Background

Up to half of all new HIV cases in Los Angeles may be caused by the 20-30% of men who have sex with men (MSM) with unrecognized HIV infection. Racial/ethnic minority MSM are at particularly high risk for being sero-unaware and due to stigma and poor healthcare access might benefit from novel private, self-testing methods, such as the recently FDA-approved OraQuick® In-Home HIV Test.

Methods

From July-November 2013, we undertook a pilot study to examine the feasibility of a voucher program for free OraQuick® tests targeting African American MSM in Los Angeles. We determined feasibility based on: (1) the establishment of a voucher redemption and third-party payment system, (2) the willingness of community-based organizations (CBOs) to disseminate vouchers, and (3) the collection of user demographics, test and linkage-to-care results with an anonymous telephone survey.

Results

We partnered with Walgreens® to create a voucher and third-party reimbursement system for free OraQuick® tests. Voucher distribution was divided into two periods. In total, 641 vouchers were supplied to CBOs: 274 (42.7%) went to clients and of those 53 (19.3%) were redeemed. Fifty (18.2%) of the 274 clients were surveyed: 44 (88%) were African American, 39 (78%) reported being likely to repeat voucher use, 44 (88%) reported reviewing pre-test information, and 37 (74%) the post-test information. Three (6%) of 50 survey respondents reported newly testing HIV-positive of whom all (100%) reported seeking medical care. Two withheld their results, both of whom also sought medical care.

Conclusions

Developing and partnering with a commercial pharmacy to institute a voucher system to facilitate HIV self-testing with linkage-to-care was feasible. Our findings suggest the voucher program was associated with increasing the identification of new cases of HIV infection with high rates of linkage to care. Expanded research and evaluation of voucher programs for HIV self-test kits among high-risk groups is warranted.

Background

Geosocial networking applications (GSN apps) used for meeting sexual partners have become increasingly popular with men who have sex with men (MSM) since 2009. The current study aimed to determine if self-identified HIV-negative, MSM clinic attendees who used GSN apps have an increased incidence of sexually transmitted infections (STI) compared to self-identified HIV-negative, MSM attendees who met sexual partners via in-person venues, such as bars or clubs or through MSM-specific hook-up websites.

Methods

Data were collected between August 2011 and January 2013 on all self-identified HIV-negative, MSM clients visiting the L.A. Gay & Lesbian Center for STI screening. A total of 7184 individuals tested for STIs and self-reported behaviours on drug use and social networking methods to meet sexual partners. Multivariate logistic regression models were used to analyse the results.

Results

Individuals who used GSN apps for meeting sexual partners had greater odds of testing positive for gonorrhoea (OR: 1.25; 95% CI 1.06 to 1.48) and for chlamydia (OR: 1.37; 95% CI 1.13 to 1.65) compared to individuals who met partners through in-person methods only. There were no significant differences in syphilis and HIV incidence between those who met partners via in-person venues only, on the internet or through GSN apps.

Conclusions

The present study concludes that sexual health clinic MSM attendees who are meeting on GSN apps are at greater risk for gonorrhoea and chlamydia than MSM attendees who meet in-person or on the internet. Future interventions should explore the use of these novel technologies for testing promotion, prevention and education.

With the goal to screen high-risk populations for oral cancer in low- and middle-income countries (LMICs), we have developed a low-cost, portable, easy to use smartphone-based intraoral dual-modality imaging platform. In this paper we present an image classification approach based on autofluorescence and white light images using deep learning methods. The information from the autofluorescence and white light image pair is extracted, calculated, and fused to feed the deep learning neural networks. We have investigated and compared the performance of different convolutional neural networks, transfer learning, and several regularization techniques for oral cancer classification. Our experimental results demonstrate the effectiveness of deep learning methods in classifying dual-modal images for oral cancer detection.