Improving school outcomes for children affected by parental HIV/AIDS: Evaluation of the ChildCARE Intervention at 6-,12-,and 18-months

Abstract

Children affected by parental HIV/AIDS are at-risk for poor school outcomes including reduced attendance, lower grades, and lower school satisfaction compared to unaffected peers. Resilience-based interventions offer promise to improve functioning across a number of domains. A four-arm randomized controlled trial was conducted with 790 children affected by parental HIV/AIDS in rural, central China to examine the effects of a multi-level, resilience-based intervention (i.e., ChildCARE) on school outcomes. Child and caregiver dyads were randomized to a control group, child-only intervention, child + caregiver intervention, or child + caregiver + community intervention. School outcomes at 6-, 12-, and 18-months suggest that participation in the ChildCARE program yielded improvements in academic performance, school satisfaction, and school interest. Opportunities for school psychologists to engage in psychosocial and educational intervention for children impacted by HIV/AIDS are discussed.

Keywords

children affected by parental HIV/AIDS China intervention resilience school outcomes

The World Health Organization (WHO) estimates there are 35 million individuals living with HIV/AIDS across the globe, with the majority of infections occurring among young adults of reproductive age (WHO, 2013). The tendency of HIV infection to occur in this demographic group creates particular challenges. At a macro level, high prevalence rates among young adults yield a number of documented socio-demographic effects, including changes in fertility, household size, and household composition (Hosegood, 2009). At the micro level, HIV infection creates a host of physical, psychological, social, and economic challenges that negatively impact the individual, family members, and children residing within the household.

Children affected by parental HIV/AIDS

Currently, the Joint United Nations Programme on HIV/AIDS (UNAIDS) estimates that 16.6 million children between the ages of 0- and 17-years-old have been orphaned by AIDS, an increase from 10.0 million in 2001 (UNAIDS, 2010). Perhaps the most obvious impact of parental HIV/AIDS is the risk of orphanhood for these children. Contrary to colloquial usage, UNAIDS (2011) defines an HIV/AIDS orphan as a child who has lost one or both parents to HIV/AIDS. In addition to the bereavement and grief that may be expected following the loss of a parent, a large body of research has established that AIDS-orphaned youth are at risk for a wide range of negative physical, psychological, and socioeconomic outcomes (Sherr et al., 2008). Children orphaned by HIV/AIDS may lack parental care and supervision, experience stigma within communities, and have limited access to financial resources (Foster & Williamson, 2000; Ntozi, Ahimbisibwe, Odwee, Ayiga, & Okurut, 1999). These orphanhood outcomes can be substantial barriers to school enrollment and school achievement, particularly in low- and middle-income countries.

In recent years, there has been increasing recognition that children of parents living with HIV/AIDS, referred to as ‘vulnerable children’, also encounter significant stressors for years prior to the death of the parent (Mishra & Bignami-Van Assche, 2008). Families impacted by HIV/AIDS must grapple with the uncertain clinical course of the virus, face the future losses to come, and ultimately experience grief as loved ones die (Chi & Li, 2013; Lin et al., 2014). Families frequently encounter economic hardship when affected adults are unable to work, and healthy family members must provide caregiving for those who are ill rather than engage in income-generating endeavors. Such shifts in roles also impact children; girls and older children in the home may be forced to take on expanded, adult roles far earlier than is typical, including caring for younger siblings, nursing sick adults, and assisting with household chores and/or farming (Gilborn, 2002). These additional demands frequently exact a large toll on school opportunities.

Impact of parental HIV/AIDS on education

In a review of the global literature on the impact of HIV/AIDS on educational outcomes, Guo, Li, and Sherr (2012) describe 23 existing studies that have included at least one educational outcome variable. Data broadly suggest that parental HIV/AIDS yields significant educational disadvantage for children, ranging from lower school enrollment, poorer daily attendance, and internalizing and externalizing behaviors that negatively impact learning. These are ultimately linked with lower likelihood of school completion and less educational attainment (Guo et al., 2012). The effects of HIV/AIDS on school experiences and trajectories are shaped by a multitude of contextual factors including timing of parental illness and death; socio-economic status of family and community; societal and gender norms; and characteristics of the local educational system. Household economic status in particular may be an important mediator of later educational outcomes; however, few longitudinal studies have been conducted, making it difficult to parse apart contributing individual and contextual factors (Ainsworth & Filmer, 2006).

In addition, the majority of previous investigations have been limited in the number and scope of educational variables included as outcome measures. Previous efforts to explore the effects of parental HIV/AIDS on school experiences have typically focused on two constructs: Enrollment and attendance (Guo et al., 2012). Measures that emphasize school ‘quantity’ fail to capture the complexities of the school experience and provide little information about the quality of education and student satisfaction. Other potential variables of interest include measuring student performance (e.g., grades, standardized test scores, courses passed), academic engagement (e.g., compliance with school expectations, participation in extracurricular activities, leadership positions), and positive attitudes toward school (e.g., feelings of belonging, high educational expectations, positive relationships with staff and students) (Hammond, Smink, & Drew, 2007).

Education as a protective factor

When the definition of ‘educational outcome’ is expanded, a clearer picture emerges of impacts and needs of this vulnerable group. In one of the few investigations that has examined school performance and school behavior of children affected by parental HIV/AIDS, Tu and colleagues (2009) investigated the impact of HIV/AIDS on school experiences by comparing AIDS orphans, vulnerable children (i.e., living with HIV-infected parent), and comparison children. They found a dose-response relationship between HIV/AIDS status and academic achievement, with orphaned children receiving the lowest academic marks, comparison children the highest marks, and vulnerable children in the middle. All groups of children expressed similar desires to attain high education levels. However, when teachers were questioned about their educational expectations for children, teachers expected orphaned children to achieve the lowest end levels of education and comparison children the highest. Similarly, teachers reported that significantly fewer orphans and vulnerable children held leadership positions within the school than comparison children.

Fostering improved school outcomes for children made vulnerable by parental HIV/AIDS may be a pathway to improved well-being later in life, and thus an important protective factor for this at-risk group. Education is widely recognized to convey significant societal as well as individual benefits, and to have many positive effects on lifetime well-being. Higher levels of education are strongly linked with income levels, and earnings increase with each additional level of education one receives (Organisation for Economic Co-Operation and Development, 2008). Given the economic challenges that exist for many families affected by HIV, improved financial security may be particularly salient for this group.

Beyond pecuniary benefits, education is also among the strongest correlates of two of the most commonly used indicators of successful aging: Health and life satisfaction (Meeks & Murrell, 2001). The mechanisms though which education yields improved health are wide ranging, and include increased engagement in healthy behaviors, reductions in unhealthy and/or risky behaviors, and increased use of preventative health services (Feinstein, Sabates, Anderson, Sorhaindo, & Hammond, 2006). It is also likely that increased education yields improvement in a number of executive functioning skills (e.g., self-monitoring, self-regulation, organization, planning) that have later pay-offs in terms of improved decision making. Global evaluations of self are linked with higher education – most notably subjective well-being (e.g., satisfaction with one’s relationships or work role) as well as affective states (i.e., the presence of positive emotions and absence of unpleasant affect) (Diener, Sapyta, & Suh, 1998; Yakovlev & Leuizamon, 2012). Thus, education seems to generally promote happier, healthier lifestyles and improved psychosocial well-being.

