Effect of Handling and Levels of Microbial Contamination of Different Roadside Roasted Meats of Namawojjolo and Lukaya Highway food markets, Uganda

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This study assessed effects of handling roadside roasted meats on levels of microbial contamination sold at Namawojjolo and Lukaya Highway Food Markets in Uganda. Methods A total of 89 samples comprising of raw and ready-to-eat beef, chicken, and goat meats were collected using simple random sampling and analyzed for contamination by five key pathogens: Staphylococcus aureus , Escherichia coli, Campylobacter, Listeria, and Salmonella . Microbiological analysis was performed using standard culture and quantification technique and data were statistically analyzed using ANOVA and Bonferroni post-hoc tests. Results Raw samples exhibited highest contamination across all pathogens, where S. aureus in raw chicken (8 ± 0.56 log₁₀ CFU/g) and raw goat (8 ± 0.97 log₁₀ CFU/g) far exceeded Uganda’s National Bureau of Standards (UNBS) limits. Similarly, most of cold samples matched or surpassed hot samples in contamination. For example, cold beef showed higher Listeria counts (5 ± 1.93 log₁₀ CFU/g) than hot beef (3 ± 2.71 log₁₀ CFU/g). All tested meat types showed microbial contamination above UNBS safety limits for the five microbes examined which is ≤ 2 log₁₀ CFU/g for S.aureus and E.coli or completely absent for Salmonella , Lysteria and Campylobacter . However, S. aureus was consistently highest for all three meat types; for example, 8.4 ± 9.0 log₁₀ CFU/g for goat meat compared to 5.5 ± 5.7 shown for Salmonella in goat meat. Conclusion The findings highlight food safety gaps in the informal meat vending sector in Uganda. The pervasive microbial contamination, especially with pathogens of significant public health concern, underscores an urgent need for improved hygiene practices and regulatory oversight to safeguard consumer health. This study provides empirical evidence for targeted interventions to reduce foodborne disease risks associated with roadside meat consumption in Uganda and possibly, elsewhere. Escherichia coli Food safety Microbial contamination Ready-to-eat meat Roadside roasted meat Staphylococcus aureus Introduction Roadside roasted meat vending in Uganda dates back to pre-colonial times, when communities engaged in open-fire meat roasting as part of cultural and social gatherings [ 1 , 2 ]. However, urbanization and economic pressures transformed this practice into a widespread informal trade with limited regulatory oversight [ 3 ]. Lack of formal regulation and sanitary infrastructure in roadside food systems compromises food safety and possibly facilitates microbial contamination, leading to recurring outbreaks of foodborne illnesses [ 4 ]. Accordingly, preparation and handling of roadside roasted meats is traditionally influenced by local customs and practices, which often prioritize flavor and affordability over safety. For example, the use of communal utensils, improper storage of raw materials, and the re-use of cooking oils are common practices that contribute to microbial contamination [ 5 ]. Furthermore, environmental conditions under which roadside roasted meats are prepared and sold play critical role in determining safety from microbial contamination. Vendors often operate in open-air settings with limited access to clean water and other facilities such as proper waste disposal [ 6 ]. These conditions create an environment conducive for growth of pathogens. Additionally, the proximity of vending sites to sources of pollution, such as busy roads and waste disposal areas further exacerbates the risk of contamination [ 7 , 8 ]. Similarly, the handling practices of vendors is another critical factor influencing microbial contamination. Many vendors lack formal training in food safety and hygiene measures, leading to improper handling which leads to cross-contamination from utensils, preparation surfaces, and even mixing of raw meat with ready-to-eat meat [ 1 , 9 ]. For example, the re-use of unwashed utensils and handling of money and food without proper hand washing are common practices that contribute to contamination [ 6 , 10 ]. Primary pathogens associated with roadside roasted meats include Escherichia coli , Staphylococcus aureus , Campylobacter , Listeria , and Salmonella spp ., which are commonly isolated from contaminated meats [ 8 ]. These pathogens are often introduced through several pathways such as contaminated raw materials, meat from slaughterhouses with poor hygiene practices, or through cross-contamination during handling [ 7 , 11 ]. The ability of these pathogens to survive and proliferate in ready-to-eat meats is further influenced by factors such as temperature, humidity, and the presence of inhibitory compounds [ 5 , 8 ]. Pathogenic food-borne illnesses constitute one of the major health problems in developing countries like Uganda [ 8 ]. These illnesses are responsible for a significant number of disability-adjusted life years (DALYs), accounting for over 34% of premature deaths in children under the age of five [ 12 ]. This study therefore sought to assess effects of handling roadside roasted beef, chicken and goat meats on level of microbial contamination along Namawojjolo and Lukaya Highway Food Markets -Uganda. Materials and methods Study design and study setting A completely randomized study design involving sample analysis in the laboratory was used to collect data on the effect of handling on microbial contamination of different meats and levels of microbial contamination of different roadside roasted meats Meat samples for the study were collected from two purposively selected Highway Roadside Markets of Namawojjolo along Kampala-Jinja and Lukaya along Kampala-Masaka. The two Food Markets were selected due to their strategic locations and high amount traffic conveying travellers as potential customers, variety of ready-to-eat meats and vendors selling the products. Namawojjolo is located at a GPS reading of N 0°23'3, E 32°50'18 and altitude of 1123 m above sea level in Nama, Mukono District of Central Uganda, about 33 km East of Kampala City and 11 km away from Mukono. Kampala-Jinja Highway is the main one linking Capital City (Kampala) to the Eastern parts of Uganda and even those traveling to neighbouring Country of Kenya by road. This makes it a very busy road with heavy traffic and many travellers, making stopovers at the market to get already-prepared food items on-the-go. Lukaya is located in Kalungu District, along Kampala – Masaka Highway, approximately 107km from Kampala and 27 km from Masaka Town in Central Uganda at a GPS reading of 0°09'03.0"S, 31°52'28.0"E. Kampala-Masaka Highway leads to the Southern and Western parts of Uganda and further links to Rwanda and the Democratic Republic of Congo (DRC) borders. Each of these two markets had individual population of vendors ranging between 300–400, who were involved in sale of different food-items daily [ 8 ]. Laboratory analysis to assess microbial contamination was done at the Agricultural Biotechnology Laboratory of Makerere University, housed in the Department of Agricultural Production. Sample Collection A total of 89 meat samples were collected in different categories and proportionate sampling used to determine the exact number due to variability in meat types and numbers available in the different markets. Most vendors were found to be selling chicken followed by beef and very few sold goat meat, therefore proportionate sampling gave the numbers shown in Table 1 . Meat samples (fresh/raw meats ready to be put on fire as well as roasted beef, chicken, and goat meat) from Namawojjolo and Lukaya Food Markets were bought from randomly selected vendors and packed in sterile zipper-lock bags, sealed off, and put in a cool box (at a temperature of 2°C) then transported to laboratory on the same day for microbial determination. Microbial analysis was done to assess contamination with five microbes ( Escherichia coli, Salmonella, Staphylococcus aureus, Campylobacter , and Listeria ). Table 1 Category and number of meat samples purchased from two roadside Food Markets for determination of microbiological contamination Meat Category Roadside Food Markets Total Namawojjolo Lukaya Raw/fresh Chicken 11 06 17 Beef 04 05 09 Goat meat 00* 06 06 Sub-total (raw meat samples) 15 17 32 Roasted (ready-to-eat) Chicken 16 12 28 Beef 08 10 18 Goat meat 00* 11 11 Sub-total (roasted meat