Transmission of Cryptosporidium Species Among Human and Animal Local Contact Networks in Sub-Saharan Africa: A Multicountry Study

Abstract

Background: Cryptosporidiosis has been identified as one of the major causes of diarrhea and diarrhea-associated deaths in young children in sub-Saharan Africa. This study traces back Cryptosporidium-positive children to their human and animal contacts to identify transmission networks.

Methods: Stool samples were collected from children < 5 years of age with diarrhea in Gabon, Ghana, Madagascar, and Tanzania. Cryptosporidium-positive and -negative initial cases (ICs) were followed to the community, where stool samples from households, neighbors, and animal contacts were obtained. Samples were screened for Cryptosporidium species by immunochromatographic tests and by sequencing the 18S ribosomal RNA gene and further subtyped at the 60 kDa glycoprotein gene (gp60). Transmission clusters were identified and risk ratios (RRs) calculated.

Results: Among 1363 pediatric ICs, 184 (13%) were diagnosed with Cryptosporidium species. One hundred eight contact networks were sampled from Cryptosporidium-positive and 68 from negative ICs. Identical gp60 subtypes were detected among 2 or more contacts in 39 (36%) of the networks from positive ICs and in 1 contact (1%) from negative ICs. In comparison to Cryptosporidium-negative ICs, positive ICs had an increased risk of having Cryptosporidium-positive household members (RR, 3.6 [95% confidence interval {CI}, 1.7-7.5]) or positive neighboring children (RR, 2.9 [95% CI, 1.6-5.1]), but no increased risk of having positive animals (RR, 1.2 [95% CI, .8-1.9]) in their contact network.

Conclusions: Cryptosporidiosis in rural sub-Saharan Africa is characterized by infection clusters among human contacts, to which zoonotic transmission appears to contribute only marginally.

Keywords: Africa; cryptosporidium; molecular epidemiology; transmission.

Figure 1.

Proportion of Cryptosporidium species cases for initial patients and human contacts (household contacts and neighboring children), stratified by age.

Figure 2.

Frequency of Cryptosporidium gp60 subtypes at the 4 study sites. Frequencies of gp60 subtypes at the 4 study sites are presented for humans (first number) and animals (second number) along with strain and study site totals. The heatmap displays the lowest frequencies in yellow and the highest frequencies in red.

Figure 3.

Occurrence of gp60 subtypes over time at the 4 study sites. Gray bars represent Cryptosporidium species infections of unknown subtype. Black lines represent monthly precipitation (in millimeters, z-axis).

Figure 4.

Transmission clusters of individual study subjects (circles) with identical gp60 subtypes among contact networks at the different study sites. Cluster structures are displayed, including study subjects infected with the same Cryptosporidium species but lacking gp60 data, which could potentially be related to the detected cases. The occurrence of differing, non-cluster-defining Cryptosporidium species within networks is also shown. Abbreviations: AC, animal contact; C., Cryptosporidium; GA, Gabon; GH, Ghana; HC, household contact; IC, initial case; MG, Madagascar; NC, neighboring child; NW-ID, network identifier as defined in Supplementary Figure 1; TZ, Tanzania.