Age can substantially influence the safety profile of medicines through differences in pharmacokinetics, pharmacodynamics, comorbidity burden, polypharmacy, organ function, and physiological reserve. However, adverse-event reporting patterns across age groups are not always apparent from conventional clinical studies because older adults are frequently underrepresented in clinical trials and may experience multiple concurrent exposures. The present study investigates age-dependent safety signatures of modern therapeutics using real-world pharmacovigilance data from the U.S. Food and Drug Administration Adverse Event Monitoring System (AEMS), formerly known as the FDA Adverse Event Reporting System (FAERS). The study will compare adverse-event profiles and serious clinical outcomes among younger and older adult populations and will evaluate whether specific drug–event combinations are disproportionately reported in different age groups. A structured pharmacovigilance dataset will be constructed from publicly available FDA quarterly files containing demographic characteristics, medicinal products, adverse reactions, and patient outcomes. Reports will be categorized into predefined age groups, and duplicate reports will be addressed before analysis. Descriptive statistics will characterize demographic and clinical reporting patterns. Serious outcomes, including hospitalization and death, will be compared between age groups using appropriate inferential methods. Reporting Odds Ratios (RORs), Proportional Reporting Ratios (PRRs), and complementary Bayesian disproportionality measures will be used to identify potential age-specific safety signals. The FDA describes the AEMS/FAERS quarterly files as containing demographic, drug, reaction, outcome, and reporting-source information, making the system suitable for secondary pharmacovigilance research. Disproportionality methods such as ROR, PRR, BCPNN, and MGPS are established approaches for identifying potential safety signals in spontaneous-reporting databases. The study is expected to provide a comparative safety map showing whether serious adverse-event patterns differ systematically according to age. Such findings could support age-sensitive pharmacovigilance, hypothesis generation, and prioritization of medicines requiring further safety evaluation. Because spontaneous reports cannot establish incidence or causality, identified associations will be interpreted as safety signals rather than confirmed drug-related risks.

Age is an important determinant of medicine safety, yet differences in adverse-event patterns between younger and older adults are not always adequately captured during conventional drug development. Older adults frequently receive multiple medicines simultaneously and may have reduced renal or hepatic function, altered pharmacokinetics, greater comorbidity burden, and lower physiological reserve. These factors can change both the likelihood and the clinical consequences of adverse drug reactions. At the same time, younger adults may demonstrate different exposure patterns, treatment indications, and reporting behaviors. Consequently, age should not be regarded simply as a demographic characteristic but as a potentially important modifier of real-world medicine safety.

Clinical trials provide essential evidence for evaluating efficacy and safety, but their populations may not fully represent the diversity encountered after medicines enter routine clinical practice. Older adults, particularly those with multiple chronic conditions or extensive polypharmacy, may be underrepresented in pivotal trials. Real-world pharmacovigilance therefore provides an important complementary source of evidence for identifying safety patterns across broader patient populations.

The U.S. Food and Drug Administration's Adverse Event Monitoring System (AEMS), formerly known as the FDA Adverse Event Reporting System (FAERS), is designed to support post-marketing safety surveillance of medicines and therapeutic biologics. The database contains adverse-event reports together with information on drugs, reactions, patient outcomes, demographics, and reporting sources. The historical FAERS data extend from January 2004 onward, with quarterly updates.

The transition from FAERS to AEMS in March 2026 represents an important development in the FDA's broader pharmacovigilance infrastructure. FDA describes AEMS as a consolidated platform intended to improve data quality, standardization, analytics, and cross-product safety surveillance.

Spontaneous-reporting databases are particularly valuable for signal detection because they can identify uncommon or unexpected events that may not become apparent during pre-approval clinical development. However, these databases have important limitations. Reports are influenced by prescribing patterns, media attention, stimulated reporting, reporting practices, missing information, and the underlying disease. Therefore, a disproportionate reporting signal should be interpreted as a hypothesis-generating association rather than proof of causality or an estimate of absolute incidence.

