|Year : 2019 | Volume
| Issue : 7 | Page : 2234-2241
A multicentric cross-sectional study to characterize the scale and impact of polypharmacy in rural Indian communities, conducted as part of health workers training
Sangeetha Balaji1, Monsurul Hoq2, Jachin Velavan1, Beulah Raji1, Eva Grace1, Sara Bhattacharji1, Nathan Grills3
1 Department of Distance Education Unit, Christian Medical College, Vellore, Tamil Nadu, India
2 Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Victoria, Australia
3 Australia India Institute and Nossal Institute for Global Health, The University of Melbourne, Australia
|Date of Submission||20-May-2019|
|Date of Decision||26-May-2019|
|Date of Acceptance||06-Jun-2019|
|Date of Web Publication||31-Jul-2019|
Dr. Sangeetha Balaji
Department of Distance Education Unit, Christian Medical College, Vellore, Tamil Nadu
Source of Support: None, Conflict of Interest: None
Context: Polypharmacy and inappropriate medication usage is one of the world's most important public health issues. Yet in rural India, where medications are readily available, little is known about polypharmacy. Aim: This study explores factors related to polypharmacy in rural India to inform the response. Settings and Design: A household survey was conducted by community health trainees, across 515 Indian villages collecting medication prescription and usage information for single illness in the past month. Methods and Material: Polypharmacy was defined as the concurrent usage of four or more medications for single illness. Data from 515 rural India villages were collected on medication usage for their last illness. Respondents who consulted one healthcare provider for this illness were included for analysis. Statistical Analysis Used: Bivariate logistic regression and multivariate generalized estimating equation analysis were used to explore associations with polypharmacy. Results: Prevalence of polypharmacy was 13% (n = 273) in the sample and ranges between 1% and 35% among Indian states. Polypharmacy was common among prescriptions for nonspecific symptoms (15%, N = 404). People aged over 61 years compared with people aged between 20 and 60 years (OR 1.11, 95% CI 1.03–1.19) and people with income of over 3,000 INR/month (OR 1.04, 95% CI 1.00–1.07) were more likely to be prescribed four or more medications. Conclusions: The study demonstrates high rates of polypharmacy, identifies vulnerable populations, and provides information to improve the response to polypharmacy in rural India.
Keywords: Drug interactions, India, medication errors, polypharmacy, prescription, public health, rural communities, rural health, survey
|How to cite this article:|
Balaji S, Hoq M, Velavan J, Raji B, Grace E, Bhattacharji S, Grills N. A multicentric cross-sectional study to characterize the scale and impact of polypharmacy in rural Indian communities, conducted as part of health workers training. J Family Med Prim Care 2019;8:2234-41
|How to cite this URL:|
Balaji S, Hoq M, Velavan J, Raji B, Grace E, Bhattacharji S, Grills N. A multicentric cross-sectional study to characterize the scale and impact of polypharmacy in rural Indian communities, conducted as part of health workers training. J Family Med Prim Care [serial online] 2019 [cited 2020 Sep 21];8:2234-41. Available from: http://www.jfmpc.com/text.asp?2019/8/7/2234/263806
| Introduction|| |
Polypharmacy or the use of excess or clinically inappropriate medication is an area of growing worldwide concern.,,, The concern is particularly acute in regards to treatments issued for co-existing morbidities in ageing populations; multiple medications may be appropriate according to the clinical situation, but they are prone to misuse.,
In the context of low- and middle-income countries such as India, where prescription patterns are poorly monitored and there are few policies that govern prescription practices, documentation, and follow-up,,, it is important to study polypharmacy in the general population. There are multiple contributors to polypharmacy at the health-seeking, prescribing, dispensing, and consuming levels. Community-based factors such as illiteracy, poor availability of quality health care, and low health awareness ,,, further complicate the situation. This could be termed a “wicked” public health problem., These factors may lead to the overly frequent intake of too many or inappropriate medicines, especially for minor and self-limiting illnesses. The chances of drug-related problems, such as drug-to-drug and drug-to-disease interactions, medication errors, and adverse drug reactions are greater in these situations.,,,
Other consequences of polypharmacy such as antibiotic resistance are a serious threat especially in countries where communicable diseases are still a leading cause of death and disability. India is the largest user of antimicrobials for humans and faces some of the highest levels of antimicrobial resistance in the world., The socioeconomic effect of unnecessary prescription medications on a population making out-of-pocket expenses for health care can also be deleterious. The cost can lead to patient noncompliance to essential drugs, leading to a worsening of the health situation, which, in turn, may require hospitalization at a greater cost, loss of work and wages, the spread of communicable diseases, and/or complications from chronic illness.,,,,,
In order to address the issue of polypharmacy, we need to evaluate polypharmacy trends and predictors, accordingly develop clinical guidelines for prescription and then implement (and enforce) new regulatory policies.,,, Understanding medication prescribing and intake patterns for common illnesses in the general population is an important first step in identifying vulnerable groups and formulating appropriate guidelines for medication prescription and use.
