Knowledge Attitude and Practice to awards Food Drug Interaction among Health Care Providers at Public Primary Hospitals of Southern Ethiopia

Background: Interactions between food and medications can greatly impact patient adherence and the overall effectiveness of drug therapy. In healthcare settings, Food-Drug Interactions (FDIs) are an increasing concern. Various studies have reported FDI rates ranging from 3% to 30%. Despite clear evidence of the harmful effects linked to many FDIs, there is limited information about the factors that influence healthcare providers’ knowledge, attitudes, and practices regarding these interactions.

Objective: To assess the knowledge, attitude, and practice of healthcare providers towards fooddrug interactions and their associated factors at the public primary hospitals in Wolaita Zone.

Methods: A cross-sectional study was carried out among healthcare providers in primary hospitals within the Wolaita Zone, South Ethiopia. A total of 337 healthcare providers were selected using single population proportion formulas, population correction methods, and a multi-stage sampling technique to choose the facilities. Data collection was done using standardized, self-administered questionnaires. The collected data were entered into Epi-Data version 4.6.0.2 and then exported to SPSS version 25.0 for further analysis. The mean score was used as the threshold to categorize outcome variables. Descriptive statistics, including frequencies and percentages, were used to summarize the data. Bivariate analysis was performed to identify potential associated variables, with those having a p-value less than 0.25 considered for multivariable analysis. Statistical significance was determined at a p-value of less than 0.05 using binary logistic regression.

Result: Out of 337 respondents, 316 (94%) participated in the study, and the rest, 21 (6%), were not enrolled due to exclusion. Overall knowledge level was 89%, with 82% positive perception, despite poor practice (60%). Sex and profession were independent factors associated with knowledge. Work experience and profession were independent factors associated with attitude. The profession was associated with practice.

Conclusion: Healthcare providers demonstrated a generally good level of knowledge regarding food-drug interactions. Among the factors examined-sex, work experience, and profession— only experience and profession were found to have a significant influence on their knowledge, attitudes, and practices related to food-drug interactions.

ADR: Adverse Drug Reactions; AOR: Adjusted Odds Ratio; DI: Drug Interactions; DIC: Drug Information Centre; EDHS: Ethiopian Demographic Health Survey; FDA: Food and Drug Administration; FDI: Food Drug Interactions; HCPs: Health Care Professionals; KAP: Knowledge, Attitude and Practice; NSAID: Non-Steroidal Anti-inflammatory Drug; OR: Odds Ratio; PFDIs: Potential Food Drug Interactions; SE: Side Effects; WHO: World Health Organization; WSU: Wolaita Sodo University

Background

Interactions between food and drugs can significantly impact patient adherence and the overall effectiveness of drug therapy. In healthcare settings, Food-Drug Interactions (FDIs) are an increasing concern. Various studies report that the incidence of FDIs ranges from 3% to 30% [1,2]. These interactions occur when additional substances such as other drugs, foods, herbs, beverages, or environmental chemicals alter the pharmacological activity of a medication [3]. FDIs refer to the influence that food or nutrients have on medications. Many people assume that all herbs and foods are safe because they are natural [4], but they can affect drug pharmacokinetics and potentially cause FDIs or reduce drug effectiveness [5-7]. The extent of these interactions depends on factors such as the drug’s physical and chemical properties, its formulation, the type of food consumed, and the timing between eating and medication intake [6,8]. In clinical practice, FDIs often occur when herbal remedies are used without the treating physician’s awareness [8]. FDIs are among the fastest-growing challenges in oral drug therapy, impacting both the Pharmacokinetics (PK) and Pharmacodynamics (PD) of co-administered drugs [9]. Currently, FDIs represent a significant healthcare issue, as they can cause toxic effects or diminish the therapeutic benefits of medications [10]. Despite their importance to public health, Drug-Food Interactions (DFIs) are frequently overlooked and underestimated. Since the body’s metabolism is influenced by various vitamins and micronutrients, their interactions with drugs may lead to physiological disturbances that are critical to consider when managing patient treatment plans [11].

