Scalable Strategies to Increase Efficiency and Augment Public Health Activities During Epidemic Peaks.

OBJECTIVE: Scalable strategies to reduce the time burden and increase contact tracing efficiency are crucial during early waves and peaks of infectious transmission. DESIGN: We enrolled a cohort of SARS-CoV-2-positive seed cases into a peer recruitment study testing social network methodology and a novel electronic platform to increase contact tracing efficiency. SETTING: Index cases were recruited from an academic medical center and requested to recruit their local social contacts for enrollment and SARS-CoV-2 testing. PARTICIPANTS: A total of 509 adult participants enrolled over 19 months (384 seed cases and 125 social peers). INTERVENTION: Participants completed a survey and were then eligible to recruit their social contacts with unique "coupons" for enrollment. Peer participants were eligible for SARS-CoV-2 and respiratory pathogen screening. MAIN OUTCOME MEASURES: The main outcome measures were the percentage of tests administered through the study that identified new SARS-CoV-2 cases, the feasibility of deploying the platform and the peer recruitment strategy, the perceived acceptability of the platform and the peer recruitment strategy, and the scalability of both during pandemic peaks. RESULTS: After development and deployment, few human resources were needed to maintain the platform and enroll participants, regardless of peaks. Platform acceptability was high. Percent positivity tracked with other testing programs in the area. CONCLUSIONS: An electronic platform may be a suitable tool to augment public health contact tracing activities by allowing participants to select an online platform for contact tracing rather than sitting for an interview.

A method for intelligent allocation of diagnostic testing by leveraging data from commercial wearable devices: a case study on COVID-19.

Mass surveillance testing can help control outbreaks of infectious diseases such as COVID-19. However, diagnostic test shortages are prevalent globally and continue to occur in the US with the onset of new COVID-19 variants and emerging diseases like monkeypox, demonstrating an unprecedented need for improving our current methods for mass surveillance testing. By targeting surveillance testing toward individuals who are most likely to be infected and, thus, increasing the testing positivity rate (i.e., percent positive in the surveillance group), fewer tests are needed to capture the same number of positive cases. Here, we developed an Intelligent Testing Allocation (ITA) method by leveraging data from the CovIdentify study (6765 participants) and the MyPHD study (8580 participants), including smartwatch data from 1265 individuals of whom 126 tested positive for COVID-19. Our rigorous model and parameter search uncovered the optimal time periods and aggregate metrics for monitoring continuous digital biomarkers to increase the positivity rate of COVID-19 diagnostic testing. We found that resting heart rate (RHR) features distinguished between COVID-19-positive and -negative cases earlier in the course of the infection than steps features, as early as 10 and 5 days prior to the diagnostic test, respectively. We also found that including steps features increased the area under the receiver operating characteristic curve (AUC-ROC) by 7-11% when compared with RHR features alone, while including RHR features improved the AUC of the ITA model's precision-recall curve (AUC-PR) by 38-50% when compared with steps features alone. The best AUC-ROC (0.73 ± 0.14 and 0.77 on the cross-validated training set and independent test set, respectively) and AUC-PR (0.55 ± 0.21 and 0.24) were achieved by using data from a single device type (Fitbit) with high-resolution (minute-level) data. Finally, we show that ITA generates up to a 6.5-fold increase in the positivity rate in the cross-validated training set and up to a 4.5-fold increase in the positivity rate in the independent test set, including both symptomatic and asymptomatic (up to 27%) individuals. Our findings suggest that, if deployed on a large scale and without needing self-reported symptoms, the ITA method could improve the allocation of diagnostic testing resources and reduce the burden of test shortages.

Visualizing COVID Restrictions: Activity Patterns Before, During, and After COVID-19 Lockdowns in Uttar Pradesh, India.

Globally, restrictions implemented to limit the spread of COVID-19 have highlighted deeply rooted social divisions, raising concerns about differential impacts on members of different groups. Inequalities among households of different castes are ubiquitous in certain regions of India. Drawing on a novel data set of 8,564 households in Uttar Pradesh, the authors use radar plots to examine differences between castes in rates of activity for several typical behaviors before, during, and upon lifting strict lockdown restrictions. The visualization reveals that members of all castes experienced comparable reductions in activity rates during lockdown and recovery rates following it. Nonetheless, members of less privileged castes procure water outside the household more often than their more privileged peers, highlighting an avenue of improvement for future public health efforts.

