1 INTRODUCTION

Coronavirus disease 2019 (COVID-19), caused from infection with SARS-CoV-2, is a highly contagious, viral disease that can lead to severe health outcomes that may require hospitalization and intensive care.1 According to COVID-NET estimates, at the end of 2020, the cumulative incidence rate of COVID-19 hospitalizations in the United States was 369.3 hospitalizations per 100,000 population.2 Hospitalizations are valuable to study from an epidemiological perspective because they are more likely to accurately reflect who is getting infected with COVID-19 compared with viral testing that can be prone to testing biases.

Over the course of the pandemic, it has become evident that certain people are hospitalized with COVID-19 at disproportionately higher rates than others, including the elderly and people with underlying health conditions.3, 4 People of color, particularly Black and African American communities, have also faced an increased risk of COVID-19 infection and hospitalization compared with non-Hispanic White communities,5-10 as have Hispanic and Latinx patients who in some cases have experienced increased in-hospital mortality.7 Additionally, there is increasing evidence that low socioeconomic status (SES) is an important risk factor for hospitalization and thus, antecedent infection.5, 6 Individual-level measures of SES are not typically obtained or available through public health surveillance programs, so instead, census tract-level measures of poverty and crowding from the US Census can be linked to patients' residential addresses as a way to assess SES disparities.11 Census-tract-based metrics have been valuable to determining the role SES plays in influenza in Connecticut and in other jurisdictions contributing to FluSurv-Net.12-14

To date, we are not aware of studies that analyze COVID-19 hospitalizations and disparities solely among non-institutionalized individuals in the community (unlike congregate settings which are mostly closed environments) throughout an entire state using public health surveillance data. In Connecticut, the geographical focus of this analysis, disparities in COVID-19 hospitalizations that occurred during the state's initial “Stay Safe, Stay Home” lockdown period have been previously described but were limited to those in New Haven and Middlesex counties.10 In this analysis, we aim to describe Connecticut's statewide trends in COVID-19 hospitalization among community members after the first, initial wave of COVID-19 and before the effect vaccinations would have on epidemiology—a time when most individuals had potential for COVID-19 exposure—in order to help determine the magnitude and persistence of disparities in COVID-19 hospitalizations. In addition, we compare the magnitude of racial/ethnic and SES disparities from the initial lockdown period10 to those found in this analysis.

2 METHODS 2.1 Surveillance data

We used statewide surveillance data collected by the Connecticut Department of Public Health (DPH) to monitor COVID-19 hospitalizations beginning on July 1, 2020. Hospitalizations on or after this point were required to be reported to the DPH by hospital staff completing a case report form, which included relevant information such as the patient's age, sex, and race/ethnicity, along with the COVID-19 case classification, date of admission, whether the patient resided in a congregate setting, and the patient's residential address.

All patients' residential addresses were automatically geocoded by the DPH, assigning each its census tract identification number. For those addresses that could not be automatically geocoded, the DPH manually geocoded them. Addresses unable to be geocoded included those with PO boxes or those deemed erroneous.

2.2 Study population

The study population included all Connecticut residents who were hospitalized at an acute care facility with COVID-19 for the first time between July 1 and December 31, 2020. All hospitalized patients in the final dataset were classified as either confirmed or probable A. Confirmed cases were defined as patients hospitalized within 14 days of a positive polymerase chain reaction (PCR) test for SARS-CoV-2. Probable A cases were defined as patients hospitalized within 14 days of a positive SARS-CoV-2 antigen-based test. Probable B cases were excluded (those patients hospitalized with no SARS-CoV-2 diagnostic test but with symptoms consistent with the Council of State and Territorial Epidemiologists' COVID-19 case definition15 or an Office of Chief Medical Examiner [OCME] report of a likely COVID-19 death).

2.3 Census data

Area-based SES measures of poverty and household crowding for each patient were determined by matching each patient's census tract of residence with the corresponding census tract estimate of poverty and crowding from the 2014–2018 American Community Survey (ACS) 5-Year Estimates from the US Census (https://data.census.gov/cedsci/). Both SES measures were stratified into four levels based on precedent in Connecticut.9, 11, 12 Poverty, defined as the percentage of households living below the federal poverty level, was categorized as very low (<5%), low (5% to <10%), medium (10% to <20%), and high (≥20%). Crowding, defined as the percentage of households with more than one occupant per room, was categorized as very low (<0.9%), low (0.9% to <2.5%), medium (2.5% to <5%), and high (≥5%).