This study aimed to investigate and evaluate factors related to polypharmacy for single illnesses in the general population by conducting a household survey in selected rural Indian communities.
| Subjects and Methods|| |
Community lay-leaders from rural India are trained as health workers in a distance education certificate program. This study was part of the trainees' research curriculum with approval from the Institution Review Board. The multicentric, cross-sectional, household-level survey was conducted by the trainees, using a structured questionnaire. Training for data collection was conducted by nurse trainers in 15 regional centres across Central, North, North East, and South India. The structured questionnaire and interview guidelines were formulated and piloted by the institutional research core team. The questionnaire consisted of 32 questions with an average interview time of 30–40 min for each subject. The questionnaire was translated into two local Indian languages.
To elicit regional patterns of polypharmacy, the survey was conducted in villages across the central Indian states of Chhattisgarh, Maharashtra, and Madhya Pradesh; the northern states of Rajasthan, Haryana, Himachal Pradesh, Jammu and Kashmir, Uttar Pradesh, and Uttarakhand; the north east states of Arunachal Pradesh, Assam, Bihar, Jharkhand, Orissa, and West Bengal; and the southern states of Andhra Pradesh and Karnataka. The villages were selected by convenience sampling in that they represented the villages where the community health trainee either lived or worked.
A list of villages was assigned to the trainee based on the area where they worked. Each trainee selected one village (village one) by lot from the list of allotted villages and surveyed 50 subjects of any age who had experienced an illness in the month prior to the interview. The trainee surveyed every third house, starting from the village entrance until the assigned number of 50 subjects was achieved. If such a selection did not yield a minimum of 50 subjects in one village, then the next village (village two) was selected by lot, and the survey was done until 50 surveys were completed. If the residents of a house were not available for survey, the immediate next house was selected, after which every third house was surveyed.
The data were collected from household units spread over 515 villages in the neighborhood of the 15 regional centers, with an expected sample size of 5,000. Written or verbal consent was obtained from subjects.
Demographic data regarding age, sex, education, and caste were collected. Monthly food expenditure was recorded as a proxy for household income. The interview questions asked about symptomatology, source of prescription, and names of medications. Medication details were gathered from the original prescriptions or from the medication label on the available strips left with the subject, or by the subjects' recollection of the medications taken.
The International Classification of Primary Care (ICPC, second edition) of the World Organization of Family Doctors Classification Committee was adapted to include the illnesses stated by the subjects. Classification of illnesses was based on symptoms. Illnesses such as malaria, which are common in many of the survey areas, would be reported as fever and would legitimately require three medications as the treatment regimen. Thus, for the purpose of this study, polypharmacy was defined as the usage of four or more medications for a single illness.
The medications entered in the survey form were generic and brand names. The generic medications were categorized using the World Health Organization (WHO) index of Anatomical Therapeutic Chemical Classification with defined daily doses (ATC/DDD). A general online search was conducted using the brand names of medications in the survey forms. Some of the websites that were used to find out the generic components of brand names included mims.com, igenericdrugs.com, and brand-specific pharmaceutical company websites. The WHO ATC/DDD index was then applied to the generic constituents of the brand-named medications. A medication classification code book was maintained to ensure uniform categorization of the medications. The combination medications that included more than one pharmacotherapeutic agent were individually cataloged and categorized. In case of combination medications, the total number of pharmacotherapeutic agents was counted as the number of medications.
