Background: Human papillomavirus (HPV) testing provides a much more sensitive method of detection for high-grade lesions than cytology, but specificity is low. Here, we explore the extent to which full HPV genotyping, viral load, and multiplicity of types can be used to improve specificity.Methods: A population-based sample of 47,120 women undergoing cervical screening was tested for 13 high-risk HPV genotypes. Positive predictive values (PPV) for cervical intraepithelial neoplasia (CIN) grade 2 or worse (CIN2þ; N ¼ 3,449) and CIN3 or worse (CIN3þ; N ¼ 1,475) over 3 years of follow-up were estimated for HPV genotype and viral load. Weighted multivariate logistic regression models were used to estimate the odds of CIN2þ or CIN3þ according to genotype, multiplicity of types, and viral load.Results: High-risk HPV was detected in 15.4% of women. A hierarchy of HPV genotypes based on sequentially maximizing PPVs for CIN3þ found HPV16>33>31 to be the most predictive, followed sequentially by HPV18>35>58>45>52>59>51>39>56>68. After adjusting for higher ranked genotypes, the inclusion of multiple HPV infections added little to risk prediction. High viral loads for HPV18, 35, 52, and 58 carried more risk than low viral loads for HPV16, 31, and 33. High viral load for HPV16 was significantly more associated with CIN3þ than low viral load.Conclusions: HPV genotype and viral load, but not multiplicity of HPV infections, are important predictors of CIN2þ and CIN3þ.Impact: The ability to identify women at higher risk of CIN2þ and CIN3þ based on both HPV genotype and viral load could be important for individualizing triage plans, particularly as HPV becomes the primary screening test.

This article highlights the processes and intermediate outcomes of a pilot project to increase mammography rates of women in an American Indian tribe in New Mexico. Using a socioecological framework and principles of community-based participatory research, a community coalition was able to (a) bolster local infrastructure to increase access to mammography services; (b) build public health knowledge and skills among tribal health providers; (c) identify community-specific knowledge, attitudes, and beliefs related to breast cancer; (d) establish interdependent partnerships among community health programs and between the tribe and outside organizations; and (e) adopt local policy initiatives to bolster tribal cancer control. These findings demonstrate the value of targeting a combination of individual, community, and environmental factors, which affect community breast cancer screening rates and incorporating cultural strengths and resources into all facets of a tribal health promotion intervention.

Background: Both opioid use and COVID-19 affect respiratory and pulmonary health, potentially putting individuals with opioid use disorders (OUD) at risk for complications from COVID-19. We examine the relationship between OUD and subsequent hospitalization, length of stay, risk for invasive ventilator dependence (IVD), and COVID-19 mortality. Methods: Multivariable logistic and exponential regression models using electronic health records data from the Cerner COVID-19 De-Identified Data Cohort from January through June 2020. Findings: Out of 52,312 patients with COVID-19, 1.9% (n=1,013) had an OUD. COVID-19 patients with an OUD had higher odds of hospitalization (aOR=3.44, 95% CI=2.81À4.21), maximum length of stay (eb=1.16, 95% CI=1.09À1.22), and odds of IVD (aOR=1.26, 95% CI=1.06À1.49) than patients without an OUD, but did not differ with respect to COVID-19 mortality. However, OUD patients under age 45 exhibited greater COVID-19 mortality (aOR=3.23, 95% CI=1.59À6.56) compared to patients under age 45 without an OUD. OUD patients using opioid agonist treatment (OAT) exhibited higher odds of hospitalization (aOR=5.14, 95% CI=2.75À10.60) and higher maximum length of stay (eb=1.22, 95% CI=1.01À1.48) than patients without OUDs; however, risk for IVD and COVID-19 mortality did not differ. OUD patients using naltrexone had higher odds of hospitalization (aOR=32.19, 95% CI=4.29À4,119.83), higher maximum length of stay (eb=1.59, 95% CI=1.06À2.38), and higher odds of IVD (aOR=3.15, 95% CI=1.04À9.51) than patients without OUDs, but mortality did not differ. OUD patients who did not use treatment medication had higher odds of hospitalization (aOR=4.05, 95% CI=3.32À4.98), higher maximum length of stay (eb=1.14, 95% CI=1.08À1.21), and higher odds of IVD (aOR=1.25, 95% CI=1.04À1.50) and COVID-19 mortality (aOR=1.31, 95% CI=1.07À1.61) than patients without OUDs. Interpretation: This study suggests people with OUD and COVID-19 often require higher levels of care, and OUD patients who are younger or not using medication treatment for OUDs are particularly vulnerable to death due to COVID-19.

Factors contributing to racial inequities in outcomes from coronavirus disease 2019 (COVID-19) remain poorly understood. We compared by race the risk of 4 COVID-19 health outcomes––maximum length of hospital stay (LOS), invasive ventilation, hospitalization exceeding 24 h, and death––stratified by Elixhauser comorbidity index (ECI) ranking. Outcomes and ECI scores were constructed from retrospective data obtained from the Cerner COVID-19 De-Identified Data cohort. We hypothesized that racial disparities in COVID-19 outcomes would exist despite comparable ECI scores among non-Hispanic (NH) Blacks, Hispanics, American Indians/Alaska Natives (AI/ANs), and NH Whites. Compared with NH Whites, NH Blacks had longer hospital LOS, higher rates of ventilator dependence, and a higher mortality rate; AI/ANs, higher odds of hospitalization for ECI = 0 but lower for ECI ≥ 5, longer LOS for ECI = 0, a higher risk of death across all ECI categories except ECI ≥ 5, and higher odds of ventilator dependence; Hispanics, a lower risk of death across all ECI categories except ECI = 0, lower odds of hospitalization, shorter LOS for ECI ≥ 5, and higher odds of ventilator dependence for ECI = 0 but lower for ECI = 1–4. Our findings contest arguments that higher comorbidity levels explain elevated COVID-19 death rates among NH Blacks and AI/ANs compared with Hispanics and NH Whites.

Objective: Public health surveillance systems suffer from insufficient inclusion of American Indian/Alaska Native (AI/AN) populations. These health surveys have also gravitated to telephone administration because of the rising cost of face-to-face interviewing. Several studies have demonstrated that telephone surveys underrepresent people with low incomes, less educational attainment, and minorities. This study assessed the impact of administration mode upon survey participation in rural AI/AN tribes. Design: Using a modified Behavioral Risk Factor Surveillance System instrument, the Albuquerque Area Southwest Tribal Epidemiology Center partnered with 3 tribes to administer the survey to a target population of 900 AI/AN adults. Half of the sample was assigned to telephone survey administration and the other half was surveyed in-person by trained community interviewers. Significance testing was performed to assess differences in response rates, demographic characteristics, and costs by survey administration type. Results: Several notable differences between the survey administration modes were observed. In-person administration yielded a higher response rate (68.8%) than the telephone survey (35.7%). Likewise, in-person participants were, on average, younger and had lower household incomes and educational attainment than those who completed the survey via telephone. In-person survey administration was also slightly more cost-effective than telephone administration ($192 vs $211 per completed survey) due to the low response rate of telephone administration. Conclusions: The findings from this study have important implications for public health surveillance with rural AI/AN populations, where telephone survey administration is unlikely to yield sufficient coverage of this underserved population. This discovery is particularly disconcerting, given the fact that face-to-face interviewing has largely been replaced by telephone interviewing (and increasingly mobile phones) for public health surveillance in the United States. Without change and innovation, the AI/AN population will continue to lack meaningful health data, further challenging capacity to document and address persistent disparities and inequities witnessed among AI/ANs nationwide.