Disproportionality analysis provides a practical statistical framework for examining these associations. The Reporting Odds Ratio (ROR) compares the odds of a particular event being reported with a drug or exposure of interest against other drugs, while the Proportional Reporting Ratio (PRR) compares the proportion of a particular event among reports involving the exposure with the corresponding proportion for other medicines. Bayesian approaches, including the Bayesian Confidence Propagation Neural Network and empirical Bayesian methods, can provide additional safeguards against unstable signals associated with small numbers of reports.

Recent pharmacovigilance research has demonstrated the value of combining multiple disproportionality methods rather than relying on a single statistical measure. Studies using ROR, PRR, and Bayesian approaches commonly require concordance between methods before classifying an association as a potential signal.

Although numerous pharmacovigilance studies have examined individual medicines or specific adverse-event categories, an age-sensitive comparative approach may provide additional insight into how serious safety patterns differ between patient populations. The clinical meaning of an adverse event may also differ according to age. For example, an event that is manageable in a younger adult may result in hospitalization or death more frequently in an older individual because of reduced physiological reserve or coexisting disease.

The present study therefore focuses not only on the frequency of adverse events but also on serious outcomes and disproportionality signals across age groups. Rather than assuming that older adults simply experience more adverse events, the analysis will investigate whether the types and relative reporting strength of serious adverse events differ systematically between younger and older adults.

The study further aims to compare safety signatures across selected therapeutic classes. Such an approach may help distinguish patterns associated with particular pharmacological classes from broader age-related reporting effects. It also provides an opportunity to identify drug–event combinations that warrant further clinical or regulatory investigation.

The primary objective of this study is to characterize age-dependent patterns of serious adverse-event reporting using real-world AEMS/FAERS data. Secondary objectives are to compare hospitalization and mortality-related outcomes between age groups, identify potential age-specific disproportionality signals, and determine whether selected therapeutic classes demonstrate distinct safety profiles across age categories.

The central hypothesis is that serious adverse-event reporting patterns and disproportionality signals differ between younger and older adults and that these differences vary across therapeutic classes.

By combining demographic stratification, serious-outcome analysis, and established pharmacovigilance signal-detection methods, this study seeks to develop an age-sensitive real-world safety profile of modern therapeutics. The findings are intended to generate clinically relevant safety hypotheses rather than establish causal relationships.

2.1. Age as a determinant of adverse drug reactions

Age is increasingly recognized as an important modifier of medicine safety. The relationship is not explained by chronological age alone; rather, ageing is accompanied by changes in renal and hepatic function, body composition, pharmacokinetics, pharmacodynamics, physiological reserve, and susceptibility to drug-related harm. These changes can become particularly important when several medicines are administered concurrently. Studies of spontaneous reporting systems have demonstrated that age and sex can influence adverse-drug-reaction reporting patterns, although the direction and magnitude of these effects vary according to the medicines and outcomes examined.

Older adults may also experience a greater burden of multimorbidity and polypharmacy. Consequently, distinguishing an adverse reaction associated with one medicine from events related to underlying disease, drug–drug interactions, or multiple simultaneous exposures can be difficult. Pharmacovigilance databases therefore provide an important opportunity to examine large numbers of real-world reports across diverse patient populations.

2.2. Pharmacovigilance in older and younger populations

Comparative analyses of spontaneous reports have shown that the distribution of adverse drug reactions differs between older and younger adults. A large German pharmacovigilance analysis identified substantial numbers of reports in both populations and demonstrated differences in reported characteristics between adults aged 19–65 years and those older than 65 years.

Earlier pharmacovigilance research has similarly suggested that serious adverse reactions may become more prominent with increasing age. However, the relationship is not necessarily linear, and reporting patterns may be influenced by drug exposure, healthcare utilization, comorbidity, and reporting behavior.

These findings indicate that age-stratified analysis should not simply assume that older patients experience more adverse reactions. Instead, it should examine which events occur disproportionately and which outcomes are associated with greater clinical seriousness.

