| Literature DB >> 27296630 |
Allison Golden1, Dunia Faulx1, Michael Kalnoky1, Eric Stevens1, Lindsay Yokobe1, Roger Peck1, Potochoziou Karabou2, Méba Banla3, Ramakrishna Rao4, Kangi Adade2, Richard G Gantin3, Kossi Komlan3, Peter T Soboslay3,5, Tala de Los Santos1, Gonzalo J Domingo6.
Abstract
BACKGROUND: Diagnostics provide a means to measure progress toward disease elimination. Many countries in Africa are approaching elimination of onchocerciasis after successful implementation of mass drug administration programs as well as vector control. An understanding of how markers for infection such as skin snip microfilaria and Onchocerca volvulus-specific seroconversion perform in near-elimination settings informs how to best use these markers.Entities:
Keywords: Diagnostics; IgG4; Neglected tropical diseases; Onchocerciasis; Seroconversion
Mesh:
Substances:
Year: 2016 PMID: 27296630 PMCID: PMC4907250 DOI: 10.1186/s13071-016-1623-1
Source DB: PubMed Journal: Parasit Vectors ISSN: 1756-3305 Impact factor: 3.876
Study demographics broken out by age, total recruitment and number of participants included in the seroprevalence data analysis
| Age range | Participants recruited | Participants with Ov16 IgG4 ELISA results | ||
|---|---|---|---|---|
| Total | Microfilaria (MF)-positive (prevalence in %) | Total | Microfilaria (MF)-positive (prevalence in %) | |
| Ages 5–10 | 280 | 3 (1.1) | 193 | 3 (1.6) |
| Ages 11–20 | 528 | 5 (1.0) | 386 | 3 (0.8) |
| Ages 21–30 | 491 | 18 (3.7) | 352 | 11 (3.1) |
| Ages 31–40 | 501 | 25 (5.0) | 320 | 10 (3.1) |
| Ages 41–50 | 445 | 23 (5.2) | 293 | 13 (4.4) |
| Ages 51–60 | 287 | 10 (3.5) | 197 | 4 (2.0) |
| > 60 years of age | 391 | 9 (2.3) | 264 | 6 (2.3) |
| Total no. of participants | 2,923 | 93 (3.1) | 2,005 | 50 (2.5) |
| % female | 56 ( | 48.4 ( | 58 ( | 48 ( |
| No. of villages | 35 | 35 | ||
Fig. 1Quality control (QC) selection of specimens used in final analysis for serological response to the Ov16 antigen. Number of study participants are indicated at the top of the figure for the study performed in 2013 and the study performed in 2014 on the left and right, respectively. Progressing top to bottom, the number of specimens used in the ELISA and then remaining after each QC step are indicated
Fig. 2a Histogram of normalized absorbance (OD) for horseradish peroxidase (HRP) Ov16 enzyme-linked immunosorbent assay (ELISA) data for participants with Ov16 ELISA results (n = 2,005). b Density profile for HRP Ov16 ELISA data used for the expectations maximization (EM) mixture model. c Classification of specimens for seropositivity resulting from the EM model: 1 represents seropositive, 2 represents seronegative. d Uncertainty profile from EM specimens
Fig. 3Ov16-specific IgG4 seroprevalence (circles) and microfilaria prevalence (squares) against age groups, for study participants with Ov16 ELISA results (n = 2,005)
Fig. 4a Geographical distribution of villages surveyed; size and color intensity is related to the all-age Ov16 ELISA-positive seroprevalence. b Color-coded clustering of villages showing: left of center axis, seroprevalence across all age groups, and right of center axis, seroprevalence for study participants < 20 years of age. The seroprevalence for each age group is aligned side-by-side by village. The microfilaria (MF)-positive prevalences for the villages are shown by the red lines for the respective age groups. Village name and numerical values are provided in Additional file 1: Table S1
Fig. 5Box plots for normalized anti-Ov16 IgG4 ELISA ODs for different subgroups of ELISA-positive subjects (n = 393), as determined by expectation maximization (EM). The solid lines represent median values, the boxes the 25–75 percentiles, and whiskers the minimum to maximum range. Graph a shows data for the ≤ 15 %, 15–20 % and > 20 % seroprevalence community clusters; b shows data across age ranges (age ranges were selected statistically based on sample distribution); and c shows microfilaria (MF)-positive and MF-negative subgroups
Anti-Ov16 IgG4 seroconversion rates resulting from the reverse catalytic model fits for each age group with each village cluster shown in Fig. 6
| Village cluster by seroprevalence | 1st FOI fit | 2nd FOI fit | ||
|---|---|---|---|---|
| Age range (years) | Seroconversion rate (lambda) | Age range (years) | Seroconversion rate (lambda) | |
| ≤ 15 % | 5–25 | 0.0026 | > 25 | 0.01 |
| > 15–20 % | 5–20 | 0.0051 | > 20 | 0.02 |
| > 20 % | 5–16 | 0.0071 | > 16 | 0.02 |
The age ranges also indicate the optimal point of transition from the first force-of-infection (FOI) fit to the second FOI fit (ages 25, 20, and 16)
Fig. 6Seroprevalence against age, for the three village clusters described in Fig. 4. Two FOI curves were fitted to each cluster’s seroprevalence plot. Graph a shows FOI fits for villages with < 15 % overall seroprevalence; b shows FOI fit for villages with 15–20 % seroprevalence; and c shows FOI fits for villages with > 20 % seroprevalence. The bars for each point represent the standard deviation. The likelihood of change with respect to age, informing the FOI fits, are shown below the seroprevalence plot for each respective village cluster
2 × 2 contingency tables for anti-Ov16 antibody sensitivity and specificity calculation against microfilarial (MF) status, polymerase chain reaction (PCR) status, and the composite MF + PCR all ages and under 11 year-old
| Ov16 IgG4 all ages | Ov16 IgG4 under age 11 | ||||
|---|---|---|---|---|---|
| Positive | Negative | Positive | Negative | ||
| MF | Positive | 30 | 13 | 3 | 0 |
| Negative | 100 | 291 | 2 | 33 | |
| Total | 130 | 304 | 5 | 33 | |
| PCR | Positive | 30 | 22 | 2 | 1 |
| Negative | 100 | 282 | 3 | 32 | |
| Total | 130 | 304 | 5 | 33 | |
| MF + PCR | Positive | 39 | 26 | 4 | 1 |
| Negative | 91 | 278 | 1 | 32 | |
| Total | 130 | 304 | 5 | 33 | |