samples) 24 33 57 Grand total 39 50 89 *There was no goat meat in Namawojjolo Microbial analysis Salmonella Meat samples were weighed (5 g) and placed into sterile motor then homogenized using pestle in 45 ml of Buffered Peptone Water (BPW). The mixture was shaken thoroughly for 2 minutes to release bacteria from the sample into BPW. Homogenized sample was incubated in BPW at 37°C for 24 hours to enable recovery of any stressed Salmonella cells. After 24 hours, 1 ml of the homogenized sample was added to 9 ml of Selenite Cysteine Broth (SCB) and incubated at 42°C for 24 hours to suppress competing flora thereby promoting growth of Salmonella. After incubation in SCB, 6 dilutions were done from which 0.05 ml of the enriched broth was spread onto Xylose Lysine Deoxycholate (XLD) agar plates and then incubated at 37°C for 24 hours. After incubation, the number of Salmonella appeared as black colonies on XLD plate, which were counted and quantification was based on colony-forming units (CFU) per gram of meat using the formula: CFU/g = (Number of colonies × Dilution factor) / (Sample inoculated/ volume plated) Staphylococcus aureus Meat samples were weighed (5g) and placed into sterile motor then homogenized thoroughly using a pestle in 45 ml of Buffered Peptone Water (BPW). Serial dilutions of the homogenized sample were performed to reduce bacterial concentration, facilitating accurate quantification. Using a sterile pipette, a small volume (0.05 ml) was transferred from each dilution onto separate Mannitol Salt Agar (MSA) plates and the sample was spread evenly across the agar surface using a sterile spreader. The plates were incubated at 37°C for 24 hours. Staphylococcus aureus colonies appeared as yellow clusters. Plates with 30–300 colonies were selected and colonies on these were counted and the reading was recorded as CFU/g of meat. Escherichia coli Meat samples were weighed (5g) and placed into sterile motor then homogenized thoroughly using a pestle in 45 ml of BPW to make a 1:10 dilution. Serial dilutions of the homogenized sample were performed to achieve more dilutions. Using a sterile pipette, 0.05ml was drawn from the desired dilution and spread on a Chromogenic agar plate. Multiple dilutions were plated to ensure a countable range. The Chromogenic agar plates were incubated upside down at 37°C for 24 hours. After incubation, the plates were observed for distinctly colored colonies. The blue or purple colonies were counted as E. coli and the reading recorded as CFU/g of meat. Campylobacter Campylobacter agar base was weighed and 37g added to a liter of distilled water in a sterile flat-bottomed flask. The mixture was gently heated to facilitate dissolving. After dissolving, the agar base was allowed to cool until 50°C, defibrinated sheep blood (10% v/v) was added to the cooled agar base and mixed gently. Campylobacter supplement (vancomycin) was added and mixed well to ensure even distribution of the supplement in the medium. The prepared medium was then autoclaved at 121°C for 15 minutes and allowed to cool to 50°C. In a sterile environment (Biosafety cabinet machine) the agar was poured into sterile Petri dishes and allowed to solidify at room temperature. Then the meat sample was homogenized, serial dilutions made using sterile saline and each plate (with the prepared agar) was inoculated with 0.1 ml of diluted sample using a sterile swab. The inoculum was spread evenly across the surface of the agar and the plates incubated in microaerophilic environment (5% O₂, 10% CO₂, and 85% N₂) at 42°C for 48 hours. After incubation, the plates were examined for blue colonies and Campylobacter colonies on plates with 30–300 colonies were counted while recording the colony counts for each dilution. Listeria Meat samples were weighed where 5g was placed into sterile motor then homogenized using a pestle in 45 ml of BPW 2 minutes to create a uniform suspension. Serial dilutions of the homogenized sample were prepared and for each dilution, 0.05 ml was transferred onto Listeria Oxford Media (LOM) plates. Different dilutions were spread on separate plates and the Oxford supplement (FD071) was added to the LOM at a concentration of 1% (v/v) before inoculating. The plates were incubated at 30°C for 24 hours and after incubation, the plates were examined for typical Listeria colonies. The number of Listeria colonies on each plate were counted and recorded for each dilution to determine the concentration of Listeria in the original meat sample. For all microbes, quantification was done in form of CFU/g and the microbial load was compared with recommended microbiological reference criteria for acceptable limits as described by the Uganda National Bureau of Standards ((UNBS) guidelines US EAS 84 − 1 -Meat grades and meat cuts specification Part 1: Beef grades and cuts as well as US EAS 953 -Dressed poultry specification). Microbial contamination of chicken, beef, and goat meats were done where raw, cold, and hot samples were assessed and results compared to Uganda National Bureau of Standards (UNBS) safety limits. Samples labelled “cold” refer to the ready-to-eat roasted meat samples that had been exposed to customers for at least 30 minutes, while “hot” refers to the ready-to-eat roasted meats that were direct from fire and “raw” refers to fresh raw samples ready to be put on fire. Data analysis Statistical tests were conducted to compare the different microorganisms. Analysis of variance (ANOVA) was conducted, where assumptions of homogeneity of variances were assessed using both Levene’s test and Bartlett’s test. Levene’s test was used to evaluate whether the variances across groups were equal by testing the null hypothesis that group variances are equal. Bartlett’s test, which assumes normally, distributed data, served as a secondary check for homoscedasticity. Variables with a p -value greater than 0.05 were considered to have equal variance. For each microorganism, a one-way ANOVA was performed to determine whether there were statistically significant differences in mean microbial counts between the treatment groups. The F-statistic and associated p -values were reported. Where ANOVA results indicated significant differences ( p < 0.05), Bonferroni-adjusted post hoc comparisons were conducted to identify specific group differences. This correction was used to control the family-wise error rate due to multiple comparisons. The analysis was conducted using STATA Software, Version 14 and all results are presented in tables and graphs. Results Effect of handling on microbial contamination of roadside roasted meats of Namawojjolo and Lukaya food markets, Uganda. All samples exceeded UNBS limits for S. aureus , E. coli levels surpassed the limit (≤ 2 log₁₀ CFU/g) in 7 out of 9 samples, with only hot chicken (3 ± 2.44) and cold goat (4 ± 1.98) nearing compliance. Generally, raw samples exhibited the highest contamination across all pathogens, for instance, S. aureus in raw chicken (8 ± 0.56 log₁₀ CFU/g) and raw goat (8 ± 0.97 log₁₀ CFU/g) far exceeded the limit. Similarly, cold samples mostly matched or surpassed hot samples in contamination. For example, cold beef showed higher Listeria counts (5 ± 1.93 log₁₀ CFU/g) than hot beef (3 ± 2.71 log₁₀ CFU/g) as shown in Table 2 . Table 2 Effect of handling roadside roasted meats on microbial contamination (log 10 CFUs/g) presented as mean ± SD Pathogen Meat category UNBS limit (log₁₀ CFU/g) Raw Hot Cold S. aureus 7.63 ± 0.92 6.23 ± 0.69 6.10 ± 0.89 2 E. coli 4.70 ± 2.49 4.07 ± 1.78 3.90 ± 2.08 2 Salmonella 4.27 ± 2.75 4.07 ± 1.78 2.60 ± 2.60 Absent Listeria 6.10 ± 1.45 3.57 ± 2.34 4.57 ± 2.03 Absent Campylobacter 5.57 ± 3.05 3.80 ± 2.93 3.27 ± 3.01 Absent Effect of meat type on levels of microbial contamination of different roadside roasted meats from Namawojjolo and Lukaya Food Markets, Uganda All tested meat types (beef, chicken, and goat) showed microbial contamination far above UNBS safety limits for all microbes examined which is ≤ 2 log₁₀ CFU/g for S.aureus and E. coli or completely absent for Salmonella , Lysteria and Campylobacter . However, S. aureus was consistently the highest for all the three meat types for example 8.4 ± 9.0 log₁₀ CFU/g for goat meat compared to 5.5 ± 5.7 shown for Salmonella in the same as shown in Table 3 . Table 3 Effect of meat type on levels of microbial contamination (log 10 CFUs/g) on Raw and Ready-to-Eat Roasted (Hot and Cold) meats Microbe (log₁₀ CFUs/g) Meat Types Limit (UNBS) Beef Chicken Goats