Quantifying heterogeneities in arbovirus transmission: Description of the rationale and methodology for a prospective longitudinal study of dengue and Zika virus transmission in Iquitos, Peru (2014-2019).

Current knowledge of dengue virus (DENV) transmission provides only a partial understanding of a complex and dynamic system yielding a public health track record that has more failures than successes. An important part of the problem is that the foundation for contemporary interventions includes a series of longstanding, but untested, assumptions based on a relatively small portion of the human population; i.e., people who are convenient to study because they manifest clinically apparent disease. Approaching dengue from the perspective of people with overt illness has produced an extensive body of useful literature. It has not, however, fully embraced heterogeneities in virus transmission dynamics that are increasingly recognized as key information still missing in the struggle to control the most important insect-transmitted viral infection of humans. Only in the last 20 years have there been significant efforts to carry out comprehensive longitudinal dengue studies. This manuscript provides the rationale and comprehensive, integrated description of the methodology for a five-year longitudinal cohort study based in the tropical city of Iquitos, in the heart of the Peruvian Amazon. Primary data collection for this study was completed in 2019. Although some manuscripts have been published to date, our principal objective here is to support subsequent publications by describing in detail the structure, methodology, and significance of a specific research program. Our project was designed to study people across the entire continuum of disease, with the ultimate goal of quantifying heterogeneities in human variables that affect DENV transmission dynamics and prevention. Because our study design is applicable to other Aedes transmitted viruses, we used it to gain insights into Zika virus (ZIKV) transmission when during the project period ZIKV was introduced and circulated in Iquitos. Our prospective contact cluster investigation design was initiated by detecttion of a person with a symptomatic DENV infection and then followed that person's immediate contacts. This allowed us to monitor individuals at high risk of DENV infection, including people with clinically inapparent and mild infections that are otherwise difficult to detect. We aimed to fill knowledge gaps by defining the contribution to DENV transmission dynamics of (1) the understudied majority of DENV-infected people with inapparent and mild infections and (2) epidemiological, entomological, and socio-behavioral sources of heterogeneity. By accounting for factors underlying variation in each person's contribution to transmission we sought to better determine the type and extent of effort needed to better prevent virus transmission and disease.

The US COVID-19 surveillance environment: An ecological analysis of the relationship of testing adequacy in the context of vaccination.

BACKGROUND: COVID-19 testing is a critical component of public health surveillance and pandemic control, especially among the unvaccinated, as the nation resumes in-person activities.This study examined the relationships between COVID-19 testing rates, testing positivity rates and vaccination coverage across US counties. METHODS: Data from the Health and Human Services' Community Profile Report and 2016-2020 American Community Survey 5-Year Estimates were used. 3,114 US counties were analyzed from January through September 2021. Associations among the testing metrics and vaccination coverage were estimated using multiple linear regression models with fixed effects for states and adjusted for county demographics. COVID-19 testing rates (PCR testing per 1,000), testing positivity (percentage of all PCR tests that were positive), and vaccination coverage (percentage county population that was fully vaccinated). RESULTS: Nationally, median daily COVID-19 testing rates were highest in January and September (35.5 and 34.6 tests per capita, respectively) and lowest in July (13.2 tests per capita). Monthly testing positivity was between 0.03 and 0.12 percentage points (pp) lower for each pp of vaccination coverage, and monthly testing rates were between 0.08 and 0.22 tests per capita higher for each pp of vaccination coverage. CONCLUSIONS: The quantity of COVID-19 testing was associated with vaccination coverage, implying counties having populations with relatively lower protection against the virus are conducting less testing than counties with relatively more protection. Monitoring testing practices in relation to vaccination coverage may be used to monitor the sufficiency of COVID-19 testing based on population susceptibility to the virus.

When case reporting becomes untenable: Can sewer networks tell us where COVID-19 transmission occurs?