A Method for Intelligent Allocation of Diagnostic Testing by Leveraging Data from Commercial Wearable Devices: A Case Study on COVID-19.

Mass surveillance testing can help control outbreaks of infectious diseases such as COVID-19. However, diagnostic test shortages are prevalent globally and continue to occur in the US with the onset of new COVID-19 variants, demonstrating an unprecedented need for improving our current methods for mass surveillance testing. By targeting surveillance testing towards individuals who are most likely to be infected and, thus, increasing testing positivity rate (i.e., percent positive in the surveillance group), fewer tests are needed to capture the same number of positive cases. Here, we developed an Intelligent Testing Allocation (ITA) method by leveraging data from the CovIdentify study (6,765 participants) and the MyPHD study (8,580 participants), including smartwatch data from 1,265 individuals of whom 126 tested positive for COVID-19. Our rigorous model and parameter search uncovered the optimal time periods and aggregate metrics for monitoring continuous digital biomarkers to increase the positivity rate of COVID-19 diagnostic testing. We found that resting heart rate features distinguished between COVID-19 positive and negative cases earlier in the course of the infection than steps features, as early as ten and five days prior to the diagnostic test, respectively. We also found that including steps features increased the area under the receiver operating characteristic curve (AUC-ROC) by 7-11% when compared with RHR features alone, while including RHR features improved the AUC of the ITA model's precision-recall curve (AUC-PR) by 38-50% when compared with steps features alone. The best AUC-ROC (0.73 ± 0.14 and 0.77 on the cross-validated training set and independent test set, respectively) and AUC-PR (0.55 ± 0.21 and 0.24) were achieved by using data from a single device type (Fitbit) with high-resolution (minute-level) data. Finally, we show that ITA generates up to a 6.5-fold increase in the positivity rate in the cross-validated training set and up to a 3-fold increase in the positivity rate in the independent test set, including both symptomatic and asymptomatic (up to 27%) individuals. Our findings suggest that, if deployed on a large scale and without needing self-reported symptoms, the ITA method could improve allocation of diagnostic testing resources and reduce the burden of test shortages.

Brief Report: Blood and Genital Fluid Viral Load Trajectories Among Treated and Untreated Persons With Acute HIV Infection in Malawi.

BACKGROUND: Persons with acute HIV infection (AHI) are highly infectious and responsible for a disproportionate share of incident infections. Immediate antiretroviral therapy (ART) rapidly reduces blood viral loads (VLs), but genital VLs after ART initiation during AHI are less well described. SETTING: Lilongwe, Malawi, 2012-2014. METHODS: HIV-seronegative and HIV-serodiscordant persons aged ≥18 years were screened for AHI (RNA positive) and randomized to standard of care, behavioral intervention, or behavioral intervention plus short-term ART (raltegravir/emtricitabine/tenofovir) (1:2:2). Persons who were ART eligible under Malawi guidelines could receive first-line therapy. Blood and genital VLs were assessed at weeks 1, 4, 8, and 12. Fisher's Exact test was used to compare viral suppression by ART status. RESULTS: Overall, 46 persons with AHI were enrolled; of whom, 17 started ART within 12 weeks. Median blood VL at AHI diagnosis was 836,115 copies/mL. At week 12, 7% (1/14) of those who initiated ART had a blood VL of ≥400 copies/mL, compared with 100% (23/23; P < 0.0001) of those who did not initiate ART (median VL: 61,605 copies/mL). Median genital VL at week 1 was 772 copies/mL, with 13 of 22 (59%) having VL of ≥400 copies/mL. At week 12, 0 of 10 (0%) of those who initiated ART had genital VL of ≥400 copies/mL, compared with 7 of 15 (47%) of those who did not initiate ART (P = 0.02). CONCLUSION: Although highly correlated, VLs in blood and genital fluids occupy discrete biological compartments with distinct virologic dynamics. Our results corroborate the dramatic reduction in both compartments after ART initiation. Increasing AHI screening and rapidly initiating treatment is key to interrupting transmission.

