Abstract
Objective
To describe hospitalization patterns, major disease spectra, and clinical outcomes among middle-aged and elderly inpatients in Northwest China between 2020 and 2024.
Methods
A retrospective cohort study was conducted using electronic medical records from a tertiary hospital. A total of 108,027 admissions were reviewed, and 91,470 were included after excluding cases coded as Z51 (other medical care). Demographic features, length of stay, major diagnoses, and in-hospital mortality were summarized. Annual differences and temporal trends were assessed using appropriate statistical tests.
Results
Hospitalizations increased from 16,407 in 2020 to 27,393 in 2024. Patients aged ≥65 years accounted for roughly three-quarters of admissions, with those aged 65–74 years forming the largest subgroup (46.6%). The overall in-hospital mortality was 1.29%, which scaled significantly with age and peaked at 1.77% in 2023 alongside a surge in respiratory admissions. While males consistently accounted for ∼55% of admissions, they exhibited a higher mortality rate than females (1.22% vs. 0.88%) and dominated specific admissions like chronic obstructive pulmonary disease (70.3% male). The median length of stay declined from 9 to 7 days. Chronic non-communicable diseases dominated the inpatient spectrum, with senile cataract, cerebral infarction, angina pectoris, and COPD being the most frequent diagnoses.
Conclusion
Non-communicable diseases and age-related conditions remain the primary drivers of hospitalization, but their patterns are highly dynamic. The escalating mortality in the oldest-old and marked post-pandemic respiratory surges highlight the critical role of underlying frailty. Alongside improvements in heart failure management, healthcare systems must prioritize targeted primary prevention, aggressive multimorbidity management, and robust long-term care to address these shifting vulnerabilities.
Keywords: chronic non-communicable diseases, inpatient trends, disease spectrum, healthcare utilization, geriatric population, temporal analysis
Introduction
The global shift toward an ageing population has become a major public health challenge. 1 The World Health Organization projects that by 2050 the proportion of people aged ≥65 years will double, creating unprecedented demands on healthcare systems.2,3 China, with one of the world’s fastest-ageing populations, faces a particularly heavy burden.4,5 Older adults contribute disproportionately to hospitalizations due to their high rates of chronic illness, multimorbidity, and greater susceptibility to acute diseases. Understanding the hospitalization patterns of this demographic is therefore essential for optimizing healthcare planning, clinical practice, and policy formulation.
In recent years, chronic non-communicable diseases (NCDs) have overtaken infectious diseases as the leading drivers of morbidity and mortality in China. 6 Cardiovascular and cerebrovascular disorders, notably ischemic heart disease and stroke, remain the predominant causes of hospital admission and mortality. 7 Concurrently, age-related conditions such as cataracts and musculoskeletal diseases adversely affect quality of life and frequently necessitate surgical care, further increasing hospital use.8,9 Respiratory conditions—including chronic obstructive pulmonary disease (COPD) and pneumonia—also represent a major cause of hospitalization in older adults, linked to smoking, environmental exposures, and infection vulnerability. 10 The COVID-19 pandemic further highlighted the fragility of this group, reshaping admission patterns and intensifying the respiratory disease burden in recent years.11,12
While national and regional statistics have captured the rising prevalence of NCDs, most previous research has focused on individual diseases, specific hospital units, or cross-sectional datasets.13,14 Longitudinal hospital-based investigations encompassing multiple disease types are relatively scarce, especially for middle-aged and elderly inpatients. Such analyses are crucial to reveal both persistent long-term burdens (e.g., cerebrovascular disease, cataracts) and transient fluctuations (e.g., respiratory infections during epidemic surges). Additionally, limited evidence exists regarding how hospitalization features—such as length of stay and expenditure—have evolved alongside health reforms, clinical advances, and public health events.15–17
To fill these gaps, we carried out a retrospective review of a large inpatient dataset covering 2020–2024. This period was chosen for its inclusiveness and for capturing the COVID-19 pandemic, thus allowing assessment of both routine and extraordinary influences on hospitalization.18,19 The study population was restricted to individuals aged 45 years and older, representing the middle-aged and elderly most affected by NCDs. 20 The study objectives were fourfold: (1) to describe baseline demographic and clinical characteristics of inpatients; (2) to examine hospitalization metrics such as length of stay and costs; (3) to identify leading admission causes and their temporal variation; and (4) to evaluate hospital utilization patterns across major disease categories. By integrating these dimensions, the study seeks to generate evidence to inform clinical decision-making, support hospital management, and guide health policy in the context of rapid population ageing.
