Nursing Complexity and Health Literacy as Determinants of Patient Outcomes: A Prospective One-Year Multicenter Cohort Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design and Setting
2.2. Study Populations and Recruitment
2.3. Inclusion Criteria
2.4. Exclusion Criteria
2.5. Data Sources and Collection
2.5.1. Baseline Data
2.5.2. Follow-Up Data
2.6. Variables and Measurements
- ▪
- Nursing complexity, which was assessed by evaluating the number of NDs identified within 24 h of hospital admission and the number of NAs performed throughout the patient’s stay [1,3]. While NDs are standardized clinical judgments that identify patient responses to health conditions and guide individualized care planning, NAs encompass specific tasks and interventions documented in nursing records and performed by nurses as part of their professional responsibilities [3]. These actions are grounded in scientific knowledge, clinical judgment, and a holistic approach to addressing patients’ physical, emotional, social, and spiritual needs [22]. A higher nursing complexity reflects the intensity of required nursing care in hospital settings [4]. This measure has been validated in adult and pediatric populations, demonstrating its reliability in capturing variations in patient care needs across different clinical conditions [3]. Additionally, previous studies have shown that nursing complexity is a significant predictor of key hospital outcomes, including length of stay (LOS) and mortality risk [4,5]. NDs were collected using the Professional Assessment Instrument (PAI), a clinical nursing information system integrated into the hospital’s EHR and used by nurses during their routine practice. The PAI standardizes the nursing diagnostic process by guiding nurses in selecting appropriate NDs based on the assessment data. These suggestions, which can be accepted or rejected by nurses based on their clinical judgment, are provided by a validated algorithm embedded in the PAI system, which has been in use at the study hospitals for over a decade [23]. The PAI system has supported the development of multiple studies since its initial implementation, contributing to research on NDs, nursing complexity, and patient outcomes [1,3,4].
- ▪
- HL, which was collected during hospitalization using the Single-Item Literacy Screener (SILS) as the assessment tool [24]. The SILS is commonly used in clinical settings to evaluate an individual’s understanding of health information. It consists of a single question: “How often do you ask someone for help to read the instructions and leaflets from a doctor or pharmacy?” Responses are given on a 5-point Likert scale, ranging from “never” to “always”. A response of “never” (1) or “rarely” [25] indicates adequate HL, while responses of “sometimes” (3), “often” (4), and “always” (5) suggest potential difficulties with reading health-related materials. Scores above 2 on the SILS are used to identify patients with low HL. The Italian version of the SILS, developed and validated in 2017, demonstrates good concurrent validity when compared with the Newest Vital Sign (r = −0.679; p < 0.001) and shows high diagnostic accuracy, with a sensitivity of 83.3% and a specificity of 82.6% within the general population [26]. The SILS is considered an efficient method for assessing HL, offering a straightforward alternative to more comprehensive instruments that measure functional HL.
2.7. Outcomes Measured
2.8. Covariate Variables
- ▪
- Sociodemographic characteristics. These included age, gender, education level, monthly family income, and place of origin (rural–urban classification).
- ▪
- Clinical characteristics. This category included the modality of hospital admission (planned or emergency through ED), the number of chronic conditions, and LOS.
- ▪
- Major diagnostic categories (MDCs). MDCs categorize ICD-9-CM medical diagnoses into 25 groups. Each MDC aligns with a specific medical specialty and is associated with a particular organ system or etiology. Diagnoses within an MDC share common characteristics related to the affected organ system or underlying cause, distinguishing them from diagnoses in other MDCs.