Intersection of Syphilis and Human Immunodeficiency Virus (HIV) Networks to Identify Opportunities to Enhance HIV Prevention.

BACKGROUND: Human immunodeficiency virus (HIV) and syphilis infection continue at disproportionate rates among minority men who have sex with men (MSM) in the United States. The integration of HIV genetic clustering with partner services can provide important insight into local epidemic trends to guide interventions and control efforts. METHODS: We evaluated contact networks of index persons defined as minority men and transgender women diagnosed with early syphilis and/or HIV infection between 2018 and 2020 in 2 North Carolina regions. HIV clusters were constructed from pol sequences collected through statewide surveillance. A combined "HIV-risk" network, which included persons with any links (genetic or sexual contact) to HIV-positive persons, was evaluated by component size, demographic factors, and HIV viral suppression. RESULTS: In total, 1289 index persons were identified and 55% named 1153 contacts. Most index persons were Black (88%) and young (median age 30 years); 70% had early syphilis and 43% had prevalent HIV infection. Most people with HIV (65%) appeared in an HIV cluster. The combined HIV-risk network (1590 contact network and 1500 cluster members) included 287 distinct components; however, 1586 (51%) were in a single component. Fifty-five percent of network members with HIV had no evidence of viral suppression. Overall, fewer index persons needed to be interviewed to identify 1 HIV-positive member without viral suppression (1.3 vs 4.0 for contact tracing). CONCLUSIONS: Integration of HIV clusters and viral loads illuminate networks with high HIV prevalence, indicating recent and ongoing transmission. Interventions intensified toward these networks may efficiently reach persons for HIV prevention and care re-engagement.

Lost and found: applying network analysis to public health contact tracing for HIV.

Infectious disease surveillance is often case-based, focused on people diagnosed and their contacts in a predefined time window, and treated as independent across infections. Network analysis of partners and contacts joining multiple investigations and infections can reveal social or temporal trends, providing opportunities for epidemic control within broader networks. We constructed a sociosexual network of all HIV and early syphilis cases and contacts investigated among residents of 11 contiguous counties in North Carolina over a two-year period (2012-2013). We anchored the analysis on new HIV diagnoses ("indexes"), but also included nodes and edges from syphilis investigations that were within the same network component as any new HIV index. After adding syphilis investigations and deduplicating people included in multiple investigations (entity resolution), the final network comprised 1470 people: 569 HIV indexes, 700 contacts to HIV indexes who were not also new cases themselves, and 201 people who were either indexes or contacts in eligible syphilis investigations. Among HIV indexes, nearly half (48%; n = 273) had no located contacts during single-investigation contact tracing, though 25 (9%) of these were identified by other network members and thus not isolated in the final multiple investigation network. Constructing a sociosexual network from cases and contacts across multiple investigations mitigated some effects of unobserved partnerships underlying the HIV epidemic and demonstrated the HIV and syphilis overlap in these networks.

Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World.

As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), artificial intelligence (AI)-including machine learning (ML) and Big Data analytics-as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This article provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas, where IoT can contribute are discussed, namely: 1) tracking and tracing; 2) remote patient monitoring (RPM) by wearable IoT (WIoT); 3) personal digital twins (PDTs); and 4) real-life use case: ICT/IoT solution in South Korea. Second, the role and novel applications of AI are explained, namely: 1) diagnosis and prognosis; 2) risk prediction; 3) vaccine and drug development; 4) research data set; 5) early warnings and alerts; 6) social control and fake news detection; and 7) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including: 1) crowd surveillance; 2) public announcements; 3) screening and diagnosis; and 4) essential supply delivery. Finally, we discuss how distributed ledger technologies (DLTs), of which blockchain is a common example, can be combined with other technologies for tackling COVID-19.

Are skin color and body mass index associated with social network structure? Findings from a male sex market study.