Resilience

Positive school experiences may then be a key component of ‘resilience’ for children affected by parental HIV/AIDS, and schools a logical target of intervention. Resilience is typically characterized as a dynamic process that allows an individual to positively adapt in the face of adversity (Luther, Cicchetti, & Becker, 2007; Masten & Narayan, 2012). Resilience recognizes the potential of individuals to respond in a positive and adaptive manner when their stability, sustainability, or development is threatened (Masten, 2011). Based upon a socio-ecological model, resilience is theorized to operate at several levels, including the child, family, and community. At these different levels, numerous individual (i.e., intrapersonal assets) and contextual factors (i.e., supportive systems surrounding the individual) interact with each other in order to shape the trajectory of one’s response following adversity (Garmezy, 1985; Werner & Smith, 1992). Recently, a resilience framework specific to children affected by parental HIV/AIDS has been proposed (Li, Chi, Sherr, Cluver, & Stanton, 2015) that outlines a number of key protective factors for children of people living with HIV/AIDS and those orphaned by the disease. The model emphasizes support from the family as critical for promoting positive outcomes. At the community level, school experiences are highlighted, including the importance of teacher support, adult mentorship, and effective school experiences (Li et al., 2015).

Though resilience offers a useful framework for examining positive adaptation in the context of parental HIV/AIDS, to this point there is a lack of evidence-based interventions that seek to improve resilience among this population. Further, it is unknown whether resilience-based interventions can improve school outcomes for children made vulnerable by parental HIV infection. Thus, with support of the National Institutes of Health, we developed and implemented a multi-level, resilience-based psychosocial intervention (i.e., ChildCARE) for children affected by parental HIV/AIDS, their caregivers, and their communities. We then evaluated effects on school-based outcomes at 6-, 12-, and 18-months.

Method

Study site

The current study was conducted from 2012–2016 in the Henan province in central China where a large HIV epidemic emerged in the early 1990s, primarily as a result of unhygienic commercial blood and plasma collection practices. Recruitment of affected children and caregivers was completed in 2012, and intervention components were then delivered, with biannual follow-ups lasting until 2016.

Participants

Using HIV surveillance data provided by provincial anti-epidemic stations in the Henan province, we identified five area villages with the highest rates of HIV infection. We then generated a list of families caring for children affected by parental HIV/AIDS in each of the five villages with assistance from local schools and local social welfare systems. Children were eligible for participation if they were between the ages of 6- and 17-years-old and had at least one biological parent (i.e., alive or deceased) who had HIV/AIDS. Children with known HIV infection were excluded from the study. Child and parental infection statuses were verified by caregivers and/or community leaders responsible for this information. From a list of eligible participants, random selection was used to invite children/caregivers to participate in the study in progressive fashion until the target sample size was reached (i.e., ∼800 child/caregiver dyads). Participating children were clustered into four groups based on where they lived, and these four community clusters served as the units of randomization, with each cluster randomly assigned to either the control group or one of the three intervention groups. Control or intervention material was then delivered to children through their local village schools within each community cluster. A total of 30 schools were involved in the study. Children received intervention content through small school-based peer-groups (N = 45), with a mean of 17.5 children affected by parental HIV in each group. Peers were grouped based on similar grade-level, and intervention content modified slightly to be developmentally appropriate for younger and older groups. Use of a cluster randomization design has been shown to be less vulnerable to intervention contamination across groups and a number of other steps were undertaken to reduce the possibility of intervention content being delivered to control subjects (e.g., separate facilitators for intervention and control conditions) (Torgerson, 2001).

A total of 790 children, aged 6- to 17-years-old participated in the study, and the sample consisted of 382 girls (48.4%) and 408 boys (51.6%) (see Table 1). The majority of children (94.4%) were between the ages of 8 and 15, and mean age was 10.51 years (SD = 1.99). About 97% of the sample was of Han ethnicity, the primary ethnicity in the region. Children reported an average of 1.77 siblings (SD = 1.20). Children also provided information about parental education; 59.3% of fathers and 58.2% of mothers were reported to have less than a high school education, with 8.4% of parents having had no formal schooling whatsoever. The most endorsed parental occupations were farmers (46.2% of mothers; 32.9% of fathers) and migrant workers (28.3% of mothers; 42.6% of fathers).
Table 1.
Demographic characteristics by intervention assignment.

Intervention arms

Overall Control Child-only Child + caregiver Child + caregiver + community

N(%) 790 (100%) 195 (24.7%) 200 (25.3%) 198 (25.1%) 197 (24.9%)

Boys 408 (51.6%) 113 (57.9%) 96 (48.0%) 104(52.5%) 95 (48.2%)

Mean age (SD) 10.51 (1.99) 10.62(2.07) 11.23(2.30) 10.39(1.64) 9.77 (1.59)

Han ethnicity 757 (99.2%) 188 (98.4%) 195(99.5%) 187 (98.9%) 188 (98.4%)

# of siblings 1.77 (1.20) 1.80 (0.88) 1.72 (1.10) 1.81 (1.20) 1.74 (1.29)

Parental vital status

Single infection 563 (72.6%) 134 (73.6%) 129 (64.5%) 135 (68.2%) 165 (84.2%)

Double infection 117 (15.1%) 19 (10.4%) 46 (23.0%) 39 (19.7%) 13 (6.6%)

Single death 72 (9.3%) 22 (12.1%) 20 (10.0%) 15 (7.6%) 15 (7.7%)

Double death 24 (3.1%) 7 (3.8%) 5 (2.5%) 9 (4.5%) 3 (1.5%)

Familial HIV death & infection

# of deaths in household 0.45(0.78) 0.52 (0.88) 0.41 (0.78) 0.48 (0.70) 0.39 (0.72)

# of infections in household 1.07 (1.01) 1.14 (1.16) 1.18 (0.86) 1.15 (0.98) 0.80 (0.97)

# of deaths among relatives 0.42 (1.05) 0.49 (1.01) 0.45 (1.01) 0.40 (1.24) 0.34 (0.91)

# of infections among relatives 0.81 (1.52) 0.91 (1.54) 0.90 (1.42) 0.68 (1.26) 0.74 (1.82)

Father’s education

No formal schooling 19 (2.4%) 2 (1%) 6 (3.0%) 5 (2.5%) 6 (3.1%)

Elementary 255(32.5%) 66 (34.2%) 67 (33.5%) 66 (33.3%) 56 (29.0%)

Middle 191(24.4%) 50 (25.9%) 73 (36.5%) 37 (18.7%) 31 (16.1%)

High school 61 (7.8%) 16 (8.3%) 10 (5.0%) 17 (8.6%) 18 (9.3%)

Post-secondary 51 (6.5%) 12 (6.2%) 8 (4.0%) 15 (7.6%) 16 (8.3%)

Don’t know 207 (26.4%) 47 (24.4%) 36 (18.0%) 58 (29.3%) 66 (34.2%)

Mother’s education

No formal schooling 46 (6.0%) 11 (5.8%) 12 (6.1%) 14 (7.1%) 9 (4.8%)

Elementary 249 (32.3%) 64 (33.7%) 71 (35.9%) 63 (32.1%) 51 (27.1%)

Middle 154 (19.9%) 116 (19.9%) 55 (27.8%) 31 (15.8%) 30 (16.0%)

High School 59(7.6%) 16 (8.4%) 12(4.1%) 15(7.7%) 16 (8.5%)

Post-secondary 46 (6.0%) 8 (4.2%) 9 (4.5%) 12 (6.1%) 17 (9.0%)

Don’t Know 218 (28.2%) 53 (27.9%) 39 (19.7%) 61 (31.1%) 65 (34.6%)*

Note: p < 0.05; p < 0.01; p < 0.001; ****p < 0.0001.

Approximately 12.4% (N = 98) of the children in our sample were HIV/AIDS orphans (i.e., had lost one or both parents to HIV/AIDS). Single orphans constituted 9.3% of the sample (N = 72), and double orphans were 3.1% of the sample (N = 24). Approximately 87.7% (N = 680) of the children reported that one (72.6%) or both (15.1%) parents were currently living with HIV/AIDS. Children reported an average of 1.07 (SD = 1.01) HIV-infected individuals within their household, and 0.81 (SD = 1.52) infections among other relatives.

