Understanding healthcare workers’ risk perception and response to healthcare waste in Arba Minch town, Ethiopia: application of the extended parallel process model | BMC Health Services Research

Study area and period

Arba Minch is a city in the southern part of Ethiopia. It is located in the Gamo Zone of the South Ethiopia Regional State, about 500 km south of Addis Ababa, at an elevation of 1,285 m above sea level. It is the largest town in Gamo Zone, which is surrounded by Arba Minch Zuria woreda. There are two public hospitals: Arba Minch General Hospital and Dil-Fana Primary Hospital. Arba Minch General Hospital provides holistic service with 470 workers, including 18 specialists, 43 general physicians, 3 dental doctors, 7 Integrated Emergency Surgical Officers, 29 Public Health Officers, 43 laboratorians, 10 public health specialists, 10 radiologists, 35 pharmacists, 58 midwiferies, 14 anesthesiologists, 187 nurses and 13 other workers (janitors). Dil-fana Primary Hospital employs 206 workers including: 12 General practitioners, 1 dental doctor, 4 Integrated Emergency Surgical Officers, 26 Health Officers, 78 Nurses, 19 laboratory technicians, 13 Pharmacy technicians, 35 Midwives, 1 Environmental health, 1 Health Informatician, 7 Anesthesiologist, and 9 janitors.in total, there were 676 workers in both hospitals providing hospital-level services to the population (Fig. 1). This study was conducted in Arba Minch town from December 01 to December 28, 2024.

Study design

Population

Source population

Sample population

Study unit

Eligibility criteria

Inclusion criteria

Exclusion criteria

Sample size determination

The sample size for the main objective was calculated using the single population proportion formula, considering the following assumptions: a 95% confidence level, a 5% margin of error, and a prevalence of 50% to determine the maximum sample size. This approach was taken because, to the investigator’s knowledge, no study has been conducted that addresses the danger control response of healthcare waste risk perception.

Where: n = minimum sample size

p = proportion of danger controllers for health care waste risk perception (50%)

Z = standard normal distribution curve value for 95% level of confidence with the value of 1.96

d = margin of error to be tolerated (d = 0.05)

A report from Arba Minch public hospitals showed that health workers were 676, which is less than 10,000. Following this correction formula was used.

Adding a 10% non-response rate n = 245 + 25 = 270.

Sampling technique and procedure

This institution based cross sectional study will be conducted in the two public hospitals in the town. The sample size (270) was proportionally assigned to each hospital based on the number of healthcare workers. The total number of healthcare workers (N) was identified as 676, while the desired sample size (n) was 270. To determine the number of participants for each health facility, the sample size (n) was divided by the total eligible population (N) and then multiplied by the number of healthcare workers at each facility. Simple random sampling was used to select 270 registered health workers for the data collection using the lottery method while excluding those who were not unable to respond or were severely due different reasons. The sampling framework of the healthcare workers was obtained from the respective hospital’s human resources division (Fig. 2).

Data collection tools and procedure

Data were collected using a structured self-administered questionnaire developed based on EPPM [34, 35], considering previously conducted research [5, 8,9,10,11, 14, 24] as well as national [2] and WHO guidelines [12, 18]. This tool consisted four sections: socio-demographic characteristics (Sect. 1), knowledge about healthcare waste and management (Sect. 2), message exposure (Sect. 3), and HCW Perception (Sect. 4) (Annex I). The latter encompassed perceived severity and susceptibility regarding the consequences of healthcare waste, as well as Perceived response and self-efficacy concerning recommended HCW management practice.

Perceived severity refers to the beliefs about the extent or importance of the threat and the seriousness of its consequence; Perceived susceptibility, indicating an individuals’ consideration of the likelihood of encountering the threat. Perceived efficacy reflect thoughts regarding the effectiveness, feasibility, and ease with which the suggested response prevents or mitigates a threat, and Perceived response efficacy is the belief in the effectiveness of the suggested response to prevent the threat [35].

