Quantifying Household Medicine Disposal to Address Environmental Antimicrobial Resistance: Development and Validation of the MeDisPract Index

Quantifying Household Medicine Disposal to Address Environmental Antimicrobial Resistance: Development and Validation of the MeDisPract Index | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Quantifying Household Medicine Disposal to Address Environmental Antimicrobial Resistance: Development and Validation of the MeDisPract Index Sandip Mukhopadhyay, Melissa Glenda Lewis This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9214659/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Improper disposal of domestic unused and expired medicines is a source for environmental contamination. This pharmaceutical pollution pathway is also an under-recognized cause of antimicrobial resistance (AMR). There is a need for standardized assessment tools for quantifying disposal behaviour at the population level. Methods A methodological study was conducted to validate the MeDisPract, the Medication Disposal Practices (MeDisPract) tool. Following literature review and expert consultation, a 14-item instrument was finalized with three primary domains: behavioural (practice), cognitive (knowledge), and attitudinal (readiness). Following content validity, data were collected from 509 households over 25 villages. Reliability was evaluated using ordinal alpha, split-half reliability, Guttman Lambda-6 and Cronbach’s alpha (for Likert rated items). Construct validity was assessed using exploratory factor analysis. A weighted composite MeDisPract Index (0–100) was constructed to categorize disposal risk. Results The tool demonstrated good reliability for a behavioural surveillance instrument (Ordinal α≈0.78, Guttman Lambda-6≈0.75); Cronbach’s α for four Likert items of the behavioural domain (0.57) is acceptable for a multidimensional public health instrument. Factor analysis supported a five-domain structure (KMO=0.604 which is above cut-off indicating acceptable sampling adequacy; Bartlett p<0.001), explaining 68% of total variance. Index scores showed expected heterogeneity across households, with a substantial proportion of households reflecting unsafe disposal behaviours despite good attitude towards safer disposal of medications. Conclusions MeDisPract provides a pragmatic, field-deployable instrument to quantify medication disposal behaviour and identify communities at risk of pharmaceutical environmental exposure. The index can support AMR action plans, environmental health surveillance, and evaluation of medication take-back interventions in low and middle income settings. Medication disposal antimicrobial resistance Environmental exposure Behavioural surveillance Public health tool One health Figures Figure 1 Figure 2 Figure 3 Background Medication disposal is often a neglected facet in the traditional waste management system in many countries. Though larger hospitals and Pharmaceutical Industry follow specific policies to dispose unused and expired medications, there hardly remains any structured system for disposal of domestic pharmaceutical wastes, particularly in the low and middle income countries (LMICs). The lack of accessible disposal facilities, coupled with limited awareness of safe disposal practices, foster a widespread culture of unsafe medication disposal.[ 1 ] The conventional waste management system is not sufficiently capable of handling pharmaceutical waste. Consequently, unused and expired medicines frequently enter the environment through inappropriate disposal practices such as household garbage, open dumping, or flushing into sewage systems that finally impacts both animal and human health.[ 2 , 3 ] Pharmaceutical residues, either as intact compounds or biologically active metabolites, ultimately contaminate water bodies and soil.[ 4 ] Such wastes range from modern medicine to traditional medicine, from simple analgesic to antimicrobials including those expired, unused, or leftover medicines discarded at the household level. Low level of pharmaceutical residue persists in the soil or aquatic bodies leading to ecological niches that favour the selection of drug-resistant microorganisms.[ 5 ] Though often under-recognized, growing evidence suggests that pharmaceutical waste may contribute to the emergence and propagation of antimicrobial resistance (AMR) by creating sustained low-level antimicrobial exposure in the environment.[ 6 – 9 ] These findings underscore the environmental pharmaceutical pathways that impact both animal and human health within the ‘One Health’ framework. In the Asian context, AMR is projected to cause 4.7 million deaths in 2050 and India has one of the highest AMR burden globally and related expenditure.[ 10 , 11 ] Despite global efforts to mitigate AMR, current action plans have largely focused on antimicrobial stewardship, infection prevention, and rational prescribing, with limited attention to medication disposal as a contributory pathway. Countries with inadequate regulatory oversight, absence of structured medication take-back programs and limited pharmaceutical waste infrastructure, people often resort to environmentally unsound and unsafe disposal practices. Hence, threat of AMR becomes magnified in many LMICs of Asia or Africa.[ 3 , 12 ] Studies across different geographic regions have examined people’s knowledge, attitudes, and practices related to medication disposal. However, these studies vary widely in scope, methodology, and outcome measures. While some focus predominantly on disposal methods, others emphasize knowledge or awareness, often using non-standardized, non-quantifiable instruments.[ 1 , 13 – 17 ] The heterogeneity of existing tools limits comparability across places, studies and impedes the generation of actionable, population-level evidence. Moreover, the lack of a validated quantitative measure restricts the integration of medication disposal behaviours into public health surveillance and environmental risk assessment frameworks. To address this gap, we propose a pragmatic composite measure—the MeDisPract Index —to quantify people’s behaviour towards safe disposal of household pharmaceutical waste as well as population-level pharmaceutical disposal risk in a standardized manner based on the Medication Disposal Practices (MeDisPract) tool. The tool is a structured questionnaire designed to assess household-level medication disposal behaviours, primarily the practice, readiness or attitude and knowledge for safe disposal. The tool and index are intended not only for individual-level assessment within communities but also to support public health surveillance by enabling categorization, comparison, and monitoring of medication disposal practices using a quantifiable and reproducible approach. The tool differs from an exhaustive knowledge-attitude-practice questionnaire as it was primarily targeted to support public health surveillance, focused greater emphasis on the behavioural aspects and is characterized by field readiness with simplicity and brevity. Hence, the present study was planned with the following objectives: To develop a structured questionnaire (MeDisPract tool) to assess household medication disposal practices, knowledge, and preparedness. To validate the MeDisPract tool through reliability and validity assessment. To develop a composite MeDisPract Index for quantifying population-level pharmaceutical disposal risk. Methods Study Design A community-based methodological study was conducted that involved development, validation, and field-testing of a structured instrument—the Medication Disposal Practices (MeDisPract) Tool , and to construct a composite MeDisPract Index as a study instrument or survey tool for ‘Medication Disposal Practice in the community’, for quantifying household-level medication disposal behaviour and associated public health risk (Supplementary files 1 and 2). The study combined instrument development, psychometric evaluation, and cross-sectional field application. The study adheres to STROBE and COSMIN recommendations. Ethical approval: The study was conducted in compliance with the Declaration of Helsinki. Approval of the Institutional Ethics Committee, ICMR- National Institute for Research in Bacterial Infections (formerly ICMR-NICED) was obtained for use of human data for the tool validation (Approval no. ICMR-NICED/IEC-BMHR/BMHR-032/25). Conceptual Framework The tool was designed as a public health surveillance instrument rather than a psychological scale. A focused literature review of published literature on household medication disposal of multiple countries, WHO guidance on pharmaceutical waste management, AMR-environment linkage studies, and household disposal research was conducted resulting in a five domain conceptual model: Behavioural (Practice) Domain: Frequency based disposal behaviours in Likert items. Cognitive (Knowledge) Domain): Awareness of environmental and health risks. Attitudinal (Readiness) Domain: Willingness to adopt safe disposal practices. Household medicine stock and availability of disposal infrastructure : 2 binary items, Actual methods of medication disposal - multiple-response item which captures actual methods utilized at households. The responses are either environmentally ‘safe’ or ‘unsafe’ method. Keeping stock of medication in home and actual disposal method utilized are also behavioural components, and contains only three items in total, it was decided to get these grouped under the ‘behavioural’ domain as subdomains. Hence, the final construction was three primary domains: 1. Behavioural (Practice) Domain: Capturing actual disposal behaviour and infrastructure under three subdomains- (i) routine disposal behaviours (4 frequency-based items), (ii) household medicine stock and availability of disposal infrastructure (2 binary items), (iii) methods of medication disposal (1 multiple-response item), 2. Cognitive (Knowledge) domain, and 3. Attitudinal (Readiness) domain. The index is conceptually formative, these domains are formative contributors to disposal risk; collectively they define behaviour rather than reflect a single latent construct, weighting was theory-driven rather than factor-loading driven. Essentiality, pragmatism, simplicity, minimalism, and brevity were primary focus during development of the tool to match it as a public health surveillance tool which could be rapidly administered in approximately five minutes time. The conceptual structure and index construction pathway are illustrated in Figure 1. Figure 1: Conceptual framework and construction of MeDisPract framework The MeDisPract framework conceptualizes household medication disposal behaviour as a formative public health construct comprising three interacting domains: Behavioural or Practice (actual disposal actions and infrastructure), Cognitive or Knowledge (awareness of environmental and health risks), and Attitudinal or Readiness (willingness to adopt safe disposal mechanisms). Domain scores are standardized and combined using theory-driven weighting to generate the MeDisPract Index , which categorizes communities by pharmaceutical disposal risk and supports environmental AMR surveillance and intervention planning. Item Generation and Tool Development An initial pool of items was generated through literature review of global disposal-practice studies, review of regulatory and environmental guidance and expert consultation from the fields of pharmacology, public health, microbiology, environmental health. Items were screened for relevance, clarity and essentiality. Attention was also given to simplicity and field feasibility for surveillance use. All identified items were included as a question in a preliminary draft questionnaire. Item questions concerning similar components (e.g., practice) were grouped together into three identified domains. After iterative refinement, 14 items were retained under the three identified domains as below: Behavioural (Practice) domain: 7 items (4 Likert-frequency items, 2 binary items, 1 multiple-response item) Cognitive (Knowledge) domain: 4 binary items Attitudinal (Readiness) domain: 3 binary items The questionnaire was designed for rapid administration (≈5 minutes) to enable integration into community surveys. Content Validity A multidisciplinary expert panel rated each item for relevance, clarity, and necessity using a 4-point scale. Item-level Content Validity Index (I-CVI) was calculated as the proportion of experts rating the item ≥3. Items with I-CVI <0.78 were revised or removed. The overall Scale-level CVI (S-CVI/Ave ≥0.90) indicated excellent agreement on content adequacy. Pilot Testing The revised instrument was pilot tested among 50 community participants to assess comprehension, response variability, and feasibility. Minor