Introduction

Pharmacoepidemiology deals with the use and effects of medications in a large number of population (Maklan, Greene, & Cummings, 1994). It includes clinical pharmacology and epidemiology. It applies the epidemiology principles with drug use and effects (Strom, 1994). Study information is gathered and analyzed to identify possible causation and related factors that can be applied in clinical practice to groups of people and also individuals undergoing treatment. Pharmacoepidemiology studies the relativity between exposed drug and health outcomes in identified (Wettermark, 2013). Pharmacoepidemiological studies aimed at quantifying risk and anticipate ways of Type A effects and lessen the risk by discerning the predisposing factors and enhanced dosing guideline. Type B ("bizarre") effects are not expected due to anticipated pharmacological features of a drug in suggested doses who metabolize the drug in the usual way (Stockley, 2002). Drug-Drug interaction is the alteration of the drug effect by another drug, food or herbal constituents’ (Vonbach, 2007).

Drug interactions are categorized based on mechanism, pharmacokinetic and pharmacodynamic interactions. Drug interactions have to consider both new drugs (investigational drug) and already approved drugs that are administered as co drug. On requirement, the clinical or non-clinical drug interaction studies to be initiated to determine the possibility of drug interactions and to measure their influence on drug therapy (Porta & Hartzema, 1991). A prospective study carried out intending to identify the unintended effects that are associated with the prescriptions; those were collected from various regions of Andhra Pradesh.

• Epidemiological distribution of drug-drug interacted prescriptions.

• Drug-drug interactions rate in the collected prescriptions.

• The most commonly interacted drug pairs in total prescriptions.

• To explore the most severe interacted drug pairs.

• To check whether the drug interaction occurred by chance is the significance (or) non-significance.

Materials and Methods

Collection of Prescriptions

A prospective study performed by collecting floating prescriptions in various areas like Kadapa, Anantapur, Kurnool, and Pulivendula on consent from the retail pharmacist to note the prescribed drugs in the prescription at many retail pharmacy shops for six months and collected drug therapy details with 110 patients. Drug interaction screening intervention programs are a paramount tool to discern prescriptions of several drugs for potential drug-drug interactions (pDDIs). Many programs are present on the market. They differ in layout, update frequency, search functions, content and price. The current study aimed to critically appraise interaction screening programs in the Andhra Pradesh Patients (Corder & Foreman, 2009). Those treated with various drugs for various diseases are taken into consideration. Patients were categorized according to their age (Parthasarathi, Nyfort-Hansen, & Nahata, 2012), type of disease, and interactions are grouped based on department wise as General Medicine, Gynaec, Pediatric and Ophthalmology. The study period of June 2011 to December 2011 with inclusion (at least two drugs) and exclusion criteria(Severe illness). Following patient enrollment, baseline information of patient name, age and clinical presentation & diagnosis is recorded. The collected prescription was checked using drug interactions checker. Epidemiology is a basic science to enhance the quality health of people and mainly the health of the disadvantaged (Armitage & Colton, 2005). This prospective observational study elucidates those who consume the medication in excess or without any knowledge on administration paves to unintended effects (Hennekens et al., 1996).

Segregation of the prescriptions

After collecting the total Prescriptions, the same segregated as per the department wise interactions. The four significant departments listed are:

• General medicine

• Gynaecology

• Paediatrics

• Ophthalmology

In this exploration, the selection of study samples and the scaling of exposures and outcomes (Sackett, 1979) are correlated.

Screening for Interactions

The collected prescriptions are carefully screened for Drug-Drug Interactions through Tertiary interaction references like Medscape, Drug-Drug Interaction checker software. First, a systematic review and identification of significant drug interactions in tertiary drug interaction references, including drug interaction evaluation with facts and Multidrug interaction checker (www.medscape.com). In this study, all the comparisons were made within the study base (Wacholder, McLaughlin, Silverman, & Mandel, 1992).

The drug interaction checker reflects potential drug interaction, shares an inference on pharmacological mechanisms of the interaction (pharmacokinetic, pharmacodynamic, or unknown) and classification based on severity level and substantiating scientific evidence. The Multidrug interaction checker software possesses both sensitivity and specificity to 97% in discerning drug combinations with interaction potential. A list of all selected 'major' interactions was developed.

Rate of Interactions

After determining the entire Drug-Drug interactions in the total prescriptions, the rate of interactions in each department for total prescription was calculated.

Application of the Statistics

The statistics were applied by using Intel Instant graph pad software to test the significance.

Kruskal-Wallis one-way analysis of variance

It is used to compare more than two samples those are independent, or not related. It is the one-way analysis of variance (ANOVA). The factual null hypothesis is that the populations, from which samples originate and have the same median. This test leads to significant results, later at least one of the samples is different from the other samples. Because it is a nonparametric tool, the test does not assume a normal distribution, not like the equivalent way analysis of variance. However, the assessment does assume a similarly-shaped and scaled prevalence for each group, except for any variance in medians (Taylor, 1982). The exploration illustrates projected epidemiologic theory with examples from various biological settings (Steineck & Ahlbom, 1992).

