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Social Network Mapping and Functional Recovery Within 6 Months of Ischemic Stroke

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Objective. Stroke recovery is a multidimensional process influenced by biological and psychosocial factors. To understand the latter, we mapped the social networks of stroke patients, analyzing their changes and effects on physical function at 3 and
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  https://doi.org/10.1177/1545968319872994   Neurorehabilitation andNeural Repair 1  –11© The Author(s) 2019Article reuse guidelines: sagepub.com/journals-permissionsDOI: 10.1177/1545968319872994 journals.sagepub.com/home/nnr Original Research Article Introduction Participating in life’s activities after stroke often depends on a patient’s social milieu. 1  For example, a patient with hemiplegia is more likely to attend community events if he has a large and connected group of social contacts who can coordinate care. Major guidelines such as the International Classification of Functioning, Disability, and Health recog-nize the important role of the social environment in people’s functioning. 1  Stroke, in particular, can disrupt social life more than other disorders. 2,3  Therefore, consideration of social factors in stroke rehabilitation is an unmet need and opportunity.Here, we build on a biopsychosocial model of stroke recovery. The model highlights the interplay of emotional sustenance, active coping assistance, 4  neuroplasticity, 5  and neuroendocrine and immune functions. 6  At least 30 years of research show that higher levels of social support are linked with better outcomes in cardiovascular disease. 7  In stroke, increased social support is associated with faster and more 872994 NNR XXX10.1177/1545968319872994Neurorehabilitation and Neural Repair  Dhand etal research-article 2019 1 Harvard Medical School, Boston, MA, USA 2 Northeastern University, Boston, MA, USA 3 Washington University School of Medicine, St Louis, MO, USA 4 Washington University in St Louis, MO, USA 5 Harvard T. H. Chan School of Public Health, Boston, MA, USASupplementary material for this article is available on the Neurorehabilitation & Neural Repair   website at http://nnr.sagepub.com/content/by/supplemental-data. Corresponding Author: Amar Dhand, MD, DPhil, Department of Neurology, Brigham & Women’s Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA. Email: adhand@bwh.harvard.edu Social Network Mapping and Functional Recovery Within 6 Months of Ischemic Stroke Amar Dhand, MD, DPhil 1,2 , Catherine E. Lang, PhD 3 , Douglas A. Luke, PhD 4 , Angela Kim, Karen Li 1 , Liam McCafferty 1 , Yi Mu, MPH 5 , Bernard Rosner, PhD 5 , Steven K. Feske, MD 1 , and Jin-Moo Lee, MD, PhD 3 Abstract Objective.  Stroke recovery is a multidimensional process influenced by biological and psychosocial factors. To understand the latter, we mapped the social networks of stroke patients, analyzing their changes and effects on physical function at 3 and 6 months after stroke.  Methods.  We used a quantitative social network assessment tool to map the structure and health habits embedded in patients’ personal social networks. The physical function outcome was determined using the National Institutes of Health (NIH) Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function Scale (0-100, mean 50 for US general population). We used mixed-effects models to assess changes in social network metrics. We used multivariable models to test the association between social networks and physical function, independent of demographics, socioeconomic status, clinical characteristics, comorbidities, cognition, and depression. Results.  The cohort consisted of 172 patients, with mostly mild motor-predominant stroke (median NIH Stroke Scale of 2) with retention of 149 at 3 months and 139 at 6 months. An average patient’s network over 6 months contracted by 1.25 people and became denser and family oriented. Network composition also became healthier with pruning of ties with people who smoked or did not exercise. The baseline network size, and not density or health habits in the network, was independently associated with 3- and 6-month physical function PROMIS scores. Patients embedded in small kin-based networks reported more negative social interactions. Conclusions.  Despite social networks becoming smaller and close-knit after stroke, they also become healthier. Larger baseline social networks are independently associated with better patient-reported physical function after stroke. Keywords stroke rehabilitation, social environment, social networking, recovery of function, psychosocial support systems  2 Neurorehabilitation and Neural Repair 00(0) extensive functional recovery. 8  Conversely, social isolation in stroke survivors had an odds ratio of 1.4 in predicting recurrent stroke, myocardial infarction, or death within 5 years. 9  Basic science studies in animals and humans show that the social environment influences neuroplasticity and neurogenesis, including direct effects on angiogenesis, den-dritic remodeling, and inflammatory cascades. 5,10,11  These findings support the contention that the social determinants of health are “the most significant opportunities for reduc-ing death and disability from cardiovascular disease.” 7(p874) In this study, we use social network mapping to quantify the social environment of stroke survivors. The mapping  procedure identifies the specific persons in a patient’s social world one by one, their links to each other, and health-related characteristics. 12  Both here and in prior studies, we have demonstrated that this protocol may be feasibly deployed in clinical settings. 