Child-caregiver-advocacy resilience (ChildCARE) intervention

The Child-Caregiver-Advocacy Resilience (ChildCARE) intervention seeks to build internal resilience of the child (e.g., coping, positive hope for future) as well as improve key support systems around the child (i.e., family and community). The development of ChildCARE was guided by a theoretical framework that integrated theories of human development with the distinctive needs and experiences of children made vulnerable by HIV/AIDS (Bronfenbrenner, 1979). Specifically, the intervention design was informed by Bronfenbrenner’s (1979) socio-ecological model of human development and broad perspectives on resilience (Luther et al., 2000; Masten, 2011; Ungar, Ghazinour, & Richter, 2013). These theories influenced the development of a resilience-based psychosocial intervention that targets multiple systems and seeks to increase protective factors both within an individual and across systems. Intervention content was adapted to the unique psychosocial needs of children affected by HIV/AIDS and the particular cultural context of the target population (Li, Naar-King, Barnett, Stanton, Fang, & Thurston, 2008).

The ChildCARE intervention consists of programming at three levels: Child, caregiver, and community. At the child level, facilitators with training in psychology or education led ten sessions (i.e., total of 20 hours) of small peer-group activities designed to promote the development of personal resilience characteristics for children affected by parental HIV/AIDS, including positive thinking, emotional regulation, coping skills, problem solving, support seeking, positive future orientation, and enhanced self-esteem. Because the intervention was delivered to peer groups based on child’s grade level, intervention content could be modified to be developmentally appropriate for the broad age range of participating children. At the caregiver level, trainers conducted four sessions (i.e., total of ten hours) of positive parenting training for caregivers and/or parents of affected children. The training targeted effective parenting skills, including conflict resolution, positive behavior management, and open communication. At the community level, trained community advocates made monthly home visits to participating families and organized a series of community activities to promote cohesion and encourage support at the local village level. To ensure treatment integrity, facilitators completed a fidelity process form at the end of each intervention session to provide information about what content was delivered during the session, length of the session, and attendance/participation. In addition, all intervention sessions were audio-recorded, and 20% of sessions randomly selected for review after project completion to verify that there were no significant deviations from intervention protocol.

Study design

The current study employed a community-based, four-arm cluster randomized control trial design. The 790 children and their parent/caregiver (i.e., child-caregiver dyad) were randomly assigned to one of four intervention conditions: Child intervention (N = 200 dyads), child + caregiver intervention (N = 198 dyads), child + caregiver + community intervention (N = 197 dyads), or control (N = 195). Because of resource constraints, the three intervention components were delivered in successive fashion in the following manner: Child intervention was delivered between baseline and 6-month follow-up assessment, caregiver intervention was delivered between 6-month and 12-month follow-up assessments, and community intervention was delivered after 12-month follow-up.

This intervention delivery schedule provides the opportunity to assess effects of the child-only intervention compared to the control group at the 6-month follow-up. At the 12-month follow-up, a comparison of child-only, child+caregiver, and control groups is possible. Finally, the 18-month follow-up provides the opportunity to compare the child-only, child + caregiver, child + caregiver + community, and control arms. The current study reports longitudinal follow-ups at 6-, 12-, and 18-months in order to examine short-term effects of the various interventions on self-reported school outcomes and experiences.

Procedure

As this paper examines short-term school outcomes, only data collection procedures relevant to this data set will be reported. At baseline, children completed survey questionnaires to assess demographic information and school outcome variables. Questionnaires were administered to participating children either individually or in small groups in the presence of two trained research assistants. Around 2% of the sample had difficulty reading either due to young age or reading difficulties; for these participants, interviewers read survey items aloud in a private room and recorded participants’ oral responses. These survey procedures were repeated at 6-, 12-, and 18-month follow-ups. All children received small, age-appropriate gifts following participation as a token of appreciation.

Ethical considerations

All participants were provided with descriptions of the study design and informed of potential benefits and risks (including risks to confidentiality) prior to participation. Children and caregivers then provided appropriate assent/consent prior to participation. The research protocol and consent procedures were approved by Institutional Review Boards at Henan University, University of South Carolina, and Wayne State University.

Measures

Demographic characteristics

Children provided information on their age, gender, ethnicity, household composition (e.g., caregivers, siblings), parental education level, and parental occupation. They also provided information about HIV infections and deaths within their household (i.e., immediate family) and among extended family.

Academic performance

Children were asked to provide information about their overall academic performance by reporting their course grades for the previous school semester on a five-point scale. Possible responses included ‘5’ (most greater than 90 or mostly ‘As’ in letter grade), ‘4’ (most between 80 and 89 or mostly ‘Bs’), ‘3’ (most between 70 and 79 or mostly ‘Cs’), ‘2’ (most between 60 and 69 or mostly ‘Ds’), and ‘1’ (most grades less than 60 or mostly ‘Fs’).

School satisfaction

Children provided information about how satisfied they were with their overall school experiences using a five-point Likert scale with responses ranging from 1 (strongly dissatisfied) to 5 (strongly satisfied).

School interest

School interest was measured by a six-item school interest subscale adopted from the Child Rating Scale (CRS), a socioemotional self-report scale for children (Hightower et al., 1987). The scale was forward and back translated by a team of US and Chinese researchers fluent in both Mandarin and English, following a standard protocol (Li et al., 2009). Sample items from the school interest subscale include, ‘I like to do school work’ and ‘School is fun’. Children rated each item on a four-point scale with possible responses ranging from (1 = does not describe me at all to 4 = totally describes me). Higher scores on the subscale indicated greater interest in school. Chronbach’s alpha coefficients at each wave of the study were 0.57 at baseline, 0.55 at 6-months, 0.62 at 12-months, and 0.62 at 18-months.

Educational aspiration

Children were asked to think about the highest level of school that they expected to complete. They provided responses on a six-point Likert scale that included responses of 1) elementary/middle school, 2) high school, 3) vocational school (i.e., correspondence school, night school), 4) college, 5) master’s degree, 6) doctoral degree. Higher scores indicated higher levels of educational aspiration.

Extracurricular interest

Students’ interest in extracurricular activities was measured by five items adopted from the Children’s Loneliness Scale (CLS; Asher, Hymel, & Renshaw, 1984). The measure gauged children’s interest in extra-curricular activities likely to be included in the school curriculum. Children were asked to responds to items such as ‘I like music’ and ‘I like to paint and draw’. Children responded to items on a 1 (does not suit me at all) to 4 (suits me totally) scale, with higher scores indicating greater interest in extracurricular activities. Chronbach’s alpha coefficients at each wave of the study indicated adequate internal consistency, with 0.63 at baseline, 0.62 at 6-months, 0.64 at 12-months, and 0.67 at 18-months.

Statistical analysis

First, descriptive statistics were used to compute demographic means for control and intervention groups, and analysis of variance was used to examine the difference among the study arms at 6-, 12-, and 18-months (Table 1). Next, descriptive statistics were calculated for all school outcomes (Table 2).
Table 2.
Means and Standard Deviations of school outcomes by intervention group at baseline, 6-months, 12-months, and 18-months.