2.9. Statistical Analysis
2.10. Ethical Considerations
3. Results
3.1. General Characteristics of the Sample
3.2. Nursing Complexity and HL Levels of the Sample
3.3. Nursing Complexity, Mortality, Hospital Re-Admissions, and ED Visits
3.4. HL, Mortality, Hospital Re-Admissions, and ED Visits
3.5. Association Between Nursing Complexity, HL, and Mortality
4. Discussion
4.1. Study Contributions, Limitations, and Recommendations for Further Research
4.2. Implications for Policy, Education, and Practice
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Public Involvement Statement
Guidelines and Standards Statement
Use of Artificial Intelligence
Acknowledgments
Conflicts of Interest
Abbreviations
AIC | Akaike information criterion |
BIC | Bayesian information criterion |
CI | confidence interval |
COVID-19 | Coronavirus disease 2019 |
DDs | diseases and disorders |
ED | emergency department |
EHRs | electronic health records |
HL | health literacy |
HR | hazard ratio |
IQR | interquartile range |
LCA | latent class analysis |
LOS | length of stay |
MDC | major diagnostic category |
NDs | nursing diagnoses |
SD | standard deviation |
SILS | Single-Item Literacy Screener |
STROBE | Strengthening the Reporting of Observational Studies in Epidemiology |
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General Sample (N = 2667) | Adequate HL (N = 1797) | Inadequate HL (N = 870) | p-Value a | |
---|---|---|---|---|
Age, median (IQR), years | 65 (23) | 61 (23) | 73 (17) | <0.001 |
Gender | ||||
Male | 1224 (45.9) | 774 (43.1) | 450 (51.7) | <0.001 |
Female | 1443 (54.1) | 1023 (56.9) | 420 (48.3) | |
Education | <0.001 | |||
Less than high school | 1149 (43.1) | 639 (35.6) | 510 (58.6) | |
High school | 1116 (41.8) | 858 (47.7) | 258 (29.7) | |
University degree | 378 (14.2) | 282 (15.7) | 96 (11.0) | |
No education | 24 (0.9) | 18 (1.0) | 6 (0.7) | |
Family income per month (euros) (N = 2655) | 0.949 | |||
0–1000 | 636 (24.0) | 425 (23.8) | 211 (24.3) | |
1001–2000 | 1472 (55.4) | 994 (55.6) | 478 (55.1) | |
>2000 | 547 (20.6) | 369 (20.6) | 178 (20.5) | |
Rural–urban classification | 0.309 | |||
City | 1281 (48.1) | 863 (48.1) | 418 (48.1) | |
Town | 1067 (40.1) | 730 (40.7) | 337 (38.8) | |
Rural area | 315 (11.8) | 201 (11.2) | 114 (13.1) | |
Modality of admission | <0.001 | |||
Planned admission | 2148 (80.5) | 1512 (84.1) | 636 (73.1) | |
From ED | 519 (19.5) | 285 (15.9) | 234 (26.9) | |
Number of chronic conditions | <0.005 | |||
0 | 1105 (41.4) | 780 (43.3) | 325 (37.4) | |
1 | 412 (15.4) | 284 (15.8) | 128 (14.7) | |
≥2 | 1150 (43.1) | 733 (40.8) | 417 (47.9) | |
LOS, median (IQR), years | 4 (6) | 4 (5) | 6 (9) | <0.001 |
MDC | <0.001 | |||
Hepatobiliary and pancreatic DDs | 570 (21.4) | 300 (16.7) | 270 (31.0) | |
Cardiocirculatory system DDs | 447 (16.8) | 381 (21.2) | 66 (7.6) | |
Respiratory system DDs | 315 (11.8) | 267 (14.9) | 48 (5.5) | |
Skin, subcutaneous tissue, and breast DDs | 264 (9.9) | 180 (10.0) | 84 (9.7) | |
Nervous system DDs | 231 (8.7) | 105 (5.8) | 126 (14.5) | |
Digestive system DDs | 198 (7.4) | 108 (6.0) | 90 (10.3) | |
Musculoskeletal and connective system DDs | 171 (6.4) | 117 (6.5) | 54 (6.2) | |