Census tract-level, total population estimates were obtained from the 2010 Decennial US Census (https://data.census.gov/cedsci/).

2.4 Statistical analysis

Although we described the overall epidemiology of COVID-19 hospitalizations in Connecticut, our analyses placed emphasis on patients who resided in the community, as opposed to congregate settings. Crude and age-adjusted incidence rates of COVID-19 hospitalizations were calculated by dividing the case counts by the total population estimates for each age group, gender, race/ethnicity group, poverty level, and crowding level. Age adjustments, used to account for potential age-related confounding, were based on the 2000 US Standard Population proportions. Chi-square tests were used to compare hospitalization incidence between demographic and SES strata. Mantel-Haenszel chi-square tests for trend were used to determine whether there were significant associations between increasing poverty and crowding levels with hospital incidence, both alone and within age, gender, and race/ethnicity groups.

Additionally, we split these data into two groups: (1) patients residing in New Haven and Middlesex Counties (population: 1,028,153) and (2) patients residing in Fairfield, Litchfield, Hartford, Tolland, Windham, and New London Counties (population: 2,539,394), so that the New Haven and Middlesex County data could be compared with earlier COVID-NET estimates, which were limited to these two counties. The distribution and age-adjusted incidence among demographic and SES indicators were calculated and compared between these two county-based groups to determine if disparities were geographically widespread. Then, for patients residing in New Haven and Middlesex counties, we compared these July through December data with the March through early-May data previously analyzed by COVID-NET to determine if trends and disparities in COVID-19 hospitalizations were persistent throughout the year. Both chi-square tests and Mantel-Haenszel chi-square tests for trend were used for these county-level analyses. All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc, Cary, NC, USA) and Epi Info version 5.5.3 (Centers for Disease Control and Prevention, Atlanta, GA, USA).

3 RESULTS

There were 7062 first-time COVID-19 hospitalizations among Connecticut residents from July 1 to December 31, 2020. Approximately 98% (6901) of patients' residential addresses were successfully geocoded by the DPH. Of these, 294 patients were excluded from analyses because they did not meet this study's criteria and/or were missing data (Figure 1).

Flow diagram of patients hospitalized with COVID-19 included or excluded in analyses based on inclusion criteria

3.1 Characteristics of all patients hospitalized with COVID-19

After exclusion criteria, there were 6607 first-time COVID-19 hospitalizations between July 1 to December 31, 2020, that were confirmed with a positive molecular or antigen-based SARS-CoV-2 test and had a geocodable residential address (Table 1). Of these, there was a wide range of ages, though only 1.1% (74/6607) of patients were under the age of 18 years. Over half (52.2%) identified their race/ethnicity as non-Hispanic White, whereas non-Hispanic Black and Hispanic/Latinx patients represented 12.3% and 21.9% of all, respectively. A total of 5652 (85.5%) cases involved persons who lived in the community, whereas 955 (14.5%) lived in some type of congregate setting (i.e., long-term care facility, assisted living facility, jail or prison, or group home). The frequency of hospital admissions varied across the 6-month period, with 78.2% occurring in November and December.

TABLE 1. Characteristics of all patients with geocodable residential addresses hospitalized with laboratory-confirmed COVID-19 in CT, July to December 2020 Demographic factor No. of patients % Total hospitalized patients 6607 — Classification Confirmed by NAAT 6492 98.3 Probable A 115 1.7 Age (years) <18 74 1.1 18–49 1212 18.3 50–64 1649 25.0 65–74 1378 20.9 75–84 1286 19.5 ≥85 1008 15.3 Gender Female 3237 49.0 Male 3364 50.9 Unknown 6 0.1 Race/Ethnicity Non-Hispanic White 3449 52.2 Non-Hispanic Black 815 12.3 Non-Hispanic Asian 84 1.3 Hispanic/Latinx 1450 21.9 Non-Hispanic Othera 606 9.2 Unknown/Refused 203 3.1 Residence Community 5652 85.5 Congregate setting 955 14.5 Date of admission July 1 to August 31 401 6.1 September 1 to October 31 1038 15.7 November 1 to December 31 5168 78.2 Abbreviation: NAAT, Nucleic Acid Amplification Test. a Includes Other, Multiracial, American Indian Alaskan Native, and Native Hawaiian and Other Pacific Islander races.