S. aureus 8.3 ± 8.8 7.7 ± 8.0 8.4 ± 9.0 2 E. coli 5.9 ± 6.4 5.7 ± 6.1 5.9 ± 6.3 2 Salmonella 5.4 ± 5.5 5.6 ± 5.7 5.5 ± 5.7 Absent Listeria 6.5 ± 6.7 6.5 ± 6.7 6.2 ± 6.6 Absent Campylobacter 6.7 ± 7.6 7.3 ± 7.7 7.0 ± 7.5 Absent Comparison on level of microbial contamination in roadside roasted meats from Namawojjolo and Lukaya Food Markets, Uganda Based on market, Staphylococcus aureus was found to have contaminated all the meat samples from both markets, E. coli and Campylobacter were mostly found to have contaminated meat from Lukaya 43/53 (81.1%) and 37/53 (84.9%), respectively. While Listeria and Salmonella were mostly found to have contaminated meat from Namawojolo 32/36 (88.9%) and25/36 (69.4%) respectively, as shown in Table 4 . Table 4 Proportion of contaminated roadside roasted meats from Namawojjolo and Lukaya Food Markets, Uganda Market S. aureus E. coli Campylobacter Listeria Salmonella Freq % Freq % Freq % Freq % Freq % Lukaya 53 100 43 81.1 37 69.8 45 84.9 36 67.9 Namawojjolo 36 100 27 75 25 69.4 32 88.9 25 69.4 Levene’s and Bartlett’s tests confirm equal variances, meaning ANOVA assumptions hold because all the p-values were above 0.05 thus showing equal variance among the meat samples. The ANOVA test showed that there was a significant difference in the average CFUs of S. aureus of Lukaya and Namawojolo were (p = 0.0092) was less than 0.05. Furthermore, the Bonferroni post-hoc test confirms that Lukaya has a significantly higher bacterial load of S. aureus than Namawojolo (p = 0.009) as shown in Table 5 . Table 5 Comparison of microbial levels for the two markets (Lukaya and Namawojolo) Microbe Levene test ANOVA Bartlett's test Bonferroni Post Hoc Wo df P-value F-statistic P-value X 2 value P-value Mean difference P-value S. aureus 1.03 1, 87 0.31 7.1 0.01 0.08 0.782 -29.64 0.009 E. coli 0.051 1, 87 0.82 2.23 0.14 0.01 0.924 -9.39 0.139 Salmonella 0.166 1, 87 0.68 0.04 0.84 0.05 0.816 -1.39 0.841 Listeria 0.082 1, 87 0.78 0.98 0.32 0.00 0.989 6.73 0.324 Campylobacter 1.306 1, 87 0.20 0.73 0.40 0.59 0.441 -7.04 0.397 Discussion The effect of handling on microbial contamination of roadside roasted meats of Namawojjolo and Lukaya Food Markets, Uganda The study analyzed a total of 89 meat samples, comprising 55 ready-to-eat (RTE) roasted meat samples (61.8%) and 34 raw meat samples (38.2%). Among the RTE samples, 29 (32.6%) were categorized as "cold" (exposed to customers for at least 30 minutes), while 26 (29.2%) were "hot" (freshly obtained from fire). The microbial analysis revealed substantial contamination levels for the different pathogenic bacteria. All meat samples (100%) were contaminated with S. aureus , indicating its presence in both sampling markets. Additionally, the majority of samples showed contamination with Listeria (86.5%), E. coli (78.7%), Campylobacter (69.7%), and Salmonella (68.5%). These results are consistent with previous findings that reported significant bacterial contamination in RTE meats from Ugandan highway markets, underscoring the systemic nature of food safety issues in informal meat vending centers across the country [ 8 ]. However, the results contradict a study in Egypt that reported 68% of the RTE samples being free from S. aureus compared to only 24% of the raw samples [ 13 ]. Also, another similar study reported complete destruction of S. aureus by cooking at 80 0 C [ 14 ] yet in the current study RTE roasted samples still showed presence of S. aureus . The presence of different pathogens on raw meat samples has been attributed to contamination from bad slaughtering techniques, unhygienic abattoirs, mishandling of the animal, and unhygienic containers [ 13 ]. Statistical analysis using ANOVA and Bonferroni post-hoc tests confirmed significant differences in S. aureus contamination levels between the two locations (p = 0.0092), with Lukaya samples showing significantly higher bacterial loads. These geographical variations may reflect differences in vendor practices, environmental conditions, or source materials between the two markets. The higher S. aureus load in Lukaya suggests potentially less hygienic handling practices among vendors in this location, which aligns with findings from Loukieh et al, 2018 who identified handling practices as a critical factor in microbial safety of street foods [ 6 ]. Despite roasting being expected to reduce microbial loads, RTE meats showed substantial contamination. This supports findings in Lebanon, where it was observed that cooked street foods were susceptible to post-processing contamination due to unhygienic handling and storage [ 6 ]. Similarly, a related study in Bangladesh, reported high prevalence of E. coli and S. aureus in common street foods, which was attributed to persistent fecal and human-derived contamination pathways [ 9 ]. The findings call for enforcement of sanitation standards by authorities through ensuring clean roasting areas, proper waste disposal, and safe storage for utensils and meat to prevent cross-contamination. Levels of microbial contamination of different roadside roasted meats of Namawojjolo and Lukaya Food Markets, Uganda Out of the 89 samples analyzed, 43 were chicken (comprising 48.3%), 28 were beef, and 18 were goat meat. S. aureus was mostly found in chicken and goat meat, Campylobacter and Salmonella were more in beef, Listeria was more in beef and goat, while E. coli was present in almost equal proportions in all the different types of meat. These findings are consistent with a study in South Africa, which assessed microbial profiles of different types of meat (beef, pork, and mutton) at different stages of the distribution chain. E. coli was found in almost the same quantities in all the different meat types [ 15 ]. Similarly, in Bangladesh, a study reported high prevalence of E. coli and S. aureus in all common street foods, which was attributed to persistent fecal and human-derived contamination pathways [ 9 ]. The current study's findings of Campylobacter (69.7%) and Salmonella (68.5%) are notably higher than those reported in some similar studies. For instance, Miraz et al., 2024 found lower Campylobacter prevalence in Bangladeshi street foods [ 7 ], suggesting potentially higher risks in the Ugandan context. Furthermore, a similar study in Kenya showed that poultry products often harbor higher Campylobacter loads compared to red meats, while beef may be more susceptible to E. coli contamination due to slaughterhouse practices. The physical and biochemical properties of different meat types (pH, water activity, fat content) can significantly influence microbial growth patterns. Additionally, different meat types typically undergo varying handling procedures from slaughter to vending point, potentially introducing different contamination risks [ 11 ]. Generally, the study found that all the different types of meat were contaminated with all the five pathogens assessed all above the standard (UNBS) limits. The study thus implores Local governments and NGOs to support vendors by providing safe, convenient and consistent sources of meat to ensure the microbial safety of the different meats. Declarations Ethical approval and consent to participate Not applicable Clinical trial number Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests Funding The study was funded by a Masters scholarship awarded to AN from the Islamic Development Bank through the Uganda Ministry of Education and Sports. Author Contribution AN developed and designed the study. She supervised the data collection, data analysis and drafted the manuscript. EAM and RHA played major roles in the conceptualization and design of the study, interpretation of the data and writing of the manuscript. All the authors have read and approved the final version of the manuscript Acknowledgement The authors would like to acknowledge the management of the two markets where samples were collected and the Agricultural Biotechnology Laboratory of Makerere University, where the microbial analysis was conducted. Data Availability Datasets and materials for information in this manuscript are available upon request. References Teferi SC. Street food safety, types and microbiological quality in Ethiopia: a critical review. Am J Appl Sci. 2020;6(3):67–71. Muyanja C, et al. Practices, knowledge and risk factors of street food vendors in Uganda. Food Control. 2011;22(10):1551–8. Andrade AA, Paiva AD, Machado ABF. Microbiology of street food: understanding risks to improve safety. J Appl Microbiol. 