Using Factor Analysis to Streamline Social Screening for the Emergency Department Article Swipe
Melissa Meeker
,
Diana M. Bongiorno
,
Danielle Cullen
,
Katherine H. Schiavoni
,
Margaret Samuels‐Kalow
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1111/acem.70184
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1111/acem.70184
Introduction Emergency departments (EDs) are increasingly required to screen for social risk and social need, but existing tools are long, hindering their utility in clinical settings, and resulting in incomplete surveys. However, strategies for streamlining screening tools remain unclear. This work aimed to guide future development of an ED‐based screener by using a health system's ten‐item social risk/social need questionnaire to (1) compare differences in patient populations by questionnaire completeness, (2) observe patterns of responses (e.g., what questions cover the same constructs and can potentially be eliminated), and (3) test for variation in social risk/social need measurement by age. Methods This cross‐sectional study evaluated patients who responded to at least one question in the social risk/social need questionnaire in our regional health system from February 2019 to March 2023. Descriptive analyses examined patients stratified by questionnaire completeness: lower response (< 60%) versus higher response (≥ 60%). Within the higher response group, factor analysis extracted social risk/social need constructs and the strength of the association between each questionnaire item and its corresponding social risk/social need construct. Results Among 330,109 individuals, 248,808 (75%) completed the survey. In the lower response group (28,985; 9%), more patients were caregivers of children ≤ 4 years (18,231; 63%) and had commercial insurance (21,009; 72%) compared to the higher response group (23,873; 8% and 149,814; 50%, respectively). Factor analysis revealed a three‐factor structure of the social risk/social need framework which we labeled: core resources, housing, and ability to work. From the magnitude of factor loadings, the items with the strongest indication of social risk/social need were paying for utilities, upcoming housing instability, and unemployment. Conclusion In this health system, incomplete social risk/social need questionnaires are common. To improve response rates, the social risk/social need framework elucidated by our factor analysis will guide the development of a consolidated questionnaire for the EDs.
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Metadata
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- article
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- en
- Landing Page
- https://doi.org/10.1111/acem.70184
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/acem.70184
- OA Status
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- References
- 21
- OpenAlex ID
- https://openalex.org/W4415982035
All OpenAlex metadata
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https://openalex.org/W4415982035Canonical identifier for this work in OpenAlex
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https://doi.org/10.1111/acem.70184Digital Object Identifier
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Using Factor Analysis to Streamline Social Screening for the Emergency DepartmentWork title
- Type
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articleOpenAlex work type
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-11-06Full publication date if available
- Authors
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Melissa Meeker, Diana M. Bongiorno, Danielle Cullen, Katherine H. Schiavoni, Margaret Samuels‐KalowList of authors in order
- Landing page
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https://doi.org/10.1111/acem.70184Publisher landing page
- PDF URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/acem.70184Direct link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/acem.70184Direct OA link when available
- Cited by
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0Total citation count in OpenAlex
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21Number of works referenced by this work
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| abstract_inverted_index.elucidated | 289 |
| abstract_inverted_index.incomplete | 30, 273 |
| abstract_inverted_index.indication | 254 |
| abstract_inverted_index.resources, | 237 |
| abstract_inverted_index.strategies | 33 |
| abstract_inverted_index.stratified | 134 |
| abstract_inverted_index.ten‐item | 56 |
| abstract_inverted_index.utilities, | 262 |
| abstract_inverted_index.Descriptive | 130 |
| abstract_inverted_index.association | 164 |
| abstract_inverted_index.departments | 3 |
| abstract_inverted_index.development | 46, 297 |
| abstract_inverted_index.differences | 64 |
| abstract_inverted_index.measurement | 97 |
| abstract_inverted_index.populations | 67 |
| abstract_inverted_index.potentially | 85 |
| abstract_inverted_index.risk/social | 58, 95, 116, 156, 173, 230, 257, 275, 286 |
| abstract_inverted_index.Introduction | 1 |
| abstract_inverted_index.consolidated | 300 |
| abstract_inverted_index.eliminated), | 87 |
| abstract_inverted_index.increasingly | 6 |
| abstract_inverted_index.individuals, | 179 |
| abstract_inverted_index.instability, | 265 |
| abstract_inverted_index.streamlining | 35 |
| abstract_inverted_index.completeness, | 70 |
| abstract_inverted_index.completeness: | 137 |
| abstract_inverted_index.corresponding | 171 |
| abstract_inverted_index.questionnaire | 60, 69, 118, 136, 167, 301 |
| abstract_inverted_index.unemployment. | 267 |
| abstract_inverted_index.questionnaires | 277 |
| abstract_inverted_index.respectively). | 220 |
| abstract_inverted_index.three‐factor | 225 |
| abstract_inverted_index.cross‐sectional | 102 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5040869850 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I4210087915 |
| citation_normalized_percentile |