Academic performance
School satisfaction
Educational aspiration
School interest
Extracurricular interest

Mean Std Mean Std Mean Std Mean Std Mean Std

Baseline

Control 3.158 1.22 3.305 1.132 3.282 1.761 18.736 3.765 3.252 0.573

Child-only 3.21 1.273 3.196 1.062 3.521 1.787 18.96 3.475 3.313 0.623

Child + caregiver 3.379 1.253 3.536 1.073 3.661 1.88 18.586 3.8 3.275 0.619

Child + caregiver +community 3.644 1.306 3.778 1.088 3.782 1.902 19.266 3.507 3.248 0.649

6-months

Control 2.873 1.31 3.169 1.11 3.119 1.736 18.271 3.533 3.37 0.545

Child-only 2.967 1.445 2.984 1 3.457 1.73 18.353 3.155 3.363 0.534

Child + caregiver 3.131 1.294 3.253 1.156 3.733 1.865 18.733 3.312 3.304 0.647

Child + caregiver +community 3.545 1.282 3.528 1.106 3.625 1.875 18.688 3.556 3.261 0.635

12-months

Control 2.71 1.334 2.962 1.077 3.423 1.803 18.783 3.514 3.349 0.62

Child-only 2.916 1.287 2.912 1.008 3.378 1.728 19 3.321 3.315 0.58

Child + caregiver 2.876 1.338 2.92 1.106 3.57 1.938 18.575 3.673 3.253 0.614

Child + caregiver +community 3.632 1.207 3.557 0.988 3.844 1.825 19.057 3.374 3.226 0.636

18-months

Control 2.891 1.309 3.389 0.957 3.452 1.857 18.43 3.717 3.252 0.58

Child-only 3 1.329 2.902 0.957 3.699 1.747 18.394 3.162 3.305 0.582

Child + caregiver 3.223 1.193 3.163 1.03 3.738 1.837 18.471 3.262 3.355 0.606

Child + caregiver +community 3.817 1.137 3.651 1.03 4.189 1.809 19.17 3.533 3.165 0.642

Primary analysis utilized linear mixed effects modeling that adjusted the correlation among the repeated measures (i.e., repeated measures nested within child; child nested within school-based peer group). In the linear mixed effects model, time invariant variables included child age, gender and number of HIV infections in the child’s household at the baseline. Time was denoted as study period (i.e., 0, 6-, 12-, and 18-months). The intervention effect by time is considered to be the interaction between the time-point and intervention arm. Multiple linear mixed models were used to examine intervention effects of three different intervention groups (i.e., child-only, child + caregiver, child + caregiver + community) at 6-, 12, and 18-month time-points. Data were analysed using SPSS version 17.0 (SPSS, 2008) and SAS 9.4 (SAS Institute, 2013).

Results

Sample characteristics and attrition analysis

Descriptive statistics for the sample are presented in Table 1. There were no significant differences in terms of gender, ethnicity, or experience of parental death between the control group (N = 195) and the three intervention arms (N = 595) at baseline. The control and three intervention groups differed significantly in terms of age of children, household infections, and experience of single parental infection. Bonferroni pairwise comparison among groups showed that the child-only group was significantly older than the control group, and the child + caregiver + community group was significantly younger than the control group. The Bonferroni pairwise comparison also showed that children enrolled in the child + caregiver + community group reported significantly more single parental infections and significantly less infections among other members of the household than children in the control group. Attrition remained low throughout the various study waves, with 95.0% of children completing assessments at 6-months, 86.3% at 12-months, and 84.1% at 18-months.

Effects of the ChildCARE intervention on school outcomes over time

Results of the mixed effects regression model are presented in Table 3 and a plot of estimate of differences between control and intervention groups at 18 months are shown in Figure 1. Examining the interaction effects between intervention group and time allows for analysis of trends of improvement that emerged after exposure to the intervention. These interaction effects (i.e., childtime, child + caregivertime, child + caregiver + communitytime) in the mixed effect model suggest significant changes over time for children enrolled in intervention arms for a number of school outcome variables including academic performance, school satisfaction and school interest; no significant effects of intervention were detected for the outcome variables of educational aspiration or extracurricular interest.
Table 3.
Fixed and random effect parameters for school outcomes at 6-, 12-, and 18-months.

Academic performance
School satisfaction
Education aspiration
School interest
Extracurricular interest

Variable β std β std β std β std β std

At 6-months

Fixed effects

# of infections −0.033 0.040 −0.063 0.030 0.025 0.022 −0.171 0.105 0.006 0.018

Age −0.195 0.020 −0.185 0.015 −0.067 0.011 0.033 0.054 0.009 0.009

Gender −0.168 0.079* 0.037 0.060 −0.022 0.044 −0.591 0.210 0.145 0.035

Child 0.192 0.107^a 0.144 0.089 0.060 0.063 0.177 0.297 0.032 0.051

Time −0.057 0.017* −0.027 0.017^a −0.013 0.011 −0.084 0.052 0.020 0.009*

Child*Time 0.020 0.019 −0.016 0.019 0.016 0.013 0.021 0.059 −0.013 0.010

Random effects (Intercept) 0.708 – 0.224 – 0.168 – 3.896 – 0.098 –

Random effects (Residual) 0.846 – 0.864 – 0.382 – 8.476 – 0.263 –

At 12-months

Fixed effects

# of infections −0.023 0.038 −0.073 0.028** 0.016 0.021 −0.212 0.097* 0.007 0.016

Age −0.195 0.019** −0.182 0.014 −0.072 0.011 −0.035 0.049 0.013 0.008

Gender −0.138 0.075^a 0.031 0.055 0.000 0.041 −0.730 0.193* 0.143 0.032**

Child 0.192 0.102^a 0.101 0.083 0.085 0.060 0.193 0.279 0.027 0.048

Child + caregiver 0.387 0.145 0.059 0.127 0.048 0.089 0.604 0.421 −0.185 0.074*

Time −0.062 0.011 −0.045 0.011 −0.004 0.007 −0.068 0.034* 0.017 0.006**

Child*Time 0.027 0.010** 0.007 0.010 0.004 0.007 −0.003 0.031 −0.010 0.006^a

Child + caregiverTime 0.037 0.015 0.029 0.014* 0.005 0.010 0.137 0.046 −0.016 0.008^a

Random effects (Intercept) 0.735 – 0.273 – 0.185 – 4.002 – 0.094 –

Random effects (Residual) 0.832 – 0.792 – 0.365 – 8.206 – 0.269 –

At 18-months

Fixed effects

# of infections −0.041 0.036 −0.078 0.026 0.010 0.019 −0.257 0.092 0.008 0.015

Age −0.205 0.018 −0.184 0.013 −0.078 0.010** −0.089 0.047* 0.011 0.008

Gender −0.130 0.072^a 0.057 0.051 0.007 0.039 −0.753 0.183 0.136 0.030

Child 0.243 0.098* 0.176 0.077* 0.075 0.056 0.200 0.266 0.001 0.046

Child + caregiver −0.331 0.204 −0.370 0.189^a −0.106 0.131 −0.603 0.621 −0.173 0.112

Child + caregiver + community 0.822 0.146**** 0.286 0.131* 0.230 0.092* −0.276 0.435 −0.098 0.078

Time −0.050 0.009** −0.029 0.009 −0.005 0.006 −0.065 0.029* 0.012 0.005*

ChildTime 0.011 0.007^a −0.014 0.007 0.006 0.005 −0.009 0.021 −0.003 0.004

Child + caregiverTime 0.138 0.041 0.076 0.040 0.032 0.027 0.328 0.128* −0.010 0.023

Child + Caregiver + community * Time 0.012 0.017 0.023 0.017 0.000 0.011 −0.144 0.055** −0.001 0.010

Random effects (Intercept) 0.739 – 0.267 – 0.183 – 4.038 – 0.096 –

Random effects (Residual) 0.779 – 0.760 – 0.348 – 7.978 – 0.267 –

Note: p < 0.05; p < 0.01; p < 0.001; ****p < 0.0001; ^ap < 0.10.

Figure 1.
Estimate effects of differences between control and intervention groups at 18-months.

Academic performance

Both child-only and child + caregiver intervention arms displayed improvements in academic performance following intervention implementation as evidenced by significant interaction effects between intervention condition and time (i.e., childtime; child + caregivertime) at various time-points. Specifically, while there was no significant interaction effect for the child-only intervention and time at 6-months (p > 0.05; first time-point of assessment for child-only intervention), a significant interaction effect emerged at 12-months (p < 0.01), but was not maintained at 18-months (p < 0.10). The interaction effect of the child + caregiver intervention and time was significant for academic performance at 12-months (p < 0.05; first time-point of assessment for child + caregiver intervention) and at 18-months (p < 0.05). The interaction effect of the child + caregiver + community intervention and time was non-significant at 18-months (p > 0.05; first time-point of assessment for child + caregiver + community intervention).

