Evaluating the Impact of 2023 Beers Criteria-Defined Inappropriate Prescribing on Health Outcomes in the Elderly

The global demographic transition toward an aging population has significantly increased the burden of chronic diseases and the prevalence of polypharmacy among older adults. Polypharmacy, while often necessary for managing multiple comorbidities, is strongly associated with an elevated risk of adverse drug events (ADEs), drug–drug interactions, and drug–disease interactions. These risks are further amplified by age-related alterations in pharmacokinetics and pharmacodynamics, rendering elderly individuals particularly susceptible to medication-related harm.

Among the key contributors to such harm are potentially inappropriate medications (PIMs), which continue to be widely prescribed despite well-documented risks. PIM use in older adults has been consistently linked to adverse clinical outcomes, including cognitive impairment, delirium, falls, and functional decline. Notably, medications with anticholinergic and sedative properties are frequently utilized in this population for a range of clinical conditions; however, their central nervous system effects—such as impaired memory, reduced alertness, and confusion—pose significant threats to cognitive integrity and overall well-being.

The American Geriatrics Society (AGS) Beers Criteria is an established, evidence-based tool designed to guide clinicians in identifying and minimizing the use of PIMs in individuals aged 65 years and above. The 2023 update of the Beers Criteria provides refined recommendations, with particular emphasis on high-risk drug classes, including anticholinergics and sedatives, and their association with negative health outcomes. By promoting rational prescribing and deprescribing practices, the criteria serve as a critical framework for optimizing pharmacotherapy in geriatric populations.

In parallel, quality of life (QoL) has emerged as a key outcome measure in geriatric care, reflecting not only physical health but also psychological, social, and environmental well-being. Cognitive function plays a central role in determining QoL, as impairments can adversely affect independence, daily functioning, and social participation. Despite growing recognition of these interrelationships, existing studies have largely focused on isolated clinical outcomes or hospital-based populations.

In the Indian context, there remains a paucity of community-based research examining the combined impact of PIM use on both cognitive function and quality of life. This gap limits the understanding of real-world medication-related risks and their broader implications on elderly health outcomes.

Therefore, the present study aims to evaluate the impact of potentially inappropriate prescribing, as defined by the 2023 Beers Criteria, on health outcomes among elderly individuals, with a specific focus on anticholinergic and sedative drug classes, and to assess associated changes in cognitive function and quality of life using validated assessment tools.

2. MATERIALS AND METHODS

2.1 Study Design and Setting

This study was designed as a prospective observational study to assess the impact of potentially inappropriate medications on cognitive function and quality of life among elderly individuals. The study was conducted in a community setting at Mala Grama Panchayat, Thrissur, after obtaining necessary permission from the Panchayat authorities.

2.2 Study Population and Sample Size

The study population comprised elderly individuals aged ≥65 years residing in the selected community. A total of 100 participants were included in the study. The participants were selected from the community population and were evaluated for their demographic characteristics and medication usage patterns.

2.3 Informed Consent

Written informed consent was obtained from all participants or their relatives prior to data collection.

2.4 Inclusion Criteria

The study included:

• Elderly individuals aged ≥65 years
• Patients prescribed anticholinergic drugs, identified using Duran’s list
• Patients prescribed sedative drugs, classified under the WHO Anatomical Therapeutic Chemical (ATC) classification system
• Patients receiving anticholinergic and/or sedative medications for a minimum duration of 2 months

2.5 Exclusion Criteria

The study excluded:

• Bedridden and unresponsive patients
• Patients with serious chronic conditions
• Patients who did not provide consent

2.6   Data Collection

Data were collected using a structured data collection form developed with reference to previous studies. The collected information included:

• Demographic details
• Medical conditions
• Current medication details
• Use of over-the-counter drugs
• Use of alternative systems of medicine

Medical history was recorded to identify chronic illnesses and any previous adverse events related to treatment. Medication details were collected to determine the presence of potentially inappropriate prescribing.

2.7   Data Collection Tools

The following tools were used in the study:

• Data collection form
• ATC classification system
• Duran’s list
• WHOQOL-BREF scale
• Montreal Cognitive Assessment (MoCA) version 7.1
• AGS Beers Criteria 2023

Anticholinergic drugs were identified using Duran’s list, while sedative drugs were categorized using the ATC classification system.

