Assessment of medication adherence among elderly ophthalmic patients during transition from hospital to home: a cross-sectional study

Abstract

Background

Medication adherence is a critical determinant of treatment outcomes and prognosis in patients. This study aims to investigate the current status and influencing factors of medication adherence among elderly ophthalmic patients during the transition from hospital to home, thereby providing evidence-based support for clinical treatment and nursing.

Methods

A cross-sectional survey was conducted involving elderly patients who underwent ophthalmic surgery at our hospital between February 2024 and February 2025. To evaluate patients' medication adherence, the Morisky Medication Adherence Scale (MMAS-8) was administered. Pearson correlation analysis was performed to examine the associations between patients' demographic characteristics and their levels of medication adherence. Additionally, univariate and multivariate logistic regression analyses were utilized to identify factors influencing medication adherence among elderly ophthalmic patients.

Results

A total of 248 elderly ophthalmic patients were included in the study. The prevalence of poor medication adherence was 45.97% (114/248). Pearson correlation analyses indicated that age (r = 0.587), educational level (r = 0.619), average monthly household income (r = 0.624), number of medications taken (r = 0.596), and total daily medication frequency (r = 0.642) were significantly correlated with poor adherence. Logistic regression analysis revealed that increased age (AOR = 2.751, 95% CI: 2.262–3.425), lower educational level (AOR = 2.406, 95% CI: 1.966–3.414), lower average monthly household income (AOR = 3.031, 95% CI: 2.637–3.742), higher number of medications taken (AOR = 2.925, 95% CI: 2.541–3.326), and greater total daily medication frequency (AOR = 3.446, 95% CI: 2.736–3.808) were independently associated with poorer medication adherence (all p < 0.05).

Conclusion

Our study demonstrates that elderly ophthalmic patients often exhibit poorer medication adherence following discharge. These findings highlight the need for healthcare providers to enhance discharge medication counseling and strengthen follow-up efforts to ensure the safe and effective use of medications among elderly patients.

Introduction

Age-related visual impairment is a significant public health issue, with consequences extending beyond individual health to place considerable strain on family caregiving systems and societal healthcare resources [1]. From a pathophysiological perspective, visual impairments in the elderly can be categorized into two main types: physiological age-related changes (such as presbyopia) and pathological conditions (including cataracts, glaucoma, and age-related macular degeneration) [2]. Epidemiological study [3] has revealed that medication adherence among elderly patients with chronic diseases is generally poor, with an adherence rate of only 40.26%. This issue is particularly pronounced among ophthalmic patients following surgery. For instance, cataract patients, who often undergo day surgery, face significant challenges due to their short hospital stays and limited in-hospital health education [4]. This results in notable medication safety risks during the transition from hospital to home.

Existing research indicates that the incidence of medication discrepancies during the discharge transition period is as high as 90%, and these discrepancies can double the risk of readmission within 30 days [5]. For ophthalmic surgical patients, non-adherence to prescribed medications may lead to severe complications, such as surgical site infections, poor intraocular pressure control, and postoperative inflammation [6]. These complications significantly increase the risk of adverse drug events and unplanned readmissions [7]. Despite these concerns, research on medication management during the transition period for elderly ophthalmic patients remains limited. Therefore, this study focuses on elderly patients who have undergone ophthalmic surgery to examine the current state of medication adherence and its influencing factors during the hospital-to-home transition period. The findings may provide evidence-based insights for developing a scientific, continuity-of-care medication management plan. This research holds important clinical significance for reducing adverse drug events, improving visual function outcomes, and enhancing the quality of life for these patients.

Methods

Study design, area, and period

This study adopted a cross-sectional design to explore medication adherence among elderly ophthalmic patients during the hospital-to-home transition. The investigation was conducted at a top-tier tertiary hospital in China, which is designated as a national key medical institution with comprehensive ophthalmic services, including cataract surgery, glaucoma treatment, and retinal detachment repair. For this investigation, the data collection period was clearly defined as spanning from February 2024 to February 2025, during which time eligible participants were recruited and study-related information was systematically gathered. This temporal specification pertains exclusively to the data acquisition phase, distinct from the overall study timeline that includes protocol development, ethical review, and subsequent data analysis.

