The Diagnosis of Malingering in General Hospitals in the United States: A Retrospective Analysis of the National Inpatient Sample.
Academic Article
Overview
abstract
OBJECTIVE: To characterize the socio-demographics and comorbid medical and psychiatric diagnoses of patients in the general hospital diagnosed with malingering. METHOD: We conducted a retrospective observational cohort study using data from the 2019 National Inpatient Sample, an all-payors database of acute care general hospital discharges in the United States, querying for patients aged 18 and older discharged with a diagnosis of "malingerer [conscious simulation]," ICD-10 code Z76.5. RESULTS: 45,645 hospitalizations (95% CI: 43,503 to 47,787) during the study year included a discharge diagnosis of malingering. 56.1% were for male patients, and the median age was 43 years (IQR 33 to 54). Black patients represented 26.8% of the patients with a discharge diagnosis of malingering, compared to 14.9% of all patients sampled. Zip codes in the lowest household income quartile comprised 39.9% of malingering diagnoses. The top categories of primary discharge diagnoses of hospitalizations included medical ("Diabetes mellitus without complications"), psychiatric ("Depressive disorders"), and substance use ("Alcohol-related disorders") disorders. "Sepsis, unspecified organism," was the most common primary diagnosis. CONCLUSION: The striking overrepresentation of Black patients in hospitalizations with diagnosis of malingering raises concern about the roles of implicit and systemic biases in assigning this label. The disproportionate number of patients of low socioeconomic status is further suggestive of bias and disparity. Another potential contribution is that the lower health literacy in these populations results in a limited knowledge of traditional ways to meet one's needs and thus greater reliance on malingered behavior as an alternative means. Accurate description of these patients' socio-demographics and comorbid medical and psychiatric diagnoses with reliable data from large samples can lead to improved understanding of how the malingering label is applied and ultimately better patient care.