School satisfaction

Interaction between intervention and time also suggests significant improvements in school satisfaction for child-only and child + caregiver intervention arms following exposure to the intervention. While there was no significant interaction effect for child-only intervention and time at 6-months (p > 0.05) or 12-months (p > 0.05), by 18-months a significant child-only and time interaction effect (p < 0.05) was present for school satisfaction. A significant interaction effect was also present for child + caregiver and time at 12-months (p < 0.05), though neither the child + caregiver nor child + caregiver + community intervention arms showed significant interaction effects with time at 18-months (p > 0.05).

Educational aspiration

Interactions between intervention group and time revealed no statistically significant effects for educational aspiration following exposure to any intervention arms at 6-, 12-, or 18-months (p > 0.05 for all intervention and time interaction effects).

School interest

Interactions between intervention group and time indicated no significant improvements in school interest for the child-only group at any time-point (p > 0.05). However, the interaction effect of child + caregiver and time was significant for school interest at 12-months (p < 0.01) and at 18-months (p < 0.05). In addition, the interaction effect of child + caregiver + community group and time was significant for school interest at 18-months (p < 0.01).

Extracurricular interest

Interaction effects of intervention arm and time indicated no statistically significant improvements in extracurricular interest for the child-only, child + caregiver, or child + caregiver + community intervention groups at 6-, 12-, or 18-months (p > 0.05 for all intervention arm and time interaction effects). Negative effects were found at several time-points, though these were non-significant (p > 0.05).

Additional fixed effects

For fixed effects in the model, age was found to have a significant negative impact on academic performance (p < 0.0001 at 6-, 12-, and 18-months), school satisfaction (p < 0.0001 at 6-, 12-, and 18-months), and educational aspiration (p < 0.0001 at 6-, 12-, and 18-months). Age yielded no significant effect on school interest at 6- or 12-months or on extracurricular interest at any point. Age yielded a significant negative effect on school interest at 18-months (p < 0.05). Number of household infections did not have a significant impact (p > 0.05) on academic performance, educational aspiration, or extracurricular interest. However, number of household infections did have a significant negative impact on school satisfaction across time (p < 0.05 at 6-months; p < 0.01 at 12- and 18-months). Number of household infections also had a significant negative impact on school interest at the two latter time-points (p < 0.05 at 12-months, p < 0.01 at 18-months). Finally, gender had a significant effect on academic performance, with girls reporting lower grades (p < 0.05 at 6-months). Gender had a significant impact on school interest, with girls expressing less interest in school (p < 0.01 at 6-months; p < 0.001 at 12-months; p < 0.0001 at 18-months). Gender also had a significant impact on extracurricular interest, with girls reporting greater interest in these activities (p < 0.0001 at 6-, 12-, and 18-months).

Discussion

To our knowledge, this is the first study to examine whether a resilience-based intervention for children affected by parental HIV/AIDS yields changes in school outcomes. After controlling for differences in age, gender, and number of family members infected with HIV/AIDS, children enrolled in the ChildCARE intervention made gains in a number of key school variables important for educational success. Over an 18-month period, children enrolled in the program reported significant improvements in academic achievement, school satisfaction, and school interest. Findings highlight the need to expand the conceptualization of school success by employing multiple outcome variables, so the multi-faceted impacts of parental HIV on school experiences can be better understood. Most importantly, they demonstrate the efficacy of a resilience-based approach in promoting school well-being for this vulnerable population.

Due to the use of a four-arm randomized trial and a time-lagged delivery of intervention components, we were able to evaluate contributions of the various program components: Child peer-group sessions, caregiver training, and community advocacy, at various time-points. Trends in the analysis support the additive effects of layering intervention components, as effects were more likely to accumulate both over time and when children received multiple components of the intervention. For instance, participation in both child and caregiver arms of the intervention yielded greater effects on school satisfaction than participating in the child intervention alone, and these effects became stronger over time. The lag time for many of the significant intervention effects demonstrates the importance of employing a longitudinal approach, and future studies would do well to evaluate whether intervention effects emerge and/or persist at even later time points (e.g., two or three years post-intervention).

These findings are promising, especially in light of the fact that ChildCARE does not directly target academic skills and provides no services to teachers or other school faculty. Instead, the program appears to improve school outcomes indirectly, through enhancing internal resilience characteristics of the child and strengthening key caregiver and community support systems. The child intervention targets children’s skills in problem solving, seeking support, regulating emotions, and increasing positive cognitions. These lessons may yield behavioral, emotional, and/or cognitive changes that are responsible for the improvements in academic performance and school satisfaction. It is possible that that school achievement, interest, and satisfaction improve because of enhancements to children’s overall psychosocial well-being. Mental health is a known predictor of school outcomes (DeSocio & Hootman, 2004; Murphy et al., 2015), thus as children’s psychosocial well-being is improved, greater school performance may follow. However, it also may be the case that the intervention produces specific ‘academic resiliencies’, including academic grit—the tendency of a child to sustain interest, passion, effort, and persistence in the pursuit of a long-term goal (Duckworth, Peterson, Matthews, & Kelly, 2007) or academic buoyancy – the capacity to overcome the setbacks and challenges that are part of the everyday academic life (Martin, 2013). In addition, the child intervention targets children’s self-efficacy in the context of parental HIV, but this may also produce increases in their academic self-efficacy, a known predictor of broader educational resilience (Cassidy, 2015).

Extending beyond the child, enhancing caregiver support appears to produce academic ‘pay-offs’ for HIV-affected children. Caregivers exert enormous influence on a child’s educational opportunity and advancement, and emotional support from caregivers has been found to be significant predictor of academic motivation and performance (Gregory & Weinstein, 2004; Spera, 2006; Wentzel, Russell, & Baker, 2016). Given the myriad of challenges faced by children affected by parental HIV, improving the capacity of caregivers to provide emotional support, communicate clearly, and parent effectively likely produce benefits that extend beyond traditional psychosocial outcomes. At the community level, interventions targeting the wider support system in HIV-affected areas have been successful at evoking a wide range of changes in behaviors, knowledge, and attitudes regarding HIV/AIDS (Salam, Haroon, Ahmed, Das, & Bhutta, 2014). Current findings provide evidence that the community advocacy can also yield increases in school interest for vulnerable children. Because of the time lagged delivery, effects of the community intervention could only be assessed at one time-point, and additional research is needed to determine whether community intervention produces other changes in school outcome at later time points.

Conclusions and limitations

This efficacy evaluation provides initial evidence that a resilience-based, multi-level intervention produces improvements in school outcomes for children affected by parental HIV. Our findings suggest that strengthening key support systems and the internal resiliency of vulnerable youth can contribute to better school performance, as well as greater school satisfaction and interest. A number of limitations should be considered in the interpretation of our findings. First, this intervention targeted families and communities of rural, central China. Cultural characteristics and the unique nature of the HIV epidemic within the region may limit the generalizability of findings. Implementation of the ChildCARE intervention in other populations will require appropriate cultural tailoring.

A second area of limitation involves our measurement tools. We relied upon child self-report data to gather information on school performance and other variables. Future studies may wish to include teacher ratings, school records, and even school behavioral data to further validate reported improvements. Future studies should also attempt to collect more information about school environment in order to control for any pre-existing differences in key school variables that may be linked to students’ later educational outcomes. Schools in mainland China tend to be quite homogenous as they operate under the traditional ‘hukou’ or household registration system that maintains a highly homogenous student population (Afridi, Li, & Ren, 2015). In addition, the education system in China is hierarchically stratified, with top-down decision making flowing from the Ministry of Education in Beijing to provincial education departments; little control exists at the individual school level, promoting homogeneity and a highly uniform system in terms of school environment, curriculum, and school outcomes within a province (Ng & Pun, 2013; Xiaong, 2015). Nonetheless, future studies should seek to collect information on these important variables to reduce the risk of confounding variables when examining efficacy of school-based interventions employing multiple school sites.

