# Presentation: 501073

## County-level Characteristics Associated with Gonorrhea Rates – United States, 2002

**M Greenberg, M Sternberg, E Swint, R Kerani, E Koumans**
**mgreenberg1@cdc.gov**

## Background

**Gonorrhea is the 2****nd**** most frequently reported communicable disease in the US**
**351,852 cases reported to CDC in 2002**
**Rates have declined dramatically in the past two decades**
**Significant disparities remain geographically and by race**

## Gonorrhea — rates by race and ethnicity: United States, 1981–2002 and the Healthy People 2010 objective

## Gonorrhea — rates by region: United States, 1981–2002 and the Healthy People 2010 objective

## Objectives

**Identify county-level characteristics independently associated with the gonorrhea rate **
**Evaluate effects of race and geographic clustering on county-level predictors**

## Sources of data

**National Electronic Telecommunications System for Surveillance (NETSS)**
**US Census 2000 Summary File 3 **
**3,141 United States Counties**
**SAS USA Counties Map file**
**	****(SAS Institute Inc., Cary, NC)**

## Methods - univariate analysis

**Census variables chosen**
**socio-demographic relevance **
**comparison with other studies**
**Grouped into categories felt to measure different effects**
**Density, race/ethnicity, family structure, poverty/income, fertility/health, crime, education, h****ousing**
**Correlated variables independently with the gonorrhea rate**

**Notes:**
Log transformation of the rates were done by adding 1 to the numerator and taking the log of this.

## Most strongly correlated variable from each category included in the regression model
Ordinary least squares (OLS) residual showed spatial autocorrelation
Demonstrated need for a multivariate spatial model
Spatial model explains all variables controlling for spatial autocorrelation

**Ordinary least squares (OLS) residual showed spatial autocorrelation**
**Demonstrated need for a multivariate spatial model**
**Spatial model explains all variables controlling for spatial autocorrelation**

**Methods: ****multivariate analysis**

**Notes:**
To create the model, significant variables from the univariate analysis were rank-ordered from most to least significant. Those with absolute value of r greater than or equal to 0.2 were kept. Variables were grouped into categories felt to measure different effects, for example housing, socio-economic status, crime, etc. For example, for poverty we chose percent of families below poverty, percent all ages below poverty, etc. The most significant variable in each group was included in the baseline model. The residual, or the variation of the data from the model, was assessed to see if spatial correlation was present. 

## Measuring spatial effects

**One county and its “neighbors”**
**“****Neighbors” related by:**
**Distance**
**Contiguity**

## Contiguity

## Contiguity

## Spatial autocorrelation

**Pairs of adjacent counties where the gonorrhea rate is significantly correlated and may in some way influence one another**
**High-high**
**Low-low**
**Not significant**

## Univariate analysis:Correlation between county-level characteristics and gonorrhea rate*

| Variable | Pearson correlation coefficient (r)‡ |
| --- | --- |
| % population black* | 0.71 |
| % female head of household | 0.63 |
| % people <18 below poverty | 0.37 |
| Serious crimes/100,000 population | 0.29 |
| % renter occupied* | 0.27 |
| % ≥ 25 high school graduates | -0.26 |
| Persons/square mile* | 0.19 |

***log-transformed**

**‡****p<0.0001**

**Notes:**
This table shows the univariate analysis of the county-level variables. The first column gives the Pearson correlation coefficient, and the second column the cross-correlation coefficient. This is the correlation between the log GC rate and the county-level variable for the surrounding counties, essentially a spatially-adjusted correlation. Note that for percent over 25 years HS graduates and percent 1-unit detached housing, the association went from negative to positive after spatial adjustment.

## Slide 14

**Notes:**
This is a map of crude gonorrhea rate by county for 2002.

## Spatial autocorrelation

**Spatial autocorrelation**

**High-high**

**Low-low**

**Low-high**

**High-low**

**Not significant**

**Notes:**
Note that the map identifies clustering of high (red) and low (blue) rates. White areas show no significant clustering.

This highlights the geographic clustering, both positive and negative and also suggests that Southeast and Northwest may be meaningful sub regions.

## Multivariate models

|  | Standardized Coefficient |  |
| --- | --- | --- |
| Variable | OLS | Spatial  |
| % population Black* | 0.62 | 0.58 |
| % female-headed
households | 0.25 | 0.23 |
| Persons/sq mile* | -0.25 | -0.22 |
| Serious crimes/100,000 | 0.07 | 0.10 |
| % ≥25 HS graduate | 0.14 | 0.10‡ |
| % renter occupied* | 0.18 | 0.18‡ |
| % age 18-44 | -0.17 | -0.18 |

***log-transformed**
**‡ ****p<0.05; all others p<0.001**

## Change in gonorrhea rate for one standard deviation increase in each variable

**% Pop. Black***

**% Female HH**

**% Rent occupied***

**Serious crimes/100,000**

**% ****≥25 HS grad**

**% Pop 18-44 years**

**Persons/sq mile***

***log scale**

**Change in Gonorrhea Rate (persons/100,000 population)**

## Limitations

**Ecologic study**
**Effects of differential reporting by states not included in the model**
**Spatial autocorrelation may be a surrogate for an unmeasured county-level variable or an artifact of county partitioning**
**County may not be most appropriate area level for analysis**
**Model does not include measures of health resource utilization**

## Summary

**The gonorrhea rate varied significantly across US counties **
**There was significant geographic clustering**
**Clustering was not explained by measured population characteristics alone**
**Relative contribution of certain county-level characteristics changed when accounting for geographic clustering**

## Recommendations

**Consider geographic clustering as an important effect in analyses of STD surveillance data**
**County-level characteristics and geographic context may help direct screening and prevention efforts.**

## Acknowledgements

**CDC**
**Emily Koumans**
**Maya Sternberg**
**Emmitt Swint**
**Hillard Weinstock**
**James Heffelfinger**
**Stuart Berman**
**University of Washington**
**Roxanne Kerani**

## Slide 22

## Gonorrhea — Rates: United States, 1970–2002 and the Healthy People  2010 objective

- Note: The Healthy People 2010 objective for gonorrhea is 19.0 cases per 100,000 population.