13,14  With a long tradition in the social sciences, where it is known as egocentric network analysis or personal network analysis, 15  this approach is novel to stroke recovery research. Prior work on social fac-tors in stroke have used summative scales to provide a sin-gle metric of social support or integration. In contrast, social network mapping produces a visual social network graph with quantitative metrics of the structure, content, and func-tion of social ties.We used social network mapping to longitudinally char-acterize stroke survivors’ personal social networks over 6 months. Second, we examined the association of social net-work metrics to a granular measure of patient-reported  physical function. Finally, we analyzed qualitative data to understand the mechanisms of social network dynamics. These findings reveal detailed patterns of social environ-ment evolution and influence in stroke recovery. Methods Study Design The study was a multicenter, prospective, longitudinal cohort study of patients with mostly mild, motor-predomi-nant, first-time ischemic stroke without marked cognitive impairment. The primary aims were to characterize social network changes after stroke and to determine the associa-tion of social network variables with physical function at 3 and 6 months. To achieve these aims, we recruited consecu-tive patients from 2 academic hospitals, Barnes Jewish Hospital in St Louis and Brigham and Women’s Hospital in Boston, between May 2014 and May 2017. We completed in-person clinical and social network evaluations at the time of enrollment. We completed follow-up assessments at 3 and 6 months on the phone to determine social network change and physical function outcomes. We gave small incentives ($20-$40) after each assessment to encourage continued participation.The institutional review boards at each site approved the study protocol. All patients provided informed consent to  participate in the study. Data can be made available on request with a data sharing agreement. Participants We enrolled consecutive patients during their hospitaliza-tion if they were (1) diagnosed with a first ischemic stroke, (2) 21 years old or older, (3) within 7 days of clinical stroke, and (4) recommended for inpatient rehabilitation or home with services. The 7-day timeframe, instead of weeks to months, was chosen to establish a reasonable baseline  before there are effects of the stroke on social networks. Patients were excluded if they had any of the following: (1)  prior ischemic or hemorrhagic stroke, (2) National Institutes of Health Stroke Scale (NIHSS) score > 21, (3) significant aphasia (score > 1 on the language section of the NIHSS), (4) inability to speak English, (5) lack of capacity to consent or participate in the survey interview, or (6) diagnosis of dementia or Short Blessed Test score > 6.We chose this population because they were able to par-ticipate in the survey with low anticipated dropout allowing the best chance to measure social networks over time. Patients with hemorrhagic stroke were excluded because of the stroke’s different mechanism of insult that could lead to different recovery trajectories and outcomes. 16  Patients with aphasia were excluded because such patients could not val-idly and reliably complete the questionnaire. We did not use caregiver proxy because the instrument had not been vali-dated for proxy report. Patients who were included in the study had no history of prior stroke or cognitive deficits that may have affected the social networks prior to enrollment. This allowed better interpretations to be prospectively drawn on stroke, networks, and outcomes. Finally, patients recommended to rehabilitation are a target group who have the capacity to improve. Social Network Evaluation A trained study coordinator administered the social network survey in person between the second and fifth day of the stroke hospitalization and then at 3 and 6 months over the  phone. The instrument was an adaptation of the General Social Survey, 17  a validated national instrument. The main sections were a name generator, name interrelater, and name interpreter. In the name generator, participants named peo- ple with whom they had discussed important matters, socialized, or sought health-related support in the past 3 months. Specifically, we asked the following questions: “1. From time to time, most people discuss important personal matters with other people. Looking back over the last 3 months, who are the adults with whom you discussed an important personal matter? 2. From time to time, people  Dhand et al 3 socialize with other people. For instance, they visit each other, go together on a trip or to a dinner. In the last 3 months, who are the adults with whom you usually do these things? 3. Are there any other people not mentioned who do these supportive actions?” “These” referred to social and health supportive actions discussed in a prior question.In the name interrelater, participants determined the con-nections among all persons in the network and evaluated the strength of the relationship ties. In the name interpreter sec-tion, participants answered questions about the characteris-tics and health habits of each individual in the network. Specific question forms have been previously published, 8,9  and instrument psychometric properties have been described  by Burt. 17  This tool was chosen over other summary metrics (eg, Lubben Social Network Scale) because it offered in-depth phenotyping of the social network structure and health milieu surrounding the patient. Network structure is a quantitative description of the arrangement of social ties in a patient’s social surround.  