Data were entered using Epi Info 7 and analyzed using Stata 14.2. Basic univariate analysis of the demographic characteristics, the nature of health problem, medication number, the source of prescription, and bivariate analysis between the demographics and medication usage were conducted. Of the total number of 4,749 responses, 805 responses were excluded from analysis, as the illness stated could not be categorized according to the study guidelines. From the remaining 3,944 responses, those who consulted one healthcare provider or prescriber for a single illness were included for further analysis (N = 2,118). Chi-square tests and logistic regressions were conducted to detect association with polypharmacy. A generalized estimating equation model was applied to predict probability of polypharmacy, controlling for differences in states. Only completed cases with no missing data were considered for the generalized estimating equation model (N = 1,827). Interaction between education level and food expenses was also tested to understand the changes in probabilities of polypharmacy for the two variables.
| Results|| |
Demographic details of sample population
The sample had an almost equal distribution of male (51%, n = 1,070) and female (49%, n = 1,018) subjects, and a broad range of ages (<12 to >61 years). Their monthly food expense ranged from 100 INR to 20,000 INR, while half of them spent between 2,500 INR and 5,000 INR/month. Individuals from backward castes, scheduled castes, and tribal groups formed the sample population. [Table 1] shows the distribution of the 2,118 subjects' gender, age, education, financial status, caste, and regional location distribution.
Polypharmacy in the sample population
Among the sample population, 13% (n = 273) took four or more medications, 57% (n = 1209) took two or three medications, and 30% (n = 636) took one medication for single illness. [Table 2] shows the demographic distribution of prescription patterns based on the number of medications prescribed for single illnesses by a single prescriber.
|Table 2: Demographic distribution of prescription patterns based on the number of medications prescribed for single illness by single prescriber in the sample population (n=2118)|
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Regional distribution of polypharmacy
Polypharmacy varied from state to state. The top six states with polypharmacy were Andhra Pradesh (35.3%), Maharashtra (32.2%), Arunachal Pradesh (25%), Bihar (21.37%), Madhya Pradesh (20.89%), and Chhattisgarh (20.69%). In Jharkhand, Haryana, and Himachal Pradesh, prevalence of polypharmacy was <5%. The distribution of polypharmacy by State is shown in [Supplementary Table 1] and by States grouped as a region is shown in [Table 2].
Polypharmacy distribution in the top 15 illnesses
The top 15 illnesses accounted for 53% (n = 1,134) of the total health problems reported by the respondents who sought care from one healthcare provider for their single illness. [Table 3] shows the frequency distribution of these illnesses in the sample population and the occurrence of polypharmacy in the treatment of the illnesses. The most common illnesses that were treated by polypharmacy were fatigue-related symptoms (29%), cough (20%), pain not-otherwise-specified (16%), typhoid (14%), and diarrhea (13%). Nineteen percent (n = 404) of the illness were “unknown” and 15% (n = 61) of them received four or more medications for treatment. The category of “General weakness, tiredness and drowsiness” had the highest percentage, i.e., 29% (n = 6) of individuals treated with more than four drugs.
|Table 3: Polypharmacy in the top 15 common illnesses in the study sample of n=2,118|
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Polypharmacy and prescriber
Of the 1,814 respondents who reported the source of prescription, 233 had their illness treated by four or more medications. Allopathic prescribers accounted for 81% of these prescriptions, followed by nonallopathic prescribers (12%), pharmacists (5%), and nurses (2%).
Associations with polypharmacy
The bivariate logistic regression revealed a lack of evidence to suggest any difference in the occurrence of polypharmacy across children (<12 years) and adolescents (13–19 years) compared with adults (20–60 years). However, the odds of polypharmacy were 2.5 (95% CI 1.5–4.08, P < 0.05) more for the older age group (≥61 years) compared with the adult age group.
