Come As You Are - Assessing the Efficacy of a Nurse Case Management HIV Prevention and Care Intervention Among Homeless Youth

Sexually Transmitted Infection tested includes syphilis, chlamydia and gonorrhea.

Time frame: Baseline

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.

Time frame: 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.

Time frame: 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.

Time frame: 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.

Time frame: 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.

Time frame: 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.

Time frame: 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.

Time frame: 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. 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.

Time frame: 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.

Time frame: 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.

Time frame: 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.

Time frame: 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.

Time frame: 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.

Time frame: 6 months after intervention (Month 9)

Time frame: baseline

Time frame: baseline

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.

Time frame: 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.

Time frame: 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.

Time frame: 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.

Time frame: 9 months after intervention (Month 12)

Participants will be asked if they live in a shelter, apartment/house, with someone, outside, or in a car, etc.

Time frame: baseline

Participants will be asked if they live in a shelter, apartment/house, with someone, outside, or in a car, etc.

Time frame: At completion of the 3-month intervention (Month 3)

Population: Data were not collected for 82 participants in the NCM4HIV arm and 39 participants in the Usual Care arm due to loss to follow-up

Participants will be asked if they live in a shelter, apartment/house, with someone, outside, or in a car, etc.

Time frame: 3 months after intervention (Month 6)

Population: Data were not collected for 117 participants in the NCM4HIV arm and 62 participants in the Usual Care arm due to loss to follow-up

Participants will be asked if they live in a shelter, apartment/house, with someone, outside, or in a car, etc.

Time frame: 6 months after intervention (Month 9)

Population: Data were not collected for 129 participants in the NCM4HIV arm and 71 participants in the Usual Care arm due to loss to follow-up

Participants will be asked if they live in a shelter, apartment/house, with someone, outside, or in a car, etc.

Time frame: 9 months after intervention (Month 12)

Population: Data were not collected for 119 participants in the NCM4HIV arm and 65 participants in the Usual Care arm due to loss to follow-up

The Brief Symptom Inventory 18 (BSI-18) consists of 18 items on a 5-point (0-4) Likert scale and is designed to assess current psychological distress (over the past 7 days). Total score ranges from 0 to 72, with higher scores indicating greater distress.

Time frame: baseline

Population: Data were not collected for this outcome measure because the Brief Symptom Index-18 survey was not administered to participants. Instead, the Patient Health Questionnaire (PHQ-9) was used to assess mental health. As a result, no participants received the Brief Symptom Index-18 survey, and no data were collected for this outcome measure. Therefore, zero participants were analyzed for this outcome. Data from the PHQ-9 are reported under a different outcome measure.

The BSI-18 consists of 18 items on a 5-point (0-4) Likert scale and is designed to assess current psychological distress (over the past 7 days). Total score ranges from 0 to 72, with higher scores indicating greater distress.

Time frame: At completion of the 3-month intervention (Month 3)

Population: Data were not collected for this outcome measure because the Brief Symptom Index-18 survey was not administered to participants. Instead, the Patient Health Questionnaire (PHQ-9) was used to assess mental health. As a result, no participants received the Brief Symptom Index-18 survey, and no data were collected for this outcome measure. Therefore, zero participants were analyzed for this outcome. Data from the PHQ-9 are reported under a different outcome measure.

The BSI-18 consists of 18 items on a 5-point (0-4) Likert scale and is designed to assess current psychological distress (over the past 7 days). Total score ranges from 0 to 72, with higher scores indicating greater distress.

Time frame: 3 months after intervention (Month 6)

Population: Data were not collected for this outcome measure because the Brief Symptom Index-18 survey was not administered to participants. Instead, the Patient Health Questionnaire (PHQ-9) was used to assess mental health. As a result, no participants received the Brief Symptom Index-18 survey, and no data were collected for this outcome measure. Therefore, zero participants were analyzed for this outcome. Data from the PHQ-9 are reported under a different outcome measure.

The BSI-18 consists of 18 items on a 5-point (0-4) Likert scale and is designed to assess current psychological distress (over the past 7 days). Total score ranges from 0 to 72, with higher scores indicating greater distress.

Time frame: 6 months after intervention (Month 9)

Population: Data were not collected for this outcome measure because the Brief Symptom Index-18 survey was not administered to participants. Instead, the Patient Health Questionnaire (PHQ-9) was used to assess mental health. As a result, no participants received the Brief Symptom Index-18 survey, and no data were collected for this outcome measure. Therefore, zero participants were analyzed for this outcome. Data from the PHQ-9 are reported under a different outcome measure.

The BSI-18 consists of 18 items on a 5-point (0-4) Likert scale and is designed to assess current psychological distress (over the past 7 days). Total score ranges from 0 to 72, with higher scores indicating greater distress.

Time frame: 9 months after intervention (Month 12)

Population: Data were not collected for this outcome measure because the Brief Symptom Index-18 survey was not administered to participants. Instead, the Patient Health Questionnaire (PHQ-9) was used to assess mental health. As a result, no participants received the Brief Symptom Index-18 survey, and no data were collected for this outcome measure. Therefore, zero participants were analyzed for this outcome. Data from the PHQ-9 are reported under a different outcome measure.

The Patient Health Questionnaire (PHQ-9) total score ranges from 0 to 27, with higher scores indicating more severe depression. 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.

Time frame: 3 months after intervention (Month 6)

The Patient Health Questionnaire (PHQ-9) total score ranges from 0 to 27, with higher scores indicating more severe depression

Time frame: baseline

The Patient Health Questionnaire (PHQ-9) total score ranges from 0 to 27, with higher scores indicating more severe depression. 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.

Time frame: At completion of the 3-month intervention (Month 3)

The Patient Health Questionnaire (PHQ-9) total score ranges from 0 to 27, with higher scores indicating more severe depression. 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.

Time frame: 6 months after intervention (Month 9)

The Patient Health Questionnaire (PHQ-9) total score ranges from 0 to 27, with higher scores indicating more severe depression. 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.

Time frame: 9 months after intervention (Month 12)

An item from the Texas Christian University (TCU) drug screen II was used to assess this outcome. The item listed various drug substances and asked whether any of those listed had been used in the past 30 days. 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.

Time frame: 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)

An item from the Texas Christian University (TCU) drug screen II was used to assess this outcome. The item listed various drug substances and asked whether any of those listed had been used in the past 30 days. The number of participants who answered yes is reported.

Time frame: baseline