Network size is defined as the number of individuals in the network, excluding the patient. Density is the number of direct actual connections divided by the number of possible direct connections in a network. Similar to density, con-straint is the degree to which each network member is con-nected to the others in the network, with additional benefits of incorporating hierarchies and strength of ties. 18  For example, a patient has a high-constraint network if each network member is strongly connected to all the others, leading to a close-knit social structure. A patient has a low-constraint network if there are subgroups of friends who are not known to the other subgroup and individuals are less familiar with each other. Effective size is the number of nonredundant members in the network, conceptually an inverse metric of constraint. 18  Mean degree is the average number of ties of a network member, excluding the patient, indicating the distribution of ties in the network. Equations to calculate these measures are provided in Supplement 1. Network composition refers to the mix of characteristics and health habits of the persons in the network. For example,  percentage kin is the percentage of persons in the network who are family. The SD of network members’ age reflects the range of ages of people in the network around the patient. The diversity of sex index (or the index of qualitative varia-tion) represents the mix of men and women in the network with a value of 0 meaning all network members are one sex and a value of 1 indicating equal mix of men and women. 19  Likewise, diversity of race is the mix of races in the network, with a value of 0 indicating that all persons were of the same race. The mix of health habits in the network represents the health behavior environment around the patient. For exam- ple, the percentage of network members who do not exercise is the number of individuals who do not exercise divided by total number of persons described. Physical Function Evaluation The main physical function outcome was a standardized  patient-reported outcome of physical function, a measure that has distinct advantages in stroke patients. 20,21  Known as the NIH Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function Scale, it is a nationally validated, computer adaptive testing system to measure self-reported health in patients across a range of chronic diseases and demographics. 22  Scores from the US general population have a normal distribution with a mean score of 50 and SD of 10. The advantages of the PROMIS is that it is a continuous outcome that maps well on tradi-tional categorical stroke metrics (eg, Modified Rankin Scale), but it has better precision, reduced ceiling and floor effects, and less participant burden. We chose it for this study to delineate differences in functional status among mild stroke patients that would be lost by using less-sensi-tive metrics. Statistical and Qualitative Analyses We planned analyses to assess (1) change in network char-acteristics and contributing factors over 6 months after stroke, (2) relationship of baseline and change of network characteristics to PROMIS scores at 3 and 6 months, and (3) mechanisms from qualitative analysis to explain how and why social networks change after stroke.For aim (1), we examined the change for each network variable from baseline to 3 months to 6 months. First, we assessed the baseline distributions for each network vari-able at each time point, and then, we created change scores of the variables for each patient. We assessed statistical sig-nificance of change score trends over time by using a Wilcoxon signed-rank test. We also analyzed the turnover of network members with attention to their health habits, examining whether joiners or leavers were the primary drivers of the overall trends.We adjusted for time from stroke, age, sex, NIHSS score,  black race, years of education, marital status, median income, Patient Health Questionnaire (PHQ-9) depression score, Short Blessed Test cognition score, and Charlson Comorbidity Index. These potential covariates were deter-mined based on prior literature describing factors that may influence network change or stroke recovery. 18-21 To account for correlations among repeated measures of outcomes in the same participant, linear mixed models were used to estimate the time trend or slope of network variables over time. 23  The model was based on the repeated option of SAS PROC MIXED with an unstructured correlation struc-ture. The primary covariate was time of follow-up, and the  parameter term associated with this covariate signified the slope of each network feature as follows:  4 Neurorehabilitation and Neural Repair 00(0) Ynetwork_size = intercept + *time + *age + *sex + 123 ( )  ββ β βββ ββ 45678 *NIHSS +*black_race + *education + *married+ †*inncome+*depression + *cognition +*comorbidity, 91011 β ββ where “Y” is the network size slope after stroke, “time” is the follow-up time, and each of the covariates is described. “ β 1 ” Is the estimated change in social network size per month and “ β 2 ” through “ β 11 ” the estimated change in net-work score arising from the individual covariate. This pro-cedure was done for each of the network variables. In model  building, we sequentially added groups of potential con-founders. Each of these blocks was tested one by one to avoid overfitting. Time, age, NIHSS, years of education, income, depression score, cognition score, and comorbidity index were added as centered continuous variables. Sex, race, and marital status were added as categorical variables. We also considered interaction terms among each