Objectives: There is a growing burden of HIV and sex-related diseases in South Asia and India. Sociological research illustrates that key axes of social stratification, such as race and ethnicity, affect social network structure which, in turn, impacts sexual health and wellbeing. Research on networks has increasingly begun to examine the ways in which networks drive or harness sexual behaviors, but has largely neglected the influence of culture and cultural markers in this continuum. Furthermore, much of the existing scholarship has been conducted in the U.S. or in Western contexts.Design: As part of an exploratory effort, we examined how skin color and body mass index (BMI) affected networks among 206 men who have with men (MSM) frequenting sex markets in Hyderabad, India. A novel phone-based network generation method of respondent-driven sampling was used for recruitment. In assessing how skin color and BMI drive these structures, we also compared how these factors contribute to networks relative to two more commonly referenced markers of social difference among Indians, caste and religion.Results: Our findings suggest that skin color and BMI contribute significantly more to network structure than do caste and religion.Conclusions: These findings tentatively illuminate the importance of individual-level heterogeneity in bodily attributes, factors which are seldom considered in conventional approaches to researching how social stratification and health inequalities are animated during the formation of networks.

Community Effectiveness of Masks and Vaccines.

Recent controversies about wearing masks and getting vaccinated to slow the spread of coronavirus disease 2019 highlight the potential for individual rights and decision making to create widespread community-level outcomes. There is little work demonstrating the collective spillover effects of pandemic mitigation efforts. The authors contribute by visualizing the proportion of unvaccinated people who would become infected at different combinations of mask wearing and vaccination in a hypothetical community. A common pattern emerges across all assumptions: below some joint threshold of mask and vaccination rates, almost all unvaccinated people will eventually become infected, and beyond that threshold there is a steep drop leading to widespread community-level protection. What differs across settings is the timing and shape of the drop-off after crossing the threshold. The authors conclude that masking and vaccination are sensible and in the best interest of the population.

Factors Associated With Human Immunodeficiency Virus Infections Linked in Genetic Clusters But Disconnected in Partner Tracing.

BACKGROUND: Successful partner notification can improve community-level outcomes by increasing the proportion of persons living with human immunodeficiency virus (HIV) who are linked to HIV care and virally suppressed, but it is resource intensive. Understanding where HIV transmission pathways may be undetected by routine partner notification may help improve case finding strategies. METHODS: We combined partner notification interview and HIV sequence data for persons diagnosed with HIV in Wake County, NC in 2012 to 2013 to evaluate partner contact networks among persons with HIV pol gene sequences 2% or less pairwise genetic distance. We applied a set of multivariable generalized estimating equations to identify correlates of disparate membership in genetic versus partner contact networks. RESULTS: In the multivariable model, being in a male-male pair (adjusted odds ratio [AOR], 16.7; P = 0.01), chronic HIV infection status (AOR, 4.5; P < 0.01), and increasing percent genetic distance between each dyad member's HIV pol gene sequence (AOR, 8.3 per each 1% increase, P < 0.01) were all associated with persons with HIV clustering but not being identified in the partner notification network component. Having anonymous partners or other factors typically associated with risk behavior were not associated. CONCLUSIONS: Based on genetic networks, partnerships which may be stigmatized, may have occurred farther back in time or may have an intervening partner were more likely to be unobserved in the partner contact network. The HIV genetic cluster information contributes to public health understanding of HIV transmission networks in these settings where partner identifying information is not available.

Randomized Controlled Pilot Study of Antiretrovirals and a Behavioral Intervention for Persons With Acute HIV Infection: Opportunity for Interrupting Transmission.

BACKGROUND: Persons with acute HIV infection (AHI) have heightened transmission risk. We evaluated potential transmission reduction using behavioral and biomedical interventions in a randomized controlled pilot study in Malawi. METHODS: Persons were randomized 1:2:2 to standard counseling (SC), 5-session behavioral intervention (BI), or behavioral intervention plus 12 weeks of antiretrovirals (ARVs; BIA). All were followed for 26-52 weeks and, regardless of arm, referred for treatment according to Malawi-ARV guidelines. Participants were asked to refer partners for testing. RESULTS: Among 46 persons (9 SC, 18 BI, 19 BIA), the average age was 28; 61% were male. The median viral load (VL) was 5.9 log copies/mL at enrollment. 67% (10/15) of BIA participants were suppressed (<1000 copies/mL) at week 12 vs 25% BI and 50% SC (P = .07). Although the mean number of reported condomless sexual acts in the past week decreased from baseline across all arms (1.5 vs 0.3 acts), 36% experienced incident sexually transmitted infection by 52 weeks (12% SC, 28% BI, 18% BIA). Forty-one percent (19/46) of participants referred partners (44% SC, 44% BI, 37% BIA); 15 of the partners were HIV-infected. CONCLUSIONS: Diagnosis of AHI facilitates behavioral and biomedical risk reduction strategies during a high-transmission period that begins years before people are typically identified and started on ARVs. Sexually transmitted infection incidence in this cohort suggests ongoing risk behaviors, reinforcing the importance of early intervention with ARVs to reduce transmission. Early diagnosis coupled with standard AHI counseling and early ARV referral quickly suppresses viremia, may effectively change behavior, and could have tremendous public health benefit in reducing onward transmission.

