Long-term antibiotic exposure landscapes and resistant Escherichia coli colonization in a densely populated setting.

Antibiotic exposure is associated with resistant bacterial colonization, but this relationship can be obscured in community settings owing to horizontal bacterial transmission and broad distributions. Locality-level exposure estimates considering inhabitants' length of stay, exposure history, and exposure conditions of areas nearby could clarify these relationships. We used prescription data filled during 2010-2015 for 23 antibiotic types for members of georeferenced households in a population-based infectious disease surveillance platform. For each antibiotic and locality, we generated exposure estimates, expressed in defined daily doses (DDD) per 1000 inhabitant days of observation (IDO). We also estimated relevant environmental parameters, such as the distance of each locality to water, sanitation, and other amenities. We used data on ampicillin, ceftazidime, and trimethoprim-and-sulfamethoxazole resistant Escherichia coli colonization from stool cultures of asymptomatic individuals in randomly selected households. We tested exposure-colonization associations using permutation analysis of variance and logistic generalized linear mixed-effect models. Overall, exposure was highest for trimethoprim-sulfamethoxazole (1.8 DDD per 1000 IDO), followed by amoxicillin (0.7 DDD per 1000 IDO). Of 1,386 unique household samples from 195 locations tested between September 2015 and January 2016, 90%, 85% and 4% were colonized with E. coli resistant to trimethoprim and sulfamethoxazole, ampicillin, and ceftazidime, respectively. Ceftazidime-resistant E. coli colonization was common in areas with increased trimethoprim-sulfamethoxazole, cloxacillin, and erythromycin exposure. No association with any of the physical environmental variables was observed. We did not detect relationships between distribution patterns of ampicillin or trimethoprim-and-sulfamethoxazole resistant E. coli colonization and the risk factors assessed. Appropriate temporal and spatial scaling of raw antibiotic exposure data to account for evolution and ecological contexts of antibiotic resistance could clarify exposure-colonization relationships in community settings and inform community stewardship program.

The effect of COVID-19 pandemic on healthcare seeking in an urban informal settlement in Nairobi and a rural setting in western Kenya.

The COVID-19 pandemic caused widespread changes and disruptions to healthcare seeking behavior. There are limited studies on the effect of the COVID-19 pandemic on healthcare seeking patterns in low-and middle-income countries (LMICs), especially in settings with inequitable access to healthcare in rural and urban informal settlements. We investigated the effect of the COVID-19 pandemic on reported healthcare seeking at health facilities and chemists using morbidity data from participants in an ongoing population-based infectious disease surveillance platform in Asembo in Siaya County, a rural setting in western Kenya and Kibera, an urban informal settlement in Nairobi County. We described healthcare seeking patterns before (from 1st January 2016 to 12th March 2020) and during the pandemic (from 13th March 2020 to 31st August 2022) by gender and age for any reported illness and select clinical syndromes using frequencies and percentages. We used a generalized estimating equation with an exchangeable correlation structure to assess the effect of the pandemic on healthcare seeking adjusting for gender and age. Overall, there was a 19% (adjusted odds ratio, aOR: 0.81; 95% Confidence Interval, CI: 0.79-0.83) decline in odds of seeking healthcare at health facilities for any illness in Asembo during the pandemic, and a 30% (aOR: 0.70; 95% CI: 0.67-0.73) decline in Kibera. Similarly, there was a decline in seeking healthcare by clinical syndromes, e.g., for ARI, aOR: 0.76; 95% CI:0.73-0.79 in Asembo, and aOR: 0.68; 95% CI:0.64-0.72 in Kibera. The pandemic resulted in increased healthcare seeking at chemists (aOR: 1.23; 95% CI: 1.20-1.27 in Asembo, and aOR: 1.40; 95% CI: 1.35-1.46 in Kibera). This study highlights interruptions to healthcare seeking in resource-limited settings due to the COVID-19 pandemic. The pandemic resulted in a substantial decline in seeking care at health facilities, and an increase of the same at chemists.

Dynamic Incidence of Typhoid Fever over a 10-Year Period (2010-2019) in Kibera, an Urban Informal Settlement in Nairobi, Kenya.

Typhoid fever burden can vary over time. Long-term data can inform prevention strategies; however, such data are lacking in many African settings. We reexamined typhoid fever incidence and antimicrobial resistance (AMR) over a 10-year period in Kibera, a densely populated urban informal settlement where a high burden has been previously described. We used data from the Population Based Infectious Diseases Surveillance platform to estimate crude and adjusted incidence rates and prevalence of AMR in nearly 26,000 individuals of all ages. Demographic and healthcare-seeking information was collected through household visits. Blood cultures were processed for patients with acute fever or lower respiratory infection. Between 2010 and 2019, 16,437 participants were eligible for blood culture and 11,848 (72.1%) had a culture performed. Among 11,417 noncontaminated cultures (96.4%), 237 grew Salmonella enterica serovar Typhi (2.1%). Overall crude and adjusted incidences were 95 and 188 cases per 100,000 person-years of observation (pyo), respectively. Annual crude incidence varied from 144 to 233 between 2010 and 2012 and from 9 to 55 between 2013 and 2018 and reached 130 per 100,000 pyo in 2019. Children 5-9 years old had the highest overall incidence (crude, 208; adjusted, 359 per 100,000 pyo). Among isolates tested, 156 of 217 were multidrug resistant (resistant to chloramphenicol, ampicillin, and trimethoprim/sulfamethoxazole [71.9%]) and 6 of 223 were resistant to ciprofloxacin (2.7%). Typhoid fever incidence resurged in 2019 after a prolonged period of low rates, with the highest incidence among children. Typhoid fever control measures, including vaccines, could reduce morbidity in this setting.

Pathogen surveillance in the informal settlement, Kibera, Kenya, using a metagenomics approach.

BACKGROUND: Worldwide, the number of emerging and re-emerging infectious diseases is increasing, highlighting the importance of global disease pathogen surveillance. Traditional population-based methods may fail to capture important events, particularly in settings with limited access to health care, such as urban informal settlements. In such environments, a mixture of surface water runoff and human feces containing pathogenic microorganisms could be used as a surveillance surrogate. METHOD: We conducted a temporal metagenomic analysis of urban sewage from Kibera, an urban informal settlement in Nairobi, Kenya, to detect and quantify bacterial and associated antimicrobial resistance (AMR) determinants, viral and parasitic pathogens. Data were examined in conjunction with data from ongoing clinical infectious disease surveillance. RESULTS: A large variation of read abundances related to bacteria, viruses, and parasites of medical importance, as well as bacterial associated antimicrobial resistance genes over time were detected. Significant increased abundances were observed for a number of bacterial pathogens coinciding with higher abundances of AMR genes. Vibrio cholerae as well as rotavirus A, among other virus peaked in several weeks during the study period whereas Cryptosporidium spp. and Giardia spp, varied more over time. CONCLUSION: The metagenomic surveillance approach for monitoring circulating pathogens in sewage was able to detect putative pathogen and resistance loads in an urban informal settlement. Thus, valuable if generated in real time to serve as a comprehensive infectious disease agent surveillance system with the potential to guide disease prevention and treatment. The approach may lead to a paradigm shift in conducting real-time global genomics-based surveillance in settings with limited access to health care.