A number of measures were also limited by their brevity. Our measures of school interest and extracurricular interest displayed relatively low internal reliability. This is not surprising as brief measures often yield low Chronbach’s alphas, since alpha is dependent upon the magnitude of correlations among items, as well as the number of items (Streiner & Norman, 1989). Our measure of educational aspiration relied upon one question to assess how far in school children aspired to progress. In addition to the typical limitations of single-item scales, this measure displayed a ceiling effect as most children reported expecting to graduate from college, which may help to explain the lack of significant effects in this area. There is a need to continue to develop and employ multi-item school scales validated for use with Chinese children to provide a fuller picture of their school experiences, attitudes, and perceptions.

Implications for school psychologists

The current study yields two major lessons for school psychologists. First, school psychologists should be alert that parental HIV conveys significant educational disadvantage for children, ranging from low school enrollment, poor attendance, and a number of internal and external behaviors that can negatively impact learning (Guo et al., 2012). School psychologists have expertise in collecting and analysing data in order to monitor and intervene early for this at-risk population of students. School psychologists are well-equipped to provide evidence-based interventions if school performance suffers for a child affected by parental HIV/AIDS. They are also in an excellent position to facilitate family-school linkages, given their knowledge of family and school systems as well as their extensive training in consultation and collaboration.

Secondly, findings offer promise that, with relatively limited intervention, the academic risks for children who are made vulnerable through parental HIV/AIDS can be reduced. The ability of the brief (i.e., 20 hours) child intervention to yield improvements in academic performance and school satisfaction suggests the feasibility of employing components of ChildCARE within schools in regions of the world heavily impacted by the HIV epidemic. School-based delivery of the various components of the ChildCARE intervention may be particularly salient in resource-limited areas where community mental health services are scarce. School psychologists have the training and knowledge to adopt and effectively implement these types of resilience-based interventions, and should be on the frontlines of providing psychosocial and educational support for this vulnerable population of children.

	Intervention arms
N(%)	790 (100%)	195 (24.7%)	200 (25.3%)	198 (25.1%)	197 (24.9%)
Boys	408 (51.6%)	113 (57.9%)	96 (48.0%)	104(52.5%)	95 (48.2%)
Mean age (SD)	10.51 (1.99)	10.62(2.07)	11.23(2.30)	10.39(1.64)	9.77 (1.59)****
Han ethnicity	757 (99.2%)	188 (98.4%)	195(99.5%)	187 (98.9%)	188 (98.4%)
# of siblings	1.77 (1.20)	1.80 (0.88)	1.72 (1.10)	1.81 (1.20)	1.74 (1.29)
Parental vital status
Single infection	563 (72.6%)	134 (73.6%)	129 (64.5%)	135 (68.2%)	165 (84.2%)****
Double infection	117 (15.1%)	19 (10.4%)	46 (23.0%)	39 (19.7%)	13 (6.6%)
Single death	72 (9.3%)	22 (12.1%)	20 (10.0%)	15 (7.6%)	15 (7.7%)
Double death	24 (3.1%)	7 (3.8%)	5 (2.5%)	9 (4.5%)	3 (1.5%)
Familial HIV death & infection
# of deaths in household	0.45(0.78)	0.52 (0.88)	0.41 (0.78)	0.48 (0.70)	0.39 (0.72)
# of infections in household	1.07 (1.01)	1.14 (1.16)	1.18 (0.86)	1.15 (0.98)	0.80 (0.97)****
# of deaths among relatives	0.42 (1.05)	0.49 (1.01)	0.45 (1.01)	0.40 (1.24)	0.34 (0.91)
# of infections among relatives	0.81 (1.52)	0.91 (1.54)	0.90 (1.42)	0.68 (1.26)	0.74 (1.82)
Father’s education
No formal schooling	19 (2.4%)	2 (1%)	6 (3.0%)	5 (2.5%)	6 (3.1%)
Elementary	255(32.5%)	66 (34.2%)	67 (33.5%)	66 (33.3%)	56 (29.0%)
Middle	191(24.4%)	50 (25.9%)	73 (36.5%)	37 (18.7%)	31 (16.1%)
High school	61 (7.8%)	16 (8.3%)	10 (5.0%)	17 (8.6%)	18 (9.3%)
Post-secondary	51 (6.5%)	12 (6.2%)	8 (4.0%)	15 (7.6%)	16 (8.3%)
Don’t know	207 (26.4%)	47 (24.4%)	36 (18.0%)	58 (29.3%)	66 (34.2%)****
Mother’s education
No formal schooling	46 (6.0%)	11 (5.8%)	12 (6.1%)	14 (7.1%)	9 (4.8%)
Elementary	249 (32.3%)	64 (33.7%)	71 (35.9%)	63 (32.1%)	51 (27.1%)
Middle	154 (19.9%)	116 (19.9%)	55 (27.8%)	31 (15.8%)	30 (16.0%)
High School	59(7.6%)	16 (8.4%)	12(4.1%)	15(7.7%)	16 (8.5%)
Post-secondary	46 (6.0%)	8 (4.2%)	9 (4.5%)	12 (6.1%)	17 (9.0%)
Don’t Know	218 (28.2%)	53 (27.9%)	39 (19.7%)	61 (31.1%)	65 (34.6%)*

	Academic performance	School satisfaction	Educational aspiration	School interest	Extracurricular interest
Baseline
Control	3.158	1.22	3.305	1.132	3.282	1.761	18.736	3.765	3.252	0.573
Child-only	3.21	1.273	3.196	1.062	3.521	1.787	18.96	3.475	3.313	0.623
Child + caregiver	3.379	1.253	3.536	1.073	3.661	1.88	18.586	3.8	3.275	0.619
Child + caregiver +community	3.644	1.306	3.778	1.088	3.782	1.902	19.266	3.507	3.248	0.649
6-months
Control	2.873	1.31	3.169	1.11	3.119	1.736	18.271	3.533	3.37	0.545
Child-only	2.967	1.445	2.984	1	3.457	1.73	18.353	3.155	3.363	0.534
Child + caregiver	3.131	1.294	3.253	1.156	3.733	1.865	18.733	3.312	3.304	0.647
Child + caregiver +community	3.545	1.282	3.528	1.106	3.625	1.875	18.688	3.556	3.261	0.635
12-months
Control	2.71	1.334	2.962	1.077	3.423	1.803	18.783	3.514	3.349	0.62
Child-only	2.916	1.287	2.912	1.008	3.378	1.728	19	3.321	3.315	0.58
Child + caregiver	2.876	1.338	2.92	1.106	3.57	1.938	18.575	3.673	3.253	0.614
Child + caregiver +community	3.632	1.207	3.557	0.988	3.844	1.825	19.057	3.374	3.226	0.636
18-months
Control	2.891	1.309	3.389	0.957	3.452	1.857	18.43	3.717	3.252	0.58
Child-only	3	1.329	2.902	0.957	3.699	1.747	18.394	3.162	3.305	0.582
Child + caregiver	3.223	1.193	3.163	1.03	3.738	1.837	18.471	3.262	3.355	0.606
Child + caregiver +community	3.817	1.137	3.651	1.03	4.189	1.809	19.17	3.533	3.165	0.642