Materials and methods
Ethics statement
This retrospective study was approved by the Institutional Review Board (IRB) of 3201 Hospital (approval number: LLSC-KYLW-2025-040). The requirement for written informed consent was waived by the IRB due to the retrospective, anonymized nature of the study. All procedures were conducted in accordance with the Declaration of Helsinki.
Study population and data source
This retrospective study utilized electronic medical records (EMRs) from a large tertiary hospital in Northwest China, covering all inpatient admissions between January 1, 2020, and December 31, 2024. Each hospitalization was treated as an independent event, with repeat admissions for the same patient considered separate records. The specific inclusion criteria for this study were: (1) patients aged 45 years and older; (2) patients admitted to 3201 Hospital between January 1, 2020, and December 31, 2024; and (3) patients with complete primary diagnostic records in the electronic medical record (EMR) system. The term “admission year” referred to the calendar year of discharge. Records were retained if they contained valid data on age, sex, admission year, and a primary diagnosis coded according to the International Classification of Diseases, 10th Revision (ICD-10). Exclusion criteria included missing or implausible age, absence of primary diagnosis or ICD-10 coding, or duplicate entries. Following data cleaning, 108,027 admissions were included in the final dataset: 16,407 in 2020, 18,818 in 2021, 19,381 in 2022, 26,028 in 2023, and 27,393 in 2024. Although the hospital admits patients of all ages, individuals younger than 45 years were rarely hospitalized and were excluded. Subsequent analyses therefore focused on two groups: 45–64 years and ≥65 years.
Variables and definitions
The extracted variables included sex, age, dates of admission and discharge, length of stay (LOS), primary diagnosis and ICD-10 code, and discharge outcome. Age was recorded as a continuous variable, while sex was categorized as male, female, or unknown; the proportion of unknown values was minimal. LOS was calculated as the number of days between admission and discharge. Primary diagnosis, based on ICD-10, was used to construct annual inpatient disease spectra.
Statistical analysis
Descriptive statistics were employed to summarize baseline patient characteristics. Categorical variables are presented as frequencies and percentages, while continuous variables are reported as mean ± standard deviation (SD) and median with interquartile range (IQR). Differences across years for categorical variables (e.g., sex distribution, age categories, disease groups) were examined using chi-square tests of independence, with results expressed as χ2 values, degrees of freedom (df), and P values. Temporal changes in proportions were assessed using the Cochran–Armitage trend test, 21 with both Z and equivalent χ2 statistics reported. For continuous variables (e.g., age, LOS), comparisons across years were made using Kruskal–Wallis tests, with H values, df, and P values provided.
For disease spectrum analysis, the 10 most frequent ICD-10 diagnoses were identified annually, and chi-square tests were used to compare distributions across years. At a broader level, admissions were aggregated by major ICD-10 categories, followed by overall chi-square tests and trend analyses for each category.
All analyses were two-tailed, with statistical significance defined as P < 0.05. Data analysis was performed using Python version 3.9 (pandas, numpy, scipy), and results were cross-validated with SPSS Statistics version 26.0 (IBM Corp., Armonk, NY, USA).
Results
Study population
A total of 108027 inpatient admissions were recorded at the study hospital between 2020 and 2024. We excluded 16,557 admissions coded as Z51 (other medical care, including chemotherapy, radiotherapy, and supportive treatment; the annual distribution of these excluded cases is detailed in Supplemental Table S1), leaving a final of 91,470 eligible admissions for the disease spectrum analysis. The annual number of eligible hospitalizations increased steadily over the study period, from 16407 in 2020 to 27393 in 2024 (Figure 1).
Figure 1.
Annual number of hospital admissions, 2020–2024.
Sociodemographic characteristics
The baseline demographic and clinical characteristics of the study cohort are presented in Table 1. The distribution of sex remained relatively stable throughout the five-year period, with males consistently representing approximately 55% of admissions. A subsequent chi-square test confirmed that the interannual differences were not statistically significant (χ2=18.26, df=4, P=0.108).
Table 1.
