Injury ER Visits
Compare This MetricDescription
Number of emergency room visits for injuries, poisonings, or accidents.
Calculation
Number of ER visits for injuries per 1,000 population.
Source
Statewide Planning and Research Cooperative System (SPARCS) Outpatient Data, 2011-2013.
Years of Data
2011-2013
City Wide Average
76.0
Census Tract 1006800 Average
47.5
By Census Tract
1006800
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| Injury ER Visit Rate | Population (2011-2013) |
| All | 47.5 | 18,873 |
Sex
| Female | 46.0 | 9,768 |
| Male | 61.1 | 9,103 |
Race/Ethnicity
| Asian/Pacific Islander | 24.2 | 1,900 |
| Black | 185.1 | 902 |
| Hispanic | 156.7 | 1,404 |
| White | 24.6 | 14,115 |
Age
| 0-14 years | 202.0 | 693 |
| 15-24 years | 30.2 | 3,776 |
| 25-34 years | 23.8 | 6,556 |
| 35-44 years | 43.1 | 2,716 |
| 45-54 years | 62.6 | 1,948 |
| 55-64 years | 100.2 | 1,308 |
| 65-74 years | 42.6 | 1,127 |
| 75+ years | 93.2 | 730 |
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Overlay Local Information
All measures except rates by age are age-adjusted to facilitate comparisons across communities with different age structures. For more information on age-adjustment, read more here.
DOWNLOAD MAP (PDF)In statistics, correlation is a measure of association between two numeric variables. The strength of correlation between two variables is represented by the correlation coefficient, represented by the abbreviation r. Correlation coefficients range between -1 to 1.
Though the correlation coefficient indicates the strength of an association, it does not provide information about whether the change in one variable is caused by the other.
For example, if the correlation between adult smoking prevalence and child poverty is 0.7—a strong correlation—we cannot say either that adult smoking causes child poverty or, inversely, that child poverty causes smoking. We only know that as one of these variables increases, the other tends to increases.