PCD logo

Descriptive Epidemiology of New York City Older Adult Patients With Multiple Chronic Conditions

Sarah Conderino, DrPH1; John Dodson, MD1; Yuchen Meng, MA1; Mark G. Weiner, MD2; Catherine Rabin, BS2; Wilson Jacobs2; Parampreet Bakshi, MD3; Melissa Lee, MD3; Jenny Uguru, DNP3; Lorna E. Thorpe, PhD1 (View author affiliations)

Suggested citation for this article: Conderino S, Dodson J, Meng Y, Weiner MG, Rabin C, Jacobs W, et al. Descriptive Epidemiology of New York City Older Adult Patients With Multiple Chronic Conditions. Prev Chronic Dis 2024;21:230444. DOI: http://dx.doi.org/10.5888/pcd21.230444.

PEER REVIEWED

Summary

What is already known on this topic?

Co-occurrence of multiple chronic conditions (MCCs) is a significant public health concern among older adults, with differential burden and profiles by demographic subgroups.

What is added by this report?

We descriptively characterized older adults with MCCs using a large, diverse patient sample from New York City. We identified significant potential disparities in comorbidity burden and cardiovascular risk factors by the intersection of race and ethnicity and sex.

What are the implications for public health practice?

Public health interventions are needed to prevent chronic disease onset, and community–clinical interventions are needed to improve disease management among minority populations and enhance quality of life.

Abstract

We characterized comorbidity profiles and cardiometabolic risk factors among older adults with multiple chronic conditions (MCCs) in New York City using an intersectionality approach. Electronic health record data were obtained from the INSIGHT Clinical Research Network on 367,901 New York City residents aged 50 years or older with MCCs. Comorbidity profiles were heterogeneous. The most common profile across sex and racial and ethnic groups was co-occurring hypertension and hyperlipidemia; prevalence of these 2 conditions differed across groups (4.7%–7.3% co-occurrence alone, 65.1%–88.0% with other conditions). Significant sex and racial and ethnic differences were observed, which may reflect accumulated disparities in risk factors and health care access across the life course.

Top

Objective

Co-occurrence of multiple chronic conditions (MCCs) is a significant public health concern among older adults in the US, affecting an estimated 64% of those aged 65 years or older and contributing to worse health outcomes or quality of life (1). Prior work has demonstrated that the burden of MCCs differs by demographic factors (1,2). However, few studies have characterized MCCs using an intersectional approach to understand variation of comorbidity burden by sex and race and ethnicity, which may reflect, among other factors, effects of interdependent systems of discrimination or oppression, such as sexism and racism, on health outcomes (3). Large patient samples afforded by electronic health record (EHR) data offer the opportunity to examine people with MCCs at the granularity required for such an approach (4,5).

The goal of this study was to capitalize on the large sample size and demographic diversity found in an EHR-based data system in New York City (NYC) to characterize the burden and cardiometabolic risk factors of older adult patients with MCCs. We hypothesized that these would meaningfully differ by race and ethnicity and sex. More broadly, assessing the descriptive epidemiology of patients with MCCs by the intersection of race and ethnicity and sex may facilitate a deeper understanding of underlying structural factors influencing chronic disease onset.

Top

Methods

This cross-sectional study was conducted through use of data from the INSIGHT Clinical Research Network (CRN). INSIGHT is the largest urban CRN in the US, capturing data of more than 17 million diverse patients from 7 academic health care systems in NYC and Houston, Texas (6). This study was approved by the NYU Langone institutional review board.

To avoid the effects of pandemic-associated death or diagnosis misclassification due to disruption in care, our study population was restricted to NYC residents aged 50 years or older with at least 1 ambulatory visit in the 6 months before COVID-19 pandemic onset (September 7, 2019–March 6, 2020) and 2 or more chronic conditions (https://github.com/condes01/covid-shutdown). Those with a diagnosis for Alzheimer disease, dementia, or metastatic cancer were excluded. To examine intersectionality by major sex and racial and ethnic groups, we stratified individuals into 8 categories: female Asian, male Asian, female Black, male Black, female Latino, male Latino, female White, and male White.