wording changes were made without altering item structure. Field Administration The finalized MeDisPract tool was administered to 509 households using interviewer-assisted data collection utilizing the platform of ‘Population based health survey (PBHS)’, conducted by the Model Rural Health Research Unit-Darjeeling (MRHRU-Darjeeling), West Bengal at Darjeeling District. This PBS round 1 survey is a flagship initiative from the Department of Health Research, Government of India and conducted all over India through the networks of MRHRUs all over India at 34 sites during 2024-25. A sub-survey was co-administered to the same households utilizing the MeDisPract tool (Medication disposal practice survey) with one consenting adult respondent included (1 household= 1 participant, sample size 459). The sample size was determined pragmatically based on the available households within the Population-Based Health Survey platform and MeDisPract Survey and was considered adequate for exploratory factor analysis and validation of a multidimensional instrument. Written informed consent was obtained from the participants. Responses completed for all 14 items were included in the analysis. Construction of the MeDisPract Index Domain Scoring Each domain was scored separately (Table 1): Table 1: Domain specific scoring of MeDisPract tool Domain Scoring Method Raw Score Range Behavioural (Practice) domain Likert + behavioural items (binary + multiple input item) 0–32 Cognitive (Knowledge) domain Binary (Yes=1, No=0) 0–4 Attitudinal (Readiness) domain Binary (Yes=1, No=0) 0–3 Standardization To ensure comparability, each domain score was linearly transformed to a 0–100 scale : Weighting and Composite Index Because behavioural practice directly determines environmental exposure, the practice domain was assigned double weight : The resulting index ranges from 0 to 100 , representing increasing safety of disposal behaviour. Risk Categorization: Risk categorization from index score was done for public health interpretation and utilizing the tool to set actionable priority (Table 2). The cutoffs were pragmatically defined using equal quartile banding for interpretability. Table 2: Medication disposal practice risk category based on MeDisPract index Index Score Category Public Health Interpretation 0–25 Very Poor High disposal risk 26–50 Poor Unsafe practices prevalent 51–75 Fair Transitional behaviour 76- 100 Good Predominantly safe practice Reliability Assessment As the tool was comprising a mixed ordinal–binary structure, multiple complementary reliability estimates were used: Cronbach’s alpha for Likert items (behavioural coherence) to assess internal consistency Ordinal alpha (polychoric-based) to account for ordinal responses.[18] Guttman Lambda-6 as a conservative lower-bound estimate.[19] Split-half reliability with Spearman–Brown correction was calculated for ordinal-mixed data.[20] Reliability coefficients were interpreted pragmatically, keeping in mind that the behavioural surveillance tools measure heterogeneous constructs rather than a single latent trait. Construct Validity Exploratory factor analysis (EFA) using polychoric correlations assessed whether items grouped according to the hypothesized three-domain structure. Kaiser–Meyer–Olkin (KMO) test evaluated sampling adequacy and Bartlett’s test assessed inter-item correlation suitability. Principal component extraction with varimax rotation was applied. Factor loadings ≥0.40 were considered meaningful. Statistical Analysis Analyses included descriptive statistics (mean, SD, median, IQR), Reliability estimation, Exploratory factor analysis and distributional assessment of MeDisPract Index and risk categories. As the MeDisPract Index is a composite behavioural indicator rather than a reflective psychometric scale, reliability estimates were interpreted as measures of response coherence rather than internal homogeneity. Moderate coefficients were considered acceptable for surveillance-oriented instruments. Results Participant Characteristics Data from 509 respondents from 25 villages were analysed. A flow diagram summarizes the participant inclusion and analysis for the MeDisPract validation study (Figure 2). Figure 2: Flow diagram of participant inclusion and analysis for the MeDisPract validation study All responses were complete and suitable for index computation; therefore, no imputation or missing data handling was required. The demographic detail of the participants is summarized in Table 3. Table 3. Demographic details of the study participants Variables Categories n(%) Age in years Median (IQR) 35.00 (27.00, 47.00) Gender Male 259 (50.90) Female 250 (49.10) Religion Buddhists 3 (0.60) Christians 4 (0.80) Muslims 40 (7.90) Hindus 462 (90.80) Tribe Bengali 166 (32.60) Bihari 13 (2.60) Nepali 50 (9.80) Rajbanshi 272 (53.40) Tribal 8 (1.60) Education Status Illiterate 98 (19.25) Primary 156 (30.65) Secondary 133 (26.13) 12 th 72 (14.15) Graduate 50 (9.80) Respondent’s Employment Status Yes 105 (20.60) No 404 (79.40) Employment Category (n=105) Unskilled 53 (50.48) Semiskilled 38 (36.19) Skilled 14 (13.33) Reliability testing was done by multiple tests as the tool comprised different response formats for the items.[18,21] The MeDisPract tool, a mixed Likert-binary scale, demonstrated moderate to strong internal consistency with Ordinal α value of 0.78 and Guttman λ-6 value 0.75. The behavioural Likert items showed moderate consistency through Cronbach’s α (α=0.57) reflecting multidimensional formative construct. The attitude and cognitive domain also showed moderate internal consistency as expected reflecting distinct awareness components (Table 4). The tool exhibited moderate split-half reliability (r_sb=0.58, p<0.001). These were consistent with instruments measuring diverse real-world practices rather than redundant constructs. Table 4: Reliability of the MeDisPract Tool Domain Items Cronbach’s α Ordinal α Lambda-6 (λ6) Split half Interpretation Behavioural (Practice) 7 0.57 (item 1-4) 0.72 0.70 0.68 Acceptable Cognitive (Knowledge) 4 0.65 0.87 0.60 Moderate to Good (λ6) Attitudinal (Readiness) 3 0.58 0.55 0.52 Moderate Overall Tool 14 0.57 0.78 0.75 0.74 Good composite coherence * Q14 (attitudinal domain) showed a "ceiling effect" (universal agreement), which is a valid finding in public health (showing high community readiness despite low knowledge). In psychometrics, an item with no variance cannot contribute to reliability or factor analysis Construct Validity Exploratory factor analysis (EFA) provided construct validity evidence. Although the index is formative, EFA was used pragmatically to explore item clustering apart from construct validation. Prior to conducting exploratory factor analysis (EFA), the suitability of the data for EFA was assessed using Bartlett's test of sphericity and the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy. The KMO test resulted an acceptable value of 0.604 which exceeds the minimum threshold of 0.6, reflecting the sampling adequacy for a formative and multidimensional structure of the tool. Bartlett test of sphericity was significant with p<0.001 (non-identity matrix). Hence, ‘Factor Analysis’ is appropriate for the tool. Item Q14 was excluded from all analyses due to "ceiling effect" (universal agreement resulting zero variance) as during reliability analysis. Factor loadings ≥0.40 were considered significant. Scree plot (Figure 3) was obtained additionally for selection of the number of relevant components or factors to be considered in factor analysis.[22] It shows five factors with eigenvalues >1.0 (Kaiser rule) with a clear elbow after fifth factor indicating optimal 5-factor solution. Figure 3: Scree plot showing eigenvalues for MeDisPract items (n=509) Exploratory Factor Analysis using principal component extraction (PCA) with Varimax rotation was chosen for interpretability and domain separation and it identified a five-factor structure with eigenvalues >1 (Table 5): Factor 1 captured awareness of environmental and health risks of improper disposal (Q8–Q11). Factor 2 represented core household disposal behaviour (Q1–Q3). Factor 3 reflected access to disposal infrastructure and organized collection systems (Q6, Q12). Factor 4 described safe-use practices and routine checking behaviours (Q2, Q4, Q7). Factor 5 indicated perceived knowledge gap and medicine retention tendency (Q5, Q13). Most items demonstrated satisfactory communalities (0.56–0.88), indicating adequate representation of shared variance by the extracted factors. Q4 and Q7 communalities (0.42 and 0.44 respectively) are slightly low. These two items showed modest communalities but were retained due to conceptual relevance to safe disposal behaviour. Total explained variance of 68.4% with 5 factors was consistent with multidimensional behavioural tool. Factor analysis of MeDisPract demonstrated that, the a. items grouped as theorized, b. there is minimum cross-loadings, c. the cognitive and behaviour domain are dominant, and d. suitable for surveillance use. Factor analysis also demonstrated clear behavioural clustering (Table 6). This indicates that behaviour is not a single variable — it is distributed across multiple actionable dimensions, which is common in public-health behaviour models. Table 6: Additional insight from EFA Area Supporting Items Statistical Evidence Behaviour (actual disposal actions) Q1, Q2, Q3, Q4, Q7 Load strongly across Factors 2 & 4 Awareness/Risk perception Q8–Q11 Very high loadings (0.59–0.93) System/Infrastructure Q6, Q12 Strong Factor 3 loadings (~0.73–0.76) Perceived need / retention Q5, Q13 Factor 5 structure Distribution of MeDisPract Index The MeDisPract Index demonstrated variability across households, indicating heterogeneity in disposal behaviour. A substantial proportion of households fell into the moderate to high disposal risk categories. Unsafe disposal practices were common despite awareness of environmental harm (Table 7). Table 7: MeDisPract based medication disposal risk categories, population distribution and the public health interpretation Risk Category Percentage of participants (n=509) Proportion of Participants Interpretation Very Poor 13.75 Substantial segment Very unsafe disposal behaviour, high environmental exposure potential Poor 83.69 Largest segment Mostly unsafe behaviour, high environmental exposure potential Fair 2.16 Moderate proportion Partial adoption of safe practices Good 0.39 Smallest proportion Environmentally safe behaviour Unsafe disposal practices persisted despite awareness of environmental harm, highlighting a gap between knowledge and actionable behaviour. Variation in disposal behaviour improved with employment status and varied among the tribes but not across education level, possibly implying cultural sensitivity to cleanliness among tribes. Domain Contribution Patterns The practice domain showed the strongest influence on overall index variability, supporting its theoretical weighting as the principal determinant of pharmaceutical environmental entry. Discussion Household medication disposal remains an under-recognized interface between pharmaceutical use and environmental hazards including antimicrobial resistance. The MeDisPract tool was developed and field-tested in the present study as a pragmatic surveillance instrument to quantify household-level disposal behaviours and translate them into a measurable public health risk index. The MeDisPract tool functioned effectively as a field-ready surveillance instrument, demonstrating adequate reliability for behavioural measurement, conceptually coherent domain structure and capacity to stratify communities by disposal risk. Behaviour–Environment–AMR Link: Improper disposal of household medication wastes introduce a diffuse, low-dose pharmaceutical exposure into ecosystems, a known ecological driver of antimicrobial selection pressure.[4,7,23,24] By operationalizing disposal behaviour into a quantifiable metric, the MeDisPract Index enables this neglected pathway to be incorporated into AMR surveillance models. Comparison With Existing Studies on medication disposal: Previous studies assessed knowledge or disposal methods in isolation using non-standardized questionnaires, limiting comparability and policy translation.[15,16,23,25,26] Some tools only targeted to validate scale on medication literacy.[27] MeDisPract may be considered an advancement in this field by providing a standardized scoring framework, allowing population-level risk stratification and enabling evaluation of disposal interventions. The present study noted that positive attitude does not reflect in good behaviour. Similar occurrence was noted in previous studies also.