Health Services Needs and Utilization of Persons Aging With SCI: A Cross-Country Comparison
Bibliographic record
Abstract
OBJECTIVE: To map health care service utilization among individuals with spinal cord injury/disease (SCI/D), focusing on reported health conditions and age groups. DESIGN: Cross-sectional, multinational, observational cohort study data from the first InSCI community survey 2017-2019 (N=12,591) and the second InSCI community survey 2022-2024 (N=15,051). SETTING: Community setting with participants from 36 countries representing all 6 WHO regions. PARTICIPANTS: Individuals with traumatic or nontraumatic SCI aged ≥18 years and living in the community. In total, we analyzed data of N=27,642 persons with SCI. MAIN OUTCOMES MEASURE(S): Utilization of ten types of healthcare providers, including primary care physicians, rehabilitation specialists, other medical specialists, inpatient services, nurses, psychologists, chiropractors, physiotherapists, and occupational therapists, among others. Utilization of 10 healthcare providers, including primary care, rehabilitation physician, other medical specialities, inpatient care, nurses, psychologists, chiropractors, physiotherapists, occupational therapists, among others. RESULTS: Individuals with SCI/D experience a wide range of secondary health conditions across the life course, contributing to substantial health care utilization. The most frequently consulted providers are general practitioners (59.2%±7.9%), inpatient services (48.5%±3.5%), physiotherapists (43.5%±4.7%), and rehabilitation physicians (40.4%±3.4%). Specialists in other medical fields (36.4%±5.0%) and nursing care (24.6%±4.0%) play a comparatively smaller but consistent role in supporting individuals with SCI/D. Overall, health care needs increase with age, reflecting the cumulative burden of secondary conditions. However, some conditions-such as pain, sexual dysfunction, and spasticity-tend to decline over time, which may explain shifts in provider use. Although patterns of care remain broadly stable across countries, notable differences emerge, particularly among countries of different income levels. CONCLUSIONS: Health care utilization among individuals with SCI/D remains consistently high across the life course, reflecting the high burden of secondary health conditions. Although the demand for certain providers shifts with age, the need for multidisciplinary care persists throughout life. By providing comparative data across countries, including low- and middle-income settings where evidence has been scarce, this study highlights the importance of planning for lifelong access to essential providers and adapting care pathways to evolving health profiles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".