Who Moves After SCI? Individual, Health, and Neighborhood Predictors of Residential Mobility Among Participants in the National Spinal Cord Injury Model Systems Database

Development of Composite Measures of Neighborhood Economic Factors for Use in Spinal Cord Injury Outcomes Studies: A Spinal Cord Injury Model Systems Database Study

OBJECTIVE

To develop composite measures of neighborhood economic factors for use with the national Spinal Cord Injury Model Systems (SCIMSs) database in cross-sectional and longitudinal investigations of the social determinants of health.

DESIGN

Secondary data analysis of administrative data from the 2009, 2014, and 2019 American Community Survey (ACS) 5-year estimates and survey data collected for the SCIMS database.

SETTING

Community.

PARTICIPANTS

The validity of the neighborhood economic measures developed from the ACS data was tested with a sample of SCIMS participants who completed a follow-up interview between 2017 and 2021 (N=8,130). The predictive validity of the neighborhood measures was assessed with a subsample of cases with complete data on the outcome and covariate measures (N=6,457).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

A binary measure of self-rated health status (1=poor/fair health; 0=good/very good/excellent).

RESULTS

A combination of panel review and data reduction techniques yielded 2 distinct measuring neighborhood socioeconomic status (SES) and neighborhood socioeconomic disadvantage that were validated using 3 waves of ACS data and the SCIMS data. The odds of reporting poor health were lower among people living in moderate- and high-SES neighborhoods and highest among people living in moderately and highly disadvantaged neighborhoods. The negative association between neighborhood SES and poor health was fully attenuated by differences in participants' individual demographic and economic characteristics whereas the positive association between neighborhood disadvantage and poor health persisted after adjusting for individual differences.

CONCLUSIONS

The two composite measures of neighborhood economic factors developed by this study are robust in samples from different periods of time and valid for use with the SCIMS database. Future investigations conducting surveillance of the needs of the SCI population using this resource may consider using these measures to assess the effect of the social determinants of health in outcomes after SCI.

Development and Validation of a Novel Nomogram for Predicting Mechanical Ventilation After Cervical Spinal Cord Injury

OBJECTIVE

To investigate the risk factors relating to the need for mechanical ventilation (MV) in isolated patients with cervical spinal cord injury (cSCI) and to construct a nomogram prediction model.

DESIGN

Retrospective analysis study.

SETTING

National Spinal Cord Injury Model System Database (NSCID) observation data were initially collected during rehabilitation hospitalization.

PARTICIPANTS

A total of 5784 patients (N=5784) who had a cSCI were admitted to the NSCID between 2006 and 2021.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURE(S)

A univariate and multivariate logistic regression analysis was used to identify the independent factors affecting the use of MV in patients with cSCI, and these independent influencing factors were used to develop a nomogram prediction model. The area under the receiver operating characteristic curve (AUROC), calibration curve, and decision curve analysis (DCA) were used to evaluate the efficiency and the clinical application value of the model, respectively.

RESULTS

In a series of 5784 included patients, 926 cases (16.0%) were admitted to spinal cord model system inpatient rehabilitation with the need for MV. Logistic regression analysis demonstrated that associated injury, American Spinal Cord Injury Association Impairment Scale (AIS), the sum of unilateral optimal motor scores for each muscle segment of upper extremities (sUEM), and neurologic level of injury (NLI) were independent predictors for the use of MV (P<.05). The prediction nomogram of MV usage in patients with cSCI was established based on the above independent predictors. The AUROC of the training set, internal verification set, and external verification set were 0.871 (0.857-0.886), 0.867 (0.843-0.891), and 0.850 (0.824-0.875), respectively. The calibration curve and DCA results showed that the model had good calibration and clinical practicability.

CONCLUSIONS

The nomograph prediction model based on sUEM, NLI, associated injury, and AIS can accurately and effectively predict the risk of MV in patients with cSCI, to help clinicians screen high-risk patients and formulate targeted intervention measures.

