Trend of &i&Cryptosporidium&/i& Infection among Children below 24 Months in an Informal Urban Settlement, Kenya

摘要

Cryptosporidium infection is estimated to cause 2.9 million diarrheal cases yearly among children aged under 24 months in sub-Saharan Africa. Studies have shown long-term climatic variations can affect infectious diseases. The burden of cryptosporidiosis in rural areas of sub-Saharan Africa is well characterized. However, the trend of Cryptosporidium infection is not known, especially in informal urban settings. This study therefore sought to determine cryptosporidiosis trends, and further explore the association between year and Cryptosporidium infection among children below 24 months in Kibera urban informal settlement in Kenya. Data collected by the Kenya Medical Research Institute longitudinal study in Tabitha clinic in Kibera from 2009 to 2015 were used. At least 3000 children aged < 24 months receive free health care at the clinic. In the longitudinal study , children presenting with diarrhea were eligible for stool sample collection ( n = 477), out of which 421 stool samples were tested using TaqMan &#8482; Array Card (TAC) polymerase chain reaction panel that included a target for Cryptosporidium genus. Data for the 421 children were included in the analysis. Logistic regression was used to explore the difference between the seven years and cryptosporidiosis. Overall, the pooled data indicated that 23.5% of the children who were tested had Cryptosporidium infection, with the highest proportions of Cryptosporidium- positive cases observed in 2015 (45.2%). The logistic regression results also indicated that children who were tested in the year 2015 were more likely to have Cryptosporidium infection (OR = 3.39; 95% CI: 1.44 - 7.96; p = 0.005) than those in 2009. Watery stool was also found to be an important symptom of cryptosporidiosis. There was a high prevalence of Cryptosporidium infection among young children, especially in the most recent year. Routine testing of Cryptosporidium infection using molecular methods, constant monitoring and identification of the infection sources is therefore necessary towards reducing the disease burden in the low resource settings.