Social Risk Factor

The self-report survey was administered and proctored by study staff in classrooms at the respondents' respective schools and completed within a single day by each respondent at both Time 1 and Time 2. The Time 2 assessment was completed approximately one week after the school's scheduled Time 1 visit. Study participants selected their preferred language (either English or the local Fijian language) at Time 1 and responded to this (same) preferred language version at Time 2. Study participants received a unique study identification code, which was used for labeling study related documents and preserved capacity to link their responses at Time 1 and Time 2.
Study procedures for assessment with items adapted from GSHS content were developed to facilitate the aims of the main study as well as to follow local (FN-RERC) guidance in responding to at-risk students. Specifically, the portion of the assessment adapted from GSHS content was included in a spiral bound packet alongside additional assessments for psychological and social risk factors relating to study hypotheses. After orienting the study participants to the questionnaire and procedures, study staff responded to queries about the meaning of terms throughout the assessment (e.g., words such as, “calories” and “laxatives”). Participants returned assessments to study staff and waited while they were checked for completeness if time permitted; when missing or duplicate responses were identified, study staff invited participants to complete or clarify their intended response prior to their departure. After we ascertained that a response option to an item about suicide-attempt related injury was frequently misinterpreted, study staff sought to clarify the intended response when relevant and possible as well.
As part of the related, overarching study protocol, we collected additional self-report and anthropomorphic (measured height and weight) data from all study participants as well as interview data from an independently selected sub-sample at follow-up school site visits. These anthropomorphic and interview data overlap with topic content of the GSHS, but are not presented in this paper. Additional relevant details of data collection procedures are reported elsewhere (Becker et al., 2009 (link)) and are also available upon request from the corresponding author. Written parental (or guardian) informed consent and youth assent was obtained for each study participant. Parents and guardians were informed about the study in a letter distributed with support from the school in advance of the study; youth assent was obtained in person. Consent documents were available in both English and in the local vernacular language. This study was part of a protocol carried out in accordance with universal ethical principles (Emanuel et al 2000 (link)) and approved by the Partners Healthcare Human Subjects Committee, the Harvard Medical School Committee on Human Studies, and the Fiji National Research Ethical Review Committee.

We assessed a pool of potential frailty risk factors gleaned from prior research, consensus opinion, and practice guidelines for their associations with frailty (Andrew, Mitnitski, & Rockwood, 2008 (link); Cevenini et al., 2013 (link); De Martinis, Franceschi, Monti, & Ginaldi, 2006 (link); Fried et al., 2004 (link); Gobbens et al., 2012 (link); Iwata, Kuzuya, Kitagawa, & Iguchi, 2006 (link)), and the biopsychosocial model and their availability in the EHR. An expert panel consisting of a board-certified gerontology nurse, geriatrician with expertise in frailty, two advanced practice geriatric nurses, and two doctorally prepared nurses with geriatric expertise provided content validity.
We operationalized frailty by the presence or absence of 16 biopsychosocial risk factors drawn from evidence in previous studies to create an FRS (see Table 1). We further defined six of these risk factors by subfactors. The biological risk factors comprised eight symptoms, syndromes, and conditions and four serum biomarkers. Symptoms, syndromes, and conditions included fatigue, weakness, dyspnea, chronic pain, falls (history or admission diagnosis), vision impairment (glaucoma, cataracts, macular degeneration, retinopathy, blindness), urinary incontinence, and nutrition issues (low body mass index, unplanned weight loss, poor appetite). We selected the four biomarkers—CRP, albumin, hemoglobin, and WBC count— based on associations with frailty, availability in the EHR, and common use in practice. CRP, an acute-phase reactant, exerts catabolic effects leading to muscle atrophy, weakness, fatigue, and poor physical performance due to upregulated protein synthesis and decreased synthesis of albumin. Low albumin and hemoglobin are well-established markers of inflammation and frailty that have similar impacts on symptoms and function. Elevated WBC count is associated with inflammation and frailty and has synergistic interactions with CRP. The three psychological risk factors we included were cognition problems (delirium, dementia), depression, and smoking (current). Finally, we included one social support risk factor (single, living alone, caregiver concerns [i.e., concerns about the impact of illness and hospitalization on discharge needs and planning] or being older, disabled, and living alone). We did not include impaired physical function in the score due to perspectives that regard physical function as an outcome of frailty (Sternberg et al., 2011 (link)) and the lack of a valid proxy indicator in the EHR.
To calculate the FRS, we counted each risk factor as yes = 1 if present and no = 0 if not present. For the six risk factors further defined by more than one subfactor (i.e., nutrition issues, falls, vision impairment, fatigue, cognitive problems, social support issues), the presence of at least one subfactor resulted in counting the overall risk factor as present (see Table 1). The biomarker risk factors were operationalized by the categorical abnormal flag, which indicated that the laboratory value fell outside the reference range, high or low. We created an FRS as the unweighted count of risk factors present (theoretical range 0 = 16), where higher scores are indicative of increased frailty. We then used these FRSs to model the outcome variables of time to in-hospital mortality and rehospitalization within 30 days of discharge.