2023;134(8):lxad167. Khairuzzaman M, et al. Food safety challenges towards safe, healthy, and nutritious street foods in Bangladesh. Int J food Sci. 2014;2014(1):483519. Aruna N, Rajan V. Microbial analysis of street foods of different locations at Chennai city, India. Innovat Int J Med Pharma Sci. 2017;2:21–3. Loukieh M, et al. Street foods in Beirut city: An assessment of the food safety practices and of the microbiological quality. J Food Saf. 2018;38(3):e12455. Miraz UA, Md Iqbal Hossain MAA, Maraj, Islam MM. Microbial hazards in street foods: a comprehensive study in Dhaka, Bangladesh. Vocations Learn, 2024. 1(Issue 3). Bagumire A, Karumuna R. Bacterial contamination of ready-to-eat meats vended in highway markets in Uganda. Afr J Food Sci. 2017;11(6):160–70. Noor R. Microbiological quality of commonly consumed street foods in Bangladesh. Nutr Food Sci. 2016;46(1):130–41. Muhonja F, Kimathi GK. Assessment of hygienic and food handling practices among street food vendors in Nakuru Town in Kenya. 2014. Mwove JK. Quality and Safety of Ready-To-Eat Street-Vended Foods Sold in Selected Locations within Thika Town, Kiambu County, Kenya. JKUAT-CoANRE; 2023. Jaffee S, et al. The safe food imperative: Accelerating progress in low-and middle-income countries. World Bank; 2018. Hogoo HA. Effect of Different Cooking Methods on Bacteriological Quality of Meat. Benha Veterinary Med J. 2020;39(1):91–4. Montanari C, et al. New insights in thermal resistance of staphylococcal strains belonging to the species Staphylococcus epidermidis, Staphylococcus lugdunensis and Staphylococcus aureus. Food Control. 2015;50:605–12. Rani ZT, Mhlongo LC, Hugo A. Microbial profiles of meat at different stages of the distribution chain from the abattoir to retail outlets. International Journal of Environmental Research and Public Health, 2023. 20(3): p. 1986. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8871589","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600102226,"identity":"c6f76cbd-0990-42da-a2a2-c2148ada423e","order_by":0,"name":"Nanfuka Annet","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYJCCDwwHoKwEBhsgydh4ALdiMGCcAdXC2JDAkAamSdDCwHAYzMKrxZz97MFmnjN28ubSzccfPKg4b7e2/TDQlhqbaFxaLHvyEpt5biQb7pxzLLEh4czt5G1nEoFajqXlNuDQYnAgx/wxzwdmxg03cgwbEttuJ5sdAGphbDiMW8v5N4bNPB/q7TfcyP/YkPjvXLLZ+YcEtAANBzrscCLQFsYGoPl2ZjcI2XLjjWHjnDPHkzfcSDOckXAsOcHsBtCWBHx+OQ/0wptj1bYbbiQ/+Pijxs7e7Hz6wwcfamxwasEAiWCVCcQqBwF7UhSPglEwCkbByAAAXQZwtvljvSAAAAAASUVORK5CYII=","orcid":"","institution":"Bamunanika Technical Institute","correspondingAuthor":true,"prefix":"","firstName":"Nanfuka","middleName":"","lastName":"Annet","suffix":""},{"id":600102227,"identity":"b8ef8748-4270-4974-8935-54fb6e2b8fb2","order_by":1,"name":"Rachuonyo Harold Anindo","email":"","orcid":"","institution":"University of Eldoret","correspondingAuthor":false,"prefix":"","firstName":"Rachuonyo","middleName":"Harold","lastName":"Anindo","suffix":""},{"id":600102228,"identity":"37c2c1af-62ac-401f-842a-03ca45b4f302","order_by":2,"name":"Eunice Akello Mewa","email":"","orcid":"","institution":"University of Eldoret","correspondingAuthor":false,"prefix":"","firstName":"Eunice","middleName":"Akello","lastName":"Mewa","suffix":""},{"id":600102229,"identity":"70e6b17c-a8bb-42ea-9e0b-1d559cf5ae88","order_by":3,"name":"Peter Kageni","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Kageni","suffix":""},{"id":600102230,"identity":"564a1675-5a38-4adf-b5ed-e59fc0cbf365","order_by":4,"name":"Paul Mukama Ategyeka","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"Mukama","lastName":"Ategyeka","suffix":""}],"badges":[],"createdAt":"2026-02-13 11:54:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8871589/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8871589/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108666743,"identity":"0e807cf2-b6dc-4aee-a28f-9a03b1bf0e46","added_by":"auto","created_at":"2026-05-07 06:42:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":357978,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8871589/v1/880dd902-7c0a-4dfd-ac16-118c39cc80d8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effect of Handling and Levels of Microbial Contamination of Different Roadside Roasted Meats of Namawojjolo and Lukaya Highway food markets, Uganda","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRoadside roasted meat vending in Uganda dates back to pre-colonial times, when communities engaged in open-fire meat roasting as part of cultural and social gatherings [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, urbanization and economic pressures transformed this practice into a widespread informal trade with limited regulatory oversight [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLack of formal regulation and sanitary infrastructure in roadside food systems compromises food safety and possibly facilitates microbial contamination, leading to recurring outbreaks of foodborne illnesses [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e Accordingly, preparation and handling of roadside roasted meats is traditionally influenced by local customs and practices, which often prioritize flavor and affordability over safety. For example, the use of communal utensils, improper storage of raw materials, and the re-use of cooking oils are common practices that contribute to microbial contamination [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, environmental conditions under which roadside roasted meats are prepared and sold play critical role in determining safety from microbial contamination. Vendors often operate in open-air settings with limited access to clean water and other facilities such as proper waste disposal [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These conditions create an environment conducive for growth of pathogens. Additionally, the proximity of vending sites to sources of pollution, such as busy roads and waste disposal areas further exacerbates the risk of contamination [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSimilarly, the handling practices of vendors is another critical factor influencing microbial contamination. Many vendors lack formal training in food safety and hygiene measures, leading to improper handling which leads to cross-contamination from utensils, preparation surfaces, and even mixing of raw meat with ready-to-eat meat [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. For example, the re-use of unwashed utensils and handling of money and food without proper hand washing are common practices that contribute to contamination [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrimary pathogens associated with roadside roasted meats include \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, \u003cem\u003eCampylobacter\u003c/em\u003e, \u003cem\u003eListeria\u003c/em\u003e, and \u003cem\u003eSalmonella spp\u003c/em\u003e., which are commonly isolated from contaminated meats [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These pathogens are often introduced through several pathways such as contaminated raw materials, meat from slaughterhouses with poor hygiene practices, or through cross-contamination during handling [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The ability of these pathogens to survive and proliferate in ready-to-eat meats is further influenced by factors such as temperature, humidity, and the presence of inhibitory compounds [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePathogenic food-borne illnesses constitute one of the major health problems in developing countries like Uganda [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These illnesses are responsible for a significant number of disability-adjusted life years (DALYs), accounting for over 34% of premature deaths in children under the age of five [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study therefore sought to assess effects of handling roadside roasted beef, chicken and goat meats on level of microbial contamination along Namawojjolo and Lukaya Highway Food Markets -Uganda.