Funke Pickering, MD, senior medical director at Inovalon.

Funke Pickering, MD

Social risk factors are playing a significant role in driving greater hospitalization rates and healthcare costs among patients with

. In a 2024 analysis, lower income, lower educational attainment, and greater comorbidity burden were strongly associated with increased hospitalizations and spending among patients with MDD.

Hospitalization rates were nearly twice as high among Medicaid patients compared with those with commercial insurance (29.6% vs 14.7%), with annual costs exceeding $21,000 vs $14,500, respectively. Additional findings showed that individuals living in areas with greater access to mental health specialists experienced higher short-term hospitalization rates, potentially reflecting improved identification and management of more severe disease.

In this Q&A, Funke Pickering, MD, senior medical director at Inovalon, discusses how social determinants of health influence hospitalization risk in MDD, where current risk adjustment and care models fall short, and what clinicians can do to better identify and address these upstream drivers of utilization and cost.

 How do social risk factors, such as income, education, and comorbidity burden, shape hospitalization risk among patients with MDD?

 The data is clear; patients with MDD who face greater socioeconomic disadvantage are not just harder to reach, but they’re getting sicker, cycling through higher-acuity care, and costing the system more. In our research using real-world data, hospitalization among patients with MDD was meaningfully higher for Medicaid than commercial populations.

 We also observed…lower income and educational attainment were associated with higher hospitalization rates and total costs, and a greater comorbidity burden was associated with increased hospitalization risk.

Clinically, this matches what many of us see. Depression care is rarely “just a prescription.” Many patients need both medication management and psychotherapy…but coverage for both is not always the same. Income can often decide whether a patient can sustain their treatment or if they’ll fall through the cracks.

Comorbidities add another layer of complexity because they make mental health easier to overlook. Physicians have limited time, and patients who are trying to balance diabetes, heart failure, autoimmune disease, or other chronic conditions need more than a quick wellness visit. Mental health conditions, unfortunately, are too often treated in siloes, making continuity of care a challenge for clinicians.

 What practical steps can clinicians take to identify social risk factors that may increase the likelihood of hospitalization in patients with depression?

 By screening more consistently, this communicates to patients that their mental health is an important part of their overall health, while also allowing providers to identify changes in a patient's circumstances that may place them at risk.

While most clinicians working with high-risk patients already have a sense of when something is off…they’re often at a loss for what to do next. Connecting patients to the right resources is a challenge, and navigating health plan benefits and community-based services requires coordination that many clinicians simply don't have the bandwidth for. There needs to be an intervention further upstream before they walk into the office that starts with their health plan identifying, documenting, and communicating any barriers with the patient’s care team.

Once they leave, clinicians lose visibility: Did they fill the prescription? Could they afford it? Will transportation or scheduling make follow-up possible? I’ve seen many cases where the treatment plan appeared appropriate, but we later learn, when the patient is readmitted or ends up in the emergency room, that it wasn’t feasible because they couldn’t keep up with the regimen.

It's important to be honest about access. Provider directories can be misleading, and there is evidence that even when a region appears to have mental health providers, actual appointment availability can be far lower. A 2023 study found that 81% of entries in physician directories across 5 large national health insurers contained inaccuracies or inconsistencies, raising concerns that patients may not have a reliable, legitimate referral pathway.

It shouldn’t be up to physicians alone to do the screening. Health systems, payers, government agencies, and community organizations must work together.

 How should clinicians incorporate screening for social determinants of health into routine care for patients with MDD?

 Mental health and social risk screening should be as routine as taking vital signs and medication reconciliation during a care visit.

While screenings are more common than they used to be, they’re often not embedded in a comprehensive, standardized process. Patients may be asked a few high-level questions, but the assessment rarely captures critical factors such as food security, housing stability, financial strain, or language barriers, which can change within weeks and quickly derail an otherwise sound treatment plan. That’s why screening should be integrated into the overall health assessment and diagnosis, not treated as a one-time check-the-box step.

I also wouldn’t limit screenings to wellness visits, since many patients skip routine appointments and only seek care when something is wrong. Screenings should be conducted regularly throughout the year.

 How do current risk adjustment and care models fall short when social risk factors are not adequately accounted for?

 A big limitation is that most risk adjustment models are still built around diagnosis coding. They rely on ICD-10 clinical conditions and do not meaningfully incorporate social drivers of health, even when those drivers are documented. Utilizing ICD-10 Z codes that document the context around a patient’s health, including social and environmental risk factors, is one way to track conditions over time. Though historically, the use of ICD-10 Z codes has not been incentivized or reimbursed when treating vulnerable patients.

For example, you can screen a patient and identify food or housing insecurity, but those findings often aren’t captured in a way that “counts," and reimbursement still largely follows disease-based coding.

The practical issue is that 2 patients can have the same MDD diagnosis and look similar in a model, while their real-world utilization can vary based on their life circumstances. That is exactly what our research showed. Social and economic conditions were linked with hospitalization and spending, and models that ignore those factors underestimate expected resource use.

This dynamic is most evident in value-based care settings. When caring for high social risk populations, visitation patterns can be unpredictable as there’s a higher likelihood that they won’t come in unless they’re already sick of experiencing a crisis episode. Even if clinical staff have access to social risk data, it’s often not linked to claim outputs.

 What role can data analytics and population health tools play in helping clinicians identify patients with depression who are at higher risk due to social factors?

 Population health tools help clinical staff across the care continuum identify patients with MDD who also have high comorbidity burden, repeated no-show visits, complex medication regimens, and known barriers like transportation or language needs. This visibility allows teams to route them to proper care management earlier and get ahead of crises. When implemented correctly, predictive analytics can support proactive follow-up.

When treating depression, this matters because the condition often hides in plain sight. People may be struggling to sleep enough or eat well, losing interest in activities they once enjoyed, yet still pushing through. Stigma is still real, and some people suffer in silence, meaning the data we have on MDD could actually be worse than what we see.

 What strategies should clinicians and health systems prioritize to address the social drivers of depression-related hospitalizations?

 I see 2 recurring failures that drive hospitalization. First, we too often fail to screen for and diagnose depression early enough. Without routine screening, we miss early-stage MDD and fail to identify patients at risk of admission. Second, we are not consistently screening for and documenting social drivers of health, including the barriers patients face and how those barriers impact outcomes.

Managing these barriers should be a part of the patient’s treatment plan, not an afterthought. We shouldn't have to wait until a patient arrives at the hospital during a mental health crisis when the system already knew they hadn't filled their last 3 prescriptions.

Teigland C, Mohammadi I, Agatep BC, Boskovic DH, Velligan D. Relationship between social determinants of health and hospitalizations and costs in patients with major depressive disorder. 

Butala NM, Jiwani K, Bucholz EM. Consistency of Physician Data Across Health Insurer Directories. 

Articles

Socioeconomic disadvantage materially increases hospitalization risk and total cost of care in major depressive disorder, with Medicaid populations demonstrating substantially higher admission rates and annual spending than commercially insured peers. Lower income, lower educational attainment, and higher comorbidity burden correlate with greater utilization, reflecting gaps in sustained access to combined pharmacotherapy and psychotherapy and the complexity of competing chronic disease demands. Routine, longitudinal screening for both depression and social needs is positioned as foundational, alongside payer–provider–community coordination, improved documentation (including Z codes), and analytics-enabled population health workflows to anticipate crises.

Pickering discusses data linking social risk factors to hospitalization and costs in MDD and implications for screening and care models.