Population

The study population consisted of elderly patients (aged ≥ 60 years) who underwent ophthalmic surgery at the aforementioned hospital during the specified period. These patients were selected using a convenience sampling method, with recruitment conducted in the ophthalmology ward and post-operative outpatient clinics.

Eligibility criteria

The inclusion criteria were defined as follows: patients aged 65 years or older who either underwent ophthalmic surgery or required long-term ophthalmic medication, with at least one ophthalmic drug prescribed at the time of discharge.

With respect to exclusion criteria, only patients with severe psychiatric or intellectual disabilities (who were unable to provide informed consent or reliably participate in assessments) and those who explicitly declined to participate were excluded from the study. Importantly, patients with hearing impairments or communication barriers were not excluded; instead, alternative strategies were implemented to facilitate their participation, including the use of written questionnaires, support from trained interpreters (where available), or involvement of caregivers to assist with effective communication.

Sample size determination and sampling technique

The sample size was calculated using the formula for estimating proportions in cross-sectional studies: $n=Z^2\times p\times (1-p)/d^2$ where: Z represents the z-score corresponding to a 95% confidence level (1.96); p is the estimated prevalence of poor medication adherence in elderly ophthalmic patients. Based on a prior pilot study conducted at our institution, the prevalence was conservatively estimated at 50% (0.5) to maximize the sample size; d denotes the margin of error, set at 0.06 (6%) to ensure precision. Substituting the values and considering a potential non-response rate of 20%, the final required sample size was adjusted to 210. Therefore, at least 210 patients should be included for survey.

Study variables

We collected the demographic information and medication use information. The demographic section meticulously included fundamental details such as the participants' age, gender, educational level, living arrangements and comorbid conditions (including hypertension, diabetes mellitus). The medication use section focused on key elements, including the types of medications used and the total frequency of daily medication intake. The Morisky Medication Adherence Scale (MMAS-8) was employed to assess patients' medication adherence [8]. Originally developed by Morisky et al., the scale was adapted for use in domestic clinical research through translation and validation efforts by Wang Jie and colleagues [9]. The MMAS-8 comprises eight items, utilizing a hybrid scoring method. Specifically, items 1–4 and 6–7 are dichotomous, with responses of "yes" scored as 0 and "no" scored as 1. Item 5 is scored in reverse, while item 8 employs a 5-point Likert scale, with options ranging from "never" to "always" scored as 1.00, 0.75, 0.50, 0.25, and 0, respectively. The total score ranges from 0 to 8, with higher scores indicating better medication adherence. The scoring criteria define 8 as high adherence, 6–7.99 as moderate adherence, and less than 6 as low adherence. The Cronbach's α coefficient for the translated version of the scale is 0.810, and the internal consistency coefficient measured was 0.806, both of which meet acceptable standards [10, 11]. These results confirm the scale's reliability and suitability for assessing medication adherence in elderly ophthalmic patients.

Operational definitions

Elderly: Individuals aged 65 years or older, consistent with the World Health Organization’s definition[12].

Ophthalmic patients: In the context of this study, "ophthalmic patients" refers to elderly individuals (aged ≥ 65 years) who received ophthalmic interventions or long-term medication management at the study hospital, with a focus on those undergoing surgical treatment for age-related ocular conditions. This population includes patients diagnosed with cataract (undergoing phacoemulsification with intraocular lens implantation, the most prevalent procedure in the cohort), glaucoma (undergoing trabeculectomy or receiving long-term topical intraocular pressure-lowering medications), retinal diseases (such as age-related macular degeneration requiring intravitreal anti-vascular endothelial growth factor injections or retinal detachment managed via vitrectomy), and other conditions like pterygium excision or corneal suturing for traumatic lacerations.

Medication adherence: The extent to which a patient’s behavior (e.g., taking medications as prescribed) aligns with healthcare provider recommendations, as quantified by the MMAS-8. Specifically, the ophthalmic medications included: (1) anti-infective agents (e.g., levofloxacin eye drops, a fluoroquinolone antibiotic); (2) anti-inflammatory drugs (e.g., prednisolone acetate eye drops, a corticosteroid); (3) intraocular pressure-lowering medications (e.g., timolol maleate, a beta-blocker; latanoprost, a prostaglandin analog) for glaucoma management; and (4) lubricating agents (e.g., carboxymethylcellulose sodium drops) to alleviate post-surgical dry eye. These pharmacologic classes align with the primary diagnoses (cataract, glaucoma, retinal diseases) and procedures (surgical interventions, intravitreal injections) in our study population.