Monitoring SARS-CoV-2 in wastewater is a valuable approach to track COVID-19 transmission. Designing wastewater surveillance (WWS) with representative sampling sites and quantifiable results requires knowledge of the sewerage system and virus fate and transport. We developed a multi-level WWS system to track COVID-19 in Atlanta using an adaptive nested sampling strategy. From March 2021 to April 2022, 868 wastewater samples were collected from influent lines to wastewater treatment facilities and upstream community manholes. Variations in SARS-CoV-2 concentrations in influent line samples preceded similar variations in numbers of reported COVID-19 cases in the corresponding catchment areas. Community sites under nested sampling represented mutually-exclusive catchment areas. Community sites with high SARS-CoV-2 detection rates in wastewater covered high COVID-19 incidence areas, and adaptive sampling enabled identification and tracing of COVID-19 hotspots. This study demonstrates how a well-designed WWS provides actionable information including early warning of surges in cases and identification of disease hotspots.

The Impact of Nonrandom Missingness in Surveillance Data for Population-Level Summaries: Simulation Study.

BACKGROUND: Surveillance data are essential public health resources for guiding policy and allocation of human and capital resources. These data often consist of large collections of information based on nonrandom sample designs. Population estimates based on such data may be impacted by the underlying sample distribution compared to the true population of interest. In this study, we simulate a population of interest and allow response rates to vary in nonrandom ways to illustrate and measure the effect this has on population-based estimates of an important public health policy outcome. OBJECTIVE: The aim of this study was to illustrate the effect of nonrandom missingness on population-based survey sample estimation. METHODS: We simulated a population of respondents answering a survey question about their satisfaction with their community's policy regarding vaccination mandates for government personnel. We allowed response rates to differ between the generally satisfied and dissatisfied and considered the effect of common efforts to control for potential bias such as sampling weights, sample size inflation, and hypothesis tests for determining missingness at random. We compared these conditions via mean squared errors and sampling variability to characterize the bias in estimation arising under these different approaches. RESULTS: Sample estimates present clear and quantifiable bias, even in the most favorable response profile. On a 5-point Likert scale, nonrandom missingness resulted in errors averaging to almost a full point away from the truth. Efforts to mitigate bias through sample size inflation and sampling weights have negligible effects on the overall results. Additionally, hypothesis testing for departures from random missingness rarely detect the nonrandom missingness across the widest range of response profiles considered. CONCLUSIONS: Our results suggest that assuming surveillance data are missing at random during analysis could provide estimates that are widely different from what we might see in the whole population. Policy decisions based on such potentially biased estimates could be devastating in terms of community disengagement and health disparities. Alternative approaches to analysis that move away from broad generalization of a mismeasured population at risk are necessary to identify the marginalized groups, where overall response may be very different from those observed in measured respondents.

Bone, Brain, Heart study protocol: A resilient nested, tripartite prospective cohort study of the role of estrogen depletion on HIV pathology.

PURPOSE: We describe the rationale for and design of an innovative, nested, tripartite prospective observational cohort study examining whether relative estrogen insufficiency-induced inflammation amplifies HIV-induced inflammation to cause end organ damage and worsen age-related co-morbidities affecting the neuro-hypothalamic-pituitary-adrenal axis (Brain), skeletal (Bone), and cardiovascular (Heart/vessels) organ systems (BBH Study). METHODS: The BBH parent study is the Multicenter AIDS Cohort/Women's Interagency HIV Study Combined Cohort Study (MWCCS) with participants drawn from the Atlanta MWCCS site. BBH will enroll a single cohort of n = 120 women living with HIV and n = 60 HIV-negative women, equally distributed by menopausal status. The innovative multipart nested study design of BBH, which draws on data collected by the parent study, efficiently leverages resources for maximum research impact and requires extensive oversight and management in addition to careful implementation. The presence of strong infrastructure minimized BBH study disruptions due to changes in the parent study and the COVID-19 pandemic. CONCLUSION: BBH is poised to provide insight into sex and HIV associations with the neuro-hypothalamic-pituitary-adrenal axis, skeletal, and cardiovascular systems despite several major, unexpected challenges.

In-Person Versus Telehealth Setting for the Delivery of Substance Use Disorder Treatment: Ecologically Valid Comparison Study.