Primary outcomes included prevalence of each chronic condition, defined by having 1 or more International Classification of Diseases 10th Revision (ICD-10) diagnosis codes for the condition from January 1, 2016, through March 6, 2020 (https://github.com/condes01/covid-shutdown). Secondary outcomes were cardiometabolic risk factors of systolic blood pressure (SBP), cholesterol, hemoglobin A1c (HbA1c), and weight, obtained from vital signs or laboratory measurements taken during ambulatory visits in 2019. We calculated individuals’ average risk factor value, excluding biologically implausible measurements (https://github.com/condes01/covid-shutdown). We then defined binary variables to indicate elevated cardiometabolic risk factors of obesity (body mass index ≥30 kg/m2); high BP (SBP ≥140 mm Hg); high cholesterol (low-density lipoprotein [LDL] cholesterol ≥160 mg/dL, high-density lipoprotein cholesterol <40 mg/dL for men or <50 mg/dL for women, total cholesterol ≥240 mg/dL, or triglycerides ≥200 mg/dL); and elevated HbA1c (≥6.5%).

We calculated prevalence and 95% CIs of each chronic condition and characterized comorbidity profiles overall and by major sex and racial and ethnic group. As a sensitivity analysis, we assessed age-adjusted differences in prevalence by using logistic regression models, controlling for 5-year age group. We explored differences in cardiometabolic risk factors by group using analysis of variance tests for continuous outcomes and χ2 tests for categorical outcomes.

Top

Results

In total, 367,901 older adults had MCCs in the INSIGHT study population. A higher proportion of these patients were female (59%) than male (41%), and most patients were White (38%) and from medium poverty neighborhoods (43%). A substantial proportion of patients had unknown race and ethnicity (20%).

Substantial heterogeneity of chronic condition combinations existed in this population (Figure). The most prevalent conditions were hypertension (79.3%) and hyperlipidemia (72.1%), and the most common comorbidity profile was the co-occurrence of these 2 conditions (6% alone, 59% in combination with other conditions), overall and across all sex and race and ethnicity groups, although the prevalence of these conditions differed across groups. Prevalence of hypertension was highest among Black women (87.6%) and Black men (88.0%), and hyperlipidemia was highest among Asian (78.9%) and White men (79.0%) (Table 1).

Leading comorbidity profiles among New York City residents aged 50 years or older with multiple chronic conditions receiving care at NYC INSIGHT facilities, September 2019–March 2020.

Return to your place in the text
Figure.

Leading comorbidity profiles among New York City residents aged 50 years or older with multiple chronic conditions receiving care at NYC INSIGHT facilities, September 2019–March 2020. [A text description of this figure is available.]

The second leading comorbidity profile was hypertension, hyperlipidemia, and diabetes (4%) (Figure). This was the second leading profile across all sex and racial and ethnic groups except White men and women, where hypertension, hyperlipidemia, and arthritis and hypertension, hyperlipidemia, and ischemic heart disease were the second most common profiles, respectively (https://github.com/condes01/covid-shutdown). Prevalence of diabetes among White women (22.0%) and men (29.7%) was substantially lower than the prevalence among the other sex and racial and ethnic groups (range, 40.5%–51.0%). Differences in disease prevalence persisted after controlling for age (https://github.com/condes01/covid-shutdown).

Baseline cardiometabolic risk factors differed significantly by sex and racial and ethnic group (Table 2). Mean SBP and the proportion with high blood pressure was highest among Black women (mean = 134.8 mm Hg, proportion = 33.3%) and men (mean = 134.8 mm Hg, proportion = 33.7%). Across all racial and ethnic groups, women had a higher average LDL than men. Mean HbA1c and the proportion with elevated HbA1c was lowest among White women (mean = 6.0%, proportion = 18.8%) and men (mean = 6.2%, proportion = 26.3%). Obesity was lowest among Asian women (16.5%) and men (15.7%) and highest among Black women (56.6%).

Top

Discussion

We assessed burden and comorbidity profiles of NYC older adult patients with MCCs and observed potentially important disparities in MCC burden and cardiometabolic risk factors across the intersection of race and ethnicity and sex, with increased burden of hypertension among Black men and women and decreased burden of diabetes among White men and women. Results were generally consistent with patterns observed in a study of comparably aged English adults that examined similar risk factors by the intersection of gender, ethnicity, education, and income (7). The most common comorbidity profile was the co-occurrence of hypertension and hyperlipidemia, and Asian men and women had lowest prevalence of obesity yet high prevalence of diabetes, consistent with prior research (5,8).