factor and time in the model. However, none of these was statistically significant, so we report only on the main effects of these characteristics.For aim (2), the goal was to determine the relationship of network features to PROMIS physical function scores at 3 and 6 months. We hypothesized that network structural fea-tures (eg, size, constraint) would be related to physical function independent of typical covariates involved in stroke recovery. To test this hypothesis, we first used Spearman correlation and univariate linear regression to assess the individual correlations of network variables and PROMIS outcomes. Next, we used multivariable analysis and mixed-effects models to determine the association  between the strongest social network variables and PROMIS outcomes, after adjusting for the previously mentioned covariates. Finally, we performed sensitivity analyses to assess the impact of the missing data, examining any differ-ences in patients who were retained in the study versus those who were lost to follow-up and also differences by site. All  P   values were 2-tailed. SAS version 9.3 (SAS Institute, Inc, Cary, NC) and R version 3.3.3 were used for quantitative analyses.For aim (3), we conducted qualitative analyses to exam-ine mechanisms of the social network trajectory change. All patients who took part in the study were offered the opportunity to participate in the optional qualitative inter-views. Participants were given an additional $20 if they agreed. We conducted semistructured interviews to qualita-tively examine the social network changes in 25 partici- pants at 3 months and 24 participants at 6 months; 23  participants were from Barnes Jewish Hospital and 2 were from Brigham and Women’s Hospital. All interviews were transcribed. Using the framework method, 24  first, we read and reflected on all interviews; second, we coded recurrent ideas; third, we grouped ideas into themes and formalized these into a framework and coding index; and finally, we applied the agreed-on index to all transcripts. Major themes that emerged were types of social support, positive and negative social interactions, and functions and roles of net-work members. Finally, we examined the relationship of the qualitative themes with quantitative patterns. Two investigators independently coded transcripts, agreed on the coding index, and discussed any disagreements. Comparison of codes showed high consensus (percentage agreement 󰀽  93%). Results Participant Characteristics We enrolled 172 patients and retained 149 (87%) at 3 months and 139 (81%) at 6 months. Figure 1 shows a flow diagram and reasons for dropout; the most common reason was loss of contact after multiple attempts. Importantly, there were no differences in demographic, clinical, or social network features between persons who were lost to follow-up and those retained in the study (Supplement 2).At baseline, the cohort was well balanced in gender (49% were male) and race with regard to white and black or African American (66% white and 31% black or African American; Table 1). Although not nationally representative, the distribution of gender and race is consistent with patients who have a stroke in the United States. 25  Clinically, the majority had mild motor-predominant stroke, with median  NIHSS equal to 2 (range 0-13); 64% were right hemisphere, 42% were subcortical, and 4% of patients had aphasia. There were low rates of comorbidities, cognitive impair-ment, and depression, which make preexisting network changes caused by these issues less likely.Baseline network metrics included average size 7.77, density 0.79, and constraint 51.74. Metrics were similar to a nationwide sample using the same instrument in a younger  population. 13 Social Network Change From Baseline to 6  Months Social networks contracted over time according to both the aggregate mean and the change score per individual (Table 2). The average change per individual was −1.25  people over 6 months (SD 󰀽  4.00,  P    <  .001; Table 2; Figure 2). Degree of contraction was related to baseline network size, so that patients with large baseline networks had greater shedding of network members compared with  patients with smaller networks (Supplement 3). Network  Dhand et al 5 members also became more closely bonded, as measured by an increase in density and constraint over time (6.77 units increase, SD 󰀽  20.92,  P    <  .001; Table 2; Figure 2). This close-knit pattern was further supported by decreases in effective size and mean degree, revealing that network members increasingly occupied structurally similar posi-tions with reduced number of connections over 6 months.The networks also changed compositionally over time, with a 7.25% increase in kin within the network (SD 󰀽  25.16;  P    <  .001) but no change in the range of ages or diversity of sex or race in the network. The networks  became healthier, with 5.47% reduction in persons who smoke (SD 󰀽  26.00;  P    <  .01) and 9.19% reduction in per-sons who do not exercise (SD 󰀽  33.00;  P    <  .01). The increase in the health of networks was a result of joining of  persons with healthier habits and shedding of persons with unhealthy habits (Figure 2). The pattern was not a result of change of behaviors in network members who remained constant throughout the period. For example, of people who  joined the network by 6 months, 84.6% were nonsmokers and 13.4% were smokers. Conversely, of people who left the network by 6 months, 23.3% were nonsmokers and 76.7% were smokers. Of people who stayed constant, 6.5% changed from being a smoker to a nonsmoker. Figure 1.  Flow diagram of patients.
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