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and study setting\u003c/h2\u003e \u003cp\u003eA completely randomized study design involving sample analysis in the laboratory was used to collect data on the effect of handling on microbial contamination of different meats and levels of microbial contamination of different roadside roasted meats\u003c/p\u003e \u003cp\u003eMeat samples for the study were collected from two purposively selected Highway Roadside Markets of Namawojjolo along Kampala-Jinja and Lukaya along Kampala-Masaka. The two Food Markets were selected due to their strategic locations and high amount traffic conveying travellers as potential customers, variety of ready-to-eat meats and vendors selling the products. Namawojjolo is located at a GPS reading of N 0\u0026deg;23'3, E 32\u0026deg;50'18 and altitude of 1123 m above sea level in Nama, Mukono District of Central Uganda, about 33 km East of Kampala City and 11 km away from Mukono. Kampala-Jinja Highway is the main one linking Capital City (Kampala) to the Eastern parts of Uganda and even those traveling to neighbouring Country of Kenya by road. This makes it a very busy road with heavy traffic and many travellers, making stopovers at the market to get already-prepared food items on-the-go.\u003c/p\u003e \u003cp\u003eLukaya is located in Kalungu District, along Kampala \u0026ndash; Masaka Highway, approximately 107km from Kampala and 27 km from Masaka Town in Central Uganda at a GPS reading of 0\u0026deg;09'03.0\"S, 31\u0026deg;52'28.0\"E. Kampala-Masaka Highway leads to the Southern and Western parts of Uganda and further links to Rwanda and the Democratic Republic of Congo (DRC) borders. Each of these two markets had individual population of vendors ranging between 300\u0026ndash;400, who were involved in sale of different food-items daily [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLaboratory analysis to assess microbial contamination was done at the Agricultural Biotechnology Laboratory of Makerere University, housed in the Department of Agricultural Production.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample Collection\u003c/h3\u003e\n\u003cp\u003eA total of 89 meat samples were collected in different categories and proportionate sampling used to determine the exact number due to variability in meat types and numbers available in the different markets. Most vendors were found to be selling chicken followed by beef and very few sold goat meat, therefore proportionate sampling gave the numbers shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eMeat samples (fresh/raw meats ready to be put on fire as well as roasted beef, chicken, and goat meat) from Namawojjolo and Lukaya Food Markets were bought from randomly selected vendors and packed in sterile zipper-lock bags, sealed off, and put in a cool box (at a temperature of 2\u0026deg;C) then transported to laboratory on the same day for microbial determination. Microbial analysis was done to assess contamination with five microbes (\u003cem\u003eEscherichia coli, Salmonella, Staphylococcus aureus, Campylobacter\u003c/em\u003e, and \u003cem\u003eListeria\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eCategory and number of meat samples purchased from two roadside Food Markets for determination of microbiological contamination\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eMeat Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eRoadside Food Markets\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNamawojjolo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLukaya\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRaw/fresh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChicken\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGoat meat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e00*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSub-total (raw meat samples)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRoasted (ready-to-eat)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChicken\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGoat meat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e00*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSub-total (roasted meat samples)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e57\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrand total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*There was no goat meat in Namawojjolo\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eMicrobial analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSalmonella\u003c/h2\u003e \u003cp\u003eMeat samples were weighed (5 g) and placed into sterile motor then homogenized using pestle in 45 ml of Buffered Peptone Water (BPW). The mixture was shaken thoroughly for 2 minutes to release bacteria from the sample into BPW. Homogenized sample was incubated in BPW at 37\u0026deg;C for 24 hours to enable recovery of any stressed \u003cem\u003eSalmonella\u003c/em\u003e cells. After 24 hours, 1 ml of the homogenized sample was added to 9 ml of Selenite Cysteine Broth (SCB) and incubated at 42\u0026deg;C for 24 hours to suppress competing flora thereby promoting growth of Salmonella. After incubation in SCB, 6 dilutions were done from which 0.05 ml of the enriched broth was spread onto Xylose Lysine Deoxycholate (XLD) agar plates and then incubated at 37\u0026deg;C for 24 hours. After incubation, the number of Salmonella appeared as black colonies on XLD plate, which were counted and quantification was based on colony-forming units (CFU) per gram of meat using the formula:\u003c/p\u003e \u003cp\u003e \u003cb\u003eCFU/g =\u003c/b\u003e (Number of colonies \u0026times; Dilution factor) / (Sample inoculated/ volume plated)\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStaphylococcus aureus\u003c/h3\u003e\n\u003cp\u003eMeat samples were weighed (5g) and placed into sterile motor then homogenized thoroughly using a pestle in 45 ml of Buffered Peptone Water (BPW). Serial dilutions of the homogenized sample were performed to reduce bacterial concentration, facilitating accurate quantification. Using a sterile pipette, a small volume (0.05 ml) was transferred from each dilution onto separate Mannitol Salt Agar (MSA) plates and the sample was spread evenly across the agar surface using a sterile spreader. The plates were incubated at 37\u0026deg;C for 24 hours. \u003cem\u003eStaphylococcus aureus\u003c/em\u003e colonies appeared as yellow clusters. Plates with 30\u0026ndash;300 colonies were selected and colonies on these were counted and the reading was recorded as CFU/g of meat.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEscherichia coli\u003c/h2\u003e \u003cp\u003eMeat samples were weighed (5g) and placed into sterile motor then homogenized thoroughly using a pestle in 45 ml of BPW to make a 1:10 dilution. Serial dilutions of the homogenized sample were performed to achieve more dilutions. Using a sterile pipette, 0.05ml was drawn from the desired dilution and spread on a Chromogenic agar plate. Multiple dilutions were plated to ensure a countable range. The Chromogenic agar plates were incubated upside down at 37\u0026deg;C for 24 hours. After incubation, the plates were observed for distinctly colored colonies. The blue or purple colonies were counted as E. coli and the reading recorded as CFU/g of meat.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCampylobacter\u003c/h3\u003e\n\u003cp\u003eCampylobacter agar base was weighed and 37g added to a liter of distilled water in a sterile flat-bottomed flask. The mixture was gently heated to facilitate dissolving. After dissolving, the agar base was allowed to cool until 50\u0026deg;C, defibrinated sheep blood (10% v/v) was added to the cooled agar base and mixed gently. Campylobacter supplement (vancomycin) was added and mixed well to ensure even distribution of the supplement in the medium. The prepared medium was then autoclaved at 121\u0026deg;C for 15 minutes and allowed to cool to 50\u0026deg;C. In a sterile environment (Biosafety cabinet machine) the agar was poured into sterile Petri dishes and allowed to solidify at room temperature. Then the meat sample was homogenized, serial dilutions made using sterile saline and each plate (with the prepared agar) was inoculated with 0.1 ml of diluted sample using a sterile swab. The inoculum was spread evenly across the surface of the agar and the plates incubated in microaerophilic environment (5% O₂, 10% CO₂, and 85% N₂) at 42\u0026deg;C for 48 hours. After incubation, the plates were examined for blue colonies and \u003cem\u003eCampylobacter\u003c/em\u003e colonies on plates with 30\u0026ndash;300 colonies were counted while recording the colony counts for each dilution.