Within all racial and ethnic categories, LDL was higher among women than men, yet a higher proportion of men had a hyperlipidemia diagnosis. These findings could be partially attributed to the use of lipid-lowering medications, where men and White patients consistently report higher usage than women or patients of racial and ethnic minorities (9). Additionally, research suggests that there may be biological differences in the pharmacological effects of lipid-lowering medications by sex (10). These observed differences in cholesterol levels by sex and racial and ethnic groups could reflect a combination of structural discrimination and biological factors that influence statin efficacy.

As with any EHR-based study, key limitations need to be considered. Disease status may be misclassified, particularly among patients who are seen less frequently, as documentation of ICD-10 codes may be influenced by billing practices, and care from institutions external to INSIGHT will not be represented in these data. INSIGHT includes some academic medical centers that disproportionately serve privately insured patients, so our findings may not be generalizable and observed disparities may be underestimated relative to the overall NYC population (11). Additionally, a substantial proportion of patients had an unknown race and ethnicity, and the race and ethnicity distribution of these patients may have been systematically different from those with a recorded race and ethnicity.

Despite these limitations, the large size and diversity of these data provided an opportunity to examine NYC older adults with MCCs across the intersection of race and ethnicity and sex. Findings suggest the existence of significant differences in comorbidity burden, which are associated with meaningfully increased risk of cardiovascular events (7). These differences most likely reflect a combination of biological factors and preventable disparities driven by underlying systems of power and discrimination (12). Future research should explore disparities in continuity and quality of care in the management of older adults with MCCs.

Top

Acknowledgments

This study was sponsored by a grant (no. R01 AG073321) from the National Institute on Aging, National Institutes of Health. Dr Dodson is further supported by a midcareer mentoring award (no. K24AG080025) from the NIH/NIA. The authors received no external financial support for the research, authorship, or publication of this article. The authors declare no potential conflicts of interest with respect to the research, authorship, or publication of this article. No copyrighted material, surveys, instruments, or tools were used in this research.

Top

Author Information

Corresponding Author: Sarah Conderino, DrPH, Department of Population Health, New York University Grossman School of Medicine, 180 Madison Ave, New York, NY 10016 (Sarah.Conderino@nyulangone.org).

Author Affiliations: 1Department of Population Health, New York University Grossman School of Medicine, New York, New York. 2Department of Population Health Sciences, Weill Cornell Medicine, New York, New York. 3New York City Health and Hospitals, New York, New York.

Top

References

  1. Boersma P, Black LI, Ward BW. Prevalence of multiple chronic conditions among US adults, 2018. Prev Chronic Dis. 2020;17:E106. PubMed doi:10.5888/pcd17.200130
  2. Quiñones AR, Botoseneanu A, Markwardt S, Nagel CL, Newsom JT, Dorr DA, et al. . Racial/ethnic differences in multimorbidity development and chronic disease accumulation for middle-aged adults. PLoS One. 2019;14(6):e0218462. PubMed doi:10.1371/journal.pone.0218462
  3. Crenshaw K. Mapping the margins: intersectionality, identity politics, and violence against women of color. Stanford Law Rev. 1991;43(6):1241–1299.
  4. Ioakeim-Skoufa I, Clerencia-Sierra M, Moreno-Juste A, Elías de Molins Peña C, Poblador-Plou B, Aza-Pascual-Salcedo M, et al. . Multimorbidity clusters in the oldest old: results from the EpiChron Cohort. Int J Environ Res Public Health. 2022;19(16):10180. PubMed doi:10.3390/ijerph191610180
  5. He Z, Bian J, Carretta HJ, Lee J, Hogan WR, Shenkman E, et al. . Prevalence of multiple chronic conditions among older adults in Florida and the United States: comparative analysis of the OneFlorida Data Trust and National Inpatient Sample. J Med Internet Res. 2018;20(4):e137. PubMed doi:10.2196/jmir.8961
  6. Kaushal R, Hripcsak G, Ascheim DD, Bloom T, Campion TR Jr, Caplan AL, et al. ; New York City Clinical Data Research Network. Changing the research landscape: the New York City Clinical Data Research Network. J Am Med Inform Assoc. 2014;21(4):587–590. PubMed doi:10.1136/amiajnl-2014-002764
  7. Holman D, Salway S, Bell A. Mapping intersectional inequalities in biomarkers of healthy ageing and chronic disease in older English adults. Sci Rep. 2020;10(1):13522. PubMed doi:10.1038/s41598-020-69934-8
  8. Hsu WC, Araneta MR, Kanaya AM, Chiang JL, Fujimoto W. BMI cut points to identify at-risk Asian Americans for type 2 diabetes screening. Diabetes Care. 2015;38(1):150–158. PubMed doi:10.2337/dc14-2391
  9. Salami JA, Warraich H, Valero-Elizondo J, Spatz ES, Desai NR, Rana JS, et al. . National trends in statin use and expenditures in the US adult population from 2002 to 2013: insights from the Medical Expenditure Panel Survey. JAMA Cardiol. 2017;2(1):56–65. PubMed doi:10.1001/jamacardio.2016.4700
  10. Cangemi R, Romiti GF, Campolongo G, Ruscio E, Sciomer S, Gianfrilli D, et al. . Gender related differences in treatment and response to statins in primary and secondary cardiovascular prevention: the never-ending debate. Pharmacol Res. 2017;117:148–155. PubMed doi:10.1016/j.phrs.2016.12.027
  11. Calman NS, Golub M, Ruddock C, Le L, Hauser D; Action Committee of the Bronx Health REACH Coalition. Separate and unequal care in New York City. J Health Care Law Policy. 2006;9(1):105–120. PubMed
  12. Orom H, Sharma C, Homish GG, Underwood W III, Homish DL. Racial discrimination and stigma consciousness are associated with higher blood pressure and hypertension in minority men. J Racial Ethn Health Disparities. 2016;4(5):819–826. PubMed doi:10.1007/s40615-016-0284-2