## Slide 24

## Gonorrhea Rate – US 2002by race/ethnicity

|  | Rate 
(per 100,000 population) |
| --- | --- |
| Overall | 125.0 |
| African-American | 742.3 |
| Am. Indian/Alaska Native | 126.8 |
| Hispanic | 76.0 |
| White | 31.1 |
| Asian/Pacific Islanders | 24.1 |

## Methods - Overview

**Create a baseline OLS model; residuals still spatially correlated**

**Choose a spatial model to account for spatial correlation**

**Model results using baseline OLS and spatial term**

**2**

**4**

**5**

**Notes:**
Basic regression analysis assumes independence of all observations, so if spatial autocorrelation exists, this assumption is violated. Spatial dependence may either be a “nuisance” or may capture important information about the realities of the relationships. 

## Methods: Spatial Analysis

**Assess clustering of GC rate**
**Establish a weighting matrix – based on**
**Distance**
**Contiguity**
**Other measures of interaction**
**Global clustering**
**Moran’s I statistic **

**Notes:**
Since there may be geographic clustering of the gonorrhea rate, we need a method to assess this. The first step is establishing a weighting matrix which quantifies the relationship between one county and related counties, or “neighbors.” This can be defined on the basis of distance, contiguity or some other measure of interaction such as bordering a highway. We also assess the degree to which the gonorrhea rate is more similar in nearby counties than in those farther apart and whether or not that tendency is unlikely to be due to chance. This is quantified by the Moran’s I statistic, similar to a correlation coefficient. The higher the number, the greater departure from a spatially random pattern. A p-value assesses statistical significance.

## Methods: Spatial Analysis

**Choose a spatial model**
**spatial lag model or **
**spatial error model**
**Model results using baseline OLS model with spatial term**

**Notes:**
Once it is determined that there is spatial correlation, you must choose an alternate model that incorporates this. There two most widely used are the spatial lag model and the spatial error model. They vary by how the spatial weights are treated in the model. The spatial error model was more significant and that was the model we used. Finally, we constructed a spatial regression model incorporating the baseline OLS model and the spatial term.

## Geographic Clustering of the Gonorrhea Rate

**Global Moran’s I = 0.552 (p<0.0001)**
**Indicates significant clustering of GC rate**
**Chose spatial error model**

**Notes:**
The global Moran’s I for the log GC rate is 0.5522 and is highly significant. This is based on first-order contiguity. We chose the spatial error model as it was more significant in the models tested. 

## Multivariate model with Spatial Term

| Variable | Coefficient | Probability (p) |
| --- | --- | --- |
| Constant | 2.68163 | 0.0000000 |
| Midwest | -0.02635308 | 0.6810362 |
| North | -0.5374032 | 0.0000002 |
| West | -0.2800738 | 0.0008845 |
| % All ages < poverty* | -0.07646729 | 0.4747951 |
| % Female HH | 0.06709498 | 0.0000000 |
| % Black* | 0.2974684 | 0.0000000 |
| % Renter occupied | 0.1235981 | 0.0317941 |
| % >25 HS Grad | 0.0000251 | 0.9923115 |
| Serious crimes | 0.0000503 | 0.0000000 |
| % <18 below poverty | 0.00874715 | 0.1167759 |
| Births/1000 pop | -0.0255474 | 0.0000247 |
| Lambda | 0.4989758 | 0.0000000 |

## Ordinary Least Squares Model

| Variable | Coefficient | Probability (p) |
| --- | --- | --- |
| Constant | 1.80734 | 0.0000000 |
| Midwest | -0.05385923 | 0.2367081 |
| North | -0.6351099 | 0.0000000 |
| West | -0.260948 | 0.0000065 |
| % All ages < poverty* | 0.1790612 | 0.0781330 |
| % Female HH | 0.08337091 | 0.0000000 |
| % Black* | 0.3130289 | 0.0000000 |
| % Renter occupied | 0.1632146 | 0.0020724 |
| % >25 HS Grad | 0.003993079 | 0.0995493 |
| Serious crimes | 0.00001091 | 0.1379701 |
| % <18 below poverty | 0.00874715 | 0.1167759 |
| Births/1000 pop | -0.0255474 | 0.0000247 |

## Contiguity

**Second-order contiguity**

## Comparison of spatial to baseline model

| Variable | Ratio* |
| --- | --- |
| % population Black* | 0.93 |
| % female households no spouse | 0.93 |
| Persons/sq mile* | 0.85 |
| Serious crimes/100,000 pop | 1.44 |
| % ≥25 HS graduate | 0.73 |
| % renter occupied* | 0.97 |
| % age 18-44 | 1.08 |

## Categories of county-level characteristics

**Density**
**Race/ethnicity**
**Family structure**
**Poverty/income**

**Fertility/health**
**Crime**
**Education**
**Housing**

**Correlated variables independently with the gonorrhea rate**