The simultaneous use of food and medications requires careful attention due to the potential for interactions between food products and drugs [12]. A drug interaction occurs when a substance alters the effect of a medication [13]. Such interactions can happen between drugs (drug-drug interactions), between drugs and food (drug-food interactions), or between drugs and herbs (drug-herb interactions). Drug-Food Interactions (DFIs) pose significant challenges to the safe and effective use of pharmacotherapy. They can occur with both prescription and over-thecounter medications, potentially reducing drug effectiveness or increasing toxicity [7]. Unlike drug-drug interactions, DFIs are less frequently documented and are seldom encountered in clinical practice [8,9]. Certain foods, including specific vegetables, fruits, high-protein plantbased diets, and dairy products, are known to adversely interact with medications [8,10-15]. For example, consuming the antibiotic ciprofloxacin alongside dairy products can lower the drug’s absorption into the bloodstream, interfering with treatment effectiveness. Similarly, taking the anticoagulant warfarin with high-protein diets may increase blood clotting risk [8,16].

Healthcare providers (HCPs) play a crucial role in preventing FDIs by understanding the factors that influence drug action over time, which supports rational drug use [17]. They are responsible for recognizing how food and beverages might affect drug efficacy and for providing patients with clear guidance on possible FDIs, including appropriate timing between food intake and medication administration. Therefore, having strong knowledge about FDIs is essential for healthcare professionals [18].

Statement of the problem

Food-Drug Interactions (FDIs) are a major cause of hospital readmissions and, in some developed countries, have even led to fatalities, placing a significant burden on healthcare systems. However, data on FDIs from low- and middle-income countries remain scarce [19]. Research indicates that patients taking two drugs with food face a 13% risk of adverse drug interactions, which increases to 38% when taking four drugs with food, and rises sharply to 82% in cases of polypharmacy [20]. FDIs are a critical yet often overlooked source of medication errors, potentially leading to treatment failure or increased drug bioavailability, which can heighten the risk of adverse effects and toxicity [9,21]. Studies from the United States, Colombia, Palestine, and Jordan have shown that Healthcare Providers (HCPs) often lack sufficient knowledge about FDIs [22,23]. Similarly, research in South Africa found poor FDI knowledge among HCPs [3]. In Ethiopia, however, there is a lack of information regarding healthcare providers’ Knowledge, Attitudes, and Practices (KAP) related to FDIs. Consequently, this study focuses on evaluating the KAP of HCPs toward FDIs and the factors influencing these aspects.

The consequences of FDIs include severe toxicity, increased hospital stays, and higher medical costs for patients [8]. In the United States, managing morbidity and mortality linked to FDIs costs approximately $36.6 billion annually, with about half of this amount attributed to preventable errors [IOM, 2016; AHRQ, 2016]. As a significant source of medication errors, FDIs pose a major public health challenge. Research on KAP related to FDIs among HCPs with various educational backgrounds is limited [16]. This study aims to enhance the knowledge, attitudes, and practices of healthcare professionals-particularly physicians, pharmacists, health officers, midwives, and nurses-regarding FDIs [24].

Study setting and period

Wolaita Zone is located 329 km south of the country's capital, Addis Ababa. According to the most recent estimate, the total population of the zone is 5.83 million, of which 2,687,021 are males and 2,698,261 are females. Its climatic condition is woynadega, with an elevation between 1600m latitude and 2100m longitude above sea level. The total number of hospitals in Wolaita Zone is 16 (8 government primary hospitals, 4 private primary hospitals, 2 general hospitals, and 1 comprehensive teaching referral hospital). Primary hospitals in our study area are found at various distances from Sodo Town. This study was conducted in some selected governmental and private primary healthcare facilities in Wolaita Zone. These include: Didaye primary hospital, Bele hospital, Badesa hospital, Gasuba hospital, Humbo Tebela hospital,Boditi hospital,Bitena and Bombe primary hospitals, and 4 private primary hospitals: Anania, Grace, Dr. Dawit, and Enyat primary hospitals, of which the above five underlined hospitals were selected by multi-stage cluster sampling criteria using the lottery method (Figure 1).

Study period: The study was conducted from November 15 to December 15, 2023.

Study design: A hospital-based cross-sectional study design was used.

Source population

All health care providers working in public primary hospitals in Wolaita Zone were the source population for this study.

Study population

HCPs selected by a simple random sampling technique were physicians, pharmacists, health officers, nurses, and midwives working in public primary hospitals in Wolaita Zone during the study period.

Inclusion criteria

HCPs (physician, health officers, pharmacists, nurses, and midwives) who were directly involved in prescribing, dispensing, and counselling, as well as administering the drugs to patients in hospitals, were included in the study.

Exclusion criteria

➢   HCPs on sick leave and annual leave and duty off.

➢   HCPs who were not willing to participate.