\u003c/p\u003e\n\u003ch3\u003eListeria\u003c/h3\u003e\n\u003cp\u003eMeat samples were weighed where 5g was placed into sterile motor then homogenized using a pestle in 45 ml of BPW 2 minutes to create a uniform suspension. Serial dilutions of the homogenized sample were prepared and for each dilution, 0.05 ml was transferred onto Listeria Oxford Media (LOM) plates. Different dilutions were spread on separate plates and the Oxford supplement (FD071) was added to the LOM at a concentration of 1% (v/v) before inoculating. The plates were incubated at 30\u0026deg;C for 24 hours and after incubation, the plates were examined for typical Listeria colonies. The number of Listeria colonies on each plate were counted and recorded for each dilution to determine the concentration of Listeria in the original meat sample.\u003c/p\u003e \u003cp\u003e For all microbes, quantification was done in form of CFU/g and the microbial load was compared with recommended microbiological reference criteria for acceptable limits as described by the Uganda National Bureau of Standards ((UNBS) guidelines US EAS 84\u0026thinsp;\u0026minus;\u0026thinsp;1 -Meat grades and meat cuts specification Part 1: Beef grades and cuts as well as US EAS 953 -Dressed poultry specification).\u003c/p\u003e \u003cp\u003eMicrobial contamination of chicken, beef, and goat meats were done where raw, cold, and hot samples were assessed and results compared to Uganda National Bureau of Standards (UNBS) safety limits. Samples labelled \u0026ldquo;cold\u0026rdquo; refer to the ready-to-eat roasted meat samples that had been exposed to customers for at least 30 minutes, while \u0026ldquo;hot\u0026rdquo; refers to the ready-to-eat roasted meats that were direct from fire and \u0026ldquo;raw\u0026rdquo; refers to fresh raw samples ready to be put on fire.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eStatistical tests were conducted to compare the different microorganisms. Analysis of variance (ANOVA) was conducted, where assumptions of homogeneity of variances were assessed using both Levene\u0026rsquo;s test and Bartlett\u0026rsquo;s test. Levene\u0026rsquo;s test was used to evaluate whether the variances across groups were equal by testing the null hypothesis that group variances are equal. Bartlett\u0026rsquo;s test, which assumes normally, distributed data, served as a secondary check for homoscedasticity. Variables with a \u003cem\u003ep\u003c/em\u003e-value greater than 0.05 were considered to have equal variance. For each microorganism, a one-way ANOVA was performed to determine whether there were statistically significant differences in mean microbial counts between the treatment groups. The F-statistic and associated \u003cem\u003ep\u003c/em\u003e-values were reported. Where ANOVA results indicated significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), Bonferroni-adjusted post hoc comparisons were conducted to identify specific group differences. This correction was used to control the family-wise error rate due to multiple comparisons. The analysis was conducted using STATA Software, Version 14 and all results are presented in tables and graphs.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eEffect of handling on microbial contamination of roadside roasted meats of Namawojjolo and Lukaya food markets, Uganda.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAll samples exceeded UNBS limits for \u003cem\u003eS. aureus\u003c/em\u003e, E. \u003cem\u003ecoli\u003c/em\u003e levels surpassed the limit (\u0026le;\u0026thinsp;2 log₁₀ CFU/g) in 7 out of 9 samples, with only hot chicken (3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.44) and cold goat (4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98) nearing compliance. Generally, raw samples exhibited the highest contamination across all pathogens, for instance, \u003cem\u003eS. aureus\u003c/em\u003e in raw chicken (8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56 log₁₀ CFU/g) and raw goat (8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97 log₁₀ CFU/g) far exceeded the limit. Similarly, cold samples mostly matched or surpassed hot samples in contamination. For example, cold beef showed higher \u003cem\u003eListeria\u003c/em\u003e counts (5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.93 log₁₀ CFU/g) than hot beef (3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.71 log₁₀ CFU/g) as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of handling roadside roasted meats on microbial contamination (log\u003csub\u003e10\u003c/sub\u003e CFUs/g) presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePathogen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMeat category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUNBS limit (log₁₀ CFU/g)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaw\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHot\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCold\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eS. aureus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.70\u0026thinsp;\u0026plusmn;\u0026thinsp;2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.90\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.27\u0026thinsp;\u0026plusmn;\u0026thinsp;2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.60\u0026thinsp;\u0026plusmn;\u0026thinsp;2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eListeria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCampylobacter\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.57\u0026thinsp;\u0026plusmn;\u0026thinsp;3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.80\u0026thinsp;\u0026plusmn;\u0026thinsp;2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.27\u0026thinsp;\u0026plusmn;\u0026thinsp;3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEffect of meat type on levels of microbial contamination of different roadside roasted meats from Namawojjolo and Lukaya Food Markets, Uganda\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAll tested meat types (beef, chicken, and goat) showed microbial contamination far above UNBS safety limits for all microbes examined which is \u0026le;\u0026thinsp;2 log₁₀ CFU/g for \u003cem\u003eS.aureus\u003c/em\u003e and \u003cem\u003eE. coli\u003c/em\u003e or completely absent for \u003cem\u003eSalmonella\u003c/em\u003e, \u003cem\u003eLysteria\u003c/em\u003e and \u003cem\u003eCampylobacter\u003c/em\u003e. However, \u003cem\u003eS. aureus\u003c/em\u003e was consistently the highest for all the three meat types for example 8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0 log₁₀ CFU/g for goat meat compared to 5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7 shown for \u003cem\u003eSalmonella\u003c/em\u003e in the same as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of meat type on levels of microbial contamination (log\u003csub\u003e10\u003c/sub\u003e CFUs/g) on Raw and Ready-to-Eat Roasted (Hot and Cold) meats\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMicrobe (log₁₀ CFUs/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMeat Types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLimit (UNBS)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChicken\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGoats\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eS. aureus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eListeria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCampylobacter\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eComparison on level of microbial contamination in roadside roasted meats from Namawojjolo and Lukaya Food Markets, Uganda\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBased on market, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e was found to have contaminated all the meat samples from both markets, \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eCampylobacter\u003c/em\u003e were mostly found to have contaminated meat from Lukaya 43/53 (81.1%) and 37/53 (84.9%), respectively. While \u003cem\u003eListeria\u003c/em\u003e and \u003cem\u003eSalmonella\u003c/em\u003e were mostly found to have contaminated meat from Namawojolo 32/36 (88.9%) and25/36 (69.4%) respectively, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProportion of contaminated roadside roasted meats from Namawojjolo and Lukaya Food Markets, Uganda\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMarket\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eS. aureus\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCampylobacter\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eListeria\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFreq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFreq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFreq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFreq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLukaya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e69.