[14] Though the detected behaviour seems extremely skewed towards unsafe disposal, this is not unexpected in an area with low literacy, low background knowledge of disposal and practically zero exposure to any take back system.[15–17] Emphasis on behavioural determinants: Behaviour-related items (Q1–Q7) were assigned higher (double) weightage in the index which was an ‘a priori’ decision because the objective of the index was to measure actionable disposal practices rather than awareness alone. Exploratory factor analysis supported this decision, as behavioural items loaded across two independent but related factors representing routine disposal actions and safety-check practices, This indicates that ‘behaviour’ constitutes a multidimensional construct with substantial variance contribution (24%) to the overall model. This is acceptable because: Behaviour items explain variance across two extracted factors (not one). Awareness items cluster tightly into a single cognitive domain . As a Public health index, action > knowledge (KAP framework logic). The explained variance from behaviour-linked factors (~23-25%) is substantial. Reliability is Acceptable: The MeDisPract tool captures diverse behavioural, infrastructural, and awareness elements. The instrument incorporates real-world behaviours e.g. checking expiry, discarding habits, storage and willingness to change. Such diversity is not expected to exhibit high internal correlation, as the tool captures distinct real-world behaviours that collectively define disposal risk. Such multidimensional public health constructs may not exhibit high internal consistency because they represent actionable system conditions rather than a single psychological trait.[28] In spite of that, the tool achieved Ordinal α value of 0.78 and Guttman λ-6 value 0.75 marking good reliability. Cronbach’s α (0.57) for the four behavioural Likert items reflects expected heterogeneity of real-world disposal and it actually shows a) items are not redundant; b) each item contributes unique information and c) the index behaves like a public health indicator (desired). There are other examples of relatively low Cronbach’s alpha value which were considered acceptable.[29,30] Validity was supported through content validation, domain structure, and usability. Moreover, the limited number of items enhances field applicability of the tool to identify high-risk communities and monitor behavioural change longitudinally as a rapidly applicable decision-support metric and not merely a research questionnaire, nor a latent-trait psychometric scale. Though from a single district, data collection from 25 villages with discrete locations adds to the external validity of the tool. Limitations: Despite promise as a field ready tool, rapid quantification of risk, field testing in adequate sample size, acceptable reliability and validity, certain limitations are notable. The self-reported practices may overestimate safe behaviour due to self-report bias. The cross-sectional design limits causal inference between knowledge, attitudes, and disposal behaviour. Test-retest reliability and confirmatory factor analysis was also not conducted. Though the tool provides clear indication to safe or unsafe disposal of household pharmaceutical waste at the community level, the environmental contamination not directly measured. The study was conducted in one district of eastern India, which may limit generalizability to urban or other cultural settings. Conclusion The MeDisPract tool demonstrates acceptable validity and reliability for assessing household medication disposal practices. The MeDisPract Index offers a novel, quantifiable approach to classify pharmaceutical disposal risk at the population level. The tool bridges the gap between pharmaceutical consumption and environmental stewardship which is an essential but operationally missing dimension of AMR containment strategies. It has immense potential from the policy point of view through embedding in the policies like AMR National Action Plan monitoring, Community pharmacy take-back pilots and Environmental surveillance programs. In the Indian context, MeDisPract tool and Index may add additional value to the National Action Plan on Antimicrobial Resistance (NAP-AMR) of India or the Delhi Declaration on AMR as it can be used by regional health ministries to monitor "One Health" targets. Incorporation of medication disposal surveillance into public health and AMR strategies may strengthen environmental risk mitigation and promote responsible pharmaceutical stewardship, particularly in low- and middle-income settings. Declarations Ethics approval and consent to participate: Approval of the Institutional Ethics Committee, ICMR- National Institute for Research in Bacterial Infections (formerly ICMR-NICED) was obtained for the tool validation (Approval no. ICMR-NICED/IEC-BMHR/BMHR-032/25). Written informed consent was obtained from all participants before collection of data. Consent for publication: Not applicable Availability of data and materials: The deidentified participant dataset underlying the findings of this study, including the MeDisPract Index instrument and the statistical analysis code used for validation, will be available upon reasonable request from the corresponding author, Sandip Mukhopadhyay. Data will be available beginning three months following publication and will remain accessible for five years. Requests for access should include a brief proposal describing the intended use of the data and will be subject to approval by the authors and the host institution to ensure compliance with ethical and data protection regulations. No additional unpublished data are available. Competing interests: The authors declare no competing interest Funding: No direct funding involved. The Department of Health Research, Government of India funded the infrastructure and activities of the Model Rural Health Research Units including the ‘population based health survey’ where the present study was nested and were essential for field validation of the tool. Authors' contributions: Author Sandip Mukhopadhyay was involved in the Conceptualization; Methodology; Investigation; Data Curation; Validation; Formal Analysis (supporting); Visualization; Writing – Original Draft; Writing – Review & Editing; Supervision; Project Administration. Author Melissa Glenda Lewis was involved in the Formal Analysis; Methodology (statistical); Writing – Review & Editing. Acknowledgements: Prof. Ashok Shenoy, Prof. Aditi Chaturvedi, Dr. Ravindra Kumar G and Dr. Soume Pyne for supporting content validation. Declaration of generative AI and AI-assisted technologies in the manuscript preparation process: During the preparation of this work the author(s) used large language models for language check, suggestions and revision. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article. References Kanyari SS, Senapati TR, Kar A, Kanyari SS, Senapati TR, Sr AK. Disposal Practices of Unused and Expired Medicines Among the General Public and Pharmacies: A Mixed-Method Study in the Odisha State of Eastern India. Cureus. 2024;16. https://doi.org/10.7759/cureus.52359 . Monteiro SC, Boxall ABA. Factors affecting the degradation of pharmaceuticals in agricultural soils. Environ Toxicol Chem. 2009;28:2546–54. https://doi.org/10.1897/08-657.1 . Sharma BM, Bečanová J, Scheringer M, Sharma A, Bharat GK, Whitehead PG, et al. Health and ecological risk assessment of emerging contaminants (pharmaceuticals, personal care products, and artificial sweeteners) in surface and groundwater (drinking water) in the Ganges River Basin, India. Sci Total Environ. 2019;646:1459–67. https://doi.org/10.1016/j.scitotenv.2018.07.235 . Bojarski B, Kot B, Witeska M. Antibacterials in Aquatic Environment and Their Toxicity to Fish. Pharmaceuticals. 2020;13:189. https://doi.org/10.3390/ph13080189 . Berglund B. Environmental dissemination of antibiotic resistance genes and correlation to anthropogenic contamination with antibiotics. Infect Ecol Epidemiol. 2015;5:28564. https://doi.org/10.3402/iee.v5.28564 . Baquero F, Martínez J-L, Cantón R. Antibiotics and antibiotic resistance in water environments. Curr Opin Biotechnol. 2008;19:260–5. https://doi.org/10.1016/j.copbio.2008.05.006 . Larsson DGJ, Flach C-F. Antibiotic resistance in the environment. Nat Rev Microbiol. 2022;20:257–69. https://doi.org/10.1038/s41579-021-00649-x . Cycoń M, Mrozik A, Piotrowska-Seget Z. Antibiotics in the Soil Environment-Degradation and Their Impact on Microbial Activity and Diversity. Front Microbiol. 2019;10:338. https://doi.org/10.3389/fmicb.2019.00338 . Woolhouse M, Ward M, van Bunnik B, Farrar J. Antimicrobial resistance in humans, livestock and the wider environment. Philos Trans R Soc Lond B Biol Sci. 2015;370:20140083. https://doi.org/10.1098/rstb.2014.0083 . Singh VP, Jha D, Rehman BU, Dhayal VS, Dhar MS, Sharma N. A mini-review on the burden of antimicrobial resistance and its regulation across one health sectors in India. J Agric Food Res. 2024;15:100973. https://doi.org/10.1016/j.jafr.2024.100973 . Taneja N, Sharma M. Antimicrobial resistance in the environment: The Indian scenario. Indian J Med Res. 2025;149:119–28. https://doi.org/10.4103/ijmr.IJMR_331_18 . Bataduwaarachchi VR, Weeraratne CL. Global medication waste management practices: challenges and opportunities in developing countries. Int J Basic Clin Pharmacol. 2016;5:2290–4. https://doi.org/10.18203/2319-2003.ijbcp20164081 . Udupa N, Muragundi PM, Naik AN, Janodia M. Disposal Of Date Expired And Unused Medicines In India– A Conceptual Framework. Value Health. 2013;16:A269. https://doi.org/10.1016/j.jval.2013.03.1387 . Khatri J, Shakya R, Shrestha R, Shrestha S. Disposal of unused and expired medications: A study of knowledge, attitudes, and practices among community pharmacy visitors. SAGE Open Med. 2025;13:20503121251375355. https://doi.org/10.1177/20503121251375355 . Akande-Sholabi W, Adebisi YA, Abdul-Azeez IA. Disposal practices for unused and expired medications among outpatients in three healthcare facilities in Ibadan, Nigeria: a cross-sectional analysis. BMC Public Health. 2025;25:4002. https://doi.org/10.1186/s12889-025-25058-0 . Hategekimana JC, Niyonsenga F, Ntabwoba A, Niyombabazi JB, Nsengimana A. Disposal Practices of Leftover Medicines Among University of Rwanda Undergraduate Students. Integr Pharm Res Pract. 2025;14:17–29. https://doi.org/10.2147/IPRP.S499431 . Bashaar M, Thawani V, Hassali MA, Saleem F. Disposal practices of unused and expired pharmaceuticals among general public in Kabul. BMC Public Health. 2017;17:45. https://doi.org/10.1186/s12889-016-3975-z . Gadermann AM, Guhn M, Zumbo BD. Estimating ordinal reliability for Likert-type and ordinal item response data: A conceptual, empirical, and practical guide. Pract Assess Res Eval. 2012;17. https://doi.org/10.7275/n560-j767 . Reliability Analysis. XLSTAT, Your data analysis solution. https://www.xlstat.com/solutions/features/reliability-analysis . Accessed 26 Feb 2026. Dima AL. Scale validation in applied health research: tutorial for a 6-step R-based psychometrics protocol. Health Psychol Behav Med. 2018;6:136–61. https://doi.org/10.1080/21642850.2018.1472602 . Park CG. Implementing alternative estimation methods to evaluate the reliability of Likert-scale instruments. Womens Health Nurs. 2024;30:18–25. https://doi.org/10.4069/whn.2024.03.12 . Irribarra DT. Scree Plot. The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation. SAGE Publications, Inc.; 2018. pp. 1483–1483. https://doi.org/10.4135/9781506326139 . Aryal A, Anuba PA, Arun GR, Sarumathy S. Nanda kumar R. A review on status of drug disposal practice of unused and expired drugs among different countries. J Appl Pharm Sci. 2023;13:045–52. https://doi.org/10.7324/JAPS.2023.32158 . Heuer H, Schmitt H, Smalla K. Antibiotic resistance gene spread due to manure application on agricultural fields. Curr Opin Microbiol. 2011;14:236–43. https://doi.org/10.1016/j.mib.2011.04.009 . Vaseem DB, C A. A study on awareness and disposal practices of unused and expired medicines by consumers. Int J Basic Clin Pharmacol. 2020;9:556–61. https://doi.org/10.18203/2319-2003.ijbcp20201176 . Manocha S, Suranagi UD, Sah RK, Chandane RD, Kulhare S, Goyal N, et al. Current Disposal Practices of Unused and Expired Medicines Among General Public in Delhi and National Capital Region, India. Curr Drug Saf. 2020;15:13–9. https://doi.org/10.2174/1574886314666191008095344 . Zhenzhen C, Jiabao R, Tingyu D, Ke C, Ruyi H, Yimiao L et al. Development and validation of a short form of the medication literacy scale for Chinese College Students. 