Top

Tables

Return to your place in the text

Table 1. Demographic Characteristics and Comorbidity Prevalence Among New York City Residents Aged 50 Years or Older with Multiple Chronic Conditions Receiving Care at NYC INSIGHT Facilities, September 2019 Through March 2020
Demographic characteristic Asian (n = 12,684) Black (n = 53,403) Latino (n = 75,431) White (n = 138,552)
Female sex, no. 7,048 35,527 48,645 75,866
Age, no. (%), y
50–64 2,771 (39.32) 15,372 (43.27) 20,288 (41.71) 18,543 (24.44)
≥65 4,277 (60.68) 20,155 (56.73) 28,357 (58.29) 57,323 (75.56)
Poverty level, no. (%)a
Low 1,823 (25.87) 4,056 (11.42) 3,243 (6.67) 30,056 (39.62)
Medium 3,546 (50.31) 15,632 (44.00) 17,194 (35.35) 35,865 (47.27)
High 1,331 (18.88) 9,046 (25.46) 15,074 (30.99) 8,225 (10.84)
Very high 348 (4.94) 6,792 (19.12) 13,130 (26.99) 1,708 (2.25)
Unknown 0 (0) 1 (0) 4 (0.01) 12 (0.02)
Comorbidities, % (95% CI)
AMI 3.1 (2.7–3.6) 2.7 (2.6–2.9) 2.9 (2.8–3.1) 3.4 (3.3–3.5)
Arthritis 41.4 (40.3–42.6) 50.5 (50.0–51.0) 49.8 (49.3–50.2) 50.4 (50.0–50.7)
Asthma 12.8 (12.0–13.6) 20.9 (20.5–21.3) 26.7 (26.3–27.1) 13.4 (13.2–13.6)
Atrial fibrillation 14.4 (13.6–15.2) 12.6 (12.2–12.9) 12.1 (11.8–12.4) 20.9 (20.6–21.2)
Breast cancer 10.0 (9.3–10.7) 8.0 (7.7–8.3) 7.3 (7.0–7.5) 12.6 (12.4–12.9)
Chronic kidney disease 13.9 (13.1–14.8) 22.9 (22.4–23.3) 17.0 (16.6–17.3) 9.0 (8.8–9.2)
Colorectal cancer 1.3 (1.1–1.6) 1.5 (1.4–1.6) 1.4 (1.3–1.5) 1.7 (1.6–1.8)
COPD 9.8 (9.1–10.5) 14.0 (13.6–14.3) 14.1 (13.8–14.4) 17.1 (16.8–17.3)
Depression 8.9 (8.2–9.5) 15.3 (14.9–15.7) 29.0 (28.6–29.4) 19.0 (18.7–19.3)
Diabetes 40.5 (39.3–41.6) 45.3 (44.8–45.9) 44.8 (44.3–45.2) 22.0 (21.7–22.3)
Heart failure 7.2 (6.6–7.8) 11.2 (10.9–11.6) 10.3 (10.0–10.5) 8.3 (8.1–8.5)
Hyperlipidemia 72.5 (71.5–73.5) 65.1 (64.7–65.6) 69.7 (69.3–70.1) 73.2 (72.9–73.5)
Hypertension 74.3 (73.3–75.4) 87.6 (87.3–87.9) 81.0 (80.7–81.4) 69.2 (68.9–69.5)
Ischemic heart disease 20.3 (19.4–21.3) 18.7 (18.3–19.1) 21.5 (21.2–21.9) 23.5 (23.2–23.8)
Lung cancer 1.7 (1.4–2.0) 0.8 (0.7–0.9) 0.7 (0.6–0.8) 1.8 (1.7–1.9)