A questionnaire for Perceived threat (perceived severity and susceptibility) was developed for the study by adapting reviews and guidelines [8,9,10,11] in line with WHO recommendations [12, 18]. Items related to perceived efficacy (perceived response and self-efficacy) items were developed based on recommended actions on healthcare waste management from national [2] and WHO guidelines [18]. Knowledge of healthcare waste and management was assessed using items adapted from previous studies [5, 9, 14, 23, 24], as well as national [2], and WHO [18] guidelines. Data collection was conducted by three graduating classes of first-degree students under the supervision of two MSC students.

Study variables

Dependent variable

Intermediate variable

• ◦ Perceived threat (perceive severity and susceptibility towards health acre waste).

• ◦ Perceived efficacy (perceived response and self-efficacy towards recommendations on health care waste management).

Independent variable

• • Socio-demographic characteristics (Age, sex, religion, marital status, residence, educational status, occupation).

• • Knowledge about Health care waste and its management.

• • Message exposure: (exposure and source of information, type of message heard, preference of future message source).

Operational definitions and measurements

Health care waste risk perception response

It is the control response that participants engage to control the fear (health care waste risks). The control response are either danger control response or fear control response [34, 35].

Danger control response

Danger control involves a cognitive process that stimulates protection motivation. This occurs when an individual believes they can effectively mitigate a significant and pertinent threat through self-protective measures [35]. participants were in danger control response if he or she had positive discriminative value [35, 36].

Fear control response

Conversely, fear control is an emotional process that operates at a subconscious level, rendering it challenging to quantify [35]. A negative discriminative value was used to claim respondents into Fear Control Response [35, 36].

Healthcare waste management practice

Eight items were adapted from previous studies [17, 23, 24, 37] national [2], and WHO [18] guideline. Those who scored mean and above were taken as having good health care waste management practice while the contrary as having poor practice [9, 23, 24].

Perceived threat

Subjectively assessing the threat within the message. It comprises two sub-dimensions known as perceived severity and susceptibility, as referenced in sources [34, 35]. The perceived susceptibility and severity responses were summed up to give an overall threat score. The overall threat score was standardized to a response ranging from 0 to 100. The perceived threat was treated as a continuous variable [34].

Perceived severity

It pertains to beliefs regarding the extent or importance of the threat and the seriousness of its consequences [35]. This was assessed through four items utilizing a five-point Likert scale (where strongly agree = 5, agree = 4, neutral = 3, disagree = 2, and strongly disagree = 1). The responses were summed and standardized to yield a score ranging from 0 to 100, which was treated as a continuous variable [34, 35].

Perceived susceptibility

It refers to one’s consideration of the likelihood of encountering the threat [35]. This will be assessed through four items utilizing a five-point Likert scale (where strongly agree = 5, agree = 4, neutral = 3, disagree = 2, and strongly disagree = 1). The responses were summed and standardized, resulting in a score between 0 and 100. It was treated as a continuous variable [34, 35].

Perceived efficacy

It refers to thoughts regarding the effectiveness, feasibility, and ease with which the suggested response prevents or mitigates a threat [35]. The perceived response efficacy and self-efficacy responses were summed up to give an overall efficacy score. The overall efficacy score was rescaled to a response ranging from 0 to 100 while it was treated as a continuous variable [34, 35].

Perceived response efficacy

The belief in the effectiveness of the suggested response to prevent the threat [35]. This was assessed with five items utilizing a five-point Likert scale (where strongly agree = 5, agree = 4, neutral = 3, disagree = 2, and strongly disagree = 1). The responses were totaled and standardized to yield a score ranging from 0 to 100. It was treated as a continuous variable [34, 35].

Perceived self-efficacy

Self-efficacy pertains to the belief in one’s capability to execute the suggested response [35]. This was assessed through five items on a five-point Likert scale (strongly agree = 5, agree = 4, neutral = 3, disagree = 2, and strongly disagree = 1). Responses were summed, and standardized on a scale from 0 to 100 while it was treated as a continuous variable [34, 35].

Knowledge about healthcare waste

Seven items regarding healthcare waste and its management were adapted from previous studies [5, 9, 14, 23, 24], national [2], and WHO [18] guidelines were used to assess the knowledge status of health workers. All items were recoded into correct and incorrect which mean value was used to determine whether they had good or poor knowledge [23, 24].