Ear, nose, mouth, and throat DDs | 153 (5.7) | 93 (5.2) | 60 (6.9) | |
Myeloproliferative DDs, poorly differentiated neoplasms | 111 (4.2) | 81 (4.5) | 30 (3.4) | |
Reproductive system DDs | 75 (2.8) | 63 (3.5) | 12 (1.4) | |
Infectious and parasitic, systemic, or unspecified site DDs | 69 (2.6) | 51 (2.8) | 18 (2.1) | |
Other | 63 (2.3) | 51 (2.9) | 12 (1.4) | |
NDs, mean (SD) | 4.12 (3.0) | 3.64 (1.89) | 5.11 (4.33) | <0.001 |
NAs, mean (SD) | 7.05 (4.32) | 6.81 (3.28) | 7.55 (5.88) | <0.001 |
General Sample (N = 2667) | Low Nursing Complexity (N = 1176) | High Nursing Complexity (N = 1491) | p-Value a | |||
---|---|---|---|---|---|---|
Adequate HL (N = 828) | Inadequate HL (N = 348) | Adequate HL (N = 969) | Inadequate HL (N = 522) | |||
Variables | A | B | C | D | ||
Age, median (IQR), years | 65 (23) | 61 (25) | 73 (15) | 62 (21) | 73.5 (20) | |
Gender | <0.001 | |||||
Male | 1224 (45.9) | 375 (45.3) | 174 (50.0) | 399 (41.2) | 276 (52.9) | |
Female | 1443 (54.1) | 453 (54.7) | 174 (50) | 570 (58.8) | 246 (47.1) | |
Education | ||||||
Less than high school | 1149 (43.1) | 327 (39.5) | 186 (53.4) | 312 (32.2) | 324 (62.1) | |
High school | 1116 (41.8) | 387 (46.7) | 111 (31.9) | 471 (48.6) | 147 (28.2) | |
University degree | 378 (14.2) | 102 (12.3) | 51 (14.7) | 180 (18.6) | 45 (8.6) | |
No education | 24 (0.9) | 12 (1.4) | 0 (0) | 6 (0.6) | 6 (1.1) | <0.001 |
Family income per month (euros) (N = 2655) | 0.599 | |||||
0–1000 | 636 (24.0) | 208 (25.3) | 82 (23.6) | 217 (22.4) | 129 (24.8) | |
1001–2000 | 1472 (55.4) | 437 (53.2) | 189 (54.5) | 557 (57.6) | 289 (55.6) | |
>2000 | 547 (20.6) | 176 (21.4) | 76 (21.9) | 193 (20.0) | 102 (19.6) | |
Rural–urban classification | 0.426 | |||||
City | 1281 (48.1) | 398 (48.1) | 172 (49.4) | 465 (48.1) | 246 (47.2) | |
Town | 1067 (40.1) | 327 (39.5) | 135 (38.8) | 403 (41.7) | 202 (38.8) | |
Rural area | 315 (11.8) | 103 (12.4) | 41 (11.8) | 98 (10.1) | 73 (4.0) | |
Modality of admission | <0.001 | |||||
Planned admission | 2148 (80.5) | 657 (79.3) | 273 (78.4) | 855 (88.2) | 363 (69.5) | |
From ED | 519 (19.5) | 171 (20.7) | 75 (21.6) | 114 (11.8) | 159 (30.5) | |
Number of chronic conditions | <0.005 | |||||
0 | 1105 (41.4) | 351 (42.4) | 145 (41.7) | 429 (44.3) | 180 (34.5) | |
1 | 412 (15.4) | 139 (16.8) | 54 (15.5) | 145 (15.0) | 74 (14.2) | |
≥2 | 1150 (43.1) | 338 (40.8) | 149 (42.8) | 395 (40.8) | 268 (51.3) | |
LOS, median (IQR), years | 4(6) | 4 (5) | 5 (10) | 3 (6) | 7 (9) | <0.001 |
MDC | <0.001 | |||||
Hepatobiliary and pancreatic DDs | 570 (21.4) | 165(19.9) | 165 (47.4) | 135 (13.9) | 105 (20.1) | |
Cardiocirculatory system DDs | 447 (16.8) | 180 (21.7) | 0 (0) | 201 (20.7) | 66 (12.6) | |
Respiratory system DDs | 315 (11.8) | 75 (9.1) | 9 (2.6) | 192 (19.8) | 39 (7.5) | |
Skin, subcutaneous tissue, and breast DDs | 264 (9.9) | 90 (10.9) | 33 (9.5) | 90 (9.3) | 51 (9.8) | |
Nervous system DDs | 231 (8.7) | 69 (8.3) | 57 (16.4) | 36 (3.7) | 69 (13.2) | |
Digestive system DDs | 198 (7.4) | 39 (4.7) | 42 (12.1) | 69 (7.1) | 48 (9.2) | |
Musculoskeletal and connective system DDs | 171 (6.4) | 54 (6.5) | 12 (3.4) | 63 (6.5) | 42 (8.0) | |
Ear, nose, mouth, and throat DDs | 153 (5.7) | 39 (4.7) | 18 (5.2) | 54 (5.6) | 42 (8.0) | |