BACKGROUND: The COVID-19 pandemic has profoundly transformed substance use disorder (SUD) treatment in the United States, with many web-based treatment services being used for this purpose. However, little is known about the long-term treatment effectiveness of SUD interventions delivered through digital technologies compared with in-person treatment, and even less is known about how patients, clinicians, and clinical characteristics may predict treatment outcomes. OBJECTIVE: This study aims to analyze baseline differences in patient demographics and clinical characteristics across traditional and telehealth settings in a sample of participants (N=3642) who received intensive outpatient program (IOP) substance use treatment from January 2020 to March 2021. METHODS: The virtual IOP (VIOP) study is a prospective longitudinal cohort design that follows adult (aged ≥18 years) patients who were discharged from IOP care for alcohol and substance use-related treatment at a large national SUD treatment provider between January 2020 and March 2021. Data were collected at baseline and up to 1 year after discharge from both in-person and VIOP services through phone- and web-based surveys to assess recent substance use and general functioning across several domains. RESULTS: Initial baseline descriptive data were collected on patient demographics and clinical inventories. No differences in IOP setting were detected by race (χ22=0.1; P=.96), ethnicity (χ22=0.8; P=.66), employment status (χ22=2.5; P=.29), education level (χ24=7.9; P=.10), or whether participants presented with multiple SUDs (χ28=11.4; P=.18). Significant differences emerged for biological sex (χ22=8.5; P=.05), age (χ26=26.8; P<.001), marital status (χ24=20.5; P<.001), length of stay (F2,3639=148.67; P<.001), and discharge against staff advice (χ22=10.6; P<.01). More differences emerged by developmental stage, with emerging adults more likely to be women (χ23=40.5; P<.001), non-White (χ23=15.8; P<.001), have multiple SUDs (χ23=453.6; P<.001), have longer lengths of stay (F3,3638=13.51; P<.001), and more likely to be discharged against staff advice (χ23=13.3; P<.01). CONCLUSIONS: The findings aim to deepen our understanding of SUD treatment efficacy across traditional and telehealth settings and its associated correlates and predictors of patient-centered outcomes. The results of this study will inform the effective development of data-driven benchmarks and protocols for routine outcome data practices in treatment settings.

Post-lockdown changes of age-specific susceptibility and its correlation with adherence to social distancing measures.

Social distancing measures are effective in reducing overall community transmission but much remains unknown about how they have impacted finer-scale dynamics. In particular, much is unknown about how changes of contact patterns and other behaviors including adherence to social distancing, induced by these measures, may have impacted finer-scale transmission dynamics among different age groups. In this paper, we build a stochastic age-specific transmission model to systematically characterize the degree and variation of age-specific transmission dynamics, before and after lifting the lockdown in Georgia, USA. We perform Bayesian (missing-)data-augmentation model inference, leveraging reported age-specific case, seroprevalence and mortality data. We estimate that overall population-level transmissibility was reduced to 41.2% with 95% CI [39%, 43.8%] of the pre-lockdown level in about a week of the announcement of the shelter-in-place order. Although it subsequently increased after the lockdown was lifted, it only bounced back to 62% [58%, 67.2%] of the pre-lockdown level after about a month. We also find that during the lockdown susceptibility to infection increases with age. Specifically, relative to the oldest age group (> 65+), susceptibility for the youngest age group (0-17 years) is 0.13 [0.09, 0.18], and it increases to 0.53 [0.49, 0.59] for 18-44 and 0.75 [0.68, 0.82] for 45-64. More importantly, our results reveal clear changes of age-specific susceptibility (defined as average risk of getting infected during an infectious contact incorporating age-dependent behavioral factors) after the lockdown was lifted, with a trend largely consistent with reported age-specific adherence levels to social distancing and preventive measures. Specifically, the older groups (> 45) (with the highest levels of adherence) appear to have the most significant reductions of susceptibility (e.g., post-lockdown susceptibility reduced to 31.6% [29.3%, 34%] of the estimate before lifting the lockdown for the 6+ group). Finally, we find heterogeneity in case reporting among different age groups, with the lowest rate occurring among the 0-17 group (9.7% [6.4%, 19%]). Our results provide a more fundamental understanding of the impacts of stringent lockdown measures, and finer evidence that other social distancing and preventive measures may be effective in reducing SARS-CoV-2 transmission. These results may be exploited to guide more effective implementations of these measures in many current settings (with low vaccination rate globally and emerging variants) and in future potential outbreaks of novel pathogens.

Telehealth Services for Substance Use Disorders During the COVID-19 Pandemic: Longitudinal Assessment of Intensive Outpatient Programming and Data Collection Practices.

