We conducted a cross-sectional study in March 2024 among health care providers in Kalemie, Tanganyika Province, eastern DRC. The city comprises 2 health zones (Kalemie and Nyemba) covering 11 health areas and a total of 45 public, private, and faith-based health facilities.

The sample size was calculated using the single population proportion formula [24]: n = [(zα/2)² × p (1 − p)]/d², where z=1.96 (for 95% confidence), p=73.2% (estimated BEA prevalence from the aforementioned Lubumbashi study), and d=0.05 (margin of error). After accounting for a 10% nonresponse rate, the final sample size was 335.

A 2‑stage sampling approach was applied. Although clustering was considered, no formal adjustment for design effect was performed as the calculated sample size was deemed sufficient to ensure representativeness. The final number of respondents was 316, corresponding to a response rate of 94.3% (316/335); the difference was due to nonresponses.

From the 45 health facilities listed in the National Health Information System (NHIS), 34 (75.6%) were randomly selected using a computer‑generated list, ensuring proportional representation of public, private, and faith‑based institutions. Within each selected facility, staff rosters were systematically validated by managers against official NHIS records to guarantee accuracy and completeness. The number of participants per facility was determined proportionally to staff size, and providers were then chosen using a random number generator. This systematic validation of lists and proportional allocation ensured representativeness and minimized selection bias.

Inclusion criteria were nurses, physicians, midwives, and laboratory technicians with direct contact with blood or body fluids. Exclusion criteria were providers with less than 1 month of professional experience to reduce onboarding variability.

Data were collected using a structured questionnaire adapted from validated WHO and Centers for Disease Control and Prevention (CDC) instruments [25-27]. The tool comprised closed‑ended items covering sociodemographic characteristics, knowledge of BEAs, attitudes toward reporting and prevention, compliance with standard precautions, and history of occupational exposure. Prior to implementation, the questionnaire was pilot‑tested at Baraka Health Post and Shalom Polyclinic to ensure clarity and contextual relevance. Seven data collectors and 1 supervisor received 2 days of standardized training focused on interview techniques, ethical considerations, and data quality assurance. Reliability testing yielded a Cronbach α coefficient of 0.766, indicating acceptable internal consistency. Data collection was conducted through face-to-face interviews using the KoboCollect (Kobo) software installed on tablets. Each interviewer completed approximately 10 provider interviews per day over a 5-day period. Supervisory checks were performed daily to validate the completeness and accuracy of entries. The use of electronic data capture minimized transcription errors and ensured secure storage of anonymized records.

The dependent variable was occupational exposure to blood within the previous 12 months, defined as any percutaneous or mucocutaneous contact with potentially infectious biological fluids. Independent variables included the type of health facility, categorized as secondary level (general referral hospitals, referral health centers, and polyclinics) vs primary level (health centers, health posts, and dispensaries). Compliance with standard precautions was dichotomized as “always” vs “not always.” Needle recapping was assessed through self‑reported frequency and classified as “never” vs “ever.” Availability and use of PPE were measured on an ordinal scale (“rarely,” “usually,” and “always”). Syringe reuse was recorded as a binary variable (“yes” or “no”). Sociodemographic characteristics were also collected, including age (categorized), sex, marital status, professional category, years of professional experience, and daily working hours.

Data were exported from KoboCollect to Microsoft Excel 2016 and analyzed using SPSS (version 25.0; IBM Corp). Descriptive statistics were computed, including frequencies, percentages, medians, and IQRs. Normality of continuous variables was assessed using the Kolmogorov-Smirnov test [28]. Knowledge scores were derived from 31 items and categorized as optimal (>85%), moderate (70%-85%), or poor (<70%). Bivariate analyses were performed using chi‑square tests, and variables with a P value of .20 or lower were retained for multivariate modeling. Logistic regression was conducted to identify independent predictors of BEAs, with AORs and 95% CIs reported. Both crude (odds ratio) and adjusted (AOR) estimates were presented, and potential confounding factors were explicitly discussed. Model diagnostics included assessment of multicollinearity using the variance inflation factor (<2), evaluation of model fit using the Hosmer-Lemeshow goodness‑of‑fit test, and discrimination using the area under the receiver operating characteristic curve [29]. Statistical significance was set at a P value below .05.

This study was conducted and reported in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines [30]. Ethics approval was obtained from the ethics committee of the School of Public Health, University of Kinshasa (reference ESP/CE/38/2024). Written informed consent was obtained from all participants prior to their inclusion. Confidentiality was safeguarded by anonymizing the data and storing them securely, accessible only to authorized researchers. No financial compensation was provided to participants for their involvement in the study.

A total of 316 health care providers participated (response rate: 316/335, 94.3%). Most (n=256, 81%) worked in secondary‑level facilities (general referral hospitals, referral health centers, and polyclinics). Most were female (n=179, 56.6%), married (n=230, 72.8%), and aged 40 years or older (n=128, 40.5%). Nurses represented 78.2% (n=247) of the sample. Median professional experience was 9 (IQR 5‑15) years, and median workload was 8 (IQR 7‑9) hours per day, as shown in Table 1.

Overall, 65.8% (208/316) reported at least one BEA during their career, and 60.4% (191/316) reported one within the previous 12 months. The leading causes were unexpected patient movement (81/191, 42.4%) and lack of attention (66/191, 34.6%). Most incidents occurred in treatment rooms (64/191, 33.5%), delivery rooms (33/191, 17.3%), and operating rooms (27/191, 14.1%).