Data collection techniques and data quality control

At the time of discharge, the patient's medication list was meticulously formulated by the attending physician based on the patient's specific medical condition and treatment requirements. The responsible nurse then provided detailed discharge medication instructions to the patient and their family members, based on this list. The instructions covered specific methods of medication use, such as the timing of drug administration, dosage, and methods of intake (e.g., before meals, after meals, or on an empty stomach), as well as important considerations during the medication process, including potential drug side effects, warnings about drug interactions, and storage conditions for special medications. Additionally, the responsible nurse explained the specific methods and importance of follow-up to the patient and their family, ensuring that the patient received continuous medical support after discharge.

A follow-up assessment was conducted by the research team one week post-discharge, during which participants completed a pre-validated structured questionnaire. The instrument was designed to be concise and unambiguous, prioritizing the efficient collection of key data points, with the administration process typically lasting approximately 15 min. To minimize response bias and ensure comprehension, trained researchers provided immediate, detailed clarifications for any questions or ambiguities raised by participants, thereby facilitating accurate self-reporting of their experiences and behaviors. To safeguard data integrity, questionnaires were collected on-site immediately upon completion and subjected to preliminary review—including checks for missing values and logical consistency—before being finalized for data entry. This standardized protocol aimed to enhance the reliability of the collected information while respecting the time constraints of elderly participants.

Data analysis

Statistical analyses were performed using SPSS 24.0 software to ensure rigor and reproducibility. Quantitative data were summarized as mean ± standard deviation (x̄ ± s), and between-group comparisons were conducted using independent samples t-tests, which are appropriate for assessing differences in continuous variables between two independent groups. Categorical data were presented as counts (n) and percentages (%), with intergroup comparisons performed using the chi-square (χ²) test to evaluate associations between categorical variables. Pearson correlation analysis was utilized to examine the linear relationships between patients' demographic characteristics (e.g., age, education level) and medication adherence scores. To identify factors influencing medication adherence, binary logistic regression models were employed, where the dependent variable was dichotomized into "low adherence" (MMAS-8 score < 6) and "adequate adherence" (MMAS-8 score ≥ 6) based on validated cutoffs. In the initial stage, univariate binary logistic regression was employed to evaluate the crude associations between each candidate independent variable (e.g., gender, comorbidity burden, caregiver support) and the adherence outcome. This step aimed to preliminarily screen potential predictors, with a threshold of p < 0.10 adopted for variable retention. The rationale for this relatively lenient criterion lies in the exploratory nature of univariate analysis: by allowing inclusion of variables with modest associations, we sought to minimize the risk of excluding potentially relevant predictors prematurely—particularly critical in this context, where sample size constraints and the complexity of adherence behaviors might mask true associations in crude analyses. In the second stage, all variables meeting the p < 0.10 threshold from the univariate screening were incorporated into a multivariate binary logistic regression model to adjust for confounding effects and quantify independent associations. For both stages, odds ratios (ORs) and 95% confidence intervals (CIs) were computed to characterize the magnitude and precision of associations. Notably, the final determination of statistical significance in the multivariate model adhered to a stricter two-tailed threshold of p < 0.05, ensuring rigorous validation of the retained associations. This tiered approach—balancing sensitivity in variable selection with rigor in significance testing—aligns with methodological best practices for identifying robust predictors in observational studies, where the tension between Type I and Type II errors necessitates a strategic trade-off during the screening phase to optimize model validity.

Ethical considerations and consent to participate

This study employed a cross-sectional survey design and was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. The research protocol was reviewed and approved by the Institutional Review Board of hospital No.: 2025369). Prior to participation, written informed consent was obtained from all enrolled patients following a detailed explanation of the study objectives, procedures, potential risks, and benefits. Participants were assured of anonymity and the right to withdraw from the study at any time without repercussions. All data were stored securely in password-protected files to ensure confidentiality.