Baseline demographic and clinical characteristics of hospitalized patients (2020–2024).
| Variable | 2020 (n=16,407) | 2021 (n=18,818) | 2022 (n=19,381) | 2023 (n=26,028) | 2024 (n=27,393) | χ2 / H (df) | P value |
|---|---|---|---|---|---|---|---|
| Sex, n (%) | χ2=18.26 (4) | 0.108 | |||||
| Male | 9,255 (56.4) | 10,527 (55.9) | 10,831 (55.9) | 14,407 (55.4) | 15,134 (55.2) | | |
| Female | 7,152 (43.6) | 8,289 (44.0) | 8,549 (44.1) | 11,620 (44.6) | 12,259 (44.8) | | |
| Age, years | H=26.11 (4) | <0.001 | |||||
| Mean ± SD | 70.4 ± 7.3 | 70.4 ± 7.2 | 70.5 ± 7.4 | 70.7 ± 7.4 | 70.5 ± 7.3 | | |
| Median (IQR) | 69 (65–75) | 69 (65–75) | 70 (65–75) | 70 (65–76) | 70 (65–75) | | |
| Age group, n (%) | χ2=41.39 (12) | <0.001 | |||||
| 45–64 years | 3934 (24.0) | 4535 (24.1) | 4730 (24.4) | 6065 (23.3) | 6822 (24.9) | | |
| 65-74 years | 7869 (48.0) | 9,035 (48.0) | 9106 (47.0) | 12340 (47.4) | 12758 (46.6) | | |
| 75-84 years | 3968 (24.2) | 4489 (23.9) | 4712 (24.3) | 6420 (24.7) | 6645 (24.3) | | |
| ≥85 years | 636 (3.9) | 759 (4.0) | 833 (4.3) | 1,203 (4.6) | 1,168 (4.3) | | |
| Length of stay, days | H=1545.92 (4) | <0.001 | |||||
| Mean ± SD | 16.9 ± 23.6 | 16.4 ± 23.4 | 16.7 ± 24.1 | 10.2 ± 12.2 | 9.3 ± 8.2 | | |
| Median (IQR) | 9 (5–15) | 8 (5–14) | 8 (5–14) | 7 (4–11) | 7 (5–12) | | |
| In-hospital Mortality, n (%) | 191 (1.16) | 215 (1.14) | 221 (1.14) | 461 (1.77) | 308 (1.12) | χ2=61.78 (4) | <0.001 |
The mean age of patients was approximately 70 years, with modest but statistically significant variation across years (Kruskal–Wallis H=26.11, df=4, P<0.001). Individuals aged 65 years and over accounted for almost three-quarters of all hospital admissions, while those aged 45–64 years comprised the remaining quarter (Figure S1). Despite the significant variation in the age-group distribution between years (χ2=19.71, df=4, P<0.001), trend analysis failed to identify a discernible monotonic pattern of increase or decrease (Cochran–Armitage χ2=1.72, P=0.190). The demographic findings under consideration are, in general, consistent with the findings of earlier hospital-based investigations of elderly inpatients in China. 22
Further sociodemographic and clinical nuances
Beyond the broad age categorization, further stratification of the elderly cohort revealed that patients aged 65–74 years constituted the largest subgroup (approximately 47%), followed by those aged 75–84 years (24%), and the oldest-old aged 85 years and above (4–5%). Analysis of discharge outcomes demonstrated an overall in-hospital mortality rate of 1.29%. Notably, mortality remained stable between 1.12% and 1.16% from 2020 to 2022 and in 2024, but peaked significantly in 2023 (1.77%, n=461), temporally aligning with the concurrent surge in respiratory disease admissions.
Significant sex disparities also emerged within specific major diagnoses. For instance, hospitalizations for chronic obstructive pulmonary disease (COPD) were predominantly male (70.3%), whereas admissions for senile cataract were more frequently female (54.5%).
Hospitalization characteristics
The median length of hospital stay exhibited a downward trend, decreasing from nine days in 2020 to seven days in 2024, indicating a general tendency towards reduced hospitalisation durations. The interquartile range also narrowed over time, reflecting reduced variability in the duration of hospitalisation. The interannual differences observed were found to be statistically significant (Kruskal–Wallis H=1545.92, df=4, P<0.001; Figure S2). Analogous reductions in mean LOS have been documented in extensive multicentre surveys, indicative of enhanced care efficiency and hospital management reforms. 23
Hospitalisation costs demonstrated a downward trend from 2020 to 2023, followed by a slight recovery in 2024. This fluctuation is consistent with the evidence that inpatient expenditures in China remain sensitive to both system-level reforms and acute public health events. 24
Overall disease spectrum (2020–2024 combined)
The overall top 10 causes of hospitalization in middle-aged and elderly patients across the five-year period are summarized in Table 2. The most prevalent diagnosis was senile cataract (H25), accounting for 5.0% of admissions, followed by cerebral infarction (I63), angina pectoris (I20), and chronic obstructive pulmonary disease (J44). Other significant contributors included heart failure (I50), type 2 diabetes mellitus (E11), chronic kidney disease (N18), intervertebral disc disorders (M51), pneumonia (J18), and cholelithiasis (K80). Collectively, these conditions underscore the preeminence of chronic non-communicable diseases (NCDs) in motivating hospitalisation among middle-aged and elderly patients.