The occurrence of polypharmacy was influenced by the family food expenditure in the study population (Chi-square 42.92; P < 0.001). The findings of the logistic regression suggest that the odds of polypharmacy are 3.8 (95% CI 2.37–6.18) for those who spend >9,000 INR/month compared with those who spend <3,000 INR/month. Similarly, the odds of polypharmacy are 2.35 (95% CI 1.53–3.59) for those who spend 6,000–9,000 INR/month compared with those spending under 3,000 INR/month. There is a lack of evidence to suggest any difference in polypharmacy between those who spend 3,000–6,000 INR and those spending under 3,000 INR/month [Table 4].
|Table 4: Findings of logistic regression – odds of concurrent usage of four or more medications for single illness by demographic characteristics|
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There was no evidence of polypharmacy differing across education categories, gender or caste [Table 4]. The generalized estimating equation model – considering sex, age, caste, education, and income as covariates and states as cluster – reveals that food expenditure, as a proxy for income, and age both influence prescription of four or more medications. There was also no evidence of interaction between education level and food expenses.
| Discussion|| |
Main findings of the study
Thirteen percent (n = 273) of the respondents who reported having one prescriber for a single illness were treated with four or more medications. The five illnesses with the highest polypharmacy account for 14% (n = 303) of the study population. There were several associations with polypharmacy in our study that could explain the higher levels of polypharmacy. Polypharmacy was significantly associated with those who were of higher socioeconomic status. It is possible that this could be due to factors like ability to pay, a higher demand for medical treatments and even altered prescriber perception of patient expectations. People aged 61 years were more likely to be prescribed four or more medications, compared with people aged between 20 and 60 years. Regional distribution of polypharmacy in the Indian states was varied in the sample population, from <1% to 35%. Illnesses with vague presentations such as general weakness/tiredness/drowsiness, pain (not otherwise specified), stomach pain (not otherwise specified), and whole-body pain were more likely to receive more than four medications.
What is already known on this topic and what this study adds
This study likely underestimates the rates of polypharmacy, as our definition excludes those who saw multiple providers or were being treated for multiple conditions. Much higher rates of polypharmacy would be anticipated among people who consulted multiple providers and if multiple illnesses were included. This is likely to be a significant problem in India given the lack of coordinated medical care and a tendency for rural populations to seek multiple prescribers for the same illness.,
Older people are more likely to have multiple chronic illnesses and comorbid conditions due to age-related changes, and therefore require more medications for appropriate therapy. This study did not include treatment for multiple illnesses or chronic illness, it nevertheless demonstrates higher odds of persons aged 61 years and older receiving more than four medications for a single illness. This group is vulnerable to the complications of polypharmacy, including medication errors and drug interactions. Including nonspecific symptoms, the side effects of polypharmacy in older age groups can result in growing numbers of prescriptions and compounding adverse side effects. Polypharmacy in older persons may well be a growing issue for India as the proportion of people in the geriatric age group increases.
Interestingly, our study did not show any significant differences on account of gender or caste when controlling for other factors, although other international studies have reported differences by gender in the occurrence of polypharmacy.,, The wide range of polypharmacy from <1% to 35% across Indian states could be a reflection of the general variation in education level, socioeconomic status, government health policy, or access to health care in the different states. However, since the sample was not weighted by population, the derived evidence may not be representative of the general population.
The study specifically showed “general weakness, tiredness and drowsiness” had the greatest occurrence and percentage of polypharmacy, and 15% illnesses categorized as unknown (19%, n = 404) were treated by four or more medications. The spectrum of diagnoses of nonspecific symptoms can include nutritional anaemia, highly challenging psychosomatic disorders, and others, such as irritable bowel syndrome and fibromyalgia. Patients with such illnesses also have a greater tendency to increase the number of healthcare visits and prescribers. Intensive consultation periods are often required for accurate diagnosis of such uncertain patient presentations. However, most of the first-contact physicians have limited time and poor access diagnostic facilities  and may be anxious about losing clients to other providers. It would seem that healthcare providers prescribe a greater number of medications in order to cover a range of health problems at the undifferentiated stage of illness. This shotgun approach, which is used to treat vague symptoms that have no clear diagnosis, may be one of the important reasons for the increased prevalence of polypharmacy.