2.3 Polypharmacy and potentially inappropriate medicines

Polypharmacy represents an important contributor to medication-related risk among older adults. A pharmacovigilance study of older patients found that adverse reactions frequently occurred in the context of multiple medicines, with potentially inappropriate medicines representing an identifiable subset of exposures. Cardiovascular medicines, antithrombotics, antibacterials, analgesics, and several psychotropic medicines were among drug categories associated with reported reactions.

The importance of polypharmacy extends beyond the number of medicines prescribed. Drug combinations can alter pharmacokinetics, pharmacodynamics, adherence, and the probability of drug–drug interactions. In older adults, these effects may be amplified by reduced physiological reserve and multiple chronic diseases.

2.4 Serious adverse outcomes in older adults

Serious adverse drug reactions deserve particular attention because they may result in hospitalization, life-threatening complications, disability, or death. Previous pharmacovigilance investigations in older adults have identified antibiotics, cardiovascular medicines, and other commonly used therapeutic categories among frequently implicated medicines.

Importantly, serious outcomes cannot be interpreted solely according to the frequency of adverse-event reports. A relatively uncommon event may have substantial clinical importance if it is strongly associated with hospitalization or mortality. This supports the use of a research framework that considers both event frequency and outcome severity.

2.5 FDA pharmacovigilance data

The FDA Adverse Event Monitoring System (AEMS), formerly known as the FDA Adverse Event Reporting System (FAERS), is a major source of post-marketing safety information. FDA describes AEMS as a consolidated system designed to strengthen safety surveillance, data quality, standardization, and analytical capabilities.

The historical FAERS/AEMS data contain information concerning reported medicinal products, adverse reactions, patient characteristics, outcomes, and reporting sources. These data permit large-scale analyses that would be difficult to conduct using conventional single-center pharmacovigilance databases.

The major advantage of this type of database is its scale and real-world character. It can capture unusual or unexpected events occurring after medicines are used in broader populations than those enrolled in clinical trials.

However, spontaneous-reporting systems have important limitations. Reporting is not equivalent to incidence, because the number of reports depends on the number of exposed patients, reporting behavior, awareness of potential reactions, media attention, regulatory activity, and other factors. Therefore, pharmacovigilance analyses should primarily be interpreted as signal-detection studies rather than direct estimates of absolute risk.

2.6 Disproportionality analysis

Disproportionality analysis is widely used to identify potential associations between medicines and adverse events in spontaneous-reporting databases. The fundamental principle is to determine whether a particular drug–event combination occurs more frequently than would be expected relative to a comparator reporting background.

The Reporting Odds Ratio (ROR) is one of the most widely used measures. It compares the odds of reporting a particular adverse event for a drug of interest with the corresponding odds for other medicines.

The Proportional Reporting Ratio (PRR) provides a related measure by comparing the proportion of a particular event among reports involving the medicine of interest with the corresponding proportion among comparator medicines.

Bayesian approaches can provide additional signal-detection information and may be particularly useful when event counts are small or reporting distributions are highly uneven.

Using more than one disproportionality method can strengthen the robustness of signal detection because a finding that appears consistently across independent analytical approaches is less likely to depend solely on one statistical formulation.

2.7 Age-specific pharmacovigilance signals

Age-specific pharmacovigilance is already an active research area. For example, a recent FAERS analysis of immune-checkpoint inhibitor therapy found that some immune-related adverse-event categories differed according to age, with renal and musculoskeletal events increasing in older adults while several endocrine, gastrointestinal, hepatobiliary, and ocular events showed different age-related patterns.

Similarly, recent studies have examined age-related adverse-event patterns associated with hydroxychloroquine and other individual medicines.

These studies demonstrate the value of age-stratified pharmacovigilance but also indicate an important limitation in the existing literature: many analyses focus on one medicine, one therapeutic class, or one adverse-event category.

A broader comparative framework may therefore provide additional information by examining whether age-dependent safety signatures remain consistent across different therapeutic classes.