Sample size determinations and sampling procedure: Sample size determinations A single population proportion formula (using open Epi 3.1) was used to determine the sample size of the study. The total sample size was determined to be 549 by taking a 95% confidence interval, 50% population proportions (since there was no published article information on KAP of the topic in Ethiopia; indeed, there was data only dealing with knowledge), a 5% margin of error, and a 10% non-response rate. Using these assumptions, the sample size was calculated as follows: (n = (Zα/2)2pq/d2), where Zα/2 = confidence level at 95% = 1.96 d= Margin of error = 0.05 p = Population proportions 50% (0.5)

n = (Zα/2)2 p (1-p) = (1.96)^{2 *} (0.5)*(0.5) = 422

d² (0.05)²

Since we used multistage cluster sampling methods to find out five primary hospitals from 12 hospitals (40% were assumed), the sample size was multiplied by 1.3 to represent the design effect and to make the sample size adequate. So the final sample size was

nf= 422*1.3 = 549

But the source population was less than 10,000 then it was important to use the population correction formula to get final sample size as follows:

$$n=\frac{n_o}{1+\frac{(n_o-1)}{N}}$$

where N = source population=870 no = initial sample size from proportion = 549 n = final sample size

So, the final sample size was 337.

Sample size determination for the second objective: The sample size for the factors associated with HCPs towards food-drug interactions KAP was determined by considering various factors that were significantly associated with outcome variables, with a two-sided confidence level of 95%, a margin of error of 5%, and a power of 80% using open Epi Info version 3.1. The possible calculated sample sizes for selected factors are shown in table 1.

Table 1: Sample size calculation for different factors associated with KAP.
Associated factors	Level of (KAP) proportions (study in Jordan)		CI %	Pow er	AOR	Sample Size + non response rate (10%)*1.3
	Expose	Non expose
	d	d
Level of educations	46.1	53.9	95	80	4.24	82
Profession	66.7	33.3	95	80	2.833	192

 From the calculated sample size above a single population formula, the larger sample size was (337) was obtained and it was used as the final sample size.

Sampling procedure: Wolaita Zone has 12 public primary hospitals, of which 8 governmental primary hospitals and 4 private primary hospitals provide health care services for the public. There were 980 HCPs working in the 12 primary hospitals, of which 870 were medical doctors, pharmacists, health officers, nurses, and midwives. A multi-stage sampling technique was employed to select five hospitals out of 12 hospitals. To select the study units from the HCPs working in five hospitals, a stratified random sampling technique with proportionate allocation was used to obtain a representative sample from each profession. Hence, the determined sample size was proportionately allocated to the five professionals based on the total number of HCPs in each profession. The sampling frame was prepared after a list of selected HCPs working in each hospital was obtained from the respective hospital's human resources office. Finally, HCPs were selected from each profession by a simple random sampling method (Figure 2).

A self-administered questionnaire, first prepared in English and then translated into Amharic, was used to collect data from the consenting study participants. In the presence of ambiguity, trained data collectors helped the participants. The questionnaire was adapted and modified from the previous tool used to assess the KAP of HCPs about FDIs, incorporating questions for HCPs pertaining to demographic characteristics such as age, sex, level of education, profession, and work experience. The questionnaire contained 26 dichotomous and multiple-choice questions and consisted of four sections: Section I included socio-demographics; Section II included general knowledge towards FDI (1–16), with a mean of 8 as the cut-off points to decide whether the level of knowledge was good or poor; and Section III consisted of the attitude of HCPs towards FDI, with 4 (17–20) questions and a mean of 6 as the cut-off points to rate whether the HCPs had a positive or negative attitude towards FDI. Section IV consisted of 6 questions (2126) that assessed the practice of HCPs towards FDIs, and the mean score of 10 was the cut-off point for practicing well or poorly. HCPs attitudes were assessed through four statements measured using a 3-point Likert scale ranging between agree and disagree to review the main concepts and importance of FDI. The positively stated statements ranged from disagree (1) to agree (3), while the negatively stated statements ranged from disagree (3) to agree (1). The total score ranged from 4 to 12, and HCP practices regarding FDI were assessed by four statements using a fourpoint frequency response scale ranging from "never" to "always" and two multiple-choice questions constructed by the researcher. The four statements using a 4-point-frequency response scale ranged from never (0) to always (4), with two multiple-choice questions scoring 0 and 2. The first was given a 0 score for those who said nothing, while a score of 2 was given for those who said that the source of information was a leaflet, treatment guidelines, or books. The second was given a 0 score for those who said nothing, while a score of 2 was given for those who said patients, HCPs, and health institutions were responsible for managing FDI-associated problems. The total score was calculated by summing up (4x4) + (2x2) =16+ 4 = 20. The score ranged from 0 to 20 to measure the variables. Questionnaires were grouped and arranged according to the main objectives, and the mean score was the cut-off point for KAP that the study had addressed.

