Cancer does not equally burden all Pennsylvanians. Beyond the factors highlighted in previous sections, many things influence a patient’s chance of survival: overall health, tumor size, treating hospital, treatment plan, ability to pay, and so on. Public health initiatives often focus on patients’ socioeconomic status (SES) and health insurance coverage. This report uses race and poverty levels to estimate SES. The Pennsylvania Cancer Registry does not collect personal income data, so neighborhood poverty is used. The neighborhoods are Census tracts, and they’re grouped by the proportion of households below the poverty line. See Technical Notes for more details.

Race and neighborhood poverty

Research shows race can affect cancer survival (LeHew et al. 2017). Possible reasons include differences in the treatment process (Green et al. 2018) and early detection. While race can have a direct medical effect on survival, it more often acts as a proxy for SES.

White Pennsylvanians had a markedly better 5-year net cancer survival rate than black and African American Pennsylvanians.

Figure 9: 5-year Net Cancer Survival Among Pennsylvanians, Aged 15+, by Race

Higher neighborhood poverty also correlates with lower net survival. Race had less of an effect for patients living in neighborhoods with similar poverty levels.

Figure 10: 5-year Net Cancer Survival Among Pennsylvanians, Aged 15+, by Race and Neighborhood Poverty Level

For the moderate to very high poverty levels, white and black and African American patients had similar net survival rates. There were not enough black or African American patients to accurately detect any difference in neighborhoods with low poverty.


Halpern (2008) found that patients with private insurance or Medicare were more likely to be diagnosed with cancers at an early stage, possibly because they have higher screening participation. Again, earlier detection often leads to a better prognosis.

In this report, “insured” includes private insurance, Medicare, and insurance provided by the military. Patients are grouped by the primary payer at the time of diagnosis. A patients’ category does not change if they changed insurance status after diagnosis. Specifically, some of those without insurance when diagnosed could have bought insurance or applied for Medicaid because of the cancer diagnosis. See Technical Notes for more details.

The insurance status of patients varied by age. Among the Pennsylvanians included in the analysis, those between ages 15 and 64 were 5 times more likely to be uninsured and 2 times more likely to receive Medicaid. Comparing insurance status within each age group gives a clearer picture of how much insurance helps.

Figure 11: 5-year Net Cancer Survival Among Pennsylvanians by Age Group and Primary Payer at Diagnosis

For each age group, the net survival rate among Pennsylvanians using Medicaid was lower than among those with other insurance. Medicaid often serves citizens with low income or disabilities. We could not separate the risk from these qualities and the risk from cancer. This data only highlights disparities which need to be studied.

Patients aged 65 and older with Medicaid had poorer net cancer survival than those who initially had no insurance. Those initially without insurance had poorer rates than those with insurance before age 75. However, the two rates were very similar for ages 75 and older.

The differences in net survival between insurance payers can also be examined by years since diagnosis. From the time of diagnosis onward, insured Pennsylvanians had a much higher net survival rate compared to those with Medicaid or no insurance.

Insurance over time

This report categorizes patients by their insurance providers from when they were diagnosed. This status can change after diagnosis. The net survival curve below shows the change in risk at different times after diagnosis.

Figure 12: Net Cancer Survival Among Pennsylvanians, Aged 15+, by Years Since Diagnosis and Primary Payer at Diagnosis

During the first 6 months, patients with Medicaid saw higher net survival rates than those without insurance. By the end of the first year after diagnosis, the rate among those with Medicaid was declining more rapidly than among the uninsured.

Possible reasons this may have been:

  • Having no insurance means treatment is delayed, so the net mortality risk spikes in the first year.
  • An uninsured but previously healthy patient might have purchased insurance after the cancer diagnosis. The gap during the first year may reflect the period until the patient was covered.