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Table 2 Descriptive Characteristics

From: The impact of payer status on hospital admissions: evidence from an academic medical center

Variables Admissions1 P-value2
  =1 =0  
Sex    0.000
Female 48.1 51.9 -
Race    0.000
Asian 2.77 5.25 -
Hispanic 19.1 19.1 -
Other Race 5.08 7.69 -
Unknown Race 0.7 7.11 -
White 37.4 33.1 -
Marital    0.000
Divorced 5.75 2.72 -
Other Marital 1.92 5.05 -
Seperated 2.53 1.36 -
Single 56.1 65.9 -
Unknown Marital 0.02 0.13 -
Widow 5.47 1.58 -
PrscNum 0.52 0.39 0.000
Chronic 19.7 80.3 0.000
Combinations 6.65 2.18 0.000
Diagnosis    0.000
Blood 0.96 0.36 -
Circulatory 13.3 4.12 -
Congential Anomalies 0.21 0.32 -
Digestive 9.7 6.97 -
Endocrine 2.33 3.04 -
Genitourinary 4.72 5.45 -
Illdefined 4.38 12.2 -
Infections 4.02 2.94 -
Injury & Poison 13.1 8.92 -
Mental 1.64 6.56 -
Metabolic & Immune 1.35 1.38 -
Musculoskeletal 5.81 10.77 -
Neoplasms 6.37 2.94 -
Nutrition 2.68 7.73 -
Pregnancy 16.0 1.44 -
Respiratory 6.18 3.42 -
Secondary 1.33 0.50 -
Skin 2.75 4.64 -
Supplementary 1 1.9 13.0 -
Unknown Diagnosis 0.08 3.13 -
Payer    0.000
Multiple payers 8.24 5.08 -
Government 65.7 50.8 -
Private 23.1 35.0 -
Other Payers 1.51 3.70 -
Uninsured 1.44 5.44 -
ER Visits    0.000
=0 ER visit record 86.2 73.9 -
=1 ER visit record 8.22 19.6 -
>1 ER visit records 5.61 6.48 -
Prior Admissions    0.000
=0 prior admissions 66.6 99.3 -
=1 prior admissions 16.6 0.53 -
>1 prior admissions 16.7 0.15 -
  1. 1The 3rd and 4th column report the percentage of admitted and non-admitted patients, respectively, with the given feature for categorical variables or the mean over admitted patients for continuous features.
  2. 2A Chi-square test is used to check whether the difference of proportions (percentages) among different categories of a categorical variable is significant. A t-test is used to check the null hypothesis that means of continuous variables among different categories are equal. The p-values are presented in the 5th column