Needlestick injuries accounted for 59.2% (113/191) of exposures, followed by splashes on damaged skin (43/191, 22.5%) and on mucous membranes (18/191, 9.4%). Hollow needles (77/191, 40.3%) and defective surgical equipment (49/191, 25.7%) were the objects most frequently responsible, as shown in Table 2. Only 36.1% (69/191) of BEAs were formally reported; among the 122 non‑reported cases, the main reason was the perception that the incident was not risky (70/122, 57.4%).

Nearly all participants (309/316, 97.8%) had heard of BEAs, mainly through formal education (125/316, 40.5%) and training (125/316, 40.5%). However, 72.8% (230/316) had never received specific training on standard precautions, and 94.3% (298/316) demonstrated poor knowledge levels.

Attitudes were mixed: 48.4% (153/316) strongly agreed on the importance of reporting BEAs, 72.2% (228/316) emphasized the importance of knowing the patient’s serological status, and 73.4% (232/316) expressed willingness to initiate postexposure prophylaxis.

In practice, 76.6% (242/316) claimed to always follow standard precautions, but only 25% (79/316) consistently washed their hands before and after patient contact. Needle recapping was reported by 27.8% (88/316), PPE was always used by 19.6% (62/316), and syringe reuse occurred in 16.5% (52/316) of cases, as shown in Table 3.

Bivariate analysis identified 6 factors associated with BEAs, with exact P values: facility type (P<.001), daily working hours (P=.001), compliance with standard precautions (P<.001), needle recapping (P<.001), PPE availability (P=.03), and PPE use (P=.003).

Multivariate logistic regression confirmed two independent predictors: (1) noncompliance with standard precautions (AOR 2.921, 95% CI 1.503‑5.677; P=.001) and (2) facility type (AOR 0.310, 95% CI 0.144‑0.670; P=.003), as shown in Table 4.

This study highlights that BEAs remain a major occupational hazard among health care providers in Kalemie, Tanganyika Province, DRC. The strongest predictor was noncompliance with standard precautions [15], whereas working in secondary‑level facilities appeared protective.

Our findings are consistent with those of recent African studies reporting high BEA prevalence: 93% in Cameroon [10], 78.9% in Morocco [11], more than 60% in Ethiopia [13,17], and more than 60% in Nigeria [14]. The Kalemie results reinforce the persistence of occupational risks across sub‑Saharan Africa.

The protective effect of secondary‑level facilities may be explained by better infrastructure, availability of PPE, and more structured infection prevention programs, as observed in Ethiopia [17] and Benin [18]. Conversely, primary‑level facilities often lack resources, increasing exposure risks [20].

The association between noncompliance with standard precautions and BEAs has been consistently documented [27]. In Kalemie, this factor nearly tripled the risk, confirming that most accidents are preventable through adherence to basic infection control measures. The apparent contradiction between bivariate and multivariate analyses regarding facility type likely reflects confounding factors: secondary facilities host more complex procedures and higher patient volumes, increasing crude risk, but once adjusted for PPE availability, training, and compliance, their structured infection control systems reduced overall risk. Similar methodological challenges have been highlighted in recent epidemiological modeling studies [31].

The methodological rigor of this study, including the use of a validated questionnaire adapted from WHO and CDC tools [22,26], random sampling, and robust statistical diagnostics such as the Hosmer-Lemeshow test and the area under the receiver operating characteristic curve [28,29], supports the representativeness of the findings. Nevertheless, limitations must be acknowledged. Reliance on self‑reported data may have introduced social desirability bias, as suggested by the discrepancy between the high proportion of providers claiming to “always” follow standard precautions (242/316, 76.6%) and the finding that noncompliance was the strongest predictor. Recall bias may also have affected lifetime prevalence estimates. Measurement limitations exist because exposure was assessed exclusively through self‑report without external validation, and the cross‑sectional design precludes causal inference. Finally, generalizability is limited as this study was conducted in Kalemie and may not fully represent health care providers in other provinces of the DRC.

BEAs remain highly prevalent among health care providers in Kalemie. Most incidents are preventable through adherence to standard precautions, adequate provision of PPE, and establishment of surveillance systems. Strengthening institutional policies and continuous training are essential to reduce occupational risks and safeguard health care workers.

Beyond Kalemie, these findings highlight systemic gaps in infection prevention and control across sub‑Saharan Africa. Addressing these gaps requires coordinated action: investment in PPE, integration of infection control committees, and reinforcement of reporting and monitoring systems. At the global level, the persistence of BEAs underscores the need for harmonized occupational health policies and stronger advocacy for health care worker safety.

BEAs remain a major occupational hazard for health care providers in Kalemie, DRC. With nearly two-thirds of providers reporting lifetime exposure and more than half exposed within the past year, the burden is substantial. Most incidents were preventable, and noncompliance with standard precautions emerged as the strongest predictor. Conversely, working in secondary‑level facilities appeared protective once confounding factors were accounted for.

These findings underscore the urgent need to strengthen infection prevention and control measures, ensure consistent availability and use of PPE, and establish effective surveillance and reporting systems. Beyond Kalemie, the results highlight systemic gaps across sub‑Saharan Africa, calling for coordinated national and international efforts to safeguard health care workers. Protecting those at the frontline is essential not only for their safety but also for the resilience of health systems.