2.8   Assessment of Potentially Inappropriate Medications

Potentially inappropriate medications were identified using the American Geriatrics Society (AGS) Beers Criteria 2023, which is applicable to individuals aged 65 years and above. The criteria include:

• Level 1: Medications to avoid in older adults
• Level 2: Medications to avoid in older adults with specific diseases or syndromes
• Level 3: Medications to be used with caution
• Level 4: Drug–drug interactions to avoid
• Level 5: Medications requiring dose adjustment based on kidney function

In this study, only Levels 1, 2, and 4 were analyzed. Level 3 was excluded as it requires detailed clinical assessment, and Level 5 was excluded due to lack of laboratory data in the community setting.

2.9   Assessment of Cognitive Function

Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) version 7.1, a standardized 30-point screening tool used to detect mild cognitive impairment and early dementia. The tool evaluates multiple cognitive domains including memory, attention, language, visuospatial skills, executive function, abstraction, delayed recall, and orientation.

MoCA scores range from 0 to 30, with lower scores indicating greater cognitive impairment:

• 26–30: Normal
• 18–25: Mild cognitive impairment
• 10–17: Moderate cognitive impairment
• <10: Severe cognitive impairment

2.10   Assessment of Quality of Life

Quality of life was assessed using the WHOQOL-BREF scale, which consists of 26 questions. Each question is scored on a scale of 1 to 5 based on patient responses. The tool evaluates four domains:

• Physical health
• Psychological well-being
• Social relationships
• Environmental factors

The raw scores obtained were converted into transformed scores for analysis.

2.11   Data Processing and Statistical Analysis

The collected data were entered into Microsoft Excel for initial organization and coding. The data were then subjected to statistical analysis and represented in graphical form for easier interpretation.

Statistical analysis was performed using IBM SPSS Statistics version 31.0.2.0 for Windows. The association between continuous variables, including MoCA scores, WHOQOL-BREF scores, and Beers Criteria levels, was assessed.

Multiple linear regression analysis was performed to evaluate the relationship between potentially inappropriate medication use and health outcomes. The hypothesis tested was that an increase in PIM exposure leads to a decrease in cognitive function and quality of life. Interpretations were made based on regression coefficients and p-values.

3. RESULT AND DISCUSSION

3.1 Demographics

3.1.1 Age Distribution

Fig.1: Age Distribution of patients

3.1.2   Gender

Fig.2: Gender Distribution of patient

3.2 Medication details

The medication data revealed that patients were receiving various drug classes along with anticholinergic and/or sedative medications.

• Sedatives: The most commonly prescribed sedative was Clonazepam (20 patients), followed by Alprazolam (7), Lorazepam (6), Chlordiazepoxide (3), Zolpidem (3), and Melatonin (2).
• Anticholinergics: The most commonly used anticholinergic drug was Trihexyphenidyl (8 patients), with other frequently used drugs including Escitalopram (15), Theophylline (11), Risperidone (10), and others.

Based on medication usage:

Table 1: Medication Pattern

3.3 BEERS criteria

Table 2: Distribution of patient details based on beers criteria

Table 3: Distribution of patients according to Beers Criteria

3.4 Montreal cognitive assessment (MoCA)(Version 7.1)

Table 4: Distribution of patients by MoCA Score Range

3.3.1 statistical analysis of MoCA

Fig .3 statistical analysis output of gender, age, PIM and MoCA using multiple linear regression analysis

3.5 WHOQOL- BREF

Table 5: Beers categories and average transformed score of patients in each domain

3.5 Statistical analysis of  WHOQOL- BREF

Multiple linear regression analysis demonstrated that potentially inappropriate medication (PIM) use was a significant independent predictor of reduced quality of life across all domains of the WHOQOL-BREF scale.

A significant negative association was observed between PIM exposure and:

• Physical domain (β = -0.269, p = 0.009)
• Psychological domain (β = -0.481, p < 0.001)
• Social domain (β = -0.242, p = 0.020)
• Environmental domain (β = -0.298, p = 0.004)

These findings indicate that increased PIM use is associated with a significant decline in overall quality of life. Age and gender were not found to be significantly associated with any of the WHOQOL-BREF domains.

Fig.4 Statistical analysis output of age, gender, PIM and WHOQOL Bref scale distribution in physical domain using multiple linear regression analysis.

Fig.5 Statistical analysis output of age, gender, PIM and WHOQOL Bref scale distribution in psychological domain using multiple linear regression analysis.

Fig.6 Statistical analysis output of age, gender, PIM and WHOQOL Bref scale distribution in Social domain using multiple linear regression analysis.

Fig.7 Statistical analysis output of age, gender, PIM and WHOQOL Bref scale distribution in Environmental domain using multiple linear regression analysis.