This study showed that allopathic (81%, n = 188) physicians were the main proponents of polypharmacy compared with other prescribers including nurses, pharmacists, spiritual healers, self, and family members. This may be appropriate, as one would hope that if polypharmacy is genuinely required, then allopathic doctors are perhaps best placed to dispense the treatment. However, a study by Jisnu Das et al. showed evidence of primary healthcare contact being predominantly with untrained allopathic practitioners, in urban and rural India. This raises the question of how many of these allopathic physicians who were accounting for 81% of polypharmacy were, in fact, formally trained medical practitioners. In that vein, the study also identified deficiency in the quality of medical training of the formally trained healthcare providers, which emphasizes the need for inclusion of rational prescription and ethics into the medical curriculum in India.
There are many issues that need to be addressed when responding to polypharmacy in a context such as India. These include health illiteracy, poor general awareness about the problem of polypharmacy, and lack of access to quality primary care providers and diagnostic facilities. Solutions to this could include increasing community awareness, providing specific training to physicians, improving diagnostic services, further regulating and enforcing prescription and dispensing practices, and minimizing the profit motives of prescribers and pharmaceutical companies.
The purpose of this study was not only to measure the occurrence of and associations with polypharmacy in the Indian rural community but also to build momentum toward resolving and addressing the problem at the community level. As part of the institution's health worker training program, trainees gained an opportunity to appreciate the need for raising awareness around medication misuse and overuse in the communities where they worked.
Limitations of this study
A limitation of this study is that the survey population is not representative of the entire Indian population but rather represents the areas where the health trainees worked. The gender and age distribution of the sample are representative of the general Indian population, but it had larger proportions from scheduled castes (30%, n = 575, c.f. 16.2%) and scheduled tribes (36%, n = 703, c.f. 8.2%). This reflects the regional focus of the nonprofit programs that these health workers come from. Thus, although the sample is not representative of the Indian population, it is representative of the neglected, hard-to-access Indian communities where medication usage data are less documented. In particular, it may over-represent poor and vulnerable communities because the trainees and their charitable organizations intentionally serve in low-resource areas. Despite this limitation, the study context with its cultural and geographical diversity does represent a broad section of the Indian community.
The varied backgrounds and education levels among the data collectors were managed by a central research body that regularly liaised with the data collectors. Additionally, local coordinators reviewed copies of every tenth survey form for accuracy.
| Conclusion|| |
This study shows that polypharmacy is prevalent in rural communities in India and is common among older persons (>61 years) and among groups with higher socioeconomic status. Polypharmacy varies by region, but is more apparent in the central Indian states where there is a higher population density and fewer trained providers. The practice of polypharmacy is found to be more common in treating nonspecific symptoms, such as weakness/tiredness, pain, and cough, which points to the possibility of the providers using a shotgun therapy to cover all possible illnesses that result in these symptoms. Allopathic providers dominate in prescribing multiple drugs, but their level of training is variable. Further research would be required to understand providers' relative contributions to polypharmacy, patient health-seeking behavior and the influences of poor regulation and business incentives. This would help us to propose or build strategic approaches toward appropriate medication usage in primary care and in the wider community.
Distance Education Unit, Christian Medical College, Vellore, and the Nossal Institute for Global health for supporting the research implementation, analysis, and write up. We acknowledge the research trainers (CLHTC nurses) and data collectors (CLHTC candidates) from various charitable programs across India.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Mangin D, Bahat G, Golomb BA, Mallery LH, Moorhouse P, Onder G, et al
. International group for reducing inappropriate medication use and polypharmacy (IGRIMUP): Position statement and 10 recommendations for action. Drugs Aging 2018;35:575-87.
Burt J, Elmore N, Campbell SM, Rodgers S, Avery AJ, Payne RA. Developing a measure of polypharmacy appropriateness in primary care: Systematic review and expert consensus study. BMC Med 2018;16:91.
McIntosh J, Alonso A, MacLure K, Stewart D, Kempen T, Mair A, et al
. SIMPATHY consortium. A case study of polypharmacy management in nine European countries: Implications for change management and implementation. PLoS One 2018;13:e0195232.