Table 2.
The overall situation of the top 10 primary diagnoses among hospitalized middle-aged and elderly patients from 2020 to 2024.
| Rank | ICD-10 | Disease | n (%) |
|---|---|---|---|
| 1 | H25 | Senile cataract | 4605 (5.0) |
| 2 | I63 | Cerebral infarction | 3687 (4.0) |
| 3 | I20 | Angina pectoris | 3379 (3.7) |
| 4 | J44 | Chronic obstructive pulmonary disease | 3128 (3.4) |
| 5 | I50 | Heart failure | 2643 (2.9) |
| 6 | E11 | Type 2 diabetes mellitus | 2353 (2.6) |
| 7 | N18 | Chronic kidney disease (stage 5) | 2268 (2.5) |
| 8 | M51 | Intervertebral disc disorders | 2257 (2.5) |
| 9 | J18 | Pneumonia, unspecified | 1967 (2.2) |
| 10 | K80 | Cholelithiasis | 1891 (2.1) |
Statistical analysis demonstrated that the distribution of these top 10 diagnoses varied significantly across years (χ2=2248.18, df=36, P<0.001).
Temporal trends of disease spectrum
Temporal variations in the proportions of the top five diagnoses are shown in Figure 2. Overall, their distribution differed significantly across years (χ2=51.39, df=16, P<0.001). Senile cataract (H25) remained stable (χ2=0.80, P=0.370), as did cerebral infarction (I63) (χ2=0.79, P=0.373). By contrast, angina pectoris (I20) exhibited a significant upward trajectory (χ2=14.89, P<0.001), chronic obstructive pulmonary disease (J44) demonstrated a modest but statistically significant increase (χ2=4.23, P=0.040), while heart failure (I50) showed a significant decline (χ2=5.86, P=0.016).
Figure 2.
Temporal trends of the top five diagnoses, 2020–2024.
These findings underscore the dynamic nature of the inpatient disease spectrum: ophthalmologic and cerebrovascular disorders remained relatively unchanged, whereas cardiovascular and respiratory conditions underwent notable shifts, indicating evolving healthcare burdens among middle-aged and elderly patients during the study period. The annual top 10 disease spectra are provided in Supplemental Tables S2–S6, with detailed temporal distributions illustrated in Supplemental Figure S3.
System-level analysis
The distribution of hospital admissions across major system categories is presented in Table 3 and Figure S4. Circulatory diseases have been observed to account for the largest proportion of admissions on a consistent basis. However, it is notable that their relative share has seen a decline since 2022. There was notable variability in respiratory conditions, with a sharp peak in 2023 followed by a reduction in 2024. Digestive and endocrine/metabolic disorders demonstrated stability over the study period, whereas ophthalmologic conditions (eye/ear diseases) contributed modestly but persistently each year.
Table 3.
The distribution of inpatient numbers in major system categories from 2020 to 2024.
| System category | 2020 n (%) | 2021 n (%) | 2022 n (%) | 2023 n (%) | 2024 n (%) | Total n (%) |
|---|---|---|---|---|---|---|
| Circulatory system | 3341 (20.4) | 3799 (20.2) | 3945 (20.4) | 4685 (18.0) | 4793 (17.5) | 20,563 (19.0) |
| Respiratory system | 1100 (6.7) | 1069 (5.7) | 1187 (6.1) | 2920 (11.2) | 2170 (7.9) | 8446 (7.8) |
| Digestive system | 1344 (8.2) | 1564 (8.3) | 1573 (8.1) | 1963 (7.5) | 2174 (7.9) | 8618 (8.0) |
| Endocrine/metabolic | 485 (3.0) | 499 (2.7) | 541 (2.8) | 748 (2.9) | 801 (2.9) | 3074 (2.8) |
| Eye/ear diseases | 913 (5.6) | 1064 (5.7) | 1210 (6.2) | 1506 (5.8) | 1687 (6.2) | 6380 (5.9) |
The application of the chi-squared test yielded a highly statistically significant result, confirming the presence of considerable interannual variation in the overall distribution of system-level categories (χ2=668.46, df=16, P<0.001). Further trend testing identified distinct trajectories. There was a significant decline in circulatory diseases (χ2=91.84, P<0.001), an pronounced upward trend in respiratory diseases (χ2=184.26, P<0.001), a slight decrease in digestive diseases (χ2=4.48, P=0.034), and a moderate increase in eye/ear diseases (χ2=5.81, P=0.016). In contrast, endocrine/metabolic disorders remained largely unchanged (χ2=0.42, P=0.517).