Dependent variables

Knowledge, attitude and practice of food-drug interactions

Independent variables

Socio demographic factors (Age, sex, level of educations, occupations and work experience etc.

Food-drug interactions: Food-Drug Interactions (FDIs) were defined as alterations in drug pharmacokinetics and pharmacodynamics as a result of food or changes to nutrients caused by drugs, which may enhance or inhibit the absorption, distribution, metabolism, and excretion of drugs or alter their clinical or physiological effects on the body [18].

Food: Any substance consumed by an organism for nutritional support essential for growth, repair and maintenance of body tissues, and regulation of vital processes.

Health Care providers: HCPs are any doctors, pharmacists, health officers and nurses working in any hospital setting or health facilities.                                           

Poor knowledge: HCPs who scored less than the mean knowledge score or within the exact mean cut-off were categorized as having poor knowledge of FDIs [19].

Good knowledge: HCPs who will score above mean score was considered as having good knowledge towards FDIs [25].                                               

Good attitude: HCPs who scored above the mean were categorized as having a positive attitude towards FDIs [21].

Poor attitude: HCPs who score below the mean and exactly within the mean were categorized as having a negative attitude towards FDIs [21].

Good practice: HCPs who score above the mean were categorized as practicing good towards FDIs [9].

Poor practice: HCPs who scored below the mean and exactly the mean value were categorized as practicing poorly towards FDIs [9].

Data collectors were trained before the data collection process. A pre-test was done on 5% of the total study population at Saint Merry Primary Hospital (Dubbo) before the start of data collection. Any error found during the pre-test process was corrected, and modifications were made to the final version of the questionnaire. All data were examined for completeness and consistency during data management, storage, and analysis. The data collected during the pretest was not included in the main study analysis. Data collectors and supervisors were trained for two days on data collection instruments and the objectives of the study. The investigators and supervisors were under on-site supervision during the whole period of data collection. The collected data was checked each day by the PI and supervisors for possible corrections to enhance the quality of the data.

Data was carefully checked, coded, and fed to Epi-data version 4.6.0.2, and then transferred to the Statistical Package for Social Science (SPSS) version 25 for analysis. Socio-demographic characteristics were analysed using descriptive statistics. Continuous variables were expressed as mean + SD, while categorical variables were expressed as frequency and percentages. The overall mean score percentage was calculated as the percentage of the overall mean score divided by the total score per category. The overall questions were grouped into three categories: 16, 4, and 6, and the overall knowledge score was for questions 1–16. Each correct response was assigned a maximum score of 1, and incorrect answers were assigned a score of 0. The mean cutoff points were less than or equal to 8. Questions 17–20 each correct response was given a score of 3 and incorrect answers a score of 0, and the mean score <=6 is cut-off points; and questions 21–26 each correct response was given a score of 4 and incorrect answers a score of 0, and the mean score<=10 was cut-off points to classify the knowledge level as either poor or good and the attitude as positive or negative and practice as good or poor, respectively. HCPs that scored less than the mean knowledge score level or exactly within the cut-off points were considered to have poor knowledge levels, while those who scored above the mean score were classified as having good drug-food interaction knowledge. Binary logistic regression analysis was done to identify candidate variables (p-value <0.25) for the final multivariable analysis model. A multivariate analysis was conducted to identify factors associated with knowledge of HCPs towards FDIs. A P-value less than or equal to 0.05 was considered to be statistically significant.

Ethical approval for the study was granted by the Institutional Review Board (IRB) of WSU through the College of Public Health. Prior to data collection, a formal permission letter was obtained from the respective hospital. Written informed consent was secured from each participant, who were also assured of their right to withdraw from the study at any time. To maintain confidentiality, data were coded and accessed exclusively by the research team. Participants were informed that their inclusion or exclusion from the study posed no risk. Therefore, since this study involved human participants, it adhered fully to the principles outlined in the Declaration of Helsinki.

Socio-demographic characteristics of study participants

The study achieved a response rate of 94%, with 316 out of 337 selected participants completing the survey. The average age of participants was 32.35 years, with a standard deviation of 7.264 years. Of the 316 respondents, 199 (63%) were male. Most participants held a Bachelor of Science (BSc) degree, accounting for 58.5% (185), while the remainder included 19% (59) with diplomas, 21% (66) who were medical doctors, and 2% (6) with a Master’s (MSc) degree.

