HIV testing approaches to reach the first UNAIDS 95% target in sub-Saharan Africa.

When developing a clinical prediction model, assuming a linear relationship between the continuous predictors and outcome is not recommended. Incorrect specification of the functional form of continuous predictors could reduce predictive accuracy. We examine how continuous predictors are handled in studies developing a clinical prediction model.We searched PubMed for clinical prediction model studies developing a logistic regression model for a binary outcome, published between July 01, 2020, and July 30, 2020.In total, 118 studies were included in the review (18 studies (15%) assessed the linearity assumption or used methods to handle nonlinearity, and 100 studies (85%) did not). Transformation and splines were commonly used to handle nonlinearity, used in 7 (n = 7/18, 39%) and 6 (n = 6/18, 33%) studies, respectively. Categorization was most often used method to handle continuous predictors (n = 67/118, 56.8%) where most studies used dichotomization (n = 40/67, 60%). Only ten models included nonlinear terms in the final model (n = 10/18, 56%).Though widely recommended not to categorize continuous predictors or assume a linear relationship between outcome and continuous predictors, most studies categorize continuous predictors, few studies assess the linearity assumption, and even fewer use methodology to account for nonlinearity. Methodological guidance is provided to guide researchers on how to handle continuous predictors when developing a clinical prediction model. 

Poor handling of continuous predictors in clinical prediction models using logistic regression: a systematic review.

To inform targeted HIV testing, we developed and externally validated a risk-score algorithm that incorporated behavioral characteristics. Outpatient data from five health facilities in western Kenya, comprising 19,458 adults ≥ 15 years tested for HIV from September 2017 to May 2018, were included in univariable and multivariable analyses used for algorithm development. Data for 11,330 adults attending one high-volume facility were used for validation. Using the final algorithm, patients were grouped into four risk-score categories: ≤ 9, 10–15, 16–29 and ≥ 30, with increasing HIV prevalence of 0.6% [95% confidence interval (CI) 0.46–0.75], 1.35% (95% CI 0.85–1.84), 2.65% (95% CI 1.8–3.51), and 15.15% (95% CI 9.03–21.27), respectively. The algorithm’s discrimination performance was modest, with an area under the receiver-operating-curve of 0.69 (95% CI 0.53–0.84). In settings where universal testing is not feasible, a risk-score algorithm can identify sub-populations with higher HIV-risk to be prioritized for HIV testing.

/pdf/development-and-validation-of-a-sociodemographic-and-1ac4tfzokv.pdf

Development and Validation of a Sociodemographic and Behavioral Characteristics-Based Risk-Score Algorithm for Targeting HIV Testing Among Adults in Kenya

Effective ways to diagnose the remaining people living with HIV who do not know their status are a global priority. We reviewed the use of risk-based tools, a set of criteria to identify individuals who would not otherwise be tested (screen in) or excluded people from testing (screen out).Recent studies suggest that there may be value in risk-based tools to improve testing efficiency (i.e. identifying those who need to be tested). However, there has not been any systematic reviews to synthesize these studies. We identified 18,238 citations, and 71 were included. The risk-based tools identified were most commonly from high-income (51%) and low HIV (<5%) prevalence countries (73%). The majority were for "screening in" (70%), with the highest performance tools related to identifying MSM with acute HIV. Screening in tools may be helpful in settings where it is not feasible or recommended to offer testing routinely. Caution is needed for screening out tools, where there is a trade-off between reducing costs of testing with missing cases of people living with HIV. 

/pdf/risk-based-screening-tools-to-optimise-hiv-testing-services-2fukjvle.pdf

Risk-Based Screening Tools to Optimise HIV Testing Services: a Systematic Review

While primary care providers in New York State (NYS) are mandated to offer all patients a HIV test, still many NYS residents miss the HIV screening opportunity. To fill the gap, and as the CDC recommends, this study aimed to examine the feasibility of implementing HIV screening in dental setting, identify patient characteristics associated with acceptance of HIV rapid testing, and discuss best practices of HIV screening in dental setting. New York State Department of Health (NYSDOH) collaborated with the Northeast/Caribbean AIDS Education and Training Center (NECA AETC) and three dental schools in New York State to offer free HIV screening tests as a component of routine dental care between February 2016 and March 2018. Ten clinics in upstate New York and Long Island participated in the study. HIV screening was performed using the OraQuick™ In-Home HIV Test. 14,887 dental patients were offered HIV screening tests; 9,057 (60.8%) were screened; and one patient (0.011%) was confirmed HIV positive and linked to medical care. Of all dental patients, 33% had never been screened for HIV; and 56% had not had a primary care visit or had not been offered an HIV screening test by primary care providers in the previous 12 months. Multi-level generalized linear modeling analysis indicated that test acceptance was significantly associated with patient's age, race/ethnicity, gender, country of origin, primary payer (or insurance), past primary care visits, past HIV testing experiences, and the poverty level of patient's community. HIV screening is well accepted by dental patients and can be effectively integrated into routine dental care. HIV screening in the dental setting can be a good option for first-time testers, those who have not seen a primary care provider in the last 12 months, and those who have not been offered HIV screening at their last primary care visit.

HIV screening in the dental setting in New York State

Homa Bay, Siaya, and Kisumu counties in western Kenya have the highest estimated HIV prevalence (16.3-21.0%) in the country, and struggle to meet program targets for HIV testing services (HTS). The Kenya Ministry of Health (MOH) recommends annual HIV testing for the general population. We assessed the degree to which reducing the interval for retesting to less than 12 months increased diagnosis of HIV in outpatient departments (OPD) in western Kenya. We conducted a retrospective analysis of routinely collected program data from seven high-volume (>800 monthlyOPD visits) health facilities in March-December, 2017. Data from persons ≥15 years of age seeking medical care (patients) in the OPD and non-care-seekers (non-patients) accompanying patients to the OPD were included. Outcomes were meeting MOH (routine) criteria versus criteria for a reduced retesting interval (RRI) of 12 months, and 5% (4,832) met other criteria. The remaining 80% (74,033) met criteria for a RRI of < 12 months. In total 1.3% (1,185) of clients had a positive test. Although the percent yield was over 2-fold higher among those meeting routine criteria (2.4% vs. 1.0%; p<0.001), 63% (750) of all HIV infections were found among clients tested less than 12 months ago, the majority (81%) of whom reported having a negative test in the past 3-12 months. Non-patients accounted for 45% (539) of all HIV-positive persons identified. Percent yield was higher among non-patients as compared to patients (1.5% vs. 1.2%; p-value = <0.001) overall and across eligibility criteria and age categories. The majority of HIV diagnoses in the OPD occurred among clients reporting a negative HIV test in the past 12 months, clients ineligible for testing under the current MOH guidelines. Nearly half of all HIV-positive individuals identified in the OPD were non-patients. Our findings suggest that in the setting of a generalized HIV epidemic, retesting persons reporting an HIV-negative test in the past 3-12 months, and routine testing of non-patients accessing the OPD are key strategies for timely diagnosis of persons living with HIV.

/pdf/expanded-eligibility-for-hiv-testing-increases-hiv-diagnoses-4eashfjv85.pdf

Expanded eligibility for HIV testing increases HIV diagnoses-A cross-sectional study in seven health facilities in western Kenya.

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Development and Validation of a Sociodemographic and Behavioral Characteristics-Based Risk-Score Algorithm for Targeting HIV Testing Among Adults in Kenya

Expanded eligibility for HIV testing increases HIV diagnoses-A cross-sectional study in seven health facilities in western Kenya.