Myeloproliferative DDs, poorly differentiated neoplasms | 111 (4.2) | 48 (5.8) | 0 (0) | 33 (3.4) | 30 (5.7) | |
Reproductive system DDs | 75 (2.8) | 42 (5.1) | 9 (2.6) | 21 (2.2) | 3 (0.6) | |
Infectious and parasitic, systemic, or unspecified site DDs | 69 (2.6) | 21 (2.5) | 3 (0.9) | 30 (3.1) | 15 (2.9) | |
Other | 63 (2.3) | 6 (0.8) | 0 (0.0) | 45 (4.7) | 12 (2.4) | |
NAs (number of activities documented in HR) Mean (SD) | 7.05 (4.32) | 3.34 (4.13) | 4.85 (3.05) | 8.49 (2.44) | 10.4 (5.15) | <0.001 |
Variables | Hazard Ratio | 95% CI | p-Value |
---|---|---|---|
Simple Cox regression without adjustment | |||
Nursing complexity (NDs) a | |||
Low nursing complexity | 1.0 | ||
High nursing complexity | 1.84 | 1.35–2.51 | <0.001 |
Cox Regression with adjustment † | |||
Nursing complexity (NDs) b | |||
Low nursing complexity | 1.0 | ||
High nursing complexity | 1.81 | 1.32–2.48 | <0.001 |
General Sample (N = 2280) | Adequate HL (N = 1569) | Inadequate HL (N = 711) | p-Value a | |
---|---|---|---|---|
Mortality (1 year) | 189 (8.3) | 33 (2.1) | 156 (21.9) | <0.001 |
Re-admission (1 year) | 621 (27.2) | 264 (16.8) | 357 (50.2) | <0.001 |
ED visits (1 year) | 333 (16.8) | 45 (3.5) | 288 (40.5) | <0.001 |
Mortality | Re-Admissions | ED Visits | |||||||
---|---|---|---|---|---|---|---|---|---|
Variables | HR | 95% CI | p-Value | HR | 95% CI | p-Value | HR | 95% CI | p-Value |
Simple Cox regression without adjustment | |||||||||
HL | |||||||||
Adequate | 1.0 | 1.0 | 1.0 | ||||||
Inadequate | 11.21 | 7.70–16.32 | <0.001 | 3.61 | 3.06–4.20 | <0.001 | 20.78 | 14.16–30.50 | <0.001 |
= 23.059 | <0.001 | = 37.962 | <0.001 | = 42.330 | <0.001 | ||||
Cox regression with adjustment † | |||||||||
HL | |||||||||
Adequate | 1.0 | 1.0 | 1.0 | ||||||
Inadequate | 7.75 | 5.25–11.45 | <0.001 | 3.58 | 2.95–4.10 | <0.001 | 14.45 | 10.52–19.86 | <0.001 |
= 36.863 | <0.001 | = 51.532 | <0.001 | = 66.621 | <0.001 |
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Cocchieri, A.; Cristofori, E.; Nurchis, M.C.; Nursing and Public Health Group; Damiani, G.; Cesare, M. Nursing Complexity and Health Literacy as Determinants of Patient Outcomes: A Prospective One-Year Multicenter Cohort Study. Nurs. Rep. 2025, 15, 135. https://doi.org/10.3390/nursrep15040135
Cocchieri A, Cristofori E, Nurchis MC, Nursing and Public Health Group, Damiani G, Cesare M. Nursing Complexity and Health Literacy as Determinants of Patient Outcomes: A Prospective One-Year Multicenter Cohort Study. Nursing Reports. 2025; 15(4):135. https://doi.org/10.3390/nursrep15040135
Chicago/Turabian StyleCocchieri, Antonello, Elena Cristofori, Mario Cesare Nurchis, Nursing and Public Health Group, Gianfranco Damiani, and Manuele Cesare. 2025. "Nursing Complexity and Health Literacy as Determinants of Patient Outcomes: A Prospective One-Year Multicenter Cohort Study" Nursing Reports 15, no. 4: 135. https://doi.org/10.3390/nursrep15040135
APA StyleCocchieri, A., Cristofori, E., Nurchis, M. C., Nursing and Public Health Group, Damiani, G., & Cesare, M. (2025). Nursing Complexity and Health Literacy as Determinants of Patient Outcomes: A Prospective One-Year Multicenter Cohort Study. Nursing Reports, 15(4), 135. https://doi.org/10.3390/nursrep15040135