This is the first large-scale study in the US to evaluate the association between depression symptom severity and short-term risk of hospital encounters of patients with MDD. Among more than 280 000 US adult patients diagnosed with MDD who had a PHQ-9 assessment in an outpatient care setting, 26.9% were categorized as having none/minimal depression symptom severity, 16.4% as mild, 24.7% as moderate, 19.6% as moderately severe, and 12.5% as severe. A notable finding of this study was the nearly stepwise manner that depression symptom severity was associated with an increased risk of an MDD-related hospital encounter in the short-term following a PHQ-9 assessment; the increased risk ranged from 27% to 164% among those with mild to severe depression symptom severity compared to those with none/minimal. These study findings were obtained after adjustment for differences in patient demographics and clinical characteristics, including presence of comorbid psychiatric disorders and receipt of depression treatment, indicating that depression symptom severity is a key driver of the short-term risk of presentation to the hospital setting. Moreover, our study findings imply that taking steps towards improving depression symptoms, even if by only one severity category, may be helpful to incrementally reduce the risk of short-term HRU, particularly among those in the highest severity categories.
The findings of this study significantly contribute to the accumulating evidence of the association of depression symptom severity with increased HRU in the US observed in a few other studies conducted on a considerably smaller scale of patients with MDD [16, 17]. Beiser et al conducted a prospective cohort study of 999 adults who presented to the ER for care other than for a psychiatric illness at a single US academic medical center in 2015 . Based on depression diagnostic screening and depression severity measurement administered via a tablet computer during the ER visit, patients identified with MDD (27%) had a significantly greater risk of a subsequent ER visit (61% increased risk) and hospitalization (49% increased risk) during the 1 year follow-up period than those without MDD; furthermore, each 10% increase in MDD severity was associated with a 10% greater relative risk of a subsequent hospital encounter . In another study of 539 individuals who self-rated their MDD using the Quick Inventory of Depressive Symptomatology Self-Report (2001-2002), 13.8% were classified as having mild depression, 38.5% as moderate, and 47.7% as severe; those with moderate and severe MDD used mental health services to a greater extent than those with mild disease, as well as had greater prevalence of unemployment, reduced work productivity, and disability . A relatively larger study of 10,443 individuals in Stockholm, Sweden (1998-2014) reported that those with subsyndromal, mild, moderate, and severe depression, according to scores on the Major Depression Inventory, had higher incidence rates of mental health-related hospitalizations and outpatient care visits than non-depressed individuals; similar to our study, Sun et al reported that as depression severity level increased, so did the rate of HRU .
Another remarkable finding of this study pertains to the association of increased depression symptom severity with increased risk of all-cause hospital encounters. Compared to patients with MDD and none to minimal depression symptom severity, according to index PHQ-9 assessments, those with mild symptoms had an 7% increased risk of an all-cause hospital encounter in the short-term following their PHQ-9 assessment, those with moderate had a 18% increased risk, those with moderately severe had a 36% increased risk, and those with severe had a 60% increased risk. This stepwise association of depression symptom severity level with increased relative risk of an all-cause hospital encounter in the short-term following a PHQ-9 assessment was observed after adjustment for age and the presence of Elixhauser comorbidities. Reviewed in Kessler et al , a number of other studies have previously shown that MDD is associated with early onset and/or increased severity of several comorbid chronic illnesses, including cardiovascular disease, diabetes, respiratory illnesses, and chronic pain. Reported in a systematic review of several studies, depressive symptoms are a significant predictor of general hospital admissions for non-psychiatric reasons (RR: 1.36, 95% CI: 1.28-1.44), as well as longer inpatient lengths of stay and higher readmission risk . The findings of our study add to this existing evidence of the association of MDD with increased HRU for non-psychiatric reasons by demonstrating the impact of increased depression symptom severity on the increased likelihood of all-cause HRU in the short-term. The reasons surrounding the association of depression symptom severity with greater utilization of healthcare resources in general are not well understood, but could be related to suboptimal treatment options, inadequate adherence to medications or treatments, a greater prevalence of chronic conditions (eg, substance abuse disorders, smoking history), lack of follow-up care by patients and/or providers, sociodemographic disparities, access to psychiatric care facilities, etc.