2024. https://doi.org/10.48550/arXiv.2405.02853 Taber KS. The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education. Res Sci Educ. 2018;48:1273–96. https://doi.org/10.1007/s11165-016-9602-2 . Edelsbrunner PA, Simonsmeier BA, Schneider M. The Cronbach’s Alpha of Domain-Specific Knowledge Tests Before and After Learning: A Meta-Analysis of Published Studies. Educ Psychol Rev. 2025;37:4. https://doi.org/10.1007/s10648-024-09982-y . Wessel J, Williams R, Finch E, Gémus M. Reliability and validity of an objective structured clinical examination for physical therapy students. J Allied Health. 2003;32:266–9. Table 5 Table 5 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table5.docx GraphicalAbstractMeDisPractvalidation.pdf MeDisPractToolDESCRIPTION.docx MeDisPractToolSCORING.docx STROBEchecklistcrosssectionalMeDisPract.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 04 May, 2026 Reviews received at journal 27 Apr, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers invited by journal 14 Apr, 2026 Editor assigned by journal 13 Apr, 2026 Editor invited by journal 26 Mar, 2026 Submission checks completed at journal 26 Mar, 2026 First submitted to journal 26 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9214659","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":624578715,"identity":"d61ef18d-3884-4b11-8550-32d9e8f7d396","order_by":0,"name":"Sandip Mukhopadhyay","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYJACiQQQyc58AMSWIUELMxuIkuAhTguYZOYxAFGEtci3nz144+EOu2j+Zp7Pr27UWPAwsB8+ugGfFoMzeckWiWeSc2cc5t1mnXMM6DCetLQbeLUw5JhJJLYdyN3AzLvNOIcNqEWCxwyvFvn+NzAtPM+Mc/4RoYXhBtwWHubHuW1EaDG48cYY6hc2M+bcPgkeNkJ+ke/PMbz5c4ddbn978+PPOd/q5PjZDx/D7zAQYGwAU2zgCGIjqBxJC/MHolSPglEwCkbBiAMAtnRFrxBGiUUAAAAASUVORK5CYII=","orcid":"","institution":"ICMR National Institute for Research in Bacterial Infections","correspondingAuthor":true,"prefix":"","firstName":"Sandip","middleName":"","lastName":"Mukhopadhyay","suffix":""},{"id":624578724,"identity":"9c5aabda-e490-4e68-aa4d-1be4ff16b7a4","order_by":1,"name":"Melissa Glenda Lewis","email":"","orcid":"","institution":"ICMR National Institute for Research in Bacterial Infections","correspondingAuthor":false,"prefix":"","firstName":"Melissa","middleName":"Glenda","lastName":"Lewis","suffix":""}],"badges":[],"createdAt":"2026-03-24 16:54:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9214659/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9214659/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107465928,"identity":"01d5aaca-abf1-4cc0-a2c7-9cd4165d6233","added_by":"auto","created_at":"2026-04-21 18:24:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5081354,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Fig1Conceptualframework300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-9214659/v1/b0f8e963beb23e8fdf9ef7da.png"},{"id":107465982,"identity":"bc0fa48f-1594-4236-b5c6-f02483e94b73","added_by":"auto","created_at":"2026-04-21 18:24:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2927827,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure2Flowdiagram300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-9214659/v1/62dfbb436c7b7b5d8326b4db.png"},{"id":107465909,"identity":"3f010332-f337-41e9-ada8-09b3eb94bd93","added_by":"auto","created_at":"2026-04-21 18:24:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3299031,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScree plot showing eigenvalues for MeDisPract items (n=509)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig3Medispractvalidationscreeplot300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-9214659/v1/1dd6cd09e8be8246c3ad2c38.png"},{"id":107489553,"identity":"f7569c1d-9650-460d-9908-a8df25ce0e5c","added_by":"auto","created_at":"2026-04-22 02:48:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10569267,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9214659/v1/d959ba28-51e9-4b1c-aeff-6c24db9e113e.pdf"},{"id":107465980,"identity":"e4e776d5-d732-4f30-928e-4f12a9d579e6","added_by":"auto","created_at":"2026-04-21 18:24:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18950,"visible":true,"origin":"","legend":"","description":"","filename":"Table5.docx","url":"https://assets-eu.researchsquare.com/files/rs-9214659/v1/f04d510fab8173a1f9bd6ae7.docx"},{"id":107465962,"identity":"e7ed838c-4f8d-40c6-bec5-616a40c5aa5b","added_by":"auto","created_at":"2026-04-21 18:24:37","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":278552,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstractMeDisPractvalidation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9214659/v1/122f9e45ed7d6613fbf4b06f.pdf"},{"id":107466022,"identity":"7ea536d7-6aa3-48fd-a269-7fe81f3b808f","added_by":"auto","created_at":"2026-04-21 18:24:51","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17640,"visible":true,"origin":"","legend":"","description":"","filename":"MeDisPractToolDESCRIPTION.docx","url":"https://assets-eu.researchsquare.com/files/rs-9214659/v1/84c8b0395a94db00e5eee024.docx"},{"id":107465974,"identity":"648a1394-39b5-465a-bc93-b208d075ff0c","added_by":"auto","created_at":"2026-04-21 18:24:40","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":20687,"visible":true,"origin":"","legend":"","description":"","filename":"MeDisPractToolSCORING.docx","url":"https://assets-eu.researchsquare.com/files/rs-9214659/v1/2dbdf6ceee8f111f5e404bf6.docx"},{"id":107465965,"identity":"5a72ae35-bdfd-433b-87d1-507078cfbf6e","added_by":"auto","created_at":"2026-04-21 18:24:38","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":33559,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEchecklistcrosssectionalMeDisPract.docx","url":"https://assets-eu.researchsquare.com/files/rs-9214659/v1/953db5329598d912532578ad.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eQuantifying Household Medicine Disposal to Address Environmental Antimicrobial Resistance: Development and Validation of the MeDisPract Index \u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eMedication disposal is often a neglected facet in the traditional waste management system in many countries. Though larger hospitals and Pharmaceutical Industry follow specific policies to dispose unused and expired medications, there hardly remains any structured system for disposal of domestic pharmaceutical wastes, particularly in the low and middle income countries (LMICs). The lack of accessible disposal facilities, coupled with limited awareness of safe disposal practices, foster a widespread culture of unsafe medication disposal.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] The conventional waste management system is not sufficiently capable of handling pharmaceutical waste. Consequently, unused and expired medicines frequently enter the environment through inappropriate disposal practices such as household garbage, open dumping, or flushing into sewage systems that finally impacts both animal and human health.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003cp\u003ePharmaceutical residues, either as intact compounds or biologically active metabolites, ultimately contaminate water bodies and soil.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Such wastes range from modern medicine to traditional medicine, from simple analgesic to antimicrobials including those expired, unused, or leftover medicines discarded at the household level. Low level of pharmaceutical residue persists in the soil or aquatic bodies leading to ecological niches that favour the selection of drug-resistant microorganisms.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Though often under-recognized, growing evidence suggests that pharmaceutical waste may contribute to the emergence and propagation of antimicrobial resistance (AMR) by creating sustained low-level antimicrobial exposure in the environment.[\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] These findings underscore the environmental pharmaceutical pathways that impact both animal and human health within the \u0026lsquo;One Health\u0026rsquo; framework. In the Asian context, AMR is projected to cause 4.7\u0026nbsp;million deaths in 2050 and India has one of the highest AMR burden globally and related expenditure.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Despite global efforts to mitigate AMR, current action plans have largely focused on antimicrobial stewardship, infection prevention, and rational prescribing, with limited attention to medication disposal as a contributory pathway. Countries with inadequate regulatory oversight, absence of structured medication take-back programs and limited pharmaceutical waste infrastructure, people often resort to environmentally unsound and unsafe disposal practices. Hence, threat of AMR becomes magnified in many LMICs of Asia or Africa.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eStudies across different geographic regions have examined people\u0026rsquo;s knowledge, attitudes, and practices related to medication disposal. However, these studies vary widely in scope, methodology, and outcome measures. While some focus predominantly on disposal methods, others emphasize knowledge or awareness, often using non-standardized, non-quantifiable instruments.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] The heterogeneity of existing tools limits comparability across places, studies and impedes the generation of actionable, population-level evidence. Moreover, the lack of a validated quantitative measure restricts the integration of medication disposal behaviours into public health surveillance and environmental risk assessment frameworks.\u003c/p\u003e \u003cp\u003eTo address this gap, we propose a pragmatic composite measure\u0026mdash;the \u003cb\u003eMeDisPract Index\u003c/b\u003e\u0026mdash;to quantify people\u0026rsquo;s behaviour towards safe disposal of household pharmaceutical waste as well as population-level pharmaceutical disposal risk in a standardized manner based on the \u003cb\u003eMedication Disposal Practices (MeDisPract) tool.\u003c/b\u003e The tool is a structured questionnaire designed to assess household-level medication disposal behaviours, primarily the practice, readiness or attitude and knowledge for safe disposal. The tool and index are intended not only for individual-level assessment within communities but also to support public health surveillance by enabling categorization, comparison, and monitoring of medication disposal practices using a quantifiable and reproducible approach. The tool differs from an exhaustive knowledge-attitude-practice questionnaire as it was primarily targeted to support public health surveillance, focused greater emphasis on the behavioural aspects and is characterized by field readiness with simplicity and brevity. Hence, the present study was planned with the following objectives:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo develop a structured questionnaire (MeDisPract tool) to assess household medication disposal practices, knowledge, and preparedness.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo validate the MeDisPract tool through reliability and validity assessment.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo develop a composite \u003cb\u003eMeDisPract Index\u003c/b\u003e for quantifying population-level pharmaceutical disposal risk.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eA community-based methodological study was conducted that involved development, validation, and field-testing of a structured instrument\u0026mdash;the \u003cb\u003eMedication Disposal Practices (MeDisPract) Tool\u003c/b\u003e, and to construct a composite \u003cb\u003eMeDisPract Index\u003c/b\u003e as a study instrument or survey tool for \u0026lsquo;Medication Disposal Practice in the community\u0026rsquo;, for quantifying household-level medication disposal behaviour and associated public health risk (Supplementary files 1 and 2). The study combined instrument development, psychometric evaluation, and cross-sectional field application. The study adheres to STROBE and COSMIN recommendations.\u003c/p\u003e \u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in compliance with the Declaration of Helsinki. Approval of the Institutional Ethics Committee, ICMR- National Institute for Research in Bacterial Infections (formerly ICMR-NICED) was obtained for use of human data for the tool validation (Approval no.\u0026nbsp;ICMR-NICED/IEC-BMHR/BMHR-032/25).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptual Framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe tool was designed as a \u003cstrong\u003epublic health surveillance instrument\u003c/strong\u003e rather than a psychological scale. A focused literature review of published literature on household medication disposal of multiple countries, WHO guidance on pharmaceutical waste management, AMR-environment linkage studies, and household disposal research was conducted resulting in a five domain conceptual model:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eBehavioural (Practice) Domain:\u0026nbsp;\u003c/strong\u003eFrequency based disposal behaviours in Likert items.