Comparing those living in community with congregate settings, there was a significantly higher percentage of patients aged 75 years or older living in congregate settings (63.4% vs. 29.9%, P < 0.001) (data not shown).

3.2 Demographic-based disparities in hospitalization incidence

After excluding 188 (3.3%) patients in the community whose race, ethnicity, and/or gender were unknown, there were 5464 non-institutionalized patients included in the analysis. Incidence and trends of COVID-19 hospitalization significantly varied by age and race/ethnicity groups (Table 2). Elderly persons were disproportionately hospitalized; 75- to 84-year-old and ≥85-year-old patients were hospitalized at rates 8.4 (95% confidence interval [CI] 7.70–9.12) and 9.9 (95% CI 9.01–10.95) times higher, respectively, compared with 18- to 49-year-old patients. There were also significantly higher rates of hospitalization among patients of color, except for non-Hispanic Asian patients. The age-adjusted relative rates among non-Hispanic Black and Hispanic/Latinx cases compared with non-Hispanic White cases were 3.1 (95% CI 2.83–3.32),and 5.9 (95% CI 5.58–6.28), respectively.

TABLE 2. Characteristics, crude and age-adjusted incidence, and relative rates (RR) for all non-institutionalized patients hospitalized with COVID-19 in CT, July to December 2020 Demographic factor No. of patients (%) Total pop Crude incidence/100,000 population Crude RR Age-adjusted incidence/100,000 population Age-adjusted RR 95% CI (chi-square) Total hospitalized patients 5464 3,567,547 153.2 — 135.9 — — Age (years) <18 68 (12.4) 816,820 8.3 0.11 8.3 0.11 0.09–0.14 18–49 1125 (20.6) 1,517,378 74.1 Ref 74.1 Ref — 50–64 1458 (26.7) 727,130 200.5 2.70 200.5 2.70 2.50–2.92 65–74 1153 (21.1) 254,772 452.6 6.10 452.6 6.10 5.62–6.63 75–84 1035 (18.9) 166,602 621.2 8.38 621.2 8.38 7.70–9.12 ≥85 625 (11.4) 84,845 736.6 9.94 736.6 9.94 9.01–10.95 Gender Female 2657 (48.6) 1,833,851 144.9 Ref 121.8 Ref — Male 2807 (51.4) 1,733,696 161.9 1.12 155.0 1.27 1.20–1.35 Race/Ethnicity Non-Hispanic White 2815 (51.5) 2,542,250 110.7 Ref 82.6 Ref — Non-Hispanic Black 743 (13.6) 333,961 222.5 2.01 253.1 3.07 2.83–3.32 Non-Hispanic Asian 79 (1.4) 133,988 59.0 0.53 92.0 1.11 0.93–1.33 Hispanic/Latinx 1372 (25.1) 478,022 287.0 2.59 488.6 5.92 5.58–6.28 Non-Hispanic Othera 455 (8.3) — — — — — — Poverty level Very low (<5%) 1544 (28.3) 1,420,923 108.7 Ref 90.6 Ref — Low (5- < 10%) 1302 (23.8) 956,905 136.1 1.25 109.3 1.21 1.11–1.31 Medium (10- < 20%) 1325 (24.2) 664,155 199.5 1.84 187.4 2.07 1.91–2.24 High (≥20%) 1293 (23.7) 525,564 246.0 2.26 281.1 3.10 2.88–3.34 Crowding level Very low (<0.9%) 2036 (37.3) 1,770,352 115.0 Ref 95.1 Ref — Low (0.9% to <2.5%) 1302 (23.8) 839,568 155.1 1.35 132.9 1.40 1.30–1.51 Medium (2.5% to <5%) 1017 (18.6) 496,056 205.0 1.78 195.0 2.05 1.89–2.22 High (≥5%) 1109 (20.3) 461,571 240.3 2.09 269.0 2.83 2.63–3.05 a Includes Other, Multiracial, American Indian Alaskan Native, and Native Hawaiian and Other Pacific Islander race. 3.3 SES-based disparities in hospitalization incidence

When assessing census tract poverty and crowding levels as measures of SES, patients living in high poverty and crowding census tracts were hospitalized at an age-adjusted rate approximately three times higher (poverty 95% CI 2.88–3.30, crowding 95% CI 2.63–3.05) than patients living in very low poverty and crowding tracts (Table 2). As census tract poverty and crowding levels increased, there were strong and statistically significant trends of increased, age-adjusted hospitalization incidence (P < 0.001 chi-square for trend for each) (Figure 2A,B).