	Academic performance	School satisfaction	Education aspiration	School interest	Extracurricular interest
At 6-months
Fixed effects
# of infections	−0.033	0.040	−0.063	0.030*	0.025	0.022	−0.171	0.105	0.006	0.018
Age	−0.195	0.020****	−0.185	0.015****	−0.067	0.011****	0.033	0.054	0.009	0.009
Gender	−0.168	0.079*	0.037	0.060	−0.022	0.044	−0.591	0.210**	0.145	0.035****
Child	0.192	0.107^a	0.144	0.089	0.060	0.063	0.177	0.297	0.032	0.051
Time	−0.057	0.017***	−0.027	0.017^a	−0.013	0.011	−0.084	0.052	0.020	0.009*
Child*Time	0.020	0.019	−0.016	0.019	0.016	0.013	0.021	0.059	−0.013	0.010
Random effects (Intercept)	0.708	–	0.224	–	0.168	–	3.896	–	0.098	–
Random effects (Residual)	0.846	–	0.864	–	0.382	–	8.476	–	0.263	–
At 12-months
Fixed effects
# of infections	−0.023	0.038	−0.073	0.028**	0.016	0.021	−0.212	0.097*	0.007	0.016
Age	−0.195	0.019****	−0.182	0.014****	−0.072	0.011****	−0.035	0.049	0.013	0.008
Gender	−0.138	0.075^a	0.031	0.055	0.000	0.041	−0.730	0.193***	0.143	0.032****
Child	0.192	0.102^a	0.101	0.083	0.085	0.060	0.193	0.279	0.027	0.048
Child + caregiver	0.387	0.145**	0.059	0.127	0.048	0.089	0.604	0.421	−0.185	0.074*
Time	−0.062	0.011****	−0.045	0.011****	−0.004	0.007	−0.068	0.034*	0.017	0.006**
Child*Time	0.027	0.010**	0.007	0.010	0.004	0.007	−0.003	0.031	−0.010	0.006^a
Child + caregiver*Time	0.037	0.015*	0.029	0.014*	0.005	0.010	0.137	0.046**	−0.016	0.008^a
Random effects (Intercept)	0.735	–	0.273	–	0.185	–	4.002	–	0.094	–
Random effects (Residual)	0.832	–	0.792	–	0.365	–	8.206	–	0.269	–
At 18-months
Fixed effects
# of infections	−0.041	0.036	−0.078	0.026**	0.010	0.019	−0.257	0.092**	0.008	0.015
Age	−0.205	0.018****	−0.184	0.013****	−0.078	0.010****	−0.089	0.047*	0.011	0.008
Gender	−0.130	0.072^a	0.057	0.051	0.007	0.039	−0.753	0.183****	0.136	0.030****
Child	0.243	0.098*	0.176	0.077*	0.075	0.056	0.200	0.266	0.001	0.046
Child + caregiver	−0.331	0.204	−0.370	0.189^a	−0.106	0.131	−0.603	0.621	−0.173	0.112
Child + caregiver + community	0.822	0.146****	0.286	0.131*	0.230	0.092*	−0.276	0.435	−0.098	0.078
Time	−0.050	0.009****	−0.029	0.009**	−0.005	0.006	−0.065	0.029*	0.012	0.005*
Child*Time	0.011	0.007^a	−0.014	0.007*	0.006	0.005	−0.009	0.021	−0.003	0.004
Child + caregiver*Time	0.138	0.041*	0.076	0.040	0.032	0.027	0.328	0.128*	−0.010	0.023
Child + Caregiver + community * Time	0.012	0.017	0.023	0.017	0.000	0.011	−0.144	0.055**	−0.001	0.010
Random effects (Intercept)	0.739	–	0.267	–	0.183	–	4.038	–	0.096	–
Random effects (Residual)	0.779	–	0.760	–	0.348	–	7.978	–	0.267	–

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study described in this report was supported by NIH Research Grants # R01MH076488 and R01NR013466 by the National Institute of Mental Health and the National Institute of Nursing Research. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Mental Health or the National Institute of Nursing Research.

Author biographies

Sayward E. Harrison PhD is a Research Assistant Professor in the Department of Health Promotion, Education, and Behavior and Director of Research at the South Carolina SmartState Center for Healthcare Quality in the Arnold School of Public Health at the University of South Carolina. Dr Harrison is a pediatric school psychologist with six years of clinical experience practicing in school-based settings. Her research focuses on improving the well-being of children impacted by chronic and acute diseases. Current investigations include examining the mechanisms of resilience for children affected by HIV/AIDS and strengthening the linkages between family, school, and healthcare systems.

Xiaoming Li PhD is Professor of Health Promotion, Education, and Behavior, South Carolina SmartState Endowed Chair of Clinical Translational Research, and Director of the SmartState Center for Healthcare Quality at the University of South Carolina’s Arnold School of Public Health. Dr Li has been funded by the NIH to conduct HIV-related social and behavioral research in China since 2001. His current work addresses behavioral HIV prevention and intervention strategies among vulnerable populations and the psychosocial well-being of children and families affected by HIV/AIDS.

JiaJia Zhang PhD is Associate Professor at the University of South Carolina in the Department of Epidemiology and Biostatistics. Her research focuses on statistical methodology development and applied statistics. Specifically, Dr Zhang employs semiparametric statistical methods in survival data, spatial survival data, and longitudinal data. Applications of her work include disease-related cohort studies and intervention studies. Dr Zhang has previously conducted studies on cancer, HIV, nutrition, dental health, physical activity, and cardiovascular diseases.

Peilian Chi PhD is Assistant Professor at the University of Macau. Her research interests explore the interactions between interpersonal relationships and health, HIV prevention and intervention strategies, and the resilience of children and adolescents. She employs a variety of research methodologies in her work including qualitative interview, quantitative survey, and mixed method design.

Junfeng Zhao PhD is Professor of Psychology, Associate Dean of the College of Educational Sciences, and Director for the International Research Center for Psychosocial Wellbeing for Vulnerable Populations at Henan University in China. Dr Zhao’s research areas include child development, educational psychology, resilience development, and psychological measurement and evaluation. Dr Zhao was awarded a Global Funds grant in 2010 to study the emotional regulation of children affected by HIV in rural China.

Guoxiang Zhao PhD is Professor of Psychology and Senior Vice President of Henan University in Kaifeng, China. Dr Zhao is actively involved in both academic research and teaching. His areas of interest include child development, human development, and applied psychology. Dr Zhao also investigates psychological and educational measurement and evaluation techniques in China.

