{'fraction_non_alphanumeric': 0.018847006651884702, 'fraction_numerical': 0.0066518847006651885, 'mean_word_length': 5.289198606271777, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Insurance status influences the rates of reportable quality metrics in brain tumor patients: a nationwide inpatient sample study.

BACKGROUND
In 2010, the Patient Protection and Affordable Care Act was passed to expand health insurance, narrow health care disparities, and improve health care quality in the United States. As part of this initiative, the Agency for Healthcare Research and Quality and the Centers for Medicare and Medicaid Services are now tracking quality metrics.


OBJECTIVE
To analyze the effects of insurance on the incidence of patient safety indicators (PSIs) and hospital-acquired conditions (HACs) using the Nationwide Inpatient Sample for patients who have brain tumors.


METHODS
The Nationwide Inpatient Sample was queried for all hospitalizations between 2002 and 2011 involving patients with brain tumors. Because of the confounding age restriction with Medicare, comparisons were made between Medicaid/self-pay and private insurance. To determine which factors contributed to HACs and PSIs, odds ratios were calculated for each risk factor. Logistic regression models were used to assess the effect of payer status on individual PSIs, HACs, and patient outcomes.


RESULTS
Medicaid/self-pay patients had a higher PSI and HAC incidence compared with private insurance patients. The greater incidence of PSIs and HACs correlated with increased length of stay, worse discharge outcomes, and increased in-hospital mortality.


CONCLUSION
Variability exists in the incidence of PSIs and HACs in patients with brain tumors based on insurance status. Controlling for both patient and hospital factors can explain these differences. The cause of these disparities should be studied prospectively to begin the process of improving quality metrics in vulnerable patient populations.