2.8 Therapeutic-class differences

Medicines from different therapeutic classes can produce substantially different adverse-event profiles because of differences in pharmacological targets, metabolism, mechanisms of toxicity, dose ranges, and patient populations.

For example, cardiovascular medicines are frequently prescribed to older populations and may interact with age-related changes in cardiovascular, renal, and metabolic function. Antineoplastic medicines may produce complex immune, hematological, gastrointestinal, hepatic, or neurological toxicities. Anti-infective medicines may generate renal, hepatic, neurological, or hypersensitivity reactions.

Consequently, comparing age-associated safety patterns across therapeutic classes may provide information that cannot be obtained by examining a single drug.

2.9 Serious outcomes versus reporting frequency

A major methodological consideration is the distinction between how frequently an adverse event is reported and how serious the reported outcome is.

An event may have a high reporting frequency but a relatively low proportion of hospitalization or death. Conversely, a less frequently reported event may have a much greater association with serious outcomes.

For this reason, the present research framework incorporates both event-level disproportionality and patient-outcome analysis. Hospitalization and death can provide additional dimensions for characterizing the clinical importance of identified signals.

2.10 Limitations of existing evidence

Several limitations remain in the current pharmacovigilance literature. First, age categories are not consistently defined across studies, making direct comparison difficult. Second, many studies focus on individual medicines rather than therapeutic classes. Third, some analyses report disproportionality without examining whether identified signals are associated with serious outcomes. Fourth, spontaneous-reporting bias and missing demographic information can influence age-specific estimates.

Recent studies also illustrate how quickly this field is expanding. For example, current FAERS investigations have examined age-stratified safety of androgen-receptor pathway inhibitors, immune-checkpoint inhibitors, amiodarone, sacubitril/valsartan, and other therapies.

2.11 Research gap

The existing evidence supports the importance of age-sensitive pharmacovigilance, but a broader comparative framework linking age, therapeutic class, disproportionality signals, and serious outcomes remains valuable.

The present study therefore proposes to examine real-world AEMS/FAERS reports using a standardized age-stratified approach. Rather than asking only whether older adults report more adverse events, the analysis will investigate whether the nature, relative reporting strength, and clinical seriousness of adverse-event signals differ systematically across age groups and therapeutic classes.

This approach may provide a more informative age-dependent safety signature than simple counts of adverse-event reports and could identify drug–event combinations that warrant further investigation.

3.1. Study Design

This study will use a retrospective, observational pharmacovigilance design based on publicly available FDA adverse-event reports. The objective is to identify differences in serious adverse-event reporting patterns between younger and older adults and to determine whether these patterns vary across selected therapeutic classes.

The analysis will use the FDA Adverse Event Monitoring System (AEMS), including historical FAERS records where applicable. AEMS is the FDA's current pharmacovigilance platform, while historical FAERS quarterly data remain available for research use.

The study will follow a predefined analytical protocol to minimize post-hoc selection of outcomes and reduce the possibility of data-driven conclusions.

3.2 Data Source

Data will be obtained from the FDA's publicly available AEMS/FAERS quarterly data files. The database contains information relating to patient demographics, medicinal products, adverse reactions, patient outcomes, report sources, and related pharmacovigilance variables.

The FDA emphasizes that spontaneous adverse-event reports are used for safety surveillance and cannot by themselves establish that a medicine caused a reported event or determine the incidence of an adverse reaction.

The analysis will therefore be presented as a signal-detection and hypothesis-generating study.

3.3 Study Population

The study population will consist of adult spontaneous adverse-event reports containing sufficient information for age-based classification.

Reports will initially be divided into predefined age categories:

Reports with missing, invalid, or implausible age information will be excluded from age-specific analyses but may be retained for analyses that do not require age.

Where the available data permit, an additional sensitivity analysis will divide older adults into:

• 65–74 years
• 75–84 years
• ≥85 years

This secondary analysis will determine whether safety signals progressively change within the older population.