Human Immunodeficiency Virus (HIV)-1 Transmission Among Persons With Acute HIV-1 Infection in Malawi: Demographic, Behavioral, and Phylogenetic Relationships.

BACKGROUND: Understanding sexual networks involving acute human immunodeficiency virus (HIV)-1 infections (AHI) may lead to prevention opportunities to mitigate high rates of onward transmission. We evaluated HIV-1 phylogenetic and behavioral characteristics among persons with AHI and their referred partners. METHODS: Between 2012 and 2014, 46 persons with AHI in Malawi participated in a combined behavioral and biomedical intervention. Participants referred sexual partners by passive referral. Demographics and sexual behaviors were collected through interviews and HIV-1 genetic relationships were assessed with phylogenetics. RESULTS: Among 45 AHI participants with HIV-1 sequences, none was phylogenetically-linked with another AHI index. There were 19 (42%) AHI participants who referred a single partner that returned for testing. Most partners (n = 17) were HIV-infected, with 15 (88%) presenting with an established infection. There were 14 index-partner pairs that had sequences available; 13 (93%) pairs were phylogenetically-linked dyads. The AHI index was female in 7/13 (54%) dyads. Age-disparate relationships among dyads were common (≥5-year age difference in 67% of dyads), including 3/6 dyads involving a male index and a younger woman. Index participants with a referred partner were more likely to report no casual partners and to be living with their current partner than participants not in dyads. CONCLUSIONS: Passive-partner referral successfully identified partners with genetically-similar HIV infections-the likely source of infection-but only 40% of index cases referred partners who presented for HIV-1 testing. Future work evaluating assisted partner notification may help reach susceptible partners or more people with untreated HIV-1 infections connected to acute transmission. CLINICAL TRIALS REGISTRATION: NCT01450189.

Prediction of HIV Transmission Cluster Growth With Statewide Surveillance Data.

BACKGROUND: Prediction of HIV transmission cluster growth may help guide public health action. We developed a predictive model for cluster growth in North Carolina (NC) using routine HIV surveillance data. METHODS: We identified putative transmission clusters with ≥2 members through pairwise genetic distances ≤1.5% from HIV-1 pol sequences sampled November 2010-December 2017 in NC. Clusters established by a baseline of January 2015 with any sequences sampled within 2 years before baseline were assessed for growth (new diagnoses) over 18 months. We developed a predictive model for cluster growth incorporating demographic, clinical, temporal, and contact tracing characteristics of baseline cluster members. We internally and temporally externally validated the final model in the periods January 2015-June 2016 and July 2016-December 2017. RESULTS: Cluster growth was predicted by larger baseline cluster size, shorter time between diagnosis and HIV care entry, younger age, shorter time since the most recent HIV diagnosis, higher proportion with no named contacts, and higher proportion with HIV viremia. The model showed areas under the receiver-operating characteristic curves of 0.82 and 0.83 in the internal and temporal external validation samples. CONCLUSIONS: The predictive model developed and validated here is a novel means of identifying HIV transmission clusters that may benefit from targeted HIV control resources.

Leveraging Phylogenetics to Understand HIV Transmission and Partner Notification Networks.