Osteoporosis 26.1 (25.1–27.1) 10.9 (10.5–11.2) 19.4 (19.0–19.8) 27.8 (27.5–28.1)
Prostate cancer ––
Stroke 6.7 (6.1–7.3) 7.3 (7.1–7.6) 6.8 (6.5–7.0) 6.8 (6.7–7.0)
Male sex, no. 5,636 17,876 26,786 62,686
Age, no. (%), y
50–64 2,258 (40.06) 8,500 (47.55) 11,989 (44.76) 18,815 (30.01)
≥65 3,378 (59.94) 9,376 (52.45) 14,797 (55.24) 43,871 (69.99)
Poverty level, no. (%)a
Low 1,354 (24.02) 2,460 (13.76) 2,200 (8.21) 25,212 (40.22)
Medium 2,880 (51.10) 7,616 (42.60) 9,716 (36.27) 29,415 (46.92)
High 1,088 (19.30) 4,438 (24.83) 7,971 (29.76) 6,543 (10.44)
Very high 312 (5.54) 3,360 (18.80) 6,896 (25.74) 1,498 (2.39)
Unknown 2 (0.04) 2 (0.01) 3 (0.01) 18 (0.03)
Comorbidities, % (95% CI)
AMI 7.4 (6.7–8.1) 4.9 (4.6–5.2) 5.7 (5.5–6.0) 6.1 (5.9–6.3)
Arthritis 25.3 (24.2–26.5) 32.8 (32.1–33.5) 30.5 (30.0–31.1) 36.5 (36.2–36.9)
Asthma 8.9 (8.1–9.6) 11.8 (11.4–12.3) 13.5 (13.1–14.0) 9.4 (9.1–9.6)
Atrial fibrillation 20.4 (19.3–21.4) 17.4 (16.8–17.9) 17.8 (17.3–18.3) 28.3 (28.0–28.7)
Breast cancer 0.1 (0.0–0.2) 0.2 (0.1–0.2) 0.1 (0.1–0.1) 0.2 (0.1–0.2)
Chronic kidney disease 24.1 (23.0–25.3) 32.7 (32.0–33.4) 25.9 (25.4–26.4) 15.8 (15.5–16.1)
Colorectal cancer 2.0 (1.7–2.4) 1.8 (1.6–2.0) 1.9 (1.7–2.0) 1.9 (1.8–2.0)
COPD 14.1 (13.2–15.0) 14.1 (13.6–14.6) 13.3 (12.9–13.7) 16.3 (16.0–16.6)
Depression 5.9 (5.3–6.6) 11.5 (11.0–11.9) 16.6 (16.2–17.0) 13.2 (13.0–13.5)
Diabetes 51.0 (49.7–52.3) 48.9 (48.1–49.6) 50.6 (50.0–51.2) 29.7 (29.3–30)
Heart failure 12.2 (11.3–13.0) 16.8 (16.2–17.3) 16.6 (16.1–17.0) 12.8 (12.6–13.1)
Hyperlipidemia 78.9 (77.8–80.0) 66.5 (65.8–67.2) 73.8 (73.3–74.4) 79.0 (78.7–79.3)
Hypertension 82.8 (81.9–83.8) 88.0 (87.5–88.4) 85.8 (85.4–86.2) 78.4 (78.1–78.8)
Ischemic heart disease 43.3 (42.0–44.6) 26.8 (26.2–27.5) 34.3 (33.7–34.8) 43.2 (42.8–43.5)
Lung cancer 2.0 (1.6–2.3) 0.8 (0.6–0.9) 0.7 (0.6–0.8) 1.4 (1.3–1.5)
Osteoporosis 2.9 (2.4–3.3) 1.9 (1.7–2.1) 2.5 (2.3–2.7) 3.7 (3.6–3.9)
Prostate cancer 4.3 (3.8–4.8) 14.3 (13.8–14.8) 8.1 (7.8–8.5) 9.1 (8.9–9.4)
Stroke 8.7 (7.9–9.4) 8.6 (8.2–9.0) 8.2 (7.9–8.5) 7.7 (7.5–7.9)