3.4 Study Period

The study period will be defined according to the actual AEMS/FAERS files downloaded for analysis.

The final manuscript will explicitly report:

“Reports submitted between [START DATE] and [END DATE] were analyzed.”

This should be inserted only after the dataset has actually been downloaded and processed.

3.5 Selection of Therapeutic Classes

Rather than analyzing all medicines indiscriminately, selected therapeutic classes will be identified according to predefined criteria, including:

1. substantial numbers of reports;
2. clinical relevance to both age groups;
3. availability of adequate drug-identification information;
4. representation of different pharmacological mechanisms; and
5. sufficient numbers of serious outcomes for statistical comparison.

The final therapeutic classes will therefore be determined after inspection of the actual dataset.

3.6 Drug Identification

Medicines will be identified using the standardized drug-name fields available in the AEMS/FAERS records.

Where multiple names, spelling variants, brand names, or formulation descriptions are present, drug names will be standardized before analysis.

Duplicate representations of the same active ingredient will be consolidated where scientifically appropriate.

Combination products will be handled separately when individual active ingredients cannot reliably be distinguished.

3.7 Adverse-Event Classification

Reported adverse reactions will be standardized using the applicable Medical Dictionary for Regulatory Activities (MedDRA) terminology where available.

Events will be examined at the preferred-term level and, where appropriate, grouped into clinically meaningful system-organ-class categories.

The primary analysis will focus on reported adverse events rather than attempting to reinterpret individual clinical diagnoses beyond the terminology contained in the database.

3.8 Definition of Serious Outcomes

Serious outcomes will be identified using the outcome fields contained within the pharmacovigilance records.

The principal serious outcomes will include:

• hospitalization;
• death;
• life-threatening events;
• disability;
• congenital anomaly/birth defect; and
• other medically important events where appropriately coded.

A composite serious-outcome variable may also be created to identify reports containing at least one serious outcome.

3.9 Data Cleaning

Before statistical analysis, the dataset will undergo systematic preprocessing.

The following procedures will be applied:

1. removal of records with invalid identifiers;
2. standardization of age;
3. standardization of sex;
4. normalization of drug names;
5. normalization of adverse-event terminology;
6. identification and management of duplicate reports;
7. removal of records outside the predefined adult age range for age-specific analyses;
8. identification of missing values; and
9. verification of extreme or biologically implausible age values.

The number of records removed at each stage will be documented and presented in a study-selection flow diagram.

3.10 Primary Exposure Variable

The primary exposure variable will be age group.

The principal comparison will be:

Younger adults (18–64 years) versus older adults (≥65 years).

The main objective is to determine whether the distribution and reporting strength of serious adverse events differ between these populations.

3.11 Primary Outcomes

The primary outcomes will be:

1. frequency of serious adverse-event reports;
2. hospitalization;
3. death;
4. life-threatening outcome;
5. disability; and
6. age-specific disproportionality signals.

3.12 Secondary Outcomes

Secondary outcomes will include:

• distribution of adverse-event categories;
• therapeutic-class-specific differences;
• sex distribution;
• reporting-source differences;
• age gradients within older adults;
• drug–event combinations demonstrating significant disproportionality; and
• concordance of signals across different statistical methods.

3.13 Descriptive Statistical Analysis

Continuous variables will be summarized using mean ± standard deviation when approximately normally distributed and median with interquartile range when distributions are skewed.

Categorical variables will be summarized using frequencies and percentages.

Baseline characteristics will be compared between younger and older adults.

The χ² test will be used for categorical variables when assumptions are satisfied. Fisher's exact test will be used where expected cell counts are insufficient.

For continuous variables, the independent-samples t test will be used for approximately normally distributed variables, whereas the Mann–Whitney U test will be used for non-normally distributed variables.

3.14 Analysis of Serious Outcomes

The proportion of reports associated with hospitalization, death, life-threatening events, and other serious outcomes will be compared between age groups.

Crude odds ratios with 95% confidence intervals will be calculated for major serious outcomes.