Discussion
This five-year retrospective study, encompassing over 90,000 hospitalizations of middle-aged and elderly individuals in Northwest China, revealed that chronic non-communicable diseases (NCDs) remained the predominant causes of admission. Among them, senile cataract, cerebral infarction, angina pectoris, and chronic obstructive pulmonary disease were consistently ranked as leading diagnoses. Notably, hospital admissions rose steadily from 2020 to 2024, while the average length of stay declined significantly. Together, these findings underscore both the magnitude and the dynamic nature of inpatient care demands in this demographic.25,26
The burden of frailty and age-related conditions
Comparison with existing literature shows that the predominance of cardiovascular and cerebrovascular diseases is in line with national and international trends in ageing populations.27–29 At the same time, the prominence of senile cataract as the most frequent single diagnosis highlights the substantial influence of age-related conditions on hospital utilization, an aspect often underrepresented in large-scale surveys.30,31 The consistent rise in admissions among the oldest-old must also be interpreted through the lens of frailty in an aging population. Older adults contribute disproportionately to hospitalizations not merely due to isolated chronic illnesses, but because underlying frailty significantly increases their susceptibility to acute decompensation from minor physiological stressors. Recognizing and integrating frailty assessments into routine care is as critical as managing the primary disease itself when optimizing outcomes for geriatric inpatients. 32 Furthermore, the observed sex disparities in disease-specific admissions align with recent evidence emphasizing that sex significantly influences the clinical presentation and hospitalization outcomes of older adults with chronic conditions. 33
Shifts in cardiovascular and respiratory admissions
Our findings demonstrate a notable divergence in cardiovascular admission trends. The significant reduction in heart failure (HF) admissions warrants special attention. This decline likely reflects systemic improvements in outpatient management rather than a true decrease in epidemiological prevalence. The widespread clinical adoption of guideline-directed medical therapy (GDMT) in recent years—particularly the increasing uptake of SGLT2 inhibitors and angiotensin receptor-neprilysin inhibitors (ARNI)—has fundamentally altered the HF admission threshold by keeping patients clinically stable in outpatient settings.34–36 In contrast, the increasing prevalence of angina pectoris indicates continuing deficiencies in primary prevention and long-term ischemic disease control. 37
Similarly, the respiratory disease burden exhibited significant volatility, characterized by a sharp peak in pneumonia and COPD admissions in 2023, which coincided with the highest observed annual mortality rate (1.77%). It is critical to contextualize this within China’s specific epidemic timeline. The abrupt lifting of strict containment policies in late December 2022 shifted the massive initial “exit wave” of SARS-CoV-2 infections into early 2023.38–41 Beyond direct COVID-19 admissions, the subsequent surge strongly reflects post-pandemic respiratory vulnerability driven by the “immunity debt” phenomenon—where prolonged strict isolation temporarily suppressed endemic seasonal pathogens, resulting in a subsequent accumulation of susceptible individuals.42,43 Furthermore, the resumption of population mobility likely facilitated an increase in severe viral-bacterial co-infections, which are known to disproportionately trigger acute exacerbations of underlying cardiopulmonary diseases in older adults.44,45 This trajectory underscores the profound fragility of elderly inpatients when subjected to sudden, widespread respiratory stressors, which can subsequently escalate the risk of cardiovascular events. 46 From both clinical and policy perspectives, these results are highly relevant. These findings highlight the need for strategic resource allocation not only to cardiovascular care, but also to respiratory medicine and ophthalmology, given the growing impact of cataracts on inpatient demand.
