator

The numerator is the number of adult respondents reporting either binge drinking or heavy drinking. Binge drinking is defined as a woman consuming more than four alcoholic drinks during a single occasion or a man consuming more than five alcoholic drinks during a single occasion. Heavy drinking is defined as a woman drinking more than one drink on average per day or a man drinking more than two drinks on average per day.

Denominator

The denominator is the total number of adult BRFSS survey respondents.

Can This Measure Be Used to Track Progress?

This measure could be used to measure progress, but only after considering its substantial limitations. Methodological changes in the Behavioral Risk Factor Surveillance System, which are discussed above and were implemented in the 2016 Rankings, make comparisons with estimates prior to that release year difficult. In addition, current estimates are produced using sophisticated modeling techniques which make them difficult to use for tracking progress in small geographic areas.

Modeled estimates have specific drawbacks with regard to their usefulness in tracking progress in communities. Modeled data are not particularly good at incorporating the effects of local conditions, such as health promotion policies or unique population characteristics, into their estimates. Counties trying to measure the effects of programs and policies on the data should use great caution when using modeled estimates. In order to better understand and validate modeled estimates, confirming this data with additional sources of data at the local level is particularly valuable.

Data Source

Years of Data Used

2016

Behavioral Risk Factor Surveillance System

The Behavioral Risk Factor Surveillance System (BRFSS) is a state-based random digit dial (RDD) telephone survey that is conducted annually in all states, the District of Columbia, and U.S. territories. Data obtained from the BRFSS are representative of each state’s total non-institutionalized population over 18 years of age and have included more than 400,000 annual respondents with landline telephones or cellphones since 2011. Data are weighted using iterative proportional fitting (also called "raking") methods to reflect population distributions. For the County Health Rankings, data from the BRFSS are used to measure various health behaviors and health-related quality of life (HRQoL) indicators. HRQoL measures are age-adjusted to the 2000 U.S. standard population.

Prior to the 2016 County Health Rankings, up to seven survey years of landline only BRFSS data were aggregated to produce county estimates. However, even with multiple years of data, these did not provide reliable estimates for all counties, particularly those with smaller respondent samples. For the 2016 County Health Rankings, the CDC produced 2014 county estimates using single-year 2014 BRFSS data and a multilevel modeling approach based on respondent answers and their age, sex, and race/ethnicity, combined with county-level poverty, as well as county- and state-level contextual effects.[1] To produce estimates for those counties where there were no or limited data, the modeling approach borrowed information from the entire BRFSS sample as well as Census Vintage 2014 population estimates. CDC used a parametric bootstrapping method to produce standard errors and confidence intervals for those point estimates. This estimation methodology was validated for all U.S. counties, including those with no or small (<50 respondents) samples.[2] This same method was used in constructing the 500 cities study, which includes BRFSS data for the 500 largest cities in the U.S. and can be found here: https://www.cdc.gov/500cities.

One limitation of the BRFSS is that all measures are based on self-reported information, which cannot be validated with medical records. Another limitation is that these model-based estimates were created by borrowing information from the entire BRFSS, which may or may not accurately reflect those counties’ local intervention experiences. Additionally, the confidence intervals constructed from these methods appear much smaller than confidence intervals reported for direct survey methods in previous years.

  1. Zhang X, Holt JB, Lu H, Wheaton AG, Ford ES, Greenlund K, Croft JB. Multilevel regression and poststratification for small-area estimation of population health outcomes: a case study of chronic obstructive pulmonary disease prevalence using the Behavioral Risk Factor Surveillance System. American Journal of Epidemiology 2014;179(8):1025–1033.
  2. Zhang X, Holt JB, Yun, S, Lu H, Greenlund K, Croft JB. Validation of multilevel regression and poststratification methodology for small area estimation of health outcomes. American Journal of Epidemiology 2015;182(2):127-137.

Digging Deeper

Agetrue
Gendertrue
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Subcounty Areatrue

There are several methods to try to get more specific data than the county-level. For larger counties, you can access county- or MSA-specific data from the CDC at http://www.cdc.gov/brfss/smart/smart_data.htm. However, using this data requires somewhat advanced analytic capabilities.

The 500 Cities project also provides city- and census tract-level small area estimates for chronic disease risk factors, health outcomes, and clinical preventive service use, including drinking behaviors, for the largest 500 cities in the United States.

In many states, you can access county-level BRFSS estimates, and in some cases, you can stratify those estimates by age, gender, income, education, or race. You can find BRFSS resources for most states in our Finding More Data section.

References

[1] Centers for Disease Control and Prevention. Sociodemographic differences in binge drinking among adults-14 states, 2004. MMWR Morb Mortal Wkly Rep. 2009;58:301-304.
[2] Centers for Disease Control and Prevention Web Site: Alcohol and Public Health. http://www.cdc.gov/alcohol/index.htm. Updated January 7, 2013. Accessed February 27, 2013.
[3] Zhang X, Holt JB, Lu H, Wheaton AG, Ford ES, Greenlund K, Croft JB. Multilevel regression and poststratification for small-area estimation of population health outcomes: a case study of chronic obstructive pulmonary disease prevalence using the Behavioral Risk Factor Surveillance System. American Journal of Epidemiology 2014;179(8):1025–1033.
[4] CDC. Healthy youth! Frequently asked questions. Centers for Disease Control and Prevention Web Site. http://www.cdc.gov/HealthyYouth/yrbs/faq.htm. Updated February 27, 2013. Accessed February 27, 2013.
[5] Miller JW, Naimi TS, Brewer RD, Jones SE. Binge drinking and associated health risk behaviors among high school students. Pediatrics. 2007;119:76-85.

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