In terms of profession, 23% (73) were pharmacists or druggists, while the others included nurses at 22.8% (72), health officers at 19% (60), physicians at 18.7% (59), and midwives at 16.5% (52). Regarding work experience, the majority (76.3%; 241) had over five years of experience, while 18.4% (58) had 1–3 years, and 5.4% (17) had 3–5 years of experience of respondents was found to be 260 (82%), pharmacists and physicians had better perceived than other HCPs, respectively (75%, 66.7%, and 57.8%), but that difference was not statistically significant as shown in table 2 above, and having work experience of 3-5 years, HCPs behaved the best towards FDIs (Table 2, figure 3).

Table 2: Socio-demographic characteristics of study participants and KAP distribution (n = 316).
Knowledge towards food drug interaction
Variable	Categories	Good № (%)	Poor № (%)	P - Value
Sex	Male	175(87.9%)	24(12.1%)	0.220
	Female	108(92.3%)	9(7.7%)
Education	MD	59(100%)	0(0%)	0.017
	BSc	159(85.9%)	26(14.1%)
	Diploma	59(89.4%)	7(10.6%)
	MSc	6(100%)	0(0%)
Profession	Pharmacist	64(87.7%)	9(12.3)	0.000
	Physician	59(100%)	0(0%)
	Health officer	60(100%)	0(100%)
	Nurse	52(72.2%)	20(27.8%)
	Midwives	48(92.3%)	4(7.7%)
Experience	>5 years	208(86.3%)	33(13.7%)	0.003
	1-3 years	58(100%)	0(0%)
3-5 years                                 17(100%)                               0(0%)
Attitude towards food-drug interaction
Variable                                        Categories                               Good № (%)                            Poor № (%)				P - Value
Sex                                              Male                                          166(83.4%)                             33(16.6%)				0.489
Female                                     94(80.3%)                                23(19.7%)
Education                             MD                                            59(100.0%)                            0(0.0%)                                   0.000
BSc                                 167(90.3%)                          18(9.7%)
Diploma                                 28(42.4%)                          38(57.6%)
MSc                                 6(100.0%)                               0(0.0%)
Profession                             Pharmacist                            73(100%)                                         0(0%)                                                0.000
Physician                              59(100.0%)                          0(0%)
Health officer                          45(75.0%)                          15(25%)
Nurse                                 48(66.7%)                              24(33.3)
Midwives                              30(57.8%)                            22(46.2)
Experience                            >5 years                                   187(77.6%)                             54(22.4%)                              0.000
1-3 years                                  56(96%)                              2(3.4%)
3-5 years                               17(100%)                              0(0%)
Practices towards food-drug interaction
Variable                                  Categories                               Good № (%)                            Poor № (%)                            P - Value
Sex                                          Male                                          123(61.8%)                             76(38.2%)                              0.618
Female                                  69(59.0%)                             48(41.0%)
Education                             MD                                            51(86.4%)                               9(13.6%)                                 0.001
BSc                                  107(57.8%)                           78(42.2%)
Diploma                                  33(50%)                                 33(50%)
MSc                                     6(54.5%)                              5(45.5%)
Profession                             Pharmacist                             44(60.3%)                                29(39.7%)                              0.000
Physician                              52(88.1%)                            7(11.9%)
Health officer                            34(56.7%)                             26(43.3%)
Nurse                                        30(41.7%)                                42(58.35%)
Midwives                                32(61.5%)                                20(38.5%)
Experience                                >5 years                                   141(58.5%)                              100(41.5%)                           0.230
1-3 years                                 41(70.7%)                                17(29.3%)
3-5 years                                 10(58.8%)                               7(41.2%)

The overall % score of practice found to be 192 (60.76%), poor practice was higher among MD (86.4%) than B.Sc., M.Sc. and Diploma holders respectively (57.8%,54.5%,50%) and that difference was statistically significant (p = 0.001) (Table 2), HCPs having experience 1-3 years were found to have good practicing 41 (70.7%) (Table 3, figure 4).