BACKGROUND: Partner notification is an important component of public health test and treat interventions. To enhance this essential function, we assessed the potential for molecular methods to supplement routine partner notification and corroborate HIV networks. METHODS: All persons diagnosed with HIV infection in Wake County, NC, during 2012-2013 and their disclosed sexual partners were included in a sexual network. A data set containing HIV-1 pol sequences collected in NC during 1997-2014 from 15,246 persons was matched to HIV-positive persons in the network and used to identify putative transmission clusters. Both networks were compared. RESULTS: The partner notification network comprised 280 index cases and 383 sexual partners and high-risk social contacts (n = 131 HIV-positive). Of the 411 HIV-positive persons in the partner notification network, 181 (44%) did not match to a HIV sequence, 61 (15%) had sequences but were not identified in a transmission cluster, and 169 (41%) were identified in a transmission cluster. More than half (59%) of transmission clusters bridged sexual network partnerships that were not recognized in the partner notification; most of these clusters were dominated by men who have sex with men. CONCLUSIONS: Partner notification and HIV sequence analysis provide complementary representations of the existent partnerships underlying the HIV transmission network. The partner notification network components were bridged by transmission clusters, particularly among components dominated by men who have sex with men. Supplementing the partner notification network with phylogenetic data highlighted avenues for intervention.

Sustained Sexual Behavior Change After Acute HIV Diagnosis in Malawi.

BACKGROUND: Identification of acute HIV infection (AHI) allows for important opportunities for HIV prevention through behavior change and biomedical intervention. Here, we evaluate changes in sexual risk behaviors among persons with AHI enrolled in a combined behavioral and biomedical intervention designed to reduce onward transmission of HIV. METHODS: Participants were randomized to standard HIV counseling, a multisession behavioral intervention, or a multisession behavioral intervention plus antiretrovirals. Sexual behaviors were assessed periodically over 1 year. RESULTS: Four weeks after diagnosis, the predicted probability of reporting multiple sexual partners decreased from 24% to 9%, and the probability of reporting unprotected sex decreased from 71% to 27%. These declines in sexual risk behaviors were sustained over follow-up irrespective of study arm. CONCLUSIONS: Diagnosis of AHI alone may be sufficient to achieve immediate and sustained behavior change during this highly infectious period.

Integration of Contact Tracing and Phylogenetics in an Investigation of Acute HIV Infection.

BACKGROUND: The integration of traditional contact tracing with HIV sequence analyses offers opportunities to mitigate some of the barriers to effective network construction. We used combined analyses during an outbreak investigation of spatiotemporally clustered acute HIV infections to evaluate if the observed clustering was the product of a single outbreak. METHODS: We investigated acute and recent HIV index cases reported in North Carolina from 2013 to 2014 and their reported contacts. Contact tracing networks were constructed with surveillance data and compared with phylogenetic transmission clusters involving an index case using available HIV-1 pol sequences including 1672 references. Clusters were defined as clades of 2 or more sequences with a less than 1.5% genetic distance and a bootstrap of at least 98% on maximum-likelihood phylogenies. RESULTS: In total, 68 index cases and 210 contacts (71 HIV infected) were reported. The contact tracing network involved 58 components with low overall density (1.2% statewide); 33% of first-degree contacts could not be located. Among 38 (56%) of 68 index cases and 34 (48%) of 71 contacts with sequences, 13 phylogenetic clusters were identified (size 2-4 members). Four clusters connected network components that were not linked in contact tracing. The largest component (n = 28 cases) included 2 distinct phylogenetic clusters and spanned 2 regions. CONCLUSIONS: We identified the concurrent expansion of multiple small transmission clusters rather than a single outbreak in a largely disconnected contact tracing network. Integration of phylogenetic analyses provided timely information on transmission networks during the investigation. Our findings highlight the potential of combined methods to better identify high-risk networks for intervention.

United Airway Disease Among Crop Farmers.

Although there are observed relationships between agriculture and respiratory health problems, little attention has been given to relationship between lower and upper airway problems among farmers in the United States. The objective was to investigate the possible relationship between occupational risk factors and "united airway disease" among farmers in eastern North Carolina. Farmers and farm workers were selected from eastern North Carolina by using a cluster sampling method. A total of 180 farmers and farm workers completed the questionnaire. Data analyses were conducted to test associations and correlations between working conditions and symptoms. Lower airway symptom prevalence was 35%, and 66% had upper airway symptoms. Only 1% of farmers had physician-diagnosed rhinitis. Self-reported rhinitis and asthma symptoms were significantly correlated (r = .228, P = .003) among farmer/farm workers. Upper airway diseases are prevalent yet overlooked among farmer/farm workers. Early management of upper airway symptoms may prevent severe lower airway diseases, which will lead to increased productivity in the agricultural workforce.