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCognitive (Knowledge) Domain):\u003c/strong\u003e Awareness of environmental and health risks.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAttitudinal (Readiness) Domain:\u003c/strong\u003e Willingness to adopt safe disposal practices.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHousehold medicine stock\u003c/strong\u003e and availability of\u0026nbsp;\u003cstrong\u003edisposal infrastructure\u003c/strong\u003e : 2 binary items,\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eActual methods of\u0026nbsp;\u003cstrong\u003emedication disposal\u003c/strong\u003e - multiple-response item which captures actual methods utilized at households. The responses are either environmentally \u0026lsquo;safe\u0026rsquo; or \u0026lsquo;unsafe\u0026rsquo; method.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eKeeping stock of medication in home and actual disposal method utilized are also behavioural components, and contains only three items in total, it was decided to get these grouped under the \u0026lsquo;behavioural\u0026rsquo; domain as subdomains. Hence, the final construction was three primary domains:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003eBehavioural (Practice) Domain:\u003c/strong\u003e Capturing actual disposal behaviour and infrastructure under three subdomains- (i) routine disposal behaviours (4 frequency-based items), (ii) household medicine stock and availability of disposal infrastructure (2 binary items), (iii) methods of medication disposal (1 multiple-response item), 2. \u003cstrong\u003eCognitive (Knowledge) domain, and 3. Attitudinal (Readiness) domain.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe index is conceptually formative, these domains are \u003cstrong\u003eformative contributors\u003c/strong\u003e to disposal risk; collectively they define behaviour rather than reflect a single latent construct, weighting was theory-driven rather than factor-loading driven. Essentiality, pragmatism, simplicity, minimalism, and brevity were primary focus during development of the tool to match it as a public health surveillance tool which could be rapidly administered in approximately five minutes time. The conceptual structure and index construction pathway are illustrated in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1: Conceptual framework and construction of MeDisPract framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe \u003cstrong\u003eMeDisPract framework\u0026nbsp;\u003c/strong\u003econceptualizes household medication disposal behaviour as a formative public health construct comprising three interacting domains: \u003cstrong\u003eBehavioural\u003c/strong\u003e or \u003cstrong\u003ePractice\u0026nbsp;\u003c/strong\u003e(actual disposal actions and infrastructure), \u003cstrong\u003eCognitive or Knowledge\u0026nbsp;\u003c/strong\u003e(awareness of environmental and health risks), and \u003cstrong\u003eAttitudinal or\u003c/strong\u003e \u003cstrong\u003eReadiness\u003c/strong\u003e (willingness to adopt safe disposal mechanisms). Domain scores are standardized and combined using theory-driven weighting to generate the \u003cstrong\u003eMeDisPract Index\u003c/strong\u003e, which categorizes communities by pharmaceutical disposal risk and supports environmental AMR surveillance and intervention planning.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eItem Generation and Tool Development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn initial pool of items was generated through literature review of global disposal-practice studies, review of regulatory and environmental guidance and expert consultation from the fields of pharmacology, public health, microbiology, environmental health. Items were screened for relevance, clarity and essentiality. Attention was also given to simplicity and field feasibility for surveillance use. All identified items were included as a question in a preliminary draft questionnaire. Item questions concerning similar components (e.g., practice) were grouped together into three identified domains. After iterative refinement, \u003cstrong\u003e14 items\u003c/strong\u003e were retained under the three identified domains as below:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eBehavioural (Practice) domain: 7 items (4 Likert-frequency items, 2 binary items, 1 multiple-response item)\u003c/li\u003e\n \u003cli\u003eCognitive (Knowledge) domain: 4 binary items\u003c/li\u003e\n \u003cli\u003eAttitudinal (Readiness) domain: 3 binary items\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe questionnaire was designed for rapid administration (\u0026asymp;5 minutes) to enable integration into community surveys.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContent Validity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA multidisciplinary expert panel rated each item for relevance, clarity, and necessity using a 4-point scale. \u003cstrong\u003eItem-level Content Validity Index (I-CVI)\u003c/strong\u003e was calculated as the proportion of experts rating the item \u0026ge;3. Items with I-CVI \u0026lt;0.78 were revised or removed. The overall \u003cstrong\u003eScale-level CVI (S-CVI/Ave \u0026ge;0.90)\u003c/strong\u003e indicated excellent agreement on content adequacy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePilot Testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe revised instrument was pilot tested among 50 community participants to assess comprehension, response variability, and feasibility. Minor wording changes were made without altering item structure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eField Administration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe finalized MeDisPract tool was administered to 509 households using interviewer-assisted data collection utilizing the platform of \u0026lsquo;Population based health survey (PBHS)\u0026rsquo;, conducted by the Model Rural Health Research Unit-Darjeeling (MRHRU-Darjeeling), West Bengal at Darjeeling District. This PBS round 1 survey is a flagship initiative from the Department of Health Research, Government of India and conducted all over India through the networks of MRHRUs all over India at 34 sites during 2024-25. A sub-survey was co-administered to the same households utilizing the MeDisPract tool (Medication disposal practice survey) with one consenting adult respondent included (1 household= 1 participant, sample size 459). The sample size was determined pragmatically based on the available households within the Population-Based Health Survey platform and MeDisPract Survey and was considered adequate for exploratory factor analysis and validation of a multidimensional instrument. Written informed consent was obtained from the participants. Responses completed for all 14 items were included in the analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the MeDisPract Index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDomain Scoring\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach domain was scored separately (Table 1):\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Domain specific scoring of MeDisPract tool\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDomain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScoring Method\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRaw Score Range\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBehavioural (Practice) domain\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eLikert + behavioural items (binary + multiple input item)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0\u0026ndash;32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCognitive (Knowledge) domain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eBinary (Yes=1, No=0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0\u0026ndash;4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAttitudinal (Readiness) domain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eBinary (Yes=1, No=0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0\u0026ndash;3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStandardization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure comparability, each domain score was linearly transformed to a \u003cstrong\u003e0\u0026ndash;100 scale\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"321\" height=\"36\" src=\"https://myfiles.space/user_files/58893_b39df98f09c4a4bb/58893_custom_files/img177677963036.png\" v:shapes=\"_x0000_i1025\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeighting and Composite Index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBecause behavioural practice directly determines environmental exposure, the practice domain was assigned \u003cstrong\u003edouble weight\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"396\" height=\"36\" src=\"https://myfiles.space/user_files/58893_b39df98f09c4a4bb/58893_custom_files/img1776779630.png\" v:shapes=\"_x0000_i1025\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003eThe resulting index ranges from \u003cstrong\u003e0 to 100\u003c/strong\u003e, representing increasing safety of disposal behaviour.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk Categorization:\u0026nbsp;\u003c/strong\u003eRisk categorization from index score was done for public health interpretation and utilizing the tool to set actionable priority (Table 2). The cutoffs were pragmatically defined using equal quartile banding for interpretability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Medication disposal practice risk category based on MeDisPract index\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndex Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePublic Health Interpretation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u0026ndash;25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eVery Poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eHigh disposal risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e26\u0026ndash;50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eUnsafe practices prevalent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e51\u0026ndash;75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eFair\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eTransitional behaviour\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e76- 100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePredominantly safe practice\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReliability Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs the tool was comprising a mixed ordinal\u0026ndash;binary structure, multiple complementary reliability estimates were used:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eCronbach\u0026rsquo;s alpha\u003c/strong\u003e for Likert items (behavioural coherence) to assess internal consistency\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eOrdinal alpha (polychoric-based)\u003c/strong\u003e to account for ordinal responses.[18]\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGuttman Lambda-6\u003c/strong\u003e as a conservative lower-bound estimate.[19]\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSplit-half reliability\u003c/strong\u003e with \u003cstrong\u003eSpearman\u0026ndash;Brown correction\u003c/strong\u003e was calculated for ordinal-mixed data.[20]\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eReliability coefficients were interpreted pragmatically, keeping in mind that the behavioural surveillance tools measure heterogeneous constructs rather than a single latent trait.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruct Validity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExploratory factor analysis (EFA) using polychoric correlations assessed whether items grouped according to the hypothesized three-domain structure. Kaiser\u0026ndash;Meyer\u0026ndash;Olkin (KMO) test evaluated sampling adequacy and Bartlett\u0026rsquo;s test assessed inter-item correlation suitability. Principal component extraction with varimax rotation was applied. Factor loadings \u0026ge;0.40 were considered meaningful.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalyses included descriptive statistics (mean, SD, median, IQR), Reliability estimation, Exploratory factor analysis and distributional assessment of MeDisPract Index and risk categories.\u003c/p\u003e\n\u003cp\u003eAs the MeDisPract Index is a composite behavioural indicator rather than a reflective psychometric scale, reliability estimates were interpreted as measures of response coherence rather than internal homogeneity. Moderate coefficients were considered acceptable for surveillance-oriented instruments.