Results and Discussion

In this prospective study, the collected 653 prescriptions from the various regions of Andhra Pradesh like Kadapa, Proddatur, Pulivendula, Kurnool has shown the drug interaction, which is as follows.

• Demographic – Age & Sex

• Interaction severity – Major/Minor

• Department wise – interaction

• Most commonly interacting pairs

The distortion of the age and sex spreadability and the clinical vulnerability of disease can even affect judgments about the optimal interactions while among the medical care (Melton, 1985).

Gender Distribution

Figure 1 reveals that 265 were males and 388 were female patients out of 653 patients.

Age Distribution

Table 1 shows the dispersion of recruited patients for the study in various age groups. The maximum number of patients 271 were found to be in the age group of 0-20 years, and 191 patients belong to the age group of 21-40 years. Only 58 patients were at the age group of 61-80 years, whereas 133 patients were present in the age group of 41-60 years.

The number of prescriptions with interactions with an age range of Patients were depicted in Figure 2. The prescriptions containing two or more drugs have the highest potential drug interactions aged 0-20 years.

Interaction – Major/Minor

There were in total 653 prescriptions, of which all were poly pharmacy with two or more drugs, that is considered to possess a risk of drug interactions. Many prescriptions (257) were from the General Medicine department.

Possible drug interactions discerned within one prescription presented as either the number of interacting drug pairs and numbers of interactions with prescription are at a significant level. There was a total of 749 drug interacting pairs in 653 prescriptions. 346 drug interacting pairs were labelled as major interactions. The compiled rate of potential to interact drugs was 61.4%. Those of the main level accounted for 46.19%. The most significant usual interacting drug pair was Ceftriaxone + Furosemide (74 prescriptions), while the second common was Ceftriaxone + Diclofenac involving 44 prescriptions. Major interactions were prominent in the Department of General Medicine (125 prescriptions). The clinical pharmacist recommended alternatives (Primejdie, Mallet, Popa, & Bojita, 2014) to reduce the incidence of DDIs to reverse the interaction listed in Table 2.

These potential DDIs were identified as a drug-related problem (Lau, Kasper, Potter, Lyles, & Bennett, 2005) in the present study. Concerning all instances of inappropriate drugs use, resembled other similar studies (Halvorsen, Ruths, Granas, & Viktil, 2010). The hospital eventuality model should be applied with extreme precaution to evaluate hospital quality of care (Ballard, Bryant, & Brien, 1994) which is the reflection of a drug-drug interaction.

Department wise drug interactions

The rate of interactions are high in the department of general medicine (61%), followed by gynaecology (27.1%), pediatric (8%) and less in the ophthalmic department (3.8%).

General medicine

Out of 257 prescriptions in general medicine, 163 prescriptions belongs to males, and remaining 94 prescriptions belong to females. 186 prescriptions have unlikely drug interactions. The percentage of interactions occurred by chance was found to be 72.37%.

Gynaec Department

In the Gynaec department total, number of prescriptions is 226. In this unlikely drug interaction occurrence by chance was found to be 69.02%.

Pediatric Department

The total number of prescription in this department was found to be 156. The unlikely drug interaction was found to be 29.33%.

Ophthalmology

A total number of prescriptions found to be 20. The percentage of interaction occurred by chance was found to be 75%.

Interactions commonly observed in departmental wise

Table 3 elaborates that among all the drugs Ceftriaxone & Furosemide, Diclofenac & Furosemide, Albuterol & Losartan frequently interacted in General Medicine Department.

In Gynaec Department Ceftriaxone & Diclofenac, Atenolol & Amlodipine, Amoxicillin & Ceftriaxone interacted commonly.

In Pediatrics Department Phenytoin & Ranitidine, Ampicillin & vitamin k interact commonly.

In Ophthalmology Department Ceftriaxone, Cefotaxime, Diclofenac interacted.

Based on the above information statistics was applied to find unlikely drug interaction that occurred by chance is statistically significant or non-significant by using the Intel instant Graph Pad and found that study is extremely significant by the produced p-value 0.00002.

The strong correlation between the use of inhaled beta two agonists and mortality of asthma patients are confined to the use of drugs above recommended limits (Crane, Pearce, Burgess, & Beasley, 1995). Similarly, the potential interaction is due to the excess or non-Compliance of the prescription schedule.

Conclusion

The study results demonstrate the need for vigilant measures, the intervention of pharmacist in the prevention and detection of drug interactions. The pharmacist provides accurate advice on optimum drug usage based on the skills of drug interaction, that can significantly add to patient safety and well-being. The establishment of “Drug Interaction Monitoring Program” or by establishing “pharmacoepidemiological centres” in every hospital for proper screening of the drug interactions, to avoid adverse drug reactions while dispensing the medicines by “Pharmacist” and that demonstrates the need to elucidate the doctors and public, which will lead to better use of medication. But in future, more recognition and research work will be required to make Pharmacoepidemiological programs to succeed in preventing or minimizing drug interactions.