Come As You Are - Assessing the Efficacy of a Nurse Case Management HIV Prevention and Care Intervention Among Homeless Youth
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 474
- 试验地点
- 1
- 主要终点
- Number of Participants Who Use Preventive Prophylaxis (PrEP)
研究概览
简要总结
The purpose of this study is to to determine the efficacy of the Nurse Case Management HIV (NCM4HIV) intervention on HIV prevention compared to usual care among Youth Experiencing Homelessness (YEH).
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Prevention
- 盲法
- Single (Investigator)
入排标准
- 年龄范围
- 16 Years 至 25 Years（Child, Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •youth engaged in high-risk sexual activity or intravenous drug use
- •speak English
- •not planning to move out of the metro area during the study
排除标准
- •youth with very low literacy
- •severe acute mental symptoms
研究组 & 干预措施
NCM4HIV
Participant will receive NCM4HIV intervention which includes Personalized HIV prevention education, behavior goal-setting,behavioral self-monitoring,Pre exposure prophylaxis (PrEP) eligibility screening,PrEP/non occupational post exposure prophylaxis(nPEP)services (labs, medication), healthcare planning/coordination, Motivational Interviewing (MI) counseling approach, assisting with cognitive appraisals (clarifying misconceptions),promoting health seeking and coping behaviors that incorporate the situational, personal, social, and resource needs affecting health
干预措施: NCM4HIV (Behavioral)
Usual care
Participants will receive the usual care which includes Housing, food, and clothing needs,health assessment, basic healthcare, limited anticipatory guidance, mental health counseling,substance use treatment referrals,PrEP/nPEP referrals
干预措施: Usual Care (Behavioral)
结局指标
主要结局
Number of Participants Who Use Preventive Prophylaxis (PrEP)
时间窗: 9 months after intervention (Month 12)
Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.
Number of Participants Who Use Non-occupational Post-exposure Prophylaxis (nPEP)
时间窗: 9 months after intervention (Month 12)
Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.
Number of Participants Who Use Condoms at Last Sex as Measured by the Youth Risk Behavior Survey
时间窗: 9 months after intervention (Month 12)
An item from the Youth Risk Behavior Survey was used to assess this outcome. The items asked if a condom was used at last sex. The number of participants who answered yes is reported. Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.
Number of Participants Who Tested Positive for HIV or Sexually Transmitted Infection (STI)
时间窗: 9 months after intervention (Month 12)
Sexually Transmitted Infection tested includes syphilis, chlamydia and gonorrhea. Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.
Number of Participants Who Use Preventive Prophylaxis (PrEP)
时间窗: baseline
Number of Participants Who Use Preventive Prophylaxis (PrEP)
时间窗: At completion of the 3-month intervention (Month 3)
Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.
Number of Participants Who Use Preventive Prophylaxis (PrEP)
时间窗: 3 months after intervention (Month 6)
Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.
Number of Participants Who Use Preventive Prophylaxis (PrEP)
时间窗: 6 months after intervention (Month 9)
Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.
Number of Participants Who Use Non-occupational Post-exposure Prophylaxis (nPEP)
时间窗: baseline
Number of Participants Who Use Non-occupational Post-exposure Prophylaxis (nPEP)
时间窗: At completion of the 3-month intervention (Month 3)
Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.
Number of Participants Who Use Non-occupational Post-exposure Prophylaxis (nPEP)
时间窗: 3 months after intervention (Month 6)
Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.
Number of Participants Who Use Non-occupational Post-exposure Prophylaxis (nPEP)
时间窗: 6 months after intervention (Month 9)
Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.
Number of Participants Who Use Condoms at Last Sex as Measured by the Youth Risk Behavior Survey
时间窗: baseline
An item from the Youth Risk Behavior Survey was used to assess this outcome. The items asked if a condom was used at last sex. The number of participants who answered yes is reported.
Number of Participants Who Use Condoms at Last Sex as Measured by the Youth Risk Behavior Survey
时间窗: At completion of the 3-month intervention (Month 3)
An item from the Youth Risk Behavior Survey was used to assess this outcome. The items asked if a condom was used at last sex. The number of participants who answered yes is reported.\\ Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.
Number of Participants Who Use Condoms at Last Sex as Measured by the Youth Risk Behavior Survey
时间窗: 3 months after intervention (Month 6)
An item from the Youth Risk Behavior Survey was used to assess this outcome. The items asked if a condom was used at last sex. The number of participants who answered yes is reported. Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.
Number of Participants Who Use Condoms at Last Sex as Measured by the Youth Risk Behavior Survey
时间窗: 6 months after intervention (Month 9)
An item from the Youth Risk Behavior Survey was used to assess this outcome. The items asked if a condom was used at last sex. The number of participants who answered yes is reported. Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.
Number of Participants Who Tested Positive for HIV or Sexually Transmitted Infection (STI)
时间窗: Baseline
Sexually Transmitted Infection tested includes syphilis, chlamydia and gonorrhea.
Number of Participants Who Tested Positive for HIV or Sexually Transmitted Infection (STI)
时间窗: At completion of the 3-month intervention (Month 3)
Sexually Transmitted Infection tested includes syphilis, chlamydia and gonorrhea. Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.
Number of Participants Who Tested Positive for HIV or Sexually Transmitted Infection (STI)
时间窗: 3 months after intervention (Month 6)
Sexually Transmitted Infection tested includes syphilis, chlamydia and gonorrhea. Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.
Number of Participants Who Tested Positive for HIV or Sexually Transmitted Infection (STI)
时间窗: 6 months after intervention (Month 9)
Sexually Transmitted Infection tested includes syphilis, chlamydia and gonorrhea. Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.
次要结局
- Housing Status(9 months after intervention (Month 12))
- Mental Health as Measured by the Brief Symptom Index-18(9 months after intervention (Month 12))
- Number of Participants With Substance Use as Measured by Item 11 in the Texas Christian University (TCU) Drug Screen II(baseline)
- Mental Health as Measured by the Patient Health Questionnaire (PHQ-9)(9 months after intervention (Month 12))
- Housing Status(baseline)
- Mental Health as Measured by the Brief Symptom Index-18(baseline)
- Mental Health as Measured by the Brief Symptom Index-18(At completion of the 3-month intervention (Month 3))
- Mental Health as Measured by the Brief Symptom Index-18(3 months after intervention (Month 6))
- Mental Health as Measured by the Brief Symptom Index-18(6 months after intervention (Month 9))
- Housing Status(At completion of the 3-month intervention (Month 3))
- Housing Status(3 months after intervention (Month 6))
- Housing Status(6 months after intervention (Month 9))
- Number of Participants With Substance Use as Measured by Item 11 in the Texas Christian University (TCU) Drug Screen II(At completion of the 3-month intervention (Month 3), 3 months after intervention (Month 6), 6 months after intervention (Month 9), 9 months after intervention (Month 12))
- Mental Health as Measured by the Patient Health Questionnaire (PHQ-9)(baseline)
- Mental Health as Measured by the Patient Health Questionnaire (PHQ-9)(At completion of the 3-month intervention (Month 3))
- Mental Health as Measured by the Patient Health Questionnaire (PHQ-9)(3 months after intervention (Month 6))
- Mental Health as Measured by the Patient Health Questionnaire (PHQ-9)(6 months after intervention (Month 9))
研究者
Diane Santa Maria
Associate Professor
The University of Texas Health Science Center, Houston