BACKGROUND: The onset of the COVID-19 pandemic necessitated the rapid transition of many types of substance use disorder (SUD) treatments to telehealth formats, despite limited information about what makes treatment effective in this novel format. OBJECTIVE: This study aims to examine the feasibility and effectiveness of virtual intensive outpatient programming (IOP) treatment for SUD in the context of a global pandemic, while considering the unique challenges posed to data collection during an unprecedented public health crisis. METHODS: The study is based on a longitudinal study with a baseline sample of 3642 patients who enrolled in intensive outpatient addiction treatment (in-person, hybrid, or virtual care) from January 2020 to March 2021 at a large substance use treatment center in the United States. The analytical sample consisted of patients who completed the 3-month postdischarge outcome survey as part of routine outcome monitoring (n=1060, 29.1% response rate). RESULTS: No significant differences were detected by delivery format in continuous abstinence (χ22=0.4, P=.81), overall quality of life (F2,826=2.06, P=.13), financial well-being (F2,767=2.30, P=.10), psychological well-being (F2,918=0.72, P=.49), and confidence in one's ability to stay sober (F2,941=0.21, P=.81). Individuals in hybrid programming were more likely to report a higher level of general health than those in virtual IOP (F2,917=4.19, P=.01). CONCLUSIONS: Virtual outpatient care for the treatment of SUD is a feasible alternative to in-person-only programming, leading to similar self-reported outcomes at 3 months postdischarge. Given the many obstacles presented throughout data collection during a pandemic, further research is needed to better understand under what conditions telehealth is an acceptable alternative to in-person care.

Considerations for Improving Reporting and Analysis of Date-Based COVID-19 Surveillance Data by Public Health Agencies.

More than a year after the first domestic COVID-19 cases, the United States does not have national standards for COVID-19 surveillance data analysis and public reporting. This has led to dramatic variations in surveillance practices among public health agencies, which analyze and present newly confirmed cases by a wide variety of dates. The choice of which date to use should be guided by a balance between interpretability and epidemiological relevance. Report date is easily interpretable, generally representative of outbreak trends, and available in surveillance data sets. These features make it a preferred date for public reporting and visualization of surveillance data, although it is not appropriate for epidemiological analyses of outbreak dynamics. Symptom onset date is better suited for such analyses because of its clinical and epidemiological relevance. However, using symptom onset for public reporting of new confirmed cases can cause confusion because reporting lags result in an artificial decline in recent cases. We hope this discussion is a starting point toward a more standardized approach to date-based surveillance. Such standardization could improve public comprehension, policymaking, and outbreak response. (Am J Public Health. 2021;111(12):2127-2132. https://doi.org/10.2105/AJPH.2021.306520).

The DIOS framework for optimizing infectious disease surveillance: Numerical methods for simulation and multi-objective optimization of surveillance network architectures.

Infectious disease surveillance systems provide vital data for guiding disease prevention and control policies, yet the formalization of methods to optimize surveillance networks has largely been overlooked. Decisions surrounding surveillance design parameters-such as the number and placement of surveillance sites, target populations, and case definitions-are often determined by expert opinion or deference to operational considerations, without formal analysis of the influence of design parameters on surveillance objectives. Here we propose a simulation framework to guide evidence-based surveillance network design to better achieve specific surveillance goals with limited resources. We define evidence-based surveillance design as an optimization problem, acknowledging the many operational constraints under which surveillance systems operate, the many dimensions of surveillance system design, the multiple and competing goals of surveillance, and the complex and dynamic nature of disease systems. We describe an analytical framework-the Disease Surveillance Informatics Optimization and Simulation (DIOS) framework-for the identification of optimal surveillance designs through mathematical representations of disease and surveillance processes, definition of objective functions, and numerical optimization. We then apply the framework to the problem of selecting candidate sites to expand an existing surveillance network under alternative objectives of: (1) improving spatial prediction of disease prevalence at unmonitored sites; or (2) estimating the observed effect of a risk factor on disease. Results of this demonstration illustrate how optimal designs are sensitive to both surveillance goals and the underlying spatial pattern of the target disease. The findings affirm the value of designing surveillance systems through quantitative and adaptive analysis of network characteristics and performance. The framework can be applied to the design of surveillance systems tailored to setting-specific disease transmission dynamics and surveillance needs, and can yield improved understanding of tradeoffs between network architectures.