Ersoy S, Engin VS. Risk factors for polypharmacy in older adults in a primary care setting: A cross-sectional study. Clin Interv Aging 2018;13:2003-11.
Kumar S, Goyal A, Gupta YK. Abuse of topical corticosteroids in India: Concerns and the way forward. J Pharmacol Pharmacother 2016;7:1-5.
] [Full text]
Sheikha D, Matetia UV, Kabekkodub S, Sanal T. Assessment of medication errors and adherence to WHO prescription writing guidelines in a tertiary care hospital. Future J Pharm Sci 2017;3:60-4.
Humphries C, Jaganathan S, Panniyammakal J, Singh S, Goenka S, Dorairaj P, et al
. Investigating clinical handover and healthcare communication for outpatients with chronic disease in India: A mixed-methods study. PLoS One 2018;13:e0207511.
Gupta N, Vashist P, Tandon R, Gupta SK, Kalaivani M, Dwivedi SN. Use of traditional eye medicine and self-medication in rural India: A population-based study. PLoS One 2017;12:e0183461.
Patel V, Parikh R, Nandraj S, Balasubramaniam P, Narayan K, Paul VK. Assuring health coverage for all in India. Lancet 2015;386:2422-35.
Porter G, Grills N. Medication misuse in India: A major public health issue in India. J Public Health (Oxf) 2016;38:e150-7.
Patrick S, Badyal D. Self-medication practices in patients attending a tertiary care teaching hospital in Urban North-West India. Acta Med Int 2018;5:44-7. [Full text]
Patil KR, Mali RS, Dhangar BK, Bafna PS, Gagarani MB, Bari SB. Assessment of prescribing trends and quality of handwritten prescriptions from rural India. J Pharma Sci Tech 2015;5:54-60.
Walls HL. Wicked problems and a 'wicked' solution. Global Health 2018;14:34.
Agrawal RK, Nagpure S. A study on polypharmacy and drug interactions among elderly hypertensive patients admitted in a tertiary care hospital. Int J Health Allied Sci 2018;7:222-7. [Full text]
Dhamija P, Bansal D, Srinivasan A, Bhalla A, Hota D, Chakrabarti A. Patterns of prescription drug use and incidence of drug-drug interactions in patients reporting to medical emergency. Fundam Clin Pharmacol 2013;27:231-7.
Rakesh KB, Chowta MN, Shenoy AK, Shastry R, Pai SB. Evaluation of polypharmacy and appropriateness of prescription in geriatric patients: A cross-sectional study at a tertiary care hospital. Indian J Pharmacol 2017;49:16-20.
] [Full text]
Salwe KJ, Kalyansundaram D, Bahurupi Y. A study on polypharmacy and potential drug-drug interactions among elderly patients admitted in department of medicine of a tertiary care hospital in Puducherry. J Clin Diagn Res 2016;10:FC06-10.
Doron A, Broom A. The spectre of superbugs: Waste, structural violence and antimicrobial resistance in India. Worldwide Waste. J Interdiscip Stud 2019;2:1-10.
Laxminarayan R, Chaudhury RR. Antibiotic resistance in India: Drivers and opportunities for action. PLoS Med 2016;13:e1001974.
Sengupta A. Universal health care in India: Making it public, making it a reality. In: McDonald DA, Ruiters G, editors. Universalising Health Care for All, Monograph. Kingston; 2013.
Toh MR, Teo V, Kwan YH, Raaj S, Tan SY, Tan JZY. Association between number of doses per day, number of medications and patient's non-compliance, and frequency of readmissions in a multi-ethnic Asian population. Prev Med Rep 2014;1:43-7.
Franssen FM, Spruit MA, Wouters EF. Determinants of polypharmacy and compliance with GOLD guidelines in patients with chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis 2011;6:493-501.
Joel A, Srikanth B, Sree SU, Vyshnavi L, Mounika K. A pharmacoeconomic study of consequences, continuity and adherence of polypharmacy in elderly patients. IOSR J Pharm 2017;7:65-74.
