| Literature DB >> 29382360 |
James E Wright1,2,3, Marleen Werkman4,5,6, Julia C Dunn4,5, Roy M Anderson4,5,6.
Abstract
BACKGROUND: The human helminth infections include ascariasis, trichuriasis, hookworm infections, schistosomiasis, lymphatic filariasis (LF) and onchocerciasis. It is estimated that almost 2 billion people worldwide are infected with helminths. Whilst the WHO treatment guidelines for helminth infections are mostly aimed at controlling morbidity, there has been a recent shift with some countries moving towards goals of disease elimination through mass drug administration, especially for LF and onchocerciasis. However, as prevalence is driven lower, treating entire populations may no longer be the most efficient or cost-effective strategy. Instead, it may be beneficial to identify individuals or demographic groups who are persistently infected, often termed as being "predisposed" to infection, and target treatment at them.Entities:
Keywords: Ascaris; Helminths; Hookworm; Predisposition; Schistosomiasis; Systematic review; Trichuris
Mesh:
Substances:
Year: 2018 PMID: 29382360 PMCID: PMC5791198 DOI: 10.1186/s13071-018-2656-4
Source DB: PubMed Journal: Parasit Vectors ISSN: 1756-3305 Impact factor: 3.876
Fig. 1PRISMA diagram summarising inclusion and exclusion of all identified papers
Fig. 2Map showing the geographical distribution of all included papers (n = 43). The size of each circle is proportional to the total number of studies conducted in each country. The sections of each circle represent each helminth species investigated in that country
Fig. 3Distribution of publication year for the included papers (n = 43)
Frequency of definitions for predisposition utilised within the included papers
| Definition of predisposition | No. of papersa |
|---|---|
| Consistently infected at multiple time points | 11 |
| Consistent high intensity infections at multiple time points | 9 |
| Pre-treatment infection status as predictor for future infection | 6 |
| Pre-treatment intensity of infection amongst those infected post-treatment | 4 |
| Clustering of high-intensity infections within households | 3 |
| Consistent high intensity infections amongst family members | 2 |
| Consistently infected at same intensity level across multiple time points | 2 |
| Association between pre-treatment and post-treatment worm burdens | 1 |
| Clustering of infections within households | 1 |
| Consistently high intensity infections within families at multiple time points | 1 |
| Correlation between pre-treatment infection/intensity and reinfection intensity | 1 |
| Familial aggregation of re-infection | 1 |
| Individual predisposition | 1 |
| Ratio of proportion predicted to be infected and observed proportion | 1 |
| Trend in reinfection with increasing pre-treatment intensity | 1 |
| Whether those heavily infected at baseline were more likely to be infected at follow-up | 1 |
aTotal may exceed 43 due to some papers using multiple definitions
Frequency of statistical tests used to determine presence of predisposition within the included papers
| Statistical test used | No. of papersa |
|---|---|
| Kendall’s tau | 13 |
| Spearman’s rank | 8 |
| Logistic regression | 4 |
| Chi-square | 4 |
| ANOVA | 2 |
| Chi-square test for trend | 1 |
| Comparing percentage of participants with high egg count with percentage of houses they were present in | 1 |
| Comparison of percentages | 1 |
| Correlation (no further details provided) | 1 |
| Fisher’s exact test | 1 |
| Looking at association | 1 |
| Multiple regression | 1 |
| Odds ratio | 1 |
| Ranked correlation coefficient | 1 |
| Ratio of observed proportion infected and predicted proportion | 1 |
| Relative risks | 1 |
| Three-level hierarchical statistical model applied to worm counts | 1 |
| Transitional probability matrix | 1 |
| Variance components analysis | 1 |
aTotal may exceed 43 due to some papers employing more than one statistical technique