Is the impact of drug- food interaction depending on various factors like dosage, age and health status?   Do protein-rich foods affect the efficacy of levodopa? Can amiodarone be taken with grapefruit?                 Can atorvastatin be taken with fatty-diet? Does cauliflower consumption affect the efficacy of levothyroxine? Does caffeine consumption affect the efficacy of diazepam? Does wheat bran diet affect the efficacy of digoxin? Some foods can increase or decrease the action of a drug when taken together Grapefruit juice can be safely consumed with all antibiotics Patients can eat more leafy green vegetables with Coumadin (warfarin) Patient taking theophylline should avoid excessive coffee and tea. Does milk affect the efficacy of tetracycline Patients taking monoamine oxidase inhibitors (MAOIs) should avoid eating aged cheeses:

Table 3: Correctly answered general knowledge, specific FDIs and timing of drug intake with respect to food (n = 316).
Knowledge Questions	Correct  response (n)					%	Incorrect response(n)					%
	MD	Pha	Nurs	HO	Mid		MD	Phar	Nurse	HO	MIDW
Do you think that food combinations can affect the           efficacy   of medications?	50	66	52	47	39	80.4%	9	7	20	5	13	19.6%
	85%	90%	72%	78%	75%		15%	10%	28%	22%	25%	%
Antacids best recommended to take one hour before meal.	50	70	58	49	40	85%	9	3	14	11	12	15%
	84%*	96* %	81%	82%	77%*		16%	4%	19%	18%	23%	%
Do you know that acidic food such as tomato sauce, tea, and citrus juices can be taken along withantibiotics?	50	49	20	38	28	58%	9	24	52	22	24	42%
	84.7%	67%	27%	63%	53%		15%	33%	73%	37%	47%	%
	56	72	65	59	45	93.9%	3	1	7	1	7	6.1%
	95%*	98** %	90%	98* %	86.5%		5%	2%	10%	2%	13.5%	%
	44	53	45	48	42	73%	15	20	27	12	10	27%
	75%	73%	63%	80%	84%		25%	27%	37%	20%	16%	%
	36	56	30	27	25	55%	23	17	42	33	27	45%
	61%	77%	42%	45%	48%		39%	23%	58%	55%	52%	%
	56	62	26	26	24	61.4%	3	13	46	34	28	39.6%
	95%	85%	36%	43%	46%		5%	15%	64%	57%	54%	%
	49	60	33	43	32	71.8	10	13	39	17	20	28.1
	83%**	82*%	45.8*%	71%	62%	17%	28%	54%	29%	38%	83%	%
	53	67	32	47	25	70.8%	6	6	40	13	27	29.2%
	90%	92%	44%	78%	48%		10%	8%	56%	22%	52%	%
	53	59	31	29	23	61.7%	6	14	41	31	29	28.3%
	90%	81%	43%	48%	44%		10%	19%	57%	52%	56%	%
	59	73	36	40	30	75%	15	19	37	20	32	25%
	100%	100 %	50%	67%	58%		0	0	50%	33%	42%	%
	56	67	30	36	17	65%	3	6	40	24	35	35%
	95%*	92%	42%	60%	33%		5%	8%	58%	40%	67%	%
	42	58	26	30	20	55.7%	17	15	46	30	40	44.3%
	71%	70%	36%	50%	38%		29%	30%	64%	50%	52%	%
	50	56	50	39	32	71.8%	9	17	22	21	20	28.2
	85%	77%	69%	65%	62%		15%	23%	31%	35%	58%	%
	52	68	45	37	29	67.4%	7	5	27	23	11	32.6%
	88%* *	93**	63%	62%	56%*		12%	7%	57%	37%	38%	%
	45	57	29	34	18	57.9	14	26	23	26	34	42.1%
	76%	78%	40%	57%	35%		24%	22%	60%	43%	65%	%

This study showed that a total of 316 participants with different professional disciplines assessed general knowledge, knowledge of specific food types, interactions with drugs, and timing of drug intake with respect to food. They correctly responded to general knowledge questions, with a mean percentage of pharmacists and physicians finding a good level of general knowledge of 93% compared to other HCPs in the study. Moreover, 82% of physicians identified specific food types that interact with drugs correctly, compared to health officers 36 (60%), midwives 26 (48%), and nurses 33 (46%), respectively. Furthermore, pharmacists were found to have the highest knowledge (70%) among other professions in describing the correct timing of drug intake with respect to foods (Tables 3,4).