Kantor ED, Rehm CD, Haas JS, Chan AT, Giovannucci EL. Trends in prescription drug use among adults in the United States from 1999-2012. JAMA 2015;314:1818-31.
Moriarty F, Hardy C, Bennett K, Smith SM, Fahey T. Trends and interaction of polypharmacy and potentially inappropriate prescribing in primary care over 15 years in Ireland: A repeated cross-sectional study. BMJ Open 2015;5:e008656.
Morgan SG, Weymann D, Pratt B, Smolina K, Gladstone E, Raymond C, et al
. Sex differences in the risk of receiving potentially inappropriate prescriptions among older adults. Age Ageing 2016;45:535-42.
Jäger C, Steinhäuser J, Freund T, Kuse S, Szecsenyi J, Wensing M. A tailored programme to implement recommendations for multimorbid patients with polypharmacy in primary care practices-process evaluation of a cluster randomized trial. Implement Sci 2017;12:31.
World Organization of National Colleges, Academies and Academic Associations of General Practitioners/Family Physicians [Internet]. Wonca International Classification Committee: International Classification of Primary Care, Second edition (ICPC-2) – [cited 2019 Apr 05]. Available from: http://docpatient.net/3CGP/QC/ICPC_desk.pdf
Sharma RK, Thakor HG, Saha KB, Sonal GS, Dhariwal AC, Singh N. Malaria situation in India with special reference to tribal areas. Indian J Med Res 2015;141:537-45.
] [Full text]
Epi Info [computer program]. Version 7. Atlanta, GA: Centers for Disease Control and Prevention; 2011.
StataCorp LP [Stata Statistical Software]. Release 14. College Station, TX: StataCorp; 2015.
Cockburn J, Pit S. Prescribing behaviour in clinical practice: Patients' expectations and doctors' perceptions of patients' expectations--A questionnaire study. BMJ 1997;315:520-3.
Kumar KLN, Holyachi S, Reddy K, Nayak P, Byahatti N. Prevalence of polypharmacy and potentially inappropriate medication use among elderly people in the rural field practice area of a medical college in Karnataka. Int J Med Sci Public Health 2015;4:1071-5.
Kanungo S, Bhowmik K, Mahapatra T, Mahapatra S, Bhadra UK, Sarkar K. Perceived morbidity, healthcare-seeking behavior and their determinants in a poor-resource setting: Observation from India. PLoS One 2015;10:e0125865.
Yarnall AJ, Sayer AA, Clegg A, Rockwood K, Parker S, Hindle JV. New horizons in multimorbidity in older adults. Age Ageing 2017;46:882-8.
Rodrigues MCS, Oliveira CD. Drug-drug interactions and adverse drug reactions in polypharmacy among older adults: An integrative review. Rev Lat Am Enfermagem 2016;24:e2800.
Nickel CH, Ruedinger JM, Messmer AS, Maile S, Peng A, Bodmer M et al
. Drug-related emergency department visits by elderly patients presenting with non-specific complaints. Scand J Trauma Resusc Emerg Med 2013;21:15.
Calderón-Larrañaga A, Gimeno-Feliu LA, González-Rubio F, Poblador-Plou B, Lairla-San José M, Abad-Díez JM, et al
. Polypharmacy patterns: Unravelling systematic associations between prescribed medications. PLoS One 2013;8:e84967.
Smith RC, Dwamena FC. Classification and diagnosis of patients with medically unexplained symptoms. J Gen Intern Med 2007;22:685-91.
Guo D, Kleinstäuber M, Johnson MH, Sundram F. Evaluating commonalities across medically unexplained symptoms. Int J Environ Res Public Health 2019;16:E818.
Das J, Holla A, Das V, Mohanan M, Tabak D, Chan B. In urban and rural India, A standardized patient study showed low levels of provider training and huge quality gaps. Health Aff (Millwood) 2012;31:2774-84.
Office of the Registrar General and Census Commissioner, India [Internet]. Ministry of Home Affairs, Government of India. c2011. [Published 2011; Last accessed on 2017]. Available from: http://censusindia.gov.in/
[Table 1], [Table 2], [Table 3], [Table 4]