Limitations and future directions
The strengths of this work lie in its large sample size, inclusion of a continuous five-year period, and reliance on ICD-10 standardized coding, which enabled consistent interannual comparisons and trend analyses. By excluding admissions coded as Z51, the study better captured disease-driven hospitalizations rather than supportive care episodes. Nevertheless, several limitations should be recognized. The data were derived from a single tertiary hospital, which may not fully reflect regional or national patterns. Furthermore, our reliance on administrative records limited our access to secondary and tertiary diagnostic codes. Consequently, we were unable to analyze specific clusters of comorbidities or multimorbidity networks. This is a notable limitation, as recent studies have demonstrated that specific comorbidity patterns significantly influence clinical trajectories, hospitalization lengths, and mortality in older populations.47,48 Future research should integrate comprehensive comorbidity indices to better capture inpatient complexity. Finally, as an exploratory retrospective analysis utilizing a continuous five-year EMR database, we included all eligible patient records rather than conducting an a priori sample size calculation, which may limit the statistical power for detecting rare outcome differences. Unresolved issues remain. Future research should draw on multi-center cohorts to improve generalizability, and evaluate the influence of healthcare policies or public health initiatives on disease-specific trajectories. Furthermore, prospective studies are warranted to clarify causal mechanisms and guide targeted prevention strategies. Addressing these gaps could enhance healthcare system preparedness and optimize clinical care in an ageing society confronted with an escalating burden of chronic disease.
Conclusion
This five-year retrospective study provides a comprehensive epidemiological profile of middle-aged and elderly inpatients in Northwest China, confirming that non-communicable diseases remain the predominant driver of hospitalization. We observed a persistent structural burden from age-related conditions alongside dynamic shifts in cardiopulmonary admissions. The decline in heart failure hospitalizations suggests potential successes in enhanced outpatient management and guideline-directed therapies. Conversely, the rising burden of ischemic heart disease, coupled with the escalating mortality and marked post-pandemic respiratory surges among the oldest-old, highlight the critical role of underlying frailty. To optimize health outcomes in China’s rapidly aging population, healthcare systems must prioritize targeted primary prevention, aggressive multimorbidity management, and robust long-term respiratory and cardiovascular care.
Supplemental material
Supplemental material for Epidemiological characteristics and changing patterns of hospitalizations among middle-aged and elderly patients in Northwest China, 2020–2024: A retrospective cohort study by Shaomin Quan, Shuo Li, Shiying Li, Yue Li, Zhigang Fan in Sage Open Medicine.
Acknowledgements
We would like to express our sincere gratitude to the Department of Quality Management, Affiliated 3201 Hospital of Xi’an Jiaotong University, for their strong support in data acquisition, data quality control and standardized sorting throughout this study. We also thank all medical staff who have contributed to the collection and standardized management of the clinical data involved in this study.
Author contributions: Shaomin Quan: Formal analysis, Methodology, Writing - original draft, Writing - review & editing, Funding acquisition. Shuo Li: Conceptualization, Methodology, Supervision. Shiying Li: Investigation, Formal analysis. Yue Li: Investigation, Validation. Zhigang Fan: Methodology, Writing - review & editing, Supervision.
Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Shaanxi Province Health High-Level Talent (Team) Training Program Innovation Team.
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Supplemental material: Supplemental material for this article is available online.
ORCID iDs
Zhigang Fan https://orcid.org/0009-0004-4990-2213
Shaomin Quan https://orcid.org/0009-0005-7719-0281
Shuo Li https://orcid.org/0000-0003-0365-7115
Shiying Li https://orcid.org/0009-0004-0278-5566
Ethical considerations
This retrospective study was conducted in accordance with the principles of the Declaration of Helsinki. The study protocol was reviewed and approved by the Medical Ethics Committee of 3201 Hospital (approval number: LLSC-KYLW-2025-040).
Consent to participate
Because this study was a retrospective analysis based on fully anonymized electronic medical records, with no potential risk to the privacy and rights of the subjects, the requirement for written informed consent was formally waived by the above Institutional Review Board. All procedures were performed in compliance with relevant institutional and national guidelines.
Data Availability Statement
De-identified data supporting this study are available from the corresponding author upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplemental material for Epidemiological characteristics and changing patterns of hospitalizations among middle-aged and elderly patients in Northwest China, 2020–2024: A retrospective cohort study by Shaomin Quan, Shuo Li, Shiying Li, Yue Li, Zhigang Fan in Sage Open Medicine.
Data Availability Statement
De-identified data supporting this study are available from the corresponding author upon reasonable request.