Table 4: Both Bivariate and Multivariate analysis used to identify factors associated with KAP to wards food- drug interaction Wolaita zone southern Ethiopia (n = 316).
Knowledge towards food-drug  interaction
Variable		Categories		Good № (%)		Poor № (%)	Crude OR		AOR			p- value
Sex		Male		175(87.9%)		24(12.1%)	1		1			1
		Female		108(92.3%)		9(7.7%)	0.608(.272,1.356)		.299(.125, .714)			0.007*
Profession		Pharmacist		64(87.7%)		9(12.3)	1		1			1
		physician		59(100%)		0(0%)	0(0,0)		0(0,0)			0.997
		Health officer		60(100%)		0(100%)	0(0,0)		0(0,0)			0.997
		Nurse		52(72.2%)		20(27.8%)	2.73(1.149,6.513)		3.35(1.35,8.27)			0.009*
		Midwives		48(92.3%)		4(7.7%)	.593(.172,2.039)		.570(.163,1.98)			0.378
Attitude towards food-drug interaction
Variable		Categories		Good № (%)		Poor № (%)	Crude OR		AOR			P- Value
Education		MD		59(100.0%)		0(0.0%)	1		1			1
		BSc		167(90.3%)		18(9.7%)	1.7(0,0)		.794(0,0)			1.00
		Diploma		28(42.4%)		38(57.6%)	2.1(0,0)		6.44(0,0)			1.00
		MSc		11(100.0%)		0(0.0%)	1.0(0,0)		.820(0,0)			1.00
Profession		Pharmacist		51(69.9%)		22(30.1%)	1		1			1
		physician		59(100.0%)		0(0%)	0(0,0)		0(0,0)			.999
		Health officer		60(100.0%)		0(0%)	0(0,0)		0(0,0)			.997
		Nurse		48(66.7%)		24(33.3)	1.15(.576,2.33)		1.70(.074,3.90)			.206
		Midwives		42(80.8%)		10(19.2)	.552(.235,1.29)		1.27(.46,3.51)			.637
Experience		>5 years		187(77.6%)		54(22.4%)	1		1			1
		1-3 years		56(96%)		2(3.4%)	.124(.029, .523)		.260(.056,1.20)			0.085
		3-5 years		17(100%)		0(0%)	0(0,0)		.771(0,0)			1.00
Practices towards food-drug interaction
Variable	Categories		Good № (%)		Poor № (%)			Crude OR		AOR	P- Value
Education	MD		51(86.4%)		9(13.6%)			1			1
	BSc		107(57.8%)		78(42.2%)			4.67(2.08,10.34)		0.00(0,0)	.999
	Diploma		33(50%)		33(50%)			6.37(2.62,15.49)		0.00(0,0)	.999
	MSc		6(54.5%)		5(45.5%)			31.8(3.28,39.51)		0.00(0,0)	.999
Profession	Pharmacist		44(60.3%)		29(39.7%)			1			1
	physician		52(88.1%)		7(11.9%)			.204(.082, .511)		0.00(0,0)	.999
	Health officer		34(56.7%)		26(43.3%)			1.160(.580, 2.321)		.996(.443,2.23)	.992
	Nurse		30(41.7%)		42(58.35%)			2.124(1.095,4.121)		2.19(1.12,4.29)	0.022*
	Midwives		32(61.5%)		20(38.5%)			.948(.457,1.966)		1.009(.463,2.18)	.990
Experience	>5 years		141(58.5%)		100(41.5%)			1			1
	1-3 years		41(70.7%)		17(29.3%)			.585(.314,1.088)		.843(.418,1.69)	.632
	3-5 years		10(58.8%)		7(41.2%)			.987(.364, 2.681)		1.04(.246,4.46)	.950

Factors associated with KAP towards food-drug interaction among health care providers

The study found that knowledge about Food-Drug Interactions (FDIs) was significantly associated with sex and profession. Females were less likely to have poor knowledge, with an Adjusted Odds Ratio (AOR) of 0.299 (95% CI: 12.5%-71.4%, p = 0.007*), while nurses were significantly more likely to have poor knowledge compared to other professions, with an AOR of 3.35 (95% CI: 1.35–8.27, p = 0.009*) (Table 4).

Regarding attitude, profession was a contributing factor; nurses were nearly twice as likely to have a negative attitude toward FDIs compared to pharmacists. However, this association was not statistically significant in the multivariate analysis (AOR 1.7, 95% CI: 0.074–3.9, p = 0.206*) (Table 4).

In terms of practice, profession showed a significant association. Nurses demonstrated poorer practices related to FDIs compared to other professional groups, with an AOR of 2.19 (95% CI: 1.12–4.29, p = 0.022*) (Table 4).