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e84.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e67.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNamawojjolo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e69.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e88.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e69.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLevene\u0026rsquo;s and Bartlett\u0026rsquo;s tests confirm equal variances, meaning ANOVA assumptions hold because all the p-values were above 0.05 thus showing equal variance among the meat samples. The ANOVA test showed that there was a significant difference in the average CFUs of \u003cem\u003eS. aureus\u003c/em\u003e of Lukaya and Namawojolo were (p\u0026thinsp;=\u0026thinsp;0.0092) was less than 0.05. Furthermore, the \u003cem\u003eBonferroni post-hoc\u003c/em\u003e test confirms that Lukaya has a significantly higher bacterial load of \u003cem\u003eS. aureus\u003c/em\u003e than Namawojolo (p\u0026thinsp;=\u0026thinsp;0.009) as shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of microbial levels for the two markets (Lukaya and Namawojolo)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMicrobe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eLevene test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eANOVA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eBartlett's test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eBonferroni Post Hoc\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMean difference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eS. aureus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-29.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-9.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eListeria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCampylobacter\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-7.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cb\u003eThe effect of handling on microbial contamination of roadside roasted meats of Namawojjolo and Lukaya Food Markets, Uganda\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe study analyzed a total of 89 meat samples, comprising 55 ready-to-eat (RTE) roasted meat samples (61.8%) and 34 raw meat samples (38.2%). Among the RTE samples, 29 (32.6%) were categorized as \"cold\" (exposed to customers for at least 30 minutes), while 26 (29.2%) were \"hot\" (freshly obtained from fire). The microbial analysis revealed substantial contamination levels for the different pathogenic bacteria.\u003c/p\u003e \u003cp\u003eAll meat samples (100%) were contaminated with \u003cem\u003eS. aureus\u003c/em\u003e, indicating its presence in both sampling markets. Additionally, the majority of samples showed contamination with \u003cem\u003eListeria\u003c/em\u003e (86.5%), \u003cem\u003eE. coli\u003c/em\u003e (78.7%), \u003cem\u003eCampylobacter\u003c/em\u003e (69.7%), and \u003cem\u003eSalmonella\u003c/em\u003e (68.5%).\u003c/p\u003e \u003cp\u003eThese results are consistent with previous findings that reported significant bacterial contamination in RTE meats from Ugandan highway markets, underscoring the systemic nature of food safety issues in informal meat vending centers across the country [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, the results contradict a study in Egypt that reported 68% of the RTE samples being free from \u003cem\u003eS. aureus\u003c/em\u003e compared to only 24% of the raw samples [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Also, another similar study reported complete destruction of \u003cem\u003eS. aureus\u003c/em\u003e by cooking at 80\u003csup\u003e0\u003c/sup\u003eC [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] yet in the current study RTE roasted samples still showed presence of \u003cem\u003eS. aureus\u003c/em\u003e. The presence of different pathogens on raw meat samples has been attributed to contamination from bad slaughtering techniques, unhygienic abattoirs, mishandling of the animal, and unhygienic containers [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStatistical analysis using ANOVA and \u003cem\u003eBonferroni post-hoc\u003c/em\u003e tests confirmed significant differences in \u003cem\u003eS. aureus\u003c/em\u003e contamination levels between the two locations (p\u0026thinsp;=\u0026thinsp;0.0092), with Lukaya samples showing significantly higher bacterial loads. These geographical variations may reflect differences in vendor practices, environmental conditions, or source materials between the two markets. The higher \u003cem\u003eS. aureus\u003c/em\u003e load in Lukaya suggests potentially less hygienic handling practices among vendors in this location, which aligns with findings from Loukieh et al, 2018 who identified handling practices as a critical factor in microbial safety of street foods [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite roasting being expected to reduce microbial loads, RTE meats showed substantial contamination. This supports findings in Lebanon, where it was observed that cooked street foods were susceptible to post-processing contamination due to unhygienic handling and storage [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Similarly, a related study in Bangladesh, reported high prevalence of \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eS. aureus\u003c/em\u003e in common street foods, which was attributed to persistent fecal and human-derived contamination pathways [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe findings call for enforcement of sanitation standards by authorities through ensuring clean roasting areas, proper waste disposal, and safe storage for utensils and meat to prevent cross-contamination.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLevels of microbial contamination of different roadside roasted meats of Namawojjolo and Lukaya Food Markets, Uganda\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOut of the 89 samples analyzed, 43 were chicken (comprising 48.3%), 28 were beef, and 18 were goat meat. \u003cem\u003eS. aureus\u003c/em\u003e was mostly found in chicken and goat meat, \u003cem\u003eCampylobacter\u003c/em\u003e and \u003cem\u003eSalmonella\u003c/em\u003e were more in beef, \u003cem\u003eListeria\u003c/em\u003e was more in beef and goat, while \u003cem\u003eE. coli\u003c/em\u003e was present in almost equal proportions in all the different types of meat. These findings are consistent with a study in South Africa, which assessed microbial profiles of different types of meat (beef, pork, and mutton) at different stages of the distribution chain. \u003cem\u003eE. coli\u003c/em\u003e was found in almost the same quantities in all the different meat types [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Similarly, in Bangladesh, a study reported high prevalence of \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eS. aureus\u003c/em\u003e in all common street foods, which was attributed to persistent fecal and human-derived contamination pathways [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe current study's findings of \u003cem\u003eCampylobacter\u003c/em\u003e (69.7%) and \u003cem\u003eSalmonella\u003c/em\u003e (68.5%) are notably higher than those reported in some similar studies. For instance, Miraz et al., 2024 found lower \u003cem\u003eCampylobacter\u003c/em\u003e prevalence in Bangladeshi street foods [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], suggesting potentially higher risks in the Ugandan context.\u003c/p\u003e \u003cp\u003eFurthermore, a similar study in Kenya showed that poultry products often harbor higher \u003cem\u003eCampylobacter\u003c/em\u003e loads compared to red meats, while beef may be more susceptible to \u003cem\u003eE. coli\u003c/em\u003e contamination due to slaughterhouse practices. The physical and biochemical properties of different meat types (pH, water activity, fat content) can significantly influence microbial growth patterns. Additionally, different meat types typically undergo varying handling procedures from slaughter to vending point, potentially introducing different contamination risks [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGenerally, the study found that all the different types of meat were contaminated with all the five pathogens assessed all above the standard (UNBS) limits. The study thus implores Local governments and NGOs to support vendors by providing safe, convenient and consistent sources of meat to ensure the microbial safety of the different meats.