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			Intervention arms
	Overall	Control	Child-only	Child + caregiver	Child + caregiver + community
N(%)	790 (100%)	195 (24.7%)	200 (25.3%)	198 (25.1%)	197 (24.9%)
Boys	408 (51.6%)	113 (57.9%)	96 (48.0%)	104(52.5%)	95 (48.2%)
Mean age (SD)	10.51 (1.99)	10.62(2.07)	11.23(2.30)	10.39(1.64)	9.77 (1.59)****
Han ethnicity	757 (99.2%)	188 (98.4%)	195(99.5%)	187 (98.9%)	188 (98.4%)
# of siblings	1.77 (1.20)	1.80 (0.88)	1.72 (1.10)	1.81 (1.20)	1.74 (1.29)
Parental vital status
Single infection	563 (72.6%)	134 (73.6%)	129 (64.5%)	135 (68.2%)	165 (84.2%)****
Double infection	117 (15.1%)	19 (10.4%)	46 (23.0%)	39 (19.7%)	13 (6.6%)
Single death	72 (9.3%)	22 (12.1%)	20 (10.0%)	15 (7.6%)	15 (7.7%)
Double death	24 (3.1%)	7 (3.8%)	5 (2.5%)	9 (4.5%)	3 (1.5%)
Familial HIV death & infection
# of deaths in household	0.45(0.78)	0.52 (0.88)	0.41 (0.78)	0.48 (0.70)	0.39 (0.72)
# of infections in household	1.07 (1.01)	1.14 (1.16)	1.18 (0.86)	1.15 (0.98)	0.80 (0.97)****
# of deaths among relatives	0.42 (1.05)	0.49 (1.01)	0.45 (1.01)	0.40 (1.24)	0.34 (0.91)
# of infections among relatives	0.81 (1.52)	0.91 (1.54)	0.90 (1.42)	0.68 (1.26)	0.74 (1.82)
Father’s education
No formal schooling	19 (2.4%)	2 (1%)	6 (3.0%)	5 (2.5%)	6 (3.1%)
Elementary	255(32.5%)	66 (34.2%)	67 (33.5%)	66 (33.3%)	56 (29.0%)
Middle	191(24.4%)	50 (25.9%)	73 (36.5%)	37 (18.7%)	31 (16.1%)
High school	61 (7.8%)	16 (8.3%)	10 (5.0%)	17 (8.6%)	18 (9.3%)
Post-secondary	51 (6.5%)	12 (6.2%)	8 (4.0%)	15 (7.6%)	16 (8.3%)
Don’t know	207 (26.4%)	47 (24.4%)	36 (18.0%)	58 (29.3%)	66 (34.2%)****
Mother’s education
No formal schooling	46 (6.0%)	11 (5.8%)	12 (6.1%)	14 (7.1%)	9 (4.8%)
Elementary	249 (32.3%)	64 (33.7%)	71 (35.9%)	63 (32.1%)	51 (27.1%)
Middle	154 (19.9%)	116 (19.9%)	55 (27.8%)	31 (15.8%)	30 (16.0%)
High School	59(7.6%)	16 (8.4%)	12(4.1%)	15(7.7%)	16 (8.5%)
Post-secondary	46 (6.0%)	8 (4.2%)	9 (4.5%)	12 (6.1%)	17 (9.0%)
Don’t Know	218 (28.2%)	53 (27.9%)	39 (19.7%)	61 (31.1%)	65 (34.6%)*

	Academic performance		School satisfaction		Educational aspiration		School interest		Extracurricular interest
	Mean	Std	Mean	Std	Mean	Std	Mean	Std	Mean	Std
Baseline
Control	3.158	1.22	3.305	1.132	3.282	1.761	18.736	3.765	3.252	0.573
Child-only	3.21	1.273	3.196	1.062	3.521	1.787	18.96	3.475	3.313	0.623
Child + caregiver	3.379	1.253	3.536	1.073	3.661	1.88	18.586	3.8	3.275	0.619
Child + caregiver +community	3.644	1.306	3.778	1.088	3.782	1.902	19.266	3.507	3.248	0.649
6-months
Control	2.873	1.31	3.169	1.11	3.119	1.736	18.271	3.533	3.37	0.545
Child-only	2.967	1.445	2.984	1	3.457	1.73	18.353	3.155	3.363	0.534
Child + caregiver	3.131	1.294	3.253	1.156	3.733	1.865	18.733	3.312	3.304	0.647
Child + caregiver +community	3.545	1.282	3.528	1.106	3.625	1.875	18.688	3.556	3.261	0.635
12-months
Control	2.71	1.334	2.962	1.077	3.423	1.803	18.783	3.514	3.349	0.62
Child-only	2.916	1.287	2.912	1.008	3.378	1.728	19	3.321	3.315	0.58
Child + caregiver	2.876	1.338	2.92	1.106	3.57	1.938	18.575	3.673	3.253	0.614
Child + caregiver +community	3.632	1.207	3.557	0.988	3.844	1.825	19.057	3.374	3.226	0.636
18-months
Control	2.891	1.309	3.389	0.957	3.452	1.857	18.43	3.717	3.252	0.58
Child-only	3	1.329	2.902	0.957	3.699	1.747	18.394	3.162	3.305	0.582
Child + caregiver	3.223	1.193	3.163	1.03	3.738	1.837	18.471	3.262	3.355	0.606
Child + caregiver +community	3.817	1.137	3.651	1.03	4.189	1.809	19.17	3.533	3.165	0.642

	Academic performance		School satisfaction		Education aspiration		School interest		Extracurricular interest
Variable	β	std	β	std	β	std	β	std	β	std
At 6-months
Fixed effects
# of infections	−0.033	0.040	−0.063	0.030*	0.025	0.022	−0.171	0.105	0.006	0.018
Age	−0.195	0.020****	−0.185	0.015****	−0.067	0.011****	0.033	0.054	0.009	0.009
Gender	−0.168	0.079*	0.037	0.060	−0.022	0.044	−0.591	0.210**	0.145	0.035****
Child	0.192	0.107^a	0.144	0.089	0.060	0.063	0.177	0.297	0.032	0.051
Time	−0.057	0.017***	−0.027	0.017^a	−0.013	0.011	−0.084	0.052	0.020	0.009*
Child*Time	0.020	0.019	−0.016	0.019	0.016	0.013	0.021	0.059	−0.013	0.010
Random effects (Intercept)	0.708	–	0.224	–	0.168	–	3.896	–	0.098	–
Random effects (Residual)	0.846	–	0.864	–	0.382	–	8.476	–	0.263	–
At 12-months
Fixed effects
# of infections	−0.023	0.038	−0.073	0.028**	0.016	0.021	−0.212	0.097*	0.007	0.016
Age	−0.195	0.019****	−0.182	0.014****	−0.072	0.011****	−0.035	0.049	0.013	0.008
Gender	−0.138	0.075^a	0.031	0.055	0.000	0.041	−0.730	0.193***	0.143	0.032****
Child	0.192	0.102^a	0.101	0.083	0.085	0.060	0.193	0.279	0.027	0.048
Child + caregiver	0.387	0.145**	0.059	0.127	0.048	0.089	0.604	0.421	−0.185	0.074*
Time	−0.062	0.011****	−0.045	0.011****	−0.004	0.007	−0.068	0.034*	0.017	0.006**
Child*Time	0.027	0.010**	0.007	0.010	0.004	0.007	−0.003	0.031	−0.010	0.006^a
Child + caregiver*Time	0.037	0.015*	0.029	0.014*	0.005	0.010	0.137	0.046**	−0.016	0.008^a
Random effects (Intercept)	0.735	–	0.273	–	0.185	–	4.002	–	0.094	–
Random effects (Residual)	0.832	–	0.792	–	0.365	–	8.206	–	0.269	–
At 18-months
Fixed effects
# of infections	−0.041	0.036	−0.078	0.026**	0.010	0.019	−0.257	0.092**	0.008	0.015
Age	−0.205	0.018****	−0.184	0.013****	−0.078	0.010****	−0.089	0.047*	0.011	0.008
Gender	−0.130	0.072^a	0.057	0.051	0.007	0.039	−0.753	0.183****	0.136	0.030****
Child	0.243	0.098*	0.176	0.077*	0.075	0.056	0.200	0.266	0.001	0.046
Child + caregiver	−0.331	0.204	−0.370	0.189^a	−0.106	0.131	−0.603	0.621	−0.173	0.112
Child + caregiver + community	0.822	0.146****	0.286	0.131*	0.230	0.092*	−0.276	0.435	−0.098	0.078
Time	−0.050	0.009****	−0.029	0.009**	−0.005	0.006	−0.065	0.029*	0.012	0.005*
Child*Time	0.011	0.007^a	−0.014	0.007*	0.006	0.005	−0.009	0.021	−0.003	0.004
Child + caregiver*Time	0.138	0.041*	0.076	0.040	0.032	0.027	0.328	0.128*	−0.010	0.023
Child + Caregiver + community * Time	0.012	0.017	0.023	0.017	0.000	0.011	−0.144	0.055**	−0.001	0.010
Random effects (Intercept)	0.739	–	0.267	–	0.183	–	4.038	–	0.096	–
Random effects (Residual)	0.779	–	0.760	–	0.348	–	7.978	–	0.267	–