Incorporating Acute HIV Screening into Routine HIV Testing at Sexually Transmitted Infection Clinics, and HIV Testing and Counseling Centers in Lilongwe, Malawi.

BACKGROUND AND OBJECTIVES: Integrating acute HIV-infection (AHI) testing into clinical settings is critical to prevent transmission, and realize potential treatment-as-prevention benefits. We evaluated acceptability of AHI testing and compared AHI prevalence at sexually transmitted infection (STI) clinics and HIV testing and counseling (HTC) clinics in Lilongwe, Malawi. METHODS: We conducted HIV RNA testing for HIV-seronegative patients visiting STI and HTC clinics. AHI was defined as positive RNA and negative/discordant rapid antibody tests. We evaluated demographic, behavioral, and transmission-risk differences between STI and HTC patients and assessed performance of a risk-score for targeted screening. RESULTS: Nearly two-thirds (62.8%, 9280/14,755) of eligible patients consented to AHI testing. We identified 59 persons with AHI (prevalence = 0.64%)-a 0.9% case-identification increase. Prevalence was higher at STI [1.03% (44/4255)] than at HTC clinics [0.3% (15/5025), P < 0.01], accounting for 2.3% of new diagnoses vs 0.3% at HTC clinic. Median viral load (VL) was 758,050 copies per milliliter; 25% (15/59) had VL ≥ 10,000,000 copies per milliliter. Median VL was higher at STI (1,000,000 copies/mL) compared with HTC (153,125 copies/mL, P = 0.2). Among persons with AHI, those tested at STI clinics were more likely to report genital sores compared with those tested at HTC clinics (54.6% vs 6.7%, P < 0.01). The risk score algorithm performed well in identifying persons with AHI at HTC clinics (sensitivity = 73%, specificity = 89%). CONCLUSIONS: The majority of patients consented to AHI testing. AHI prevalence was substantially higher in STI clinics than HTC clinics. Remarkably high VLs and concomitant genital scores demonstrate the potential for transmission. Universal AHI screening at STI clinics, and targeted screening at HTC centers, should be considered.

HIV Transmission Patterns Among Immigrant Latinos Illuminated by the Integration of Phylogenetic and Migration Data.

Latinos represent a growing proportion of HIV cases in North Carolina (NC). Understanding how immigrants are involved in local HIV transmission is important to guide interventions. We used phylogenetics to characterize Latino involvement in local HIV transmission chains. Transmission clusters were identified from maximum-likelihood phylogenies constructed with HIV pol sequences from 177 Latinos and 1,496 non-Latinos receiving care in NC. Highly supported clusters involving one or more Latinos were characterized. Migration data were obtained from interviews and chart review. Factors associated with cluster membership were identified using log-binomial regression. Most Latinos were male (76%), immigrants (83%), and had HIV-1B (99%). Immigrants were more likely to report heterosexual risk (67% vs. 23%) than U.S.-born Latinos (p < 0.01). We identified 32 clusters that included one or more Latinos; these involved 53 Latinos (30%) and 41 non-Latinos. Immigrant and U.S.-born Latinos were equally likely to be in clusters, but immigrants were more likely to be in clusters with another Latino (78% vs. 29%; p = 0.02). Cluster composition by ethnicity and risk behavior varied by cluster size; larger clusters contained fewer immigrants and more men who have sex with men (MSM). Factors associated with immigrant membership in local transmission clusters included age <30 years [RR 2.34 (95% CI 1.47-3.73)], Mexican origin [RR 2.55 (95% CI 1.29-6.88)], and residing in the United States longer before diagnosis [RR 1.53 (95% CI 1.09-2.15), per 10 years]. While some Latinos immigrate with HIV infection, many immigrants are involved in transmission networks after arrival, particularly MSM. HIV testing and prevention interventions must consider this heterogeneity and may be better targeted by integrating phylogenetic analyses.