Biased health outcomes are revealed.
Poor and biased health outcomes related to health equity has been a recent theme across the healthcare industry, especially in relation to the social determinants of health (SDoH). New studies and consistent data reinforce the need for a shift in how the industry approaches general health and behavioral health across all populations, but especially among those at increased risk for issues associated with the SDoH.
A total of 700 women die from pregnancy-related causes in the United States annually. These numbers have evoked strong concern across the industry. A series of studies have revealed important considerations that must be factored into the care equation in such cases; severe maternal mortality outcomes have risen over 20 percent in the past two decades due to indicators closely associated with SDoH, such as race, ethnicity, insurer, socioeconomic status, and ZIP codes. For example:
- Black women are three to four times more likely to die from pregnancy-related causes than white women.
- Native American and Alaska Native pregnant women are over twice more likely to die than white women.
- In 2019, U.S. average maternal mortality was almost 30 deaths per 100,000 mothers, with severe maternal morbidity increasing by 200 percent.
- A total of 60 percent of deaths were likely preventable if care needs and contributing factors were identified and addressed by a primary care physician (e.g., high blood pressure, diabetes, toxoplasmosis, flu, etc.)
Washington, D.C.’s maternal mortality rate is now twice the national average. Two wards of Washington, D.C. no longer have hospitals with obstetric services. Pregnant women who reside in these areas must travel as much as an hour or more for maternity care. Women with high-risk pregnancies can easily have fatal outcomes.
Rural communities have their own unique but equally concerning maternal mortality challenges:
- Over half of rural counties in the U.S. lack hospital-based OB services.
- A total of 150 rural communities lack access to maternity care, or have seen hospitals dropping OB services or closing.
- A total of 20 percent of U.S. rural hospitals are at risk of closure.
Health Disparities and Bias Emerge as Factors in Recent Studies
Implicit bias has long been cited as a factor in managing populations dealing with the SDoH. Providers and practices have been shown to treat patients differently, whether due to race, ethnicity, social class, gender identity, religion, primary language, or even insurer. Refusal by practitioners to accept Medicaid plans has long been cited as a major factor that impedes the delivery of general and behavioral healthcare. Historically, studies have shown that as many as 31 percent of physicians are unwilling to accept Medicaid patients. Similar studies show increasing rejection of LGBTQ patients by practices, along with patients who have Medicare Advantage plans.
Racial and gender care inequities for patients has been emphasized in research on heart failure patients. Records of 2,000 patients admitted for heart failure to Brigham and Women’s Hospital in Boston over a 10-year period were reviewed, revealing the following findings:
- Patients who self-identified as black were 9 percent less likely to be admitted to specialized cardiac care units.
- Those who self-identified as Latinx, a gender-neutral term for persons of Latin American origin or descent, were 17 percent less likely to be admitted to specialized heart units.
- Female heart failure patients, or those 75 and older, were more likely to be treated on a general medicine floor.
Racial bias was also recently identified in an algorithm developed by Optum that is widely used across health systems. The algorithm aids hospitals in their efforts to identify high-risk patients for providers with chronic conditions, who may need additional resources to manage their health. Patients dealing with the SDoH have higher rates of chronic illness, along with higher rates of hospitalization and readmission. The costs associated with these populations have been a popular industry topic, as have the means to more proactively address the primary care needs of these populations.
Black patients were found to have been assigned the same level of risk by the algorithm, even though they may have been sicker than white patients. Bias occurs as the algorithm uses health costs as the measure for health needs. What results is the algorithm predicts healthcare costs versus illness. The outcome? Fewer dollars are spent caring for black patients than white patients, far short of the sum needed to create action to address the SDoH.
The race to continue to properly identify, assess, and develop sound means to address growing populations at risk for the SDoH continues. Follow along for more reporting on the State of the Social Determinants of Health weekly on Monitor Mondays.