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical approval and consent to participate\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eClinical trial number\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe study was funded by a Masters scholarship awarded to AN from the Islamic Development Bank through the Uganda Ministry of Education and Sports.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAN developed and designed the study. She supervised the data collection, data analysis and drafted the manuscript.\u003c/p\u003e\u003cp\u003eEAM and RHA played major roles in the conceptualization and design of the study, interpretation of the data and writing of the manuscript. All the authors have read and approved the final version of the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to acknowledge the management of the two markets where samples were collected and the Agricultural Biotechnology Laboratory of Makerere University, where the microbial analysis was conducted.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eDatasets and materials for information in this manuscript are available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTeferi SC. Street food safety, types and microbiological quality in Ethiopia: a critical review. Am J Appl Sci. 2020;6(3):67\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuyanja C, et al. Practices, knowledge and risk factors of street food vendors in Uganda. Food Control. 2011;22(10):1551\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndrade AA, Paiva AD, Machado ABF. Microbiology of street food: understanding risks to improve safety. J Appl Microbiol. 2023;134(8):lxad167.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhairuzzaman M, et al. Food safety challenges towards safe, healthy, and nutritious street foods in Bangladesh. Int J food Sci. 2014;2014(1):483519.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAruna N, Rajan V. Microbial analysis of street foods of different locations at Chennai city, India. Innovat Int J Med Pharma Sci. 2017;2:21\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoukieh M, et al. Street foods in Beirut city: An assessment of the food safety practices and of the microbiological quality. J Food Saf. 2018;38(3):e12455.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiraz UA, Md Iqbal Hossain MAA, Maraj, Islam MM. Microbial hazards in street foods: a comprehensive study in Dhaka, Bangladesh. Vocations Learn, 2024. 1(Issue 3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBagumire A, Karumuna R. Bacterial contamination of ready-to-eat meats vended in highway markets in Uganda. Afr J Food Sci. 2017;11(6):160\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoor R. Microbiological quality of commonly consumed street foods in Bangladesh. Nutr Food Sci. 2016;46(1):130\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuhonja F, Kimathi GK. \u003cem\u003eAssessment of hygienic and food handling practices among street food vendors in Nakuru Town in Kenya.\u003c/em\u003e 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMwove JK. Quality and Safety of Ready-To-Eat Street-Vended Foods Sold in Selected Locations within Thika Town, Kiambu County, Kenya. JKUAT-CoANRE; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJaffee S, et al. The safe food imperative: Accelerating progress in low-and middle-income countries. World Bank; 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHogoo HA. Effect of Different Cooking Methods on Bacteriological Quality of Meat. Benha Veterinary Med J. 2020;39(1):91\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontanari C, et al. New insights in thermal resistance of staphylococcal strains belonging to the species Staphylococcus epidermidis, Staphylococcus lugdunensis and Staphylococcus aureus. Food Control. 2015;50:605\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRani ZT, Mhlongo LC, Hugo A. \u003cem\u003eMicrobial profiles of meat at different stages of the distribution chain from the abattoir to retail outlets.\u003c/em\u003e International Journal of Environmental Research and Public Health, 2023. 20(3): p. 1986.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Escherichia coli, Food safety, Microbial contamination, Ready-to-eat meat, Roadside roasted meat, Staphylococcus aureus","lastPublishedDoi":"10.21203/rs.3.rs-8871589/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8871589/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHandling, preparation and sale of roadside roasted meats often predispose and may compromise such products leading to microbial contamination of both raw and ready-to-eat meat. This study assessed effects of handling roadside roasted meats on levels of microbial contamination sold at Namawojjolo and Lukaya Highway Food Markets in Uganda.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 89 samples comprising of raw and ready-to-eat beef, chicken, and goat meats were collected using simple random sampling and analyzed for contamination by five key pathogens: \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, \u003cem\u003eEscherichia coli, Campylobacter, Listeria, and Salmonella\u003c/em\u003e. Microbiological analysis was performed using standard culture and quantification technique and data were statistically analyzed using ANOVA and Bonferroni post-hoc tests.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eRaw samples exhibited highest contamination across all pathogens, where \u003cem\u003eS. aureus\u003c/em\u003e in raw chicken (8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56 log₁₀ CFU/g) and raw goat (8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97 log₁₀ CFU/g) far exceeded Uganda\u0026rsquo;s National Bureau of Standards (UNBS) limits. Similarly, most of cold samples matched or surpassed hot samples in contamination. For example, cold beef showed higher \u003cem\u003eListeria\u003c/em\u003e counts (5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.93 log₁₀ CFU/g) than hot beef (3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.71 log₁₀ CFU/g). All tested meat types showed microbial contamination above UNBS safety limits for the five microbes examined which is \u0026le;\u0026thinsp;2 log₁₀ CFU/g for \u003cem\u003eS.aureus\u003c/em\u003e and \u003cem\u003eE.coli\u003c/em\u003e or completely absent for \u003cem\u003eSalmonella\u003c/em\u003e, \u003cem\u003eLysteria\u003c/em\u003e and \u003cem\u003eCampylobacter\u003c/em\u003e. However, \u003cem\u003eS. aureus\u003c/em\u003e was consistently highest for all three meat types; for example, 8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0 log₁₀ CFU/g for goat meat compared to 5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7 shown for \u003cem\u003eSalmonella\u003c/em\u003e in goat meat.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe findings highlight food safety gaps in the informal meat vending sector in Uganda. The pervasive microbial contamination, especially with pathogens of significant public health concern, underscores an urgent need for improved hygiene practices and regulatory oversight to safeguard consumer health. This study provides empirical evidence for targeted interventions to reduce foodborne disease risks associated with roadside meat consumption in Uganda and possibly, elsewhere.\u003c/p\u003e","manuscriptTitle":"Effect of Handling and Levels of Microbial Contamination of Different Roadside Roasted Meats of Namawojjolo and Lukaya Highway food markets, Uganda","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-04 11:01:10","doi":"10.21203/rs.3.rs-8871589/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"69024c01-bb9c-4f1e-9eb5-a6be67e66755","owner":[],"postedDate":"March 4th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-07T06:36:07+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T06:41:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-04 11:01:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8871589","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8871589","identity":"rs-8871589","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}