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParticipant Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData from 509 respondents from 25 villages were analysed. A flow diagram summarizes the participant inclusion and analysis for the MeDisPract validation study (Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2: Flow diagram of participant inclusion and analysis for the MeDisPract validation study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll responses were complete and suitable for index computation; therefore, no imputation or missing data handling was required. The demographic detail of the participants is summarized in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Demographic details of the study participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"392\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eAge in years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 142px;\"\u003e\n \u003cp\u003e35.00 (27.00, 47.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 118px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 142px;\"\u003e\n \u003cp\u003e259 (50.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 142px;\"\u003e\n \u003cp\u003e250 (49.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 118px;\"\u003e\n \u003cp\u003eReligion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eBuddhists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 142px;\"\u003e\n \u003cp\u003e3 (0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eChristians\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 142px;\"\u003e\n \u003cp\u003e4 (0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eMuslims\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 142px;\"\u003e\n \u003cp\u003e40 (7.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eHindus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 142px;\"\u003e\n \u003cp\u003e462 (90.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 118px;\"\u003e\n \u003cp\u003eTribe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eBengali\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 142px;\"\u003e\n \u003cp\u003e166 (32.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eBihari\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 142px;\"\u003e\n \u003cp\u003e13 (2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eNepali\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 142px;\"\u003e\n \u003cp\u003e50 (9.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eRajbanshi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 142px;\"\u003e\n \u003cp\u003e272 (53.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eTribal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 142px;\"\u003e\n \u003cp\u003e8 (1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 118px;\"\u003e\n \u003cp\u003eEducation Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eIlliterate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 142px;\"\u003e\n \u003cp\u003e98 (19.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 142px;\"\u003e\n \u003cp\u003e156 (30.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eSecondary\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 142px;\"\u003e\n \u003cp\u003e133 (26.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003e12\u003csup\u003eth\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 142px;\"\u003e\n \u003cp\u003e72 (14.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eGraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 142px;\"\u003e\n \u003cp\u003e50 (9.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 118px;\"\u003e\n \u003cp\u003eRespondent\u0026rsquo;s Employment Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 142px;\"\u003e\n \u003cp\u003e105 (20.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 142px;\"\u003e\n \u003cp\u003e404 (79.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 118px;\"\u003e\n \u003cp\u003eEmployment Category \u003cem\u003e(n=105)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eUnskilled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 142px;\"\u003e\n \u003cp\u003e53 (50.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eSemiskilled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 142px;\"\u003e\n \u003cp\u003e38 (36.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 133px;\"\u003e\n \u003cp\u003eSkilled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 142px;\"\u003e\n \u003cp\u003e14 (13.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eReliability testing was done by multiple tests as the tool comprised different response formats for the items.[18,21] The MeDisPract tool, a mixed Likert-binary scale, demonstrated moderate to strong internal consistency with Ordinal \u0026alpha; value of 0.78 and Guttman \u0026lambda;-6 value 0.75. The behavioural Likert items showed moderate consistency through Cronbach\u0026rsquo;s \u0026alpha; \u0026nbsp; (\u0026alpha;=0.57) reflecting multidimensional formative construct. The attitude and cognitive domain also showed moderate internal consistency as expected reflecting distinct awareness components (Table 4). The tool exhibited moderate split-half reliability (r_sb=0.58, p\u0026lt;0.001). These were consistent with instruments measuring diverse real-world practices rather than redundant constructs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Reliability of the MeDisPract Tool\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"618\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDomain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eItems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCronbach\u0026rsquo;s \u0026alpha;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrdinal \u0026alpha;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLambda-6 (\u0026lambda;6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSplit half\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInterpretation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBehavioural (Practice)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003cp\u003e(item 1-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eAcceptable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCognitive (Knowledge)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eModerate to Good (\u0026lambda;6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAttitudinal (Readiness)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall Tool\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eGood\u0026nbsp;composite coherence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* \u003cem\u003eQ14 (attitudinal domain) showed a \u0026quot;ceiling effect\u0026quot; (universal agreement), which is a valid finding in public health (showing high community readiness despite low knowledge). In psychometrics, an item with no variance cannot contribute to reliability or factor analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruct Validity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExploratory factor analysis (EFA) provided construct validity evidence. Although the index is formative, EFA was used pragmatically to explore item clustering apart from construct validation. Prior to conducting exploratory factor analysis (EFA), the suitability of the data for EFA was assessed using Bartlett\u0026apos;s test of sphericity and the Kaiser\u0026ndash;Meyer\u0026ndash;Olkin (KMO) measure of sampling adequacy.\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThe KMO test resulted an acceptable value of 0.604 which exceeds the minimum threshold of 0.6, reflecting the sampling adequacy for a formative and multidimensional structure of the tool. Bartlett test of sphericity was significant with p\u0026lt;0.001 (non-identity matrix). Hence, \u0026lsquo;Factor Analysis\u0026rsquo; is appropriate for the tool. Item Q14 was excluded from all analyses due to \u003cem\u003e\u0026quot;ceiling effect\u0026quot; (universal agreement\u003c/em\u003e resulting zero variance) as during reliability analysis. Factor loadings \u0026ge;0.40 were considered significant.\u003c/p\u003e\n\u003cp\u003eScree plot (Figure 3) was obtained additionally for selection of the number of relevant components or factors to be considered in factor analysis.[22] It shows five factors with eigenvalues \u0026gt;1.0 (Kaiser rule) with a clear elbow after fifth factor indicating optimal 5-factor solution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3: Scree plot showing eigenvalues for MeDisPract items (n=509)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExploratory Factor Analysis using principal component extraction (PCA) with Varimax rotation was chosen for interpretability and domain separation and it identified a five-factor structure with eigenvalues \u0026gt;1 (Table 5):\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eFactor 1 captured awareness of environmental and health risks of improper disposal (Q8\u0026ndash;Q11).\u003c/li\u003e\n \u003cli\u003eFactor 2 represented core household disposal behaviour (Q1\u0026ndash;Q3).\u003c/li\u003e\n \u003cli\u003eFactor 3 reflected access to disposal infrastructure and organized collection systems (Q6, Q12).\u003c/li\u003e\n \u003cli\u003eFactor 4 described safe-use practices and routine checking behaviours (Q2, Q4, Q7).\u003c/li\u003e\n \u003cli\u003eFactor 5 indicated perceived knowledge gap and medicine retention tendency (Q5, Q13).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eMost items demonstrated satisfactory communalities (0.56\u0026ndash;0.88), indicating adequate representation of shared variance by the extracted factors. Q4 and Q7 communalities (0.42 and 0.44 respectively) are slightly low. These two items showed modest communalities but were retained due to conceptual relevance to safe disposal behaviour.\u003c/p\u003e\n\u003cp\u003eTotal explained variance of 68.4% with 5 factors was consistent with multidimensional behavioural tool. Factor analysis of MeDisPract demonstrated that, the a. items grouped as theorized, b. there is minimum cross-loadings, c. the cognitive and behaviour domain are dominant, and d. suitable for surveillance use.\u003c/p\u003e\n\u003cp\u003eFactor analysis also demonstrated clear behavioural clustering (Table 6). This indicates that \u003cstrong\u003ebehaviour is not a single variable\u003c/strong\u003e \u0026mdash; it is distributed across multiple actionable dimensions, which is common in public-health behaviour models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6: Additional insight from EFA\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSupporting Items\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistical Evidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBehaviour (actual disposal actions)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ1, Q2, Q3, Q4, Q7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLoad strongly across Factors 2 \u0026amp; 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAwareness/Risk perception\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ8\u0026ndash;Q11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVery high loadings (0.59\u0026ndash;0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSystem/Infrastructure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ6, Q12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStrong Factor 3 loadings (~0.73\u0026ndash;0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerceived need / retention\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ5, Q13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFactor 5 structure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDistribution of MeDisPract Index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MeDisPract Index demonstrated variability across households, indicating heterogeneity in disposal behaviour. A substantial proportion of households fell into the \u003cstrong\u003emoderate to high disposal risk\u003c/strong\u003e categories. Unsafe disposal practices were common despite awareness of environmental harm (Table 7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7: MeDisPract based medication disposal risk categories, population distribution and the public health interpretation\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage of participants (n=509)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProportion of Participants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInterpretation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVery Poor\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSubstantial segment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVery unsafe disposal behaviour, high environmental exposure potential\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e83.