For example:

Odds ratio = odds of outcome in older adults/odds of outcome in younger adults

An odds ratio greater than 1 will indicate higher odds of the specified outcome in the older group, whereas an odds ratio below 1 will indicate lower odds.

3.15 Reporting Odds Ratio

The Reporting Odds Ratio (ROR) will be calculated to evaluate disproportionate reporting of specific drug–event combinations.

Table 1: Two-by-Two Contingency Table for Reporting Odds Ratio (ROR).

The Reporting Odds Ratio (ROR) will be calculated as:

ROR=c/da/b​=b×ca×d​

ROR = (a/b) / (c/d)

where:

• a = reports involving the target drug and target adverse event; 
• b = reports involving the target drug and all other adverse events; 
• c = reports involving comparator drugs and the target adverse event; and 
• d = reports involving comparator drugs and all other adverse events. 

An ROR > 1 indicates disproportionate reporting of the target adverse event with the target drug compared with comparator drugs. The 95% confidence interval (CI) will be calculated to assess the statistical precision of the estimate.

The 95% confidence interval will be calculated for each estimate.

Age-stratified RORs will then be generated separately for younger and older adults.

This will allow identification of drug–event combinations that demonstrate stronger disproportionality in one age group than another.

3.16 Proportional Reporting Ratio

The Proportional Reporting Ratio (PRR) will provide a complementary measure of disproportionate reporting.

It will be calculated as:

PRR = [a/(a+b)] / [c/(c+d)]

The PRR will be interpreted together with the number of reports and associated statistical criteria rather than as an independent measure of causality.

3.17 Bayesian Signal Detection

Where the dataset and computational resources permit, a Bayesian disproportionality method will be incorporated as a complementary analysis.

The purpose will be to identify whether signals detected by frequentist methods remain supported after statistical shrinkage.

Concordance between ROR, PRR, and Bayesian estimates will strengthen confidence that a signal is not solely the result of a small reporting count.

3.18 Age-Specific Safety Signature

An age-specific safety signature will be constructed by integrating:

Age group + therapeutic class + adverse event + serious outcome + disproportionality estimate.

For each therapeutic class, the analysis will identify:

• most frequently reported events;
• events associated with hospitalization;
• events associated with death;
• strongest disproportionality signals;
• signals specific to younger adults;
• signals specific to older adults; and
• signals common to both groups.

This multidimensional approach represents the principal analytical component of the study.

3.19 Sensitivity Analysis

Sensitivity analyses will be performed to assess the robustness of the findings.

Potential sensitivity analyses will include:

• excluding reports with missing sex;
• restricting analysis to reports with primary suspect drugs;
• separating 65–74, 75–84, and ≥85 years;
• excluding duplicate reports where identifiable;
• restricting analysis to serious reports; and
• repeating signal detection using alternative minimum-report thresholds.

3.20 Missing Data

Missing demographic and clinical information will not be artificially imputed when the absence of information represents a characteristic of spontaneous reporting.

The amount of missing information will be reported transparently.

For each principal variable, the number and percentage of missing observations will be documented.

3.21 Statistical Significance

Two-sided statistical tests will be used where appropriate.

A conventional significance threshold of:

p < 0.05

will be applied to inferential comparisons.

Because multiple drug–event combinations may be examined simultaneously, false-discovery-rate adjustment will be considered for high-dimensional analyses.

For disproportionality analyses, statistical significance will be interpreted together with effect size, confidence intervals, report counts, and consistency across analytical methods.

3.22 Software

Data processing and statistical analysis will be performed using validated statistical software such as R, Python, or SPSS, depending on the final analytical workflow.

All data-processing steps will be documented to facilitate reproducibility.

3.23 Reproducibility

The final study will report:

• exact dataset source;
• data-download period;
• inclusion and exclusion criteria;
• data-cleaning procedures;
• drug-class definitions;
• adverse-event definitions;
• statistical methods;
• signal-detection thresholds; and
• software used.

This will allow other investigators to reproduce the analysis using the same public data source.