Results

Sociodemographic characteristic of the study participants

Among the 248 elderly ophthalmic patients included in the study, 114 patients were found to have a score of less than 6, which is indicative of poor medication adherence. This translates to a prevalence rate of poor adherence of 45.97% within this patient population. A shown in Table 1, there were statistical differences in the age, educational level, average monthly household income, number of medications taken and total daily medication frequency between poor adherence group and control group (all p < 0.05). No statistical differences in the gender, BMI, marital status, place of residence, living arrangement, hypertension and diabetes mellitus were found between poor adherence group and control group (all p > 0.05).

Table 1.

Socio-demographic characteristics of ophthalmic elderly patients at Top-tier tertiary hospital, China, February 2024 to February 2025 (n = 248)

Characteristic	Poor adherence group (MMAS-8 score < 6, n = 114)	Control group (MMAS-8 score 6–8, n = 134)	t/χ2	p
Age(years)	74.17 ± 5.03	67.08 ± 4.12	9.004	0.012
Male/female	80/34	82/52	1.253	0.085
BMI (kg/m2)	21.54 ± 2.16	21.63 ± 2.55	3.541	0.101
Marital status			2.327	0.113
Married	83 (72.81%)	102 (76.12%)
Unmarried	11 (9.65%)	14 (10.45%)
Divorced	12 (10.53%)	12 (8.96%)
Widowed	8 (7.02%)	6 (4.48%)
Place of residence			1.321	0.205
Rural area	45 (39.47%)	53 (39.55%)
Urban area	69 (60.53%)	81 (60.45%)
Educational level			1.295	0.006
Elementary school	40 (35.09%)	24 (17.91%)
Junior high school	38 (33.33%)	33 (24.63%)
Senior high school	12 (10.53%)	15 (11.19%)
Associate degree	22 (19.30%)	48 (35.82%)
Bachelor's degree or above	2 (1.75%)	14 (10.45%)
Average monthly household income per person (Yuan)			1.467	0.032
< 4000	79 (69.30%)	70 (52.24%)
≥ 4000	35 (30.70%)	64 (47.76%)
Living arrangement			1.742	0.079
Living alone	16 (14.04%)	17 (12.69%)
Living with spouse	72 (63.16%)	91 (67.91%)
Living with children	12 (10.53%)	18 (13.43%)
Other	14 (12.28%)	8 (5.98%)
Number of medications taken			2.012	0.002
< 3	19 (16.67%)	52 (38.81%)
3–4	61 (53.51%)	63 (47.01%)
≥ 5	34 (29.82%)	19 (14.18%)
Total daily medication frequency (times per day)			1.324	0.016
< 6	15 (13.16%)	40 (29.85%)
612	75 (65.79%)	88 (65.67%)
≥ 13	24 (21.05%)	6 (4.48%)
Hypertension	48 (42.11%)	40 (29.85%)	1.774	0.058
Diabetes mellitus	32 (28.07%)	31 (23.13%)	1.259	0.102

BMI, body mass index. MMAS, Morisky Medication Adherence Scale

Pearson correlation analysis on the correlation of poor adherence and characteristics of the study participants

As shown in Table 2, Pearson correlation analyses indicated that age(r = 0.587), educational level(r = 0.619), average monthly household income(r = 0.624), number of medications taken(r = 0.596) and total daily medication frequency(r = 0.642) were correlated with the poor adherence (all p < 0.05).

Table 2.

Pearson Correlation Analysis of the Associations Between Poor Medication Adherence (MMAS-8 score < 6) and Demographic/Clinical Characteristics Among Elderly Ophthalmic Patients

Characteristic	r	p
Age	0.587	0.014
Gender	0.241	0.015
BMI	0.128	0.107
Marital status	0.106	0.078
Place of residence	0.123	0.148
Educational level	0.619	0.026
Average monthly household income per person (Yuan)	0.624	0.018
Living arrangement	0.127	0.071
Number of medications taken	0.596	0.036
Total daily medication frequency (times per day)	0.642	0.005
Hypertension	0.187	0.115
Diabetes mellitus	0.204	0.072

BMI, body mass index. MMAS, Morisky Medication Adherence Scale

Factors associated with medication adherence

As presented in Table 3, the results of binary logistic regression analyses identified several significant factors associated with poor medication adherence among elderly ophthalmic patients. The dependent variable in this analysis was medication adherence status, dichotomized based on the Morisky Medication Adherence Scale-8 (MMAS-8) score: "poor adherence" (score < 6, coded as 1) and "adequate adherence" (score ≥ 6, coded as 0).