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLargest segment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMostly unsafe behaviour, high environmental exposure potential\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFair\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModerate proportion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePartial adoption of safe practices\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGood\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmallest proportion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEnvironmentally safe behaviour\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnsafe disposal practices persisted despite awareness of environmental harm, highlighting a gap between knowledge and actionable behaviour. Variation in disposal behaviour improved with employment status and varied among the tribes but not across education level, possibly implying cultural sensitivity to cleanliness among tribes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDomain Contribution Patterns\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe practice domain showed the strongest influence on overall index variability, supporting its theoretical weighting as the principal determinant of pharmaceutical environmental entry.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHousehold medication disposal remains an under-recognized interface between pharmaceutical use and environmental hazards including antimicrobial resistance. The MeDisPract tool was developed and field-tested in the present study as a pragmatic surveillance instrument to quantify household-level disposal behaviours and translate them into a measurable public health risk index. The MeDisPract tool functioned effectively as a field-ready surveillance instrument, demonstrating adequate reliability for behavioural measurement, conceptually coherent domain structure and capacity to stratify communities by disposal risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBehaviour\u0026ndash;Environment\u0026ndash;AMR Link:\u0026nbsp;\u003c/strong\u003eImproper disposal of household medication wastes introduce a diffuse, low-dose pharmaceutical exposure into ecosystems, a known ecological driver of antimicrobial selection pressure.[4,7,23,24]\u0026nbsp;By operationalizing disposal behaviour into a quantifiable metric, the MeDisPract Index enables this neglected pathway to be incorporated into AMR surveillance models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison With Existing Studies on medication disposal:\u0026nbsp;\u003c/strong\u003ePrevious studies assessed knowledge or disposal methods in isolation using non-standardized questionnaires, limiting comparability and policy translation.[15,16,23,25,26] Some tools only targeted to validate scale on medication literacy.[27] MeDisPract may be considered an advancement in this field by providing a standardized scoring framework, allowing population-level risk stratification and enabling evaluation of disposal interventions.\u003c/p\u003e\n\u003cp\u003eThe present study noted that positive attitude does not reflect in good behaviour. Similar occurrence was noted in previous studies also.[14] Though the detected behaviour seems extremely skewed towards unsafe disposal, this is not unexpected in an area with low literacy, low background knowledge of disposal and practically zero exposure to any take back system.[15\u0026ndash;17]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEmphasis on behavioural determinants:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBehaviour-related items (Q1\u0026ndash;Q7) were assigned higher (double) weightage in the index which was an \u0026lsquo;a priori\u0026rsquo; decision because the objective of the index was to measure actionable disposal practices rather than awareness alone. Exploratory factor analysis supported this decision, as behavioural items loaded across two independent but related factors representing routine disposal actions and safety-check practices, This indicates that \u0026lsquo;behaviour\u0026rsquo; constitutes a multidimensional construct with substantial variance contribution (24%) to the overall model. This is acceptable because:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eBehaviour items explain variance across \u003cstrong\u003etwo extracted factors\u003c/strong\u003e (not one).\u003c/li\u003e\n \u003cli\u003eAwareness items cluster tightly into a \u003cstrong\u003esingle cognitive domain\u003c/strong\u003e.\u003c/li\u003e\n \u003cli\u003eAs a Public health index, \u003cstrong\u003eaction \u0026gt; knowledge\u003c/strong\u003e (KAP framework logic).\u003c/li\u003e\n \u003cli\u003eThe explained variance from behaviour-linked factors (~23-25%) is substantial.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eReliability is Acceptable:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MeDisPract tool captures diverse behavioural, infrastructural, and awareness elements. The instrument incorporates real-world behaviours e.g. checking expiry, discarding habits, storage and willingness to change. Such diversity is not expected to exhibit high internal correlation, as the tool captures distinct real-world behaviours that collectively define disposal risk. Such multidimensional public health constructs may not exhibit high internal consistency because they represent actionable system conditions rather than a single psychological trait.[28] In spite of that, the tool achieved Ordinal \u0026alpha; value of 0.78 and Guttman \u0026lambda;-6 value 0.75 marking good reliability. Cronbach\u0026rsquo;s \u0026alpha; (0.57) for the four behavioural Likert items reflects expected heterogeneity of real-world disposal and it actually shows a) items are not redundant; b) each item contributes unique information and c) the index behaves like a public health indicator (desired). There are other examples of relatively low Cronbach\u0026rsquo;s alpha value which were considered acceptable.[29,30] Validity was supported through content validation, domain structure, and usability. Moreover, the limited number of items enhances field applicability of the tool to identify high-risk communities and monitor behavioural change longitudinally as a rapidly applicable decision-support metric and not merely a research questionnaire, nor a latent-trait psychometric scale. Though from a single district, data collection from 25 villages with discrete locations adds to the external validity of the tool.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations:\u0026nbsp;\u003c/strong\u003eDespite promise as a field ready tool, rapid quantification of risk, field testing in adequate sample size, acceptable reliability and validity, certain limitations are notable. The self-reported practices may overestimate safe behaviour due to self-report bias. The cross-sectional design limits causal inference between knowledge, attitudes, and disposal behaviour. Test-retest reliability and confirmatory factor analysis was also not conducted. Though the tool provides clear indication to safe or unsafe disposal of household pharmaceutical waste at the community level, the environmental contamination not directly measured. The study was conducted in one district of eastern India, which may limit generalizability to urban or other cultural settings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe MeDisPract tool demonstrates acceptable validity and reliability for assessing household medication disposal practices. The MeDisPract Index offers a novel, quantifiable approach to classify pharmaceutical disposal risk at the population level. The tool bridges the gap between pharmaceutical consumption and environmental stewardship which is an essential but operationally missing dimension of AMR containment strategies. It has immense potential from the policy point of view through embedding in the policies like AMR National Action Plan monitoring, Community pharmacy take-back pilots and Environmental surveillance programs. In the Indian context, MeDisPract tool and Index may add additional value to the National Action Plan on Antimicrobial Resistance (NAP-AMR) of India or the Delhi Declaration on AMR as it can be used by regional health ministries to monitor \u0026quot;One Health\u0026quot; targets. Incorporation of medication disposal surveillance into public health and AMR strategies may strengthen environmental risk mitigation and promote responsible pharmaceutical stewardship, particularly in low- and middle-income settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval of the Institutional Ethics Committee, ICMR- National Institute for Research in Bacterial Infections (formerly ICMR-NICED) was obtained for the tool validation (Approval no. ICMR-NICED/IEC-BMHR/BMHR-032/25). Written informed consent was obtained from all participants before collection of data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe deidentified participant dataset underlying the findings of this study, including the MeDisPract Index instrument and the statistical analysis code used for validation, will be available upon reasonable request from the corresponding author, Sandip Mukhopadhyay. Data will be available beginning three months following publication and will remain accessible for five years. Requests for access should include a brief proposal describing the intended use of the data and will be subject to approval by the authors and the host institution to ensure compliance with ethical and data protection regulations. No additional unpublished data are available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNo direct funding involved. The Department of Health Research, Government of India funded the infrastructure and activities of the Model Rural Health Research Units including the \u0026lsquo;population based health survey\u0026rsquo; where the present study was nested and were essential for field validation of the tool.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eAuthor Sandip Mukhopadhyay was involved in the Conceptualization; Methodology; Investigation; Data Curation; Validation; Formal Analysis (supporting); Visualization; Writing \u0026ndash; Original Draft; Writing \u0026ndash; Review \u0026amp; Editing; Supervision; Project Administration. Author Melissa Glenda Lewis was involved in the Formal Analysis; Methodology (statistical); Writing \u0026ndash; Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eProf. Ashok Shenoy, Prof. Aditi Chaturvedi, Dr. Ravindra Kumar G and Dr. Soume Pyne for supporting content validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the manuscript preparation process:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the author(s) used large language models for language check, suggestions and revision. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKanyari SS, Senapati TR, Kar A, Kanyari SS, Senapati TR, Sr AK. Disposal Practices of Unused and Expired Medicines Among the General Public and Pharmacies: A Mixed-Method Study in the Odisha State of Eastern India. Cureus. 2024;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7759/cureus.52359\u003c/span\u003e\u003cspan address=\"10.7759/cureus.52359\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonteiro SC, Boxall ABA. Factors affecting the degradation of pharmaceuticals in agricultural soils. Environ Toxicol Chem. 2009;28:2546\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1897/08-657.1\u003c/span\u003e\u003cspan address=\"10.1897/08-657.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma BM, Bečanov\u0026aacute; J, Scheringer M, Sharma A, Bharat GK, Whitehead PG, et al. Health and ecological risk assessment of emerging contaminants (pharmaceuticals, personal care products, and artificial sweeteners) in surface and groundwater (drinking water) in the Ganges River Basin, India. Sci Total Environ. 2019;646:1459\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2018.07.235\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2018.07.235\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBojarski B, Kot B, Witeska M. Antibacterials in Aquatic Environment and Their Toxicity to Fish. Pharmaceuticals. 2020;13:189. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ph13080189\u003c/span\u003e\u003cspan address=\"10.3390/ph13080189\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerglund B. Environmental dissemination of antibiotic resistance genes and correlation to anthropogenic contamination with antibiotics. Infect Ecol Epidemiol. 2015;5:28564. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3402/iee.v5.28564\u003c/span\u003e\u003cspan address=\"10.3402/iee.v5.28564\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaquero F, Mart\u0026iacute;nez J-L, Cant\u0026oacute;n R. Antibiotics and antibiotic resistance in water environments. Curr Opin Biotechnol. 