Abbreviations: — not applicable; AMI, acute myocardial infarction; COPD, chronic obstructive pulmonary disease; ZCTA, Zip Code Tabulation Area.
a Poverty level defined as ZCTA residents living below the federal poverty level: low, <10%; medium, 10% to <20%; high, 20% to <30%; and very high, ≥30%.

Return to your place in the text

Table 2. Cardiometabolic Risk Factors Among New York City Residents Aged 50 Years or Older with Multiple Chronic Conditions Receiving Care at NYC INSIGHT Facilities, by Patient Sex and Race and Ethnicity, 2019
Cardiometabolic risk factor Asian, mean (SD) Black, mean (SD) Hispanic, mean (SD) White, mean (SD)
Female Male Female Male Female Male Female Male
Systolic blood pressure, mm Hga 128.1 (14.4) 128.7 (14.2) 134.8 (14.7) 134.8 (15.1) 131.8 (14.3) 132.8 (14.6) 128.6 (14.2) 129.2 (13.4)
LDL cholesterol, mg/dLa 100.5 (35.3) 85.1 (33.1) 107.4 (35.9) 97.2 (35.7) 104.8 (36.3) 91.0 (34.8) 106.0 (34.8) 89.5 (32.7)
HbA1c, %a 6.5 (1.2) 6.7 (1.3) 6.6 (1.5) 6.8 (1.7) 6.6 (1.5) 6.8 (1.7) 6.0 (1.0) 6.2 (1.1)
Weight, lbsa 136.5 (28.2) 163.2 (30.1) 185.6 (45.1) 201.2 (46.1) 164.3 (37.3) 186.4 (39.3) 158.9 (39.1) 193.4 (39.4)
High blood pressure, %a,b 19.4 (0.5) 19.2 (0.5) 33.3 (0.3) 33.7 (0.4) 25.9 (0.2) 28.6 (0.3) 19.6 (0.1) 19.7 (0.2)
High cholesterol, %a,c 42.1 (0.8) 41.3 (1.0) 36.6 (0.3) 31.6 (0.5) 49.5 (0.3) 44.0 (0.4) 36.6 (0.2) 34.8 (0.3)
Elevated HbA1c, %a,d 35.1 (0.9) 44.3 (1.0) 36.4 (0.3) 41.3 (0.5) 38.0 (0.3) 44.7 (0.4) 18.8 (0.2) 26.3 (0.3)
Obesity, %a,e 16.5 (0.5) 15.7 (0.5) 56.6 (0.3) 40.5 (0.4) 46.5 (0.2) 38.4 (0.3) 34.6 (0.2) 36.3 (0.2)

Abbreviations: BMI, body mass index; HbA1c, hemoglobin A1c; HDL, high-density lipoprotein; LDL, low-density lipoprotein; SD, standard deviation.
a Significant at P < .05 using analysis of variance tests for continuous outcomes and χ2 tests for categorical outcomes.
b High blood pressure defined as mean annual systolic blood pressure ≥140 mm Hg.
c High cholesterol defined as mean annual LDL cholesterol ≥160 mg/dL; HDL <40 mg/dL male or <50 mg/dL female; total cholesterol ≥240 mg/dL; or triglyceride ≥200 mg/dL.
d Elevated HbA1c defined as mean annual HbA1c ≥6.5%.
e Obesity defined as mean BMI ≥30 kg/m2.

Top

Error processing SSI file

The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors’ affiliated institutions.