Table 3.

Binary logistic regression analysis of factors influencing poor medication adherence (MMAS-8 score < 6) in elderly ophthalmic patients

Variables	β	SE	Adjusted OR	95% Confidence Interval	p
Age	0.148	0.101	2.751	2.262–3.425	0.043
Educational level (ref: college and above)	0.128	0.115	2.406	1.966–3.414	0.019
Average monthly household income per person (Yuan, ref: ≥ 5000)	0.152	0.112	3.031	2.637–3.742	0.026
Number of medications taken	0.212	0.107	2.925	2.541–3.326	0.015
Total daily medication frequency (times per day)	0.119	0.103	3.446	2.736–3.808	0.004

The table presents results from a single multivariable binary logistic regression model. Dependent variable: poor medication adherence (MMAS-8 score < 6). Reference groups (ref) are specified for categorical variables. SE = standard error; OR = odds ratio; CI = confidence interval; MMAS-8 = 8-item Morisky Medication Adherence Scale

Table 3 reports adjusted odds ratios (AORs) derived from a single multivariable binary logistic regression model that simultaneously included all variables listed. This model controlled for potential confounding among the predictors, with the final set of variables selected based on significance in preliminary univariate analyses (p < 0.10) and theoretical relevance.

Results indicated that increased age (AOR = 2.751, 95% CI: 2.262–3.425), lower educational level (AOR = 2.406, 95% CI: 1.966–3.414), lower average monthly household income (AOR = 3.031, 95% CI: 2.637–3.742), higher number of medications taken (AOR = 2.925, 95% CI: 2.541–3.326), and greater total daily medication frequency (AOR = 3.446, 95% CI: 2.736–3.808) were independently associated with poorer medication adherence (all p < 0.05). These findings suggest that these factors collectively contribute to the likelihood of poor adherence in this patient population.

Discussion

The present study reveals that 45.97% of elderly ophthalmic patients exhibit poor medication adherence within one week post-discharge, establishing the hospital-to-home transition as a critical vulnerability window for medication management. This striking prevalence stems from a synergistic interplay of systemic and patient-specific factors that converge during this transitional period. Clinically, the disruption of continuous medical supervision during care setting transfer creates a regulatory gap, which is compounded by dynamic adjustments in patients' clinical status, evolving medication regimens, and potential breakdowns in prescription communication or implementation. These systemic discontinuities directly contribute to the divergence between prescribed regimens and actual medication-taking behaviors. Superimposed on this framework are the unique vulnerabilities of elderly patients: age-related multimorbidity necessitates complex medication regimens, while concurrent declines in cognitive function and self-care capacity undermine effective self-management. Ophthalmic care introduces additional layers of complexity, including the need for concurrent use of multiple formulations (e.g., eye drops, ointments, gels) post-surgery, frequent dosing schedules, prolonged treatment courses, and dynamic regimen adjustments during recovery [13, 14]. Collectively, these factors—mediated through the significant influences of age, educational attainment, economic status, and medication complexity—drive the suboptimal adherence observed in this population following surgical intervention.

Our finding that advancing age independently predicts poorer adherence aligns with, and extends, existing literature on geriatric medication management. Age-related declines in cognitive function, memory retention, and self-care capacity create a cumulative burden that complicates medication adherence [15]. as quantified in a meta-analysis reporting a 2.62-fold increased risk of poor adherence with advancing age [16]. This effect is amplified in our cohort by the ophthalmic context: older patients must navigate not only age-related cognitive limitations but also the specialized administration techniques required for ophthalmic formulations—creating a "double vulnerability" that explains the strong association observed. Additionally, age-related multimorbidity exacerbates this challenge through polypharmacy, which increases both cognitive load and practical demands of medication administration [17, 18]. These interconnected mechanisms underscore why age emerges as a particularly critical predictor in post-ophthalmic surgical care, where precise adherence directly impacts visual outcomes.