2008;19:260\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.copbio.2008.05.006\u003c/span\u003e\u003cspan address=\"10.1016/j.copbio.2008.05.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLarsson DGJ, Flach C-F. Antibiotic resistance in the environment. Nat Rev Microbiol. 2022;20:257\u0026ndash;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41579-021-00649-x\u003c/span\u003e\u003cspan address=\"10.1038/s41579-021-00649-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCycoń M, Mrozik A, Piotrowska-Seget Z. Antibiotics in the Soil Environment-Degradation and Their Impact on Microbial Activity and Diversity. Front Microbiol. 2019;10:338. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2019.00338\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2019.00338\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoolhouse M, Ward M, van Bunnik B, Farrar J. Antimicrobial resistance in humans, livestock and the wider environment. Philos Trans R Soc Lond B Biol Sci. 2015;370:20140083. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1098/rstb.2014.0083\u003c/span\u003e\u003cspan address=\"10.1098/rstb.2014.0083\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh VP, Jha D, Rehman BU, Dhayal VS, Dhar MS, Sharma N. A mini-review on the burden of antimicrobial resistance and its regulation across one health sectors in India. J Agric Food Res. 2024;15:100973. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jafr.2024.100973\u003c/span\u003e\u003cspan address=\"10.1016/j.jafr.2024.100973\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaneja N, Sharma M. Antimicrobial resistance in the environment: The Indian scenario. Indian J Med Res. 2025;149:119\u0026ndash;28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4103/ijmr.IJMR_331_18\u003c/span\u003e\u003cspan address=\"10.4103/ijmr.IJMR_331_18\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBataduwaarachchi VR, Weeraratne CL. Global medication waste management practices: challenges and opportunities in developing countries. Int J Basic Clin Pharmacol. 2016;5:2290\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18203/2319-2003.ijbcp20164081\u003c/span\u003e\u003cspan address=\"10.18203/2319-2003.ijbcp20164081\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUdupa N, Muragundi PM, Naik AN, Janodia M. Disposal Of Date Expired And Unused Medicines In India\u0026ndash; A Conceptual Framework. Value Health. 2013;16:A269. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jval.2013.03.1387\u003c/span\u003e\u003cspan address=\"10.1016/j.jval.2013.03.1387\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhatri J, Shakya R, Shrestha R, Shrestha S. Disposal of unused and expired medications: A study of knowledge, attitudes, and practices among community pharmacy visitors. SAGE Open Med. 2025;13:20503121251375355. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/20503121251375355\u003c/span\u003e\u003cspan address=\"10.1177/20503121251375355\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkande-Sholabi W, Adebisi YA, Abdul-Azeez IA. Disposal practices for unused and expired medications among outpatients in three healthcare facilities in Ibadan, Nigeria: a cross-sectional analysis. BMC Public Health. 2025;25:4002. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-025-25058-0\u003c/span\u003e\u003cspan address=\"10.1186/s12889-025-25058-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHategekimana JC, Niyonsenga F, Ntabwoba A, Niyombabazi JB, Nsengimana A. Disposal Practices of Leftover Medicines Among University of Rwanda Undergraduate Students. Integr Pharm Res Pract. 2025;14:17\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/IPRP.S499431\u003c/span\u003e\u003cspan address=\"10.2147/IPRP.S499431\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBashaar M, Thawani V, Hassali MA, Saleem F. Disposal practices of unused and expired pharmaceuticals among general public in Kabul. BMC Public Health. 2017;17:45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-016-3975-z\u003c/span\u003e\u003cspan address=\"10.1186/s12889-016-3975-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGadermann AM, Guhn M, Zumbo BD. Estimating ordinal reliability for Likert-type and ordinal item response data: A conceptual, empirical, and practical guide. Pract Assess Res Eval. 2012;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7275/n560-j767\u003c/span\u003e\u003cspan address=\"10.7275/n560-j767\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReliability Analysis. XLSTAT, Your data analysis solution. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.xlstat.com/solutions/features/reliability-analysis\u003c/span\u003e\u003cspan address=\"https://www.xlstat.com/solutions/features/reliability-analysis\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 26 Feb 2026.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDima AL. Scale validation in applied health research: tutorial for a 6-step R-based psychometrics protocol. Health Psychol Behav Med. 2018;6:136\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/21642850.2018.1472602\u003c/span\u003e\u003cspan address=\"10.1080/21642850.2018.1472602\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark CG. Implementing alternative estimation methods to evaluate the reliability of Likert-scale instruments. Womens Health Nurs. 2024;30:18\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4069/whn.2024.03.12\u003c/span\u003e\u003cspan address=\"10.4069/whn.2024.03.12\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIrribarra DT. Scree Plot. The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation. SAGE Publications, Inc.; 2018. pp. 1483\u0026ndash;1483. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4135/9781506326139\u003c/span\u003e\u003cspan address=\"10.4135/9781506326139\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAryal A, Anuba PA, Arun GR, Sarumathy S. Nanda kumar R. A review on status of drug disposal practice of unused and expired drugs among different countries. J Appl Pharm Sci. 2023;13:045\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7324/JAPS.2023.32158\u003c/span\u003e\u003cspan address=\"10.7324/JAPS.2023.32158\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeuer H, Schmitt H, Smalla K. Antibiotic resistance gene spread due to manure application on agricultural fields. Curr Opin Microbiol. 2011;14:236\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.mib.2011.04.009\u003c/span\u003e\u003cspan address=\"10.1016/j.mib.2011.04.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVaseem DB, C A. A study on awareness and disposal practices of unused and expired medicines by consumers. Int J Basic Clin Pharmacol. 2020;9:556\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18203/2319-2003.ijbcp20201176\u003c/span\u003e\u003cspan address=\"10.18203/2319-2003.ijbcp20201176\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManocha S, Suranagi UD, Sah RK, Chandane RD, Kulhare S, Goyal N, et al. Current Disposal Practices of Unused and Expired Medicines Among General Public in Delhi and National Capital Region, India. Curr Drug Saf. 2020;15:13\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2174/1574886314666191008095344\u003c/span\u003e\u003cspan address=\"10.2174/1574886314666191008095344\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhenzhen C, Jiabao R, Tingyu D, Ke C, Ruyi H, Yimiao L et al. Development and validation of a short form of the medication literacy scale for Chinese College Students. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48550/arXiv.2405.02853\u003c/span\u003e\u003cspan address=\"10.48550/arXiv.2405.02853\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaber KS. The Use of Cronbach\u0026rsquo;s Alpha When Developing and Reporting Research Instruments in Science Education. Res Sci Educ. 2018;48:1273\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11165-016-9602-2\u003c/span\u003e\u003cspan address=\"10.1007/s11165-016-9602-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdelsbrunner PA, Simonsmeier BA, Schneider M. The Cronbach\u0026rsquo;s Alpha of Domain-Specific Knowledge Tests Before and After Learning: A Meta-Analysis of Published Studies. Educ Psychol Rev. 2025;37:4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10648-024-09982-y\u003c/span\u003e\u003cspan address=\"10.1007/s10648-024-09982-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWessel J, Williams R, Finch E, G\u0026eacute;mus M. Reliability and validity of an objective structured clinical examination for physical therapy students. J Allied Health. 2003;32:266\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 5","content":"\u003cp\u003eTable 5 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Medication disposal, antimicrobial resistance, Environmental exposure, Behavioural surveillance, Public health tool, One health","lastPublishedDoi":"10.21203/rs.3.rs-9214659/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9214659/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImproper disposal of domestic unused and expired medicines is a source for environmental contamination. This pharmaceutical pollution pathway is also an under-recognized cause of antimicrobial resistance (AMR). There is a need for standardized assessment tools for quantifying disposal behaviour at the population level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA methodological study was conducted to validate the MeDisPract, the Medication Disposal Practices (MeDisPract) tool. Following literature review and expert consultation, a 14-item instrument was finalized with three primary domains: behavioural (practice), cognitive (knowledge), and attitudinal (readiness). Following content validity, data were collected from 509 households over 25 villages. Reliability was evaluated using ordinal alpha, split-half reliability, Guttman Lambda-6 and Cronbach’s alpha (for Likert rated items). Construct validity was assessed using exploratory factor analysis. A weighted composite MeDisPract Index (0–100) was constructed to categorize disposal risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe tool demonstrated good reliability for a behavioural surveillance instrument (Ordinal α≈0.78, Guttman Lambda-6≈0.75); Cronbach’s α for four Likert items of the behavioural domain (0.57) is acceptable for a multidimensional public health instrument. Factor analysis supported a five-domain structure (KMO=0.604 which is above cut-off indicating acceptable sampling adequacy; Bartlett p\u0026lt;0.001), explaining 68% of total variance. Index scores showed expected heterogeneity across households, with a substantial proportion of households reflecting unsafe disposal behaviours despite good attitude towards safer disposal of medications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMeDisPract provides a pragmatic, field-deployable instrument to quantify medication disposal behaviour and identify communities at risk of pharmaceutical environmental exposure. The index can support AMR action plans, environmental health surveillance, and evaluation of medication take-back interventions in low and middle income settings.\u003c/p\u003e","manuscriptTitle":"Quantifying Household Medicine Disposal to Address Environmental Antimicrobial Resistance: Development and Validation of the MeDisPract Index","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 18:23:56","doi":"10.21203/rs.3.rs-9214659/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-04T19:26:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T20:33:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"21272764983159523529348402026437856288","date":"2026-04-27T20:15:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275298766858643741950713266984872314951","date":"2026-04-16T23:12:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T15:40:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227478505587864697232859247942978817450","date":"2026-04-14T15:58:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-14T13:03:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-13T14:38:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-26T15:45:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-26T14:16:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-03-26T14:09:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"677e1ba2-ad25-433a-b98e-a28b236509b4","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-04T19:26:36+00:00","index":69,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T18:23:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 18:23:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9214659","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9214659","identity":"rs-9214659","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}