3.24 Analytical Framework

The overall analytical pathway will therefore be:

FDA AEMS/FAERS data → data cleaning → adult reports → age stratification → therapeutic-class classification → adverse-event classification → serious-outcome analysis → ROR/PRR analysis → age-specific comparison → sensitivity analysis → safety signatures.

4.1 Study Population

The AEMS/FAERS database was screened according to the predefined inclusion and exclusion criteria. Reports containing valid patient age information were classified into younger adults (18–64 years) and older adults (≥65 years). Reports with missing or implausible age information were excluded from the primary age-stratified analysis.

The final analytical dataset comprised [N] reports, including [N] reports from younger adults and [N] Table 2. Distribution of AEMS/FAERS Reports by Age Group

Table 2: Distribution of AEMS/FAERS Reports by Age Group.

Source: Created by Haider et al 2026

Figure 1. Study Selection and Analytical Workflow for AEMS/FAERS Pharmacovigilance Data.

Source: Created by Haider et al 2026

Figure 2. Age-Dependent Safety Signatures of Modern Therapeutics.

Source: Created by Haider et al 2026

Figure 3. Serious Clinical Outcomes Across Age Groups.

Source: Created by Haider et al 2026

Figure 4. Therapeutic-Class–Specific Pharmacovigilance Safety Landscape.

The present study was designed to investigate whether the safety profiles of modern therapeutics differ according to age by combining serious-outcome assessment with disproportionality analysis of real-world pharmacovigilance reports. Rather than evaluating adverse events solely according to their frequency, the study considers the relationship between age, therapeutic exposure, adverse-event patterns, and clinically serious outcomes. This approach is particularly relevant because spontaneous-reporting systems capture safety information from routine clinical practice that may not be fully represented in pre-marketing clinical trials.

Age-related differences in medicine safety may arise from several interacting mechanisms. Older adults commonly experience physiological changes affecting renal clearance, hepatic metabolism, body composition, receptor sensitivity, and homeostatic reserve. The coexistence of multimorbidity and polypharmacy can further increase the complexity of treatment and the potential for drug–drug interactions. Consequently, the same pharmacological exposure may produce different clinical consequences in an older patient compared with a younger adult.

An important aspect of the present analysis is the distinction between reporting frequency and reporting disproportionality. A frequently reported adverse event does not necessarily represent a pharmacovigilance signal because high reporting may simply reflect widespread use of a medicine. Conversely, an uncommon event may become important when it is reported disproportionately with a particular drug. ROR and PRR therefore provide complementary information to simple event counts.

The age-stratified approach is particularly valuable because an association may be stronger in one population than another. A drug–event combination with a substantially higher ROR among older adults may indicate a potential age-related safety signal requiring further investigation. However, such a finding should not automatically be interpreted as evidence that age causes the adverse event. Differences in prescribing indications, comorbidity, exposure duration, dosage, concomitant medicines, and healthcare utilization can also contribute to the observed pattern.

The analysis of serious outcomes provides an additional clinical dimension. Hospitalization and death are more informative from a clinical-priority perspective than simple reporting frequency alone. If particular adverse-event signals are disproportionately reported among older adults and are simultaneously associated with hospitalization or mortality, these findings may deserve greater attention for future pharmacoepidemiological investigation.

The findings should also be interpreted within the characteristics of spontaneous-reporting databases. AEMS/FAERS is primarily a signal-detection system rather than a population-based incidence database. Reports may be affected by under-reporting, stimulated reporting, publicity, changes in prescribing practices, reporting requirements, and differences in awareness among healthcare professionals and patients. FDA guidance also emphasizes that spontaneous adverse-event reports do not establish causality or provide reliable estimates of adverse-event incidence.

Another important limitation is the possibility of confounding by indication. Older adults may receive different medicines because of their underlying diseases, while younger adults may receive the same medicines for different clinical indications. Consequently, an observed age difference may reflect the underlying disease population rather than an intrinsic age-dependent pharmacological effect.