Educational attainment emerges as a key modulator of adherence behaviors, with lower educational levels correlating with poorer medication adherence. This relationship operates primarily through health literacy pathways: limited educational background impairs patients' ability to acquire, process, and apply medical information, thereby reducing their understanding of therapeutic rationales and weakening adherence motivation [19, 20]. Consistent with prior research documenting a positive correlation between educational level and adherence [21]. our findings highlight how educational disparities translate into differential capacity to engage with complex ophthalmic treatment guidelines. Further compounding this effect, individuals with lower educational attainment often rely more heavily on external support—support that becomes unreliable during the transition to home care. The clinical significance of this relationship is underscored by evidence that patients with educational attainment below junior high school experience a 2.3-fold higher medication error rate compared to college-educated counterparts [22, 23], directly linking educational disparities to tangible challenges in accurate self-administration of ophthalmic medications.

Economic status exerts a multifaceted influence on medication adherence in our cohort, with lower monthly income significantly associated with poorer adherence. Direct financial barriers are most immediately apparent: economic constraints limit access to multiple concurrent treatments, particularly newer, more expensive ophthalmic formulations [29]. This aligns with broader evidence identifying medication costs as a critical adherence barrier in elderly populations, where budgetary limitations force trade-offs between healthcare and other essential needs [24]. Beyond direct affordability, economic hardship indirectly undermines adherence through suboptimal medication storage and management practices [25, 26]. For instance, low-income patients frequently adopt cost-mitigating behaviors such as reducing dosing frequency, self-adjusting regimens, or discontinuing treatment altogether, with newer, more expensive ophthalmic formulations being particularly vulnerable to such modifications [27]. Quantifying this relationship, empirical data indicate that patients with monthly incomes below the local minimum wage face a 3.5-fold higher risk of medication interruptions compared to their higher-income counterparts [28]. a disparity that assumes particular significance in ophthalmic care where treatment interruptions can compromise surgical outcomes. Collectively, these findings demonstrate how economic status shapes adherence through a complex interplay of direct and indirect mechanisms, highlighting socioeconomic disparities as a critical target for intervention.

Finally, medication complexity—defined by both the number of medications and dosing frequency—emerges as an independent predictor of poor adherence, consistent with a robust body of literature identifying polypharmacy and regimen complexity as key adherence barriers [29–31]. A prior study explicitly quantified this relationship, noting that increased numbers of prescribed drugs correlate significantly with poor adherence [16]. In the ophthalmic context, this challenge is amplified: postoperative patients typically manage 3–5 distinct formulations with dosing frequencies ranging from once daily to once hourly. Such regimens impose substantial cognitive and practical burdens on elderly patients, increasing the likelihood of missed or incorrect doses [32]. Notably, this structural complexity—independent of patient characteristics—has been shown to reduce adherence rates by up to 60% [33], underscoring the need for regimen simplification in postoperative ophthalmic care. These findings highlight the critical importance of balancing therapeutic necessity with implementation feasibility when designing medication regimens for this vulnerable population.

Limitations of the study

Several limitations of this study should be considered. Firstly, the data were derived exclusively from elderly ophthalmic patients at a single tertiary hospital in China, with a relatively small sample size. Given the distinct disparities in medical resource allocation and patient demographic profiles across healthcare institutions of varying tiers and geographic regions, the findings may not be generalizable to elderly ophthalmic populations in other settings. Future research should therefore expand sampling to include participants from multi-tiered hospitals across diverse regions, thereby enhancing the representativeness of the cohort and strengthening the external validity of conclusions regarding medication adherence patterns in this population. Secondly, a notable limitation is its cross-sectional design, which measures medication adherence and associated factors at a single time point during the hospital-to-home transition, precluding the establishment of temporal causality between potential influencing factors (e.g., post-operative support) and adherence behaviors. This design also restricts our ability to capture dynamic changes in adherence patterns over time, such as fluctuations in medication compliance during the weeks following discharge, which may be critical for understanding long-term therapeutic outcomes in elderly ophthalmic patients. While cross-sectional data provide valuable insights into the prevalence and correlates of adherence at the transitional phase, they lack the longitudinal precision of prospective designs, which would allow for standardized tracking of variables (e.g., changes in caregiver availability) and their sequential effects on adherence. Consequently, our findings should be interpreted as a snapshot of adherence dynamics during a specific post-operative window, and future prospective cohort studies are warranted to validate these associations, clarify causal relationships, and explore how adherence evolves across the broader post-discharge trajectory. Finally, a notable limitation of the present study is the incomplete coverage of potential determinants of medication adherence. While we analyzed several influencing factors, critical variables recognized in the literature—including adverse drug reactions, physical activity levels, mental status (e.g., anxiety or depression), healthcare provider-patient communication patterns, and financial factors such as insurance coverage or out-of-pocket costs—were not incorporated due to initial design constraints. This omission may restrict the comprehensiveness of our findings, as medication adherence is inherently a multifactorial behavior shaped by the interplay of individual (e.g., psychological traits), social (e.g., caregiver support), systemic (e.g., healthcare accessibility), and treatment-related (e.g., medication palatability, packaging) factors. To address this gap, future research should adopt a theoretically grounded, multidimensional framework—such as the World Health Organization’s multidimensional adherence model—to guide variable selection. Expanding the scope of investigation to include the aforementioned unmeasured factors, coupled with larger sample sizes and longitudinal designs, would enable a more nuanced analysis of causal mechanisms underlying adherence behaviors.