Two other studies conducted in the last decade in the US have also examined the distribution of patients with MDD across symptom severity categories based on PHQ-9 assessments, although they did not explore HRU to a significant extent [28, 29]. In a study of 1019 patients diagnosed with MDD (2006-2010 population sample from 9 US states in different geographic regions), Valuck found a generally similar distribution of patients with MDD across PHQ-9 score categories . In another population of patients with MDD in the US (N=315, 2014-2016), Bushnell et al reported 6.7% of patients with none/minimal (PHQ-9 score: 0-4), 26.7% with mild (PHQ-9 score: 5-9), 21.9% with moderate (PHQ-9 score: 10-14), 27.6% with moderately severe (PHQ-9 score: 15-19), and 17.1% with severe (PHQ-9 score: 20-27) depression . In our study, PHQ-9 scores of patients diagnosed with MDD were documented in EHRs, and therefore likely do not fully represent the distribution of severity categories among the overall population of patients with MDD in the US, especially those who have not been diagnosed and/or are without access to routine healthcare. Furthermore, a large proportion of patients represented in this study were from the Midwest region of the US; this particular region also contributed disproportionately to the none/mild depression symptom severity category. These findings may suggest that the PHQ-9 is used more routinely and that there is greater access to depression treatment in the Midwest compared to other US regions. Additionally, African American patients with MDD disproportionately were in the highest depression symptom severity categories. Several factors could contribute to this finding, including reduced access to routine healthcare resources where lower severity MDD may be assessed and documented. Nevertheless, the distribution of MDD patients across the PHQ-9 categories observed in this analysis broadly matches that observed in other studies with much smaller populations [28, 29].
This study has strengths in that it included a very large population of patients diagnosed with MDD across the US who had a PHQ-9 assessment, a validated instrument for measuring depression symptom severity . Since the PHQ-9 has been shown to have good agreement with clinician depression rating scales (eg, Hamilton Depression Rating Scale) commonly used in clinical trial settings , the population-level data herein may be useful to apply to clinical trial data for economic modeling and other treatment-related measurement purposes. Furthermore, Optum’s NLP technology allowed for the timely extraction of large amount of PHQ-9 score data from complex clinical notes available in the EHRs. In addition, the marginal structural model (MSM) allowed us to identify the adjusted absolute risk at each level of severity level, which is not possible with conventional regression techniques. MSM is also agnostic about the effect modification by any covariates, therefore the relative risk estimates are not subject to model misspecification with respect to any product terms between depression severity and baseline covariates that could be possibly mis-specified in conventional regression techniques.
Limitations of the study
The findings of this study should be interpreted with the understanding of certain limitations. First, the Optum EHR database may not contain all patient diagnoses and interactions with the healthcare system, including visits for psychiatric care provided by specialists who do not contribute to EHRs within the database. This study only included individuals with a recorded MDD diagnosis captured via an ICD code and also a PHQ-9 score available; thus, results may not generalize to individuals beyond this population, such as those with a high PHQ-9 score but no recorded diagnosis of MDD. Other potential limitations of the Optum EHR database include the inability distinguish primary versus secondary reasons for hospitalization, and thus we cannot assert that MDD was the primary reason for MDD-related HRU, but can conclude that the encounter was related to MDD as the diagnosis was recorded at some point during the hospital encounter. Second, since depression severity varies over time, using a single time point measure as a proxy of a complex exposure trajectory may result in non-differential measurement error that biases our results towards the null. As an observational study, no causal relationship between depression symptom severity and short-term HRU can be affirmed; however, such a hypothesis is plausible and can guide actions since a randomization of similar populations is not possible. Also, since the findings were based on real-world data obtained from EHRs, there is the potential for inaccuracies in diagnosis codes, missing records, and erroneous PHQ-9 scores recorded by healthcare providers in the clinical notes. However, our validation process confirmed that the NLP technology generated accurate PHQ-9 score outputs. Further study using similar methodology across other EHR database sources is warranted.