Polypharmacy represents another potential source of confounding. A spontaneous report can contain multiple suspected or concomitant medicines, making attribution of an individual adverse event to one medicine difficult. Therefore, the present findings should be regarded as drug–event reporting signals, rather than definitive evidence of individual drug causation.

Nevertheless, combining multiple analytical approaches strengthens the interpretation of the results. Concordant evidence from ROR and PRR, particularly when supported by adequate report numbers and serious-outcome information, provides a stronger basis for prioritizing signals than reliance on a single statistical measure.

The principal contribution of this study is the integration of age stratification, therapeutic-class comparison, serious clinical outcomes, and disproportionality analysis within a single pharmacovigilance framework. This may help move pharmacovigilance beyond simple identification of frequently reported adverse reactions toward the development of more clinically informative age-dependent safety profiles.

From a clinical perspective, age-sensitive pharmacovigilance could support more individualized medication monitoring. If certain adverse-event signals are consistently stronger among older adults, clinicians and regulatory researchers could prioritize these drug–event combinations for closer surveillance. Such findings may also generate hypotheses for prospective pharmacoepidemiological studies using databases containing medication exposure denominators and clinical covariates.

Several limitations should be acknowledged. First, spontaneous reports are subject to reporting and selection biases. Second, missing demographic or clinical information may limit some analyses. Third, the database does not provide reliable denominators for calculating absolute incidence rates. Fourth, duplicate or follow-up reports may complicate interpretation despite systematic data cleaning. Fifth, disproportionality measures do not establish causality. Finally, differences between age groups may partly reflect differences in disease prevalence, prescribing practices, treatment indications, and concomitant medication use.

Despite these limitations, the study provides a useful framework for identifying potential age-dependent safety patterns in real-world medicine use. The findings should be viewed as hypothesis-generating and should be validated using longitudinal observational studies, electronic health-record datasets, claims databases, or prospective clinical investigations.

Overall, the study supports the concept that medicine safety should be evaluated not only according to the drug and adverse event but also according to the characteristics of the population receiving treatment. Age-specific pharmacovigilance may therefore represent an important component of modern real-world drug-safety surveillance.

Age is an important consideration in real-world medicine safety because physiological changes, multimorbidity, polypharmacy, and differences in treatment exposure can influence the occurrence and clinical consequences of adverse drug reactions. The present pharmacovigilance framework combines age stratification with serious-outcome assessment and disproportionality analysis to characterize potential differences in safety signals between younger and older adults.

The use of AEMS/FAERS provides an opportunity to examine a large body of post-marketing safety information and identify drug–event combinations that may warrant further investigation. Importantly, ROR and PRR findings should be interpreted as signals rather than proof of causality or estimates of incidence.

The principal value of this approach is the development of an age-dependent safety signature, integrating adverse-event patterns, therapeutic class, serious outcomes, and disproportionality. Such an approach may complement conventional pharmacovigilance and help prioritize medicines and adverse events for further epidemiological or clinical investigation.

Future research should validate important signals using databases containing reliable medication-exposure denominators and clinical covariates. Prospective studies may subsequently determine whether the observed age-associated reporting patterns represent genuine differences in drug safety.

Acknowledgment: The completion of this research assignment could not have been possible without the contributions and assistance of many individuals and groups. We’re. Deeply thankful to all those who played a role in the success of this project, I would like to thank My Mentor, Dr. Naweed Imam Syed, Prof, Department of Cell Biology at the University of Calgary, for their useful input and guidance for the duration of the research project. Their insights and understanding have been instrumental in shaping the path of this undertaking.

Authors' Contribution:I would like to express our sincere thanks to all the members of our take a look at, who generously shared their time, studies, and insights with us. Their willingness to interact with our studies became essential to the success of this assignment, and we’re deeply thankful for their participation.

The authors declare that there is no conflict of interest related to the publication of this manuscript.

Funding and Financial Support:The authors received no financial support for the research, authorship, and/or publication of this article.