Risk factors for Cryptosporidium infection in low and middle income countries: A systematic review and meta-analysis.

Risk factors for Cryptosporidium infection in low and middle income countries: A systematic review and meta-analysis.

Blog Article

BackgroundCryptosporidium infection causes gastrointestinal disease and has a worldwide distribution.The highest burden is in developing countries.ObjectivesWe sought to conduct a systematic review and meta-analysis to identify Cryptosporidium risk factors in Low and Middle Income countries (LMICs).

MethodsMedline Ovid and Scopus databases were searched with no restriction on year or language of publication.All references were screened independently in duplicate and were included if they presented data on Dice Accessories at least 3 risk factors.Meta-analyses using random effects models were used to calculate overall estimates for each exposure.

ResultsThe most frequently reported risk factors in the 15 included studies were overcrowding, household diarrhoea, poor quality drinking water, animal contact, open defecation/ lack of toilet and breastfeeding.The combined odds ratio for animal contact was 1.98 (95%CI: 1.

11-3.54) based on 11 studies and for diarrhoea in the household 1.98 (95%CI: 1.

13-3.49) based on 4 studies.Open defecation was associated with a pooled odds ratio of 1.

82 (95%CI: 1.19-2.8) based on 5 studies.

Poor drinking water quality was not associated with a significant Cryptosporidium risk, odds ratio 1.06 (95%CI: 0.77-1.

47).Breastfeeding was protective with pooled odds ratio 0.4 (95%CI: 0.

13-1.22), which was not statistically significant.ConclusionsBased on the included studies, crowded living conditions, Cartridges animal contact and open defecation are responsible for the majority of Cryptosporidium cases in LMICs.

Future studies investigating Cryptosporidium risk factors should have a good study design and duration, include appropriate number of cases, select suitable controls, investigate multiple relevant risk factors, fully report data and perform multivariate analysis.