Overall age-adjusted hospitalization incidence by census tract (A) poverty level and (B) crowding level in CT, July to December 2020. * Chi-square test for trend P < 0.05; ** Chi-square test for trend P < 0.01; *** Chi-square test for trend P < 0.001

Across increasing census tract poverty levels, there were statistically highly significant trends (P < 0.001) of increasing hospitalization within each race/ethnicity group (Figure 3A), except for non-Hispanic Blacks (P = 0.008).. For increasing census tract crowding levels, statistically insignificant findings were only observed among non-Hispanic Black patients (P = 0.167 chi-square for trend) (Figure 3B).

Age-adjusted hospitalization incidence by census tract (A) poverty level and (B) crowding level and by race/ethnicity group in CT, July to December 2020. NH, non-Hispanic; * Chi-square test for trend P < 0.05; ** Chi-square test for trend P < 0.01; *** Chi-square test for trend P < 0.001

3.4 County-level comparisons

Among non-institutionalized patients, 37.2% resided in New Haven and Middlesex Counties, whereas the remaining 62.8% resided in the other six counties (Table 3A,B). The age-adjusted incidence in New Haven and Middlesex Counties was approximately 43.6% higher than in the rest of the state; however, percentages of patients and relative incidence of COVID-19 hospitalization by demographic subgroups were comparable between these two county-based groups with some exceptions. Disparities were primarily found among patients characterized by low SES after adjusting for age. New Haven and Middlesex County patients living in high poverty and crowding were hospitalized at rates 2.5 (95% CI 2.20–2.81) and 2.1 (95% CI 1.79–2.36) times higher, respectively, than patients living in low poverty and crowding. These disparities were stronger in magnitude for patients of the other six counties, with the high poverty and crowding groups hospitalized at similar rates of 3.4 (95% CI 3.09–3.73) and 3.4 (95% CI 3.13–3.74) times higher than the low poverty and crowding groups, respectively.

TABLE 3. Characteristics, crude and age-adjusted incidence, and relative rates (RR) for non-institutionalized patients hospitalized with COVID-19 in (A) New Haven and Middlesex counties and (B) Fairfield, Litchfield, Hartford, Tolland, Windham, and New London counties in CT, July to December 2020 Demographic factor No. of patients (%) Total pop Crude incidence/100,000 population Crude RR Age-adjusted incidence/100,000 population Age-adjusted RR 95% CI (chi-square) (A) New Haven and Middlesex counties Total hospitalized patients 2035 1,028,153 197.9 — 173.2 — — Age (years) <18 18 (0.9) 228,072 7.9 0.09 7.9 0.09 0.05–0.14 18–49 406 (20.0) 441,329 92 Ref 92 Ref — 50–64 556 (27.3) 209,159 265.8 2.89 265.8 2.89 2.54–3.28 65–74 451 (22.2) 74,130 608.4 6.61 608.4 6.61 5.78–7.56 75–84 393 (19.3) 49,238 798.2 8.68 798.2 8.68 7.55–9.96 ≥85 211 (10.4) 26,225 804.6 8.75 804.6 8.75 7.41–10.32 Gender Female 995 (48.9) 532,155 187 Ref 154.2 Ref — Male 1040 (51.1) 495,998 209.7 1.12 198 1.28 1.17–1.41 Race/Ethnicity Non-Hispanic White 1148 (56.4) 725,528 158.2 Ref 114.9 Ref — Non-Hispanic Black 350 (17.2) 109,019 321 2.03 368.1 3.20 2.84–3.60 Non-Hispanic Asian 18 (0.9) 34,140 52.7 0.33 75.3 0.65 0.45–0.98 Hispanic/Latinx 406 (20.0) 137,577 295.1 1.87 543.8 4.73 4.29–5.22 Non-Hispanic Othera 113 (5.6) — — — — — — Poverty level Very low (<5%) 567 (27.9) 366,844 154.6 Ref 121.8 Ref — Low (5% to <10%) 513 (25.2) 270,104 189.9 1.23