This study evaluated the Knowledge, Attitude, and Practices (KAP) of healthcare providers regarding Food-Drug Interactions (FDIs) and the factors influencing them. Overall, 89.6% of healthcare providers demonstrated knowledge about FDIs. This percentage was slightly higher but still comparable to a study conducted in a public hospital in KwaZulu-Natal, which reported 79.9%, and significantly higher than studies from Palestine and Colombia, which found 60% awareness [15-17]. The difference may be attributed to variations in study locations, larger sample sizes, and the inclusion of a broader range of healthcare professionals in the current study. Additionally, pharmacists and physicians outperformed other healthcare professionals, answering correctly on general FDI knowledge, specific food types, drug interactions, and the timing of medication relative to food intake-by 78%, which contrasts slightly with findings from Colombia and Palestine, where the figure was 67% [25]. This may be due to pharmacists and physicians receiving more in-depth training in pharmacology and having greater involvement in medication dispensing and patient counseling, whereas nurses tend to focus more on direct patient care. Moreover, physicians and pharmacists accurately identified 63.4% of food items that interact with drugs-three times higher than others study conducted in Lebanon [26]. This may be attributed to the availability of alternative information sources and the inclusion of nutrition courses in undergraduate curricula. Likewise, pharmacists demonstrated the highest average knowledge score (76%) regarding the correct timing of drug administration in relation to food intake, which is notably higher than the 58.3% reported in a similar study conducted in the USA [27]. (Table 3).

The study found that 82% of respondents had a positive perception of Food-Drug Interactions (FDIs). This finding aligns closely with a study from Sudan, which reported a similar rate of 86.7% [21], but contrasts with a study from Lebanon, where only 46.4% held a positive view [26]. The discrepancy may be attributed to differences in the populations and contexts of the two study settings.

Although participants demonstrated adequate knowledge and a positive attitude toward Fooddrug Interactions (FDIs), their level of practice was relatively low at 60.76%. This finding is consistent with a study conducted in Egypt [16]. One possible explanation is that in hospital settings, clinical professionals other than pharmacists tend to focus more on patient care and prescribing, often leaving FDI-related considerations and counseling to pharmacists. In pharmacy settings, however, high workloads and limited attention to FDIs by pharmacists may contribute to the lower levels of practice.

Sex and profession were identified as factors influencing knowledge of Food-Drug Interactions (FDIs). Using males as a reference, females were found to be less likely to have poor FDI knowledge [AOR 0.299, 95% CI: 0.125-0.714]. Regarding profession, nurses were more likely to have poor knowledge compared to other healthcare professionals [AOR 3.35, 95% CI: 1.35- 8.27]. This finding supports a study conducted in Palestine, which also reported that nurses were three times more likely to lack adequate knowledge [17]. A possible reason is that nurses receive less training in pharmacology and hospital pharmacy courses compared to physicians and pharmacists.

In bivariate analysis, respondents’ attitudes toward FDIs appeared to be linked to years of experience, level of education, and professional role; however, none of these factors remained significant in multivariate analysis. This mirrors findings from a study in Iranian public hospitals, which also found no independent variables significantly associated with healthcare providers’ attitudes [8].

In terms of practice, profession remained a significant factor associated with poor FDI-related practices. Using pharmacists as the reference group, nurses were found to be twice

as likely to exhibit poor practices regarding FDIs [AOR 2.183, 95% CI: 1.030-4.624]. This result aligns with a study conducted in Saudi Arabian public hospitals, which showed that nurses were 2.25 times less likely to engage in good FDI practices [15]. This may be due to nurses focusing more on direct patient care and having less involvement in pharmacy-related tasks compared to pharmacists [28-30].

In conclusion, healthcare providers had a good level of knowledge and a positive attitude; however, there were gaps regarding practice. The main factors that affected knowledge and practice were educational status and profession.

This study had some limitations:

➢   To the best of our knowledge, there was paucity of research done on the same title conducted globally and nationally, it was difficult to compare and contrast the result exhaustively rather it would be the baseline study for further research.

➢   As self-administered questionnaire, responses may have been subjected to recall bias Study area setting was primary level hospitals; there may be limit of generalization

Ethics approval and consent to participate

The study was approved by the Research Ethics Committee of Wolaita Sodo University, (the committee’s reference number: CRCSD132/02/14). An informed written consent was obtained from each participant at the beginning of the study after explanation of the objectives of the study, procedures, and types of information to be obtained.this study use humans as study participants and its obey all aspects of Declaration of Helsinki Ethical Principles for Medical Research Involving Human Participants. The study was not a clinical trial.

Competing interests

Authors declare no conflicts of interest. The research was not supported by any commercial source; no financial relationships with any organizations that might have an interest in the submitted work.

Funding

There was no funding source for the work or the preparation of the tools.

Availability of data and materials

Data and materials are available on reasonable request for corresponding author in Email address:- [email protected]. Confidentiality and security of data and materials were insured through all stages of the study.

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