Discriminating value (critical value or threshold)

It represents the juncture where perceptions of threat start to surpass perceptions of efficacy, potentially resulting in fear or, conversely, prompting responses aimed at controlling danger [35]. Initially, the efficacy score underwent standardization, calculated as (observed efficacy score– normative mean) divided by the normative standard deviation. Subsequently, the threat score was standardized using the formula (Observed threat score– normative mean) divided by the normative standard deviation. Finally, the difference between the standardized efficacy and threat scores (standardized perceived efficacy score– standardized perceived threat score) served as the discriminative value [34, 35].

Responsive respondents (Quadrant I)

Health workers who undertake protective measures against health threats such as healthcare waste risk, identified as those scoring above the median for both perceived efficacy and threat [34].

Fear control respondents (Quadrant II)

Health workers who deny health threats such as healthcare waste risk and react against them are those scoring below the median for perceived efficacy but above the median for perceived threat [34].

Proactive respondents (Quadrant III)

A lower degree of danger control refers to health workers who take certain protective measures but lack strong motivation to take significant action. These health workers are identified as those scoring above the median for perceived efficacy but below the median for perceived threat [34].

Indifferent or no response (Quadrant IV)

Health workers who disregard the threat (such as health care waste risk), viewing it as neither real nor pertinent to their situation, often without even acknowledging its existence. These health workers are characterized as those scoring below the median for both perceived efficacy and threat [34].

Data quality management

The questionnaire was translated into Amharic for data collection and then back to English for analysis by experts. Data collectors and supervisors had intensive training on the objectives of the study, supervision, and data collection methods and procedures. Ten independent raters were asked to classify each item into one of the four construct categories to assess the content validity of the tool; nine respondents correctly classified all the items into the appropriate scale and subscale.

A pre-test was conducted on 5% of the sample size at Wolayita Hospital one week before the data collection period, yielding a Cronbach alpha value of 0.79. Any ambiguous, confusing, and difficult words were revised based on the appropriateness of the tool during the pretest. Supervisors and investigators closely monitored the data collection processes daily. Investigators checked the data for inconsistencies, and any necessary corrections were made during the data collection period.

Data processing and management

The collected data was exported to the SPSS version 26 statistical package for further analysis. Initially, data cleaning was conducted in SPSS, Principal component analysis (PCA) was employed on health care waste and management perception RBD scale with the Varimax rotation to explore latent variables and extract factors. Barlett’s Test of Sphericity and a Kaiser-Meyer-Oklin were run before PCA analysis. A Varimax rotation technique was applied to identify the variables under each factor.

Median values was used to categorize responses into four attitudinal responses based on threat and efficacy interactions: responsive (high threat, high efficacy), fear control (high threat, low efficacy), pro-active (low threat, high efficacy), and indifferent/no-responses (low threat, low efficacy).

Descriptive statistics were presented with frequency, mean, median, and standard deviation. Frequency tables and appropriate diagrams were used to visualize the findings. T-test and one-way-ANOVA were run to compare mean differences of perceptions based on characteristics of respondents and among attitudinal responses (efficacy by threat interaction). Data normality was checked using skewness and kurtosis, which the perceived threat resulted in a skewness and kurtosis value of 0.151 and 0.440, respectively, while perceived efficacy had a values of 0.151 and − 0.117. The data was claimed to be normally distributed since the \skewness and kurtosis values fell within the acceptable range of positive and negative one [38]. Pearson correlation was computed to examine the linear relationship among perceptions.

Assumptions were checked using crosstab and the existence of multi-collinearity was tested using Variance Inflation Factor (VIF). The data had VIF values ranging from 1.057 to 1.721. Then a bivariate logistic regression was then conducted for the independent variables to identify candidate variables for multivariable regression, with a P value of less than 0.25 [39, 40]. Multivariable logistic regression analysis was performed to identify significant factors associated with the dependent variable (Danger control response) at a P value less than 0.05 [39, 40]. The final model was fitted using the Hosmer and Lemeshow goodness of fit test, which had a value of 0.207. The results were presented using as odds ratio with 95% CI.