Neighborhood racial income inequality and cognitive health

INTRODUCTION

Neighborhood socioeconomic status (SES) has been linked to dementia, but the distribution of SES within a neighborhood may also matter.

METHODS

Data from 460 (47% Black, 46% White) older adults from the Michigan Cognitive Aging Project were linked to census tract-level data from the National Neighborhood Data Archive (NaNDA). Neighborhood SES included two composites reflecting disadvantage and affluence. Neighborhood racial income inequality was the ratio of median incomes for White versus Black residents. Generalized estimating equations examined associations between neighborhood factors and cognitive domains.

RESULTS

Neighborhood racial income inequality was uniquely associated with worse cognitive health, and these associations did not differ by participant race. Neighborhood disadvantage was only associated with worse cognitive health among Black participants.

DISCUSSION

Both the level and racial distribution of SES within a neighborhood may be relevant for dementia risk. Racial differences in the level and impact of neighborhood SES contribute to dementia inequalities.

HIGHLIGHTS

Black participants lived in neighborhoods with lower socioeconomic status (SES) than White participants, on average. Neighborhood SES and racial income inequality were associated with worse cognition. Effects of neighborhood racial income inequality did not differ across racial groups. Effects of neighborhood SES were only evident among Black participants.

Histories of neighborhood socioeconomic status contribute to race differences in later-life cognition

INTRODUCTION

Neighborhood characteristics are increasingly implicated in cognitive health disparities, but no research has investigated how the historical context of neighborhoods shapes these disparities.

METHODS

Four hundred sixty-four Black (55%) and White older adults (Mage = 63.6) were drawn from the Michigan Cognitive Aging Project, a community-based, prospective study of older adults. Participants' addresses at baseline (2017-2020) were geocoded and linked to 2000-2017 measures of neighborhood socioeconomic status (NSES): disadvantage [NDis] and affluence [NAff]. Latent class growth analysis (LCGA) characterized 18 interpolated year trajectories of NSES across 1344 census tracts. Path analysis examined whether NSES trajectory classes mediated the association between race and a global cognition composite.

RESULTS

LCGA identified three NDis and two NAff trajectory classes, which were associated with participant race. Only one NDis class was associated with cognition, and it mediated the association between the Black race and cognition.

DISCUSSION

Disinvestment in neighborhoods may be particularly salient in race disparities in cognitive function.

HIGHLIGHTS

Race is implicated in the likelihood of living in more disadvantaged neighborhoods. Historical trends in neighborhood disadvantage are associated with cognitive function in older adulthood. Identifying patterns of neighborhood change may inform neighborhood-level interventions.

Residential Mobility and Reasons for Moving Among People Living With Spinal Cord Injury: Results of a Multisite Survey Study

Background

Residential mobility after spinal cord injury (SCI) has not been extensively examined despite a growing interest in investigating the relationship between neighborhood exposures and community living outcomes.

Objectives

This study explores residential mobility patterns, the annual move rate, and reasons for moving among a community-living sample of adults with SCI.

Methods

A survey was conducted with 690 people at six SCI Model Systems centers in the United States between July 2017 and October 2020. The outcomes included move status in the past 12 months, move distance, and the primary reason for moving. A sample from the 2019 American Community Survey (ACS) 5-year pooled estimates was obtained for comparative analysis. Descriptive statistics were used to summarize the distributions of the outcomes and differences between the samples.

Results

The annual move rate for adults with SCI was 16.4%, and most moves were within the same county (56.6%). Recent movers were more likely to be young adults, be newly injured, and have low socioeconomic status. Housing quality, accessibility, and family were more frequently reported motivations for moving compared to employment. Young adults more commonly moved for family and accessibility, whereas middle-aged adults more commonly moved for housing quality. No notable difference was observed in the annual move rate between the SCI and the general population samples.

Conclusion

These findings suggest an age-related pattern of residential relocation after SCI, which may be indicative an extended search for optimal living conditions that meet the housing and accessibility needs of this population.