Urban–Rural Differences in Health Care Utilization and COVID-19 Outcomes in Patients With Type 2 Diabetes

Annemarie G. Hirsch, PhD; Cara M. Nordberg, MPH; Karen Bandeen-Roche; Jonathan Pollak, MPP; Melissa N. Poulsen, PhD; Katherine A. Moon, PhD; Brian S. Schwartz, MD

Disclosures

Prev Chronic Dis. 2022;19(7):e44 

In This Article

Results

Hospitalization Study

In 2020, 2,751 patients were hospitalized for COVID-19 and 1,020 of these patients met criteria for diabetes before admission (Table 1). Among those hospitalized, 458 died in the hospital and 105 died after discharge. During hospitalization, 650 patients were admitted to the ICU and 342 required mechanical ventilation. Among 2,300 patients with a troponin measure, 1,346 had an elevated level. Among 2,134 patients who had a D-dimer measure, 1,879 had levels ≥0.5 μg/mL.

Diabetes was associated with higher odds of ICU admission and elevated troponin levels in all models, but was not associated with death, mechanical ventilation, or elevated D-dimer levels (Table 2). Chronic kidney disease, chronic lung disease, institutional residence, and age were associated with higher odds of elevated troponin levels and death (Table 2). Age and female sex were also associated with higher odds of elevated troponin levels, while Hispanic ethnicity and hospitalization later in 2020 were associated with lower odds. The only factor associated with mechanical ventilation was the hospitalization time period, with the middle (June–September) and late (October–December) months associated with lower odds of ventilation. No comorbid diseases studied were associated with elevated D-dimer. We found no consistent evidence of effect modification of the associations between diabetes and COVID-19 outcomes by administrative community type or CSD. Administrative community type was not associated with death after discharge and the urbanicity measure was not associated with any of the outcomes (not shown).

Utilization Study

A total of 93,401 patients, with a mean age of 57 years, met the criteria for the utilization study (Table 3). Consistent with the demographics of the region that Geisinger serves, individuals were predominately White (93.6%) and the majority resided in townships (55.2%). We present findings based on visual inspection of trends for each outcome for ease of interpretation (Figure 1 and Figure 2). These trends were supported by model coefficients and tests of statistical significance, unless otherwise noted.

Figure 1.

Nonseasonal autoregressive integrated moving average time-series models with linear splines at 4 dates in 2020 (March 16, May 4, July 13, and November 30) of weekly utilization rates per 1,000 patients with type 2 diabetes of hemoglobin A1c(HbA1c) tests (A), antihyperglycemic medication orders (B), emergency department visits (C), and outpatient or telehealth visits (D). All plots were stratified by administrative community type. The gray shading indicates the intervention period: March 16, 2020–December 31, 2020.

Figure 2.

Nonseasonal autoregressive integrated moving average time-series models with linear splines at 4 dates in 2020 (March 16, May 4, July 13, and November 30) of weekly utilization rates per 1,000 patients with type 2 diabetes of hemoglobin A1c(HbA1c) tests (A), antihyperglycemic medication orders (B), emergency department visits (C), and outpatient or telehealth visits (D). All plots were stratified by quartile of community socioeconomic deprivation (quartile 4 = most deprived). The gray shading indicates the intervention period: March 16, 2020–December 31, 2020.

Administrative Community Type

Prepandemic rates of ED and outpatient visits differed by community type and urbanicity, such that cities (vs townships) (Figure 1) and urbanized areas and urban clusters (vs rural) (Appendix Figure 2) had higher rates of ED encounters and lower rates of outpatient visits. This disparity persisted throughout the pandemic, as the trajectory of ED and outpatient visits after March 2020 did not differ by administrative community type.

Before the pandemic, weekly rates of HbA1c tests were lower in cities than in townships, but rates were increasing faster in cities than in townships. The drop in weekly HbA1c tests in March was greater in cities than in townships. Statistical output from the ARIMA models indicated that HbA1c tests declined at a faster rate in cities than in townships in March and recovered at a faster rate in cities than in townships in May. However, an inspection of Figure 1A reveals that this finding may be, in part, an artifact of the model, as the nadir in utilization appears to have occurred slightly later than March 16, and hence the modeled results (lines) do not fully reflect the observed data between March and May. All administrative community types experienced the same rate of decline in HbA1c utilization from July through the end of 2020.

Similarly, before the pandemic, weekly rates of antihyperglycemic medication orders were lower in cities than in townships and in urbanized areas than in rural areas. In the week before March 16, there was an increase in the rate of medication orders in townships and boroughs that was not observed in cities, as indicated in Figure 1B by the peaks in rates before the intervention period (shaded gray). In March there was a decline in medication order rates in all administrative community types, but that decline was slower in cities than in townships. Rates started to increase in May, again at a slower rate in cities than in townships. After July, rates continued to increase in townships and boroughs, but started to decrease again in cities.

Community socioeconomic deprivation

Prepandemic rates of ED and outpatient visits and antihyperglycemic medication orders differed by CSD, such that patients from more deprived communities (quartiles 2, 3, or 4 vs quartile 1) had higher rates of ED encounters, lower rates of outpatient or telemedicine visits, and lower rates of medication orders (Figure 2). These disparities persisted throughout the pandemic, as the trajectory of visits and medication orders did not differ by level of CSD. Statistical output from the ARIMA models indicated that the frequency of HbA1c tests declined at a faster rate in the most deprived community (vs least deprived) in March and recovered at a faster rate in the most deprived community (vs least deprived) in May. However, an inspection of Figure 2A reveals that this finding may be an artifact of the model, as the nadir in utilization appears to have occurred slightly later than March 16, and hence the modeled results (lines) do not fully reflect the observed data between March and May. The rate of decline in HbA1c tests in July through the end of 2020 was the same across levels of CSD.

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