Conclusion

In conclusion, the results of this study indicate that the incidence of poor medication adherence among elderly ophthalmic patients during the hospital-to-home transition period is 45.97%. Medication adherence is poorer among elderly patients who are older, have lower educational levels, lower average monthly incomes, and take a greater number and frequency of medications. Improving medication adherence in elderly ophthalmic patients following surgery requires precise interventions tailored to different influencing factors.

Recommendation

Based on the findings, targeted recommendations for clinical treatment and nursing practice are proposed to improve medication adherence among elderly ophthalmic patients during the hospital-to-home transition. First, for older patients with lower educational levels, healthcare providers should adopt simplified and visualized medication education strategies. This may include developing personalized medication schedules with large-font instructions, using color-coded pill boxes to distinguish different ophthalmic drugs, and integrating verbal explanations with pictorial aids—approaches supported by studies showing that simplified educational materials enhance adherence in elderly populations with limited health literacy. Second, considering the association between economic burden (lower monthly income) and poor adherence, clinical teams should collaborate with social work departments to identify patients at risk of financial constraints. Referrals to medication assistance programs, guidance on accessing cost-effective generic ophthalmic formulations, and coordination with insurance providers to optimize coverage for long-term medications could alleviate economic barriers, as demonstrated in previous research on medication affordability and adherence. Third, to address the challenge of polypharmacy and high daily medication frequency, nurses and pharmacists should conduct regular medication reconciliation to streamline regimens when clinically feasible. For example, consolidating dosages into once-daily formulations (where available) or synchronizing administration times with daily routines (e.g., morning eye drops with breakfast) can reduce complexity, a strategy validated in studies on reducing medication burden in elderly patients. Additionally, post-discharge follow-up via telephone or telehealth platforms—focused on reinforcing medication schedules and addressing adherence barriers—may further support sustained compliance during the transitional phase. These recommendations emphasize a multidisciplinary approach, integrating medical, nursing, and social support to address the multifactorial nature of poor adherence, ultimately improving post-operative outcomes in this vulnerable patient group.

Author contributions

L L, Y W designed research; L L, G S, Y S, Y W conducted research; L L, G S analyzed data; L L, Y W wrote the first draft of manuscript; Y W had primary responsibility for final content. All authors read and approved the final manuscript. All authors contributed to the conception or design of the study or to the acquisition, analysis, or interpretation of the data. All authors drafted the manuscript, or critically revised the manuscript, and gave final approval of the version that was submitted for publication. All authors agree to be accountable for all aspects of the work, ensuring integrity and accuracy.

Funding

This study did not receive any funding in any form.

Data availability

The data associated with the paper are not publicly available but are available from the corresponding author on reasonable request.

Declarations

Conflict of interests

The authors declare no competing interests.

Ethics approval and consent to participate

All methods were performed in accordance with the relevant guidelines and regulations. The study has been reviewed and approved by the ethics committee of The First Affiliated Hospital of Soochow University (approval number: 2025369). And written informed consents had been obtained from all the included patients.

Consent for publication

Not applicable.