| Literature DB >> 27078156 |
Kalwaje Eshwara Vandana1, Chiranjay Mukhopadhyay1, Chaitanya Tellapragada1, Asha Kamath2, Meghan Tipre3, Vinod Bhat2, Nalini Sathiakumar3.
Abstract
BACKGROUND: Although melioidosis, is an important disease in many Southeast Asian countries and Australia, there is limited data on its prevalence and disease burden in India. However, an increase in case reports of melioidosis in recent years indicates its endemicity in India. AIMS AND METHODS: A population-based cross-sectional seroprevalence study was undertaken to determine the seroprevalence of B. pseudomallei by indirect haemagglutination assay and to investigate the associated risk determinants. Subjects were 711 adults aged 18 to 65 years residing in Udupi district, located in south-western coast of India. KEYEntities:
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Year: 2016 PMID: 27078156 PMCID: PMC4831803 DOI: 10.1371/journal.pntd.0004610
Source DB: PubMed Journal: PLoS Negl Trop Dis ISSN: 1935-2727
Number and proportion of subjects with potential risk factors by seropositive status.
| Potential risk factor | Seropositivity | Seronegativity (<20) N | Crude POR | P-value |
|---|---|---|---|---|
| Male | 84 | 324 | 1.0 | <0.001 |
| Female | 122 | 181 | ||
| Mean | 37.79 ±12.38 | 37.52 ±12.16 | - | 0.785 |
| Median | 36.5 | 36.0 | - | |
| ≤30 | 70 | 170 | 1.0 | 0.895 |
| 31–40 | 57 | 139 | 0.99 (0.65–1.50) | |
| 41–50 | 47 | 106 | 1.07 (0.69–1.67) | |
| >50 | 32 | 90 | 0.87 (0.53–1.42) | |
| Famers | 34 | 90 | 1.0 | 0.675 |
| Non-Farmers | 172 | 415 | 0.92 (0.59–1.42) | |
| Professional | 15 | 31 | 1.0 | 0.543 |
| Sales worker | 8 | 25 | 0.66 (0.24–1.81) | |
| Production worker | 48 | 144 | 0.68 (0.34–1.38) | |
| Agricultural worker | 35 | 87 | 0.83 (0.40–172) | |
| Other worker | 100 | 216 | 0.95 (0.49–1.85) | |
| Missing | - | 2 | - | |
| No | 190 | 458 | 1.0 | 0.320 |
| Yes | 12 | 42 | 0.68 (0.35–1.33) | |
| Refused to answer | 4 | 5 | - | |
| No | 182 | 425 | 1.0 | 0.350 |
| Yes | 15 | 52 | 0.67 (0.37–1.22) | |
| Refused to answer | 9 | 28 | - | |
| No | 167 | 397 | 1.0 | 0.464 |
| Yes | 39 | 108 | 0.8 (0.57–1.29) | |
| No | 179 | 419 | 1.0 | 0.194 |
| Yes | 27 | 86 | 0.73 (0.46–1.17) | |
| No | 98 | 303 | 1.0 | |
| Yes | 108 | 202 | ||
| No | 202 | 505 | 1.0 | |
| Yes | 4 | 0 | ||
| Always | 173 (29.2) | 420 | 1.0 | 0.651 |
| Sometimes | 33 (28.0) | 85 | 0.78 (0.12–3.86) | |
| No | 169 | 400 | 1.0 | 0.392 |
| Yes | 37 | 105 | 0.84 (0.55–1.28) | |
| No | 187 | 433 | 1.0 | 0.026 |
| Yes | 4 | 24 | 0.38 (0.13–1.12) | |
| Do not know | 15 | 48 | ||
| No | 186 | 442 | 1.0 | 0.449 |
| Yes | 6 | 17 | 0.83 (0.32–2.16) | |
| Do not know | 14 | 46 |
*Comparison using student’s t-test
**Crude Prevalence Odds ratio.
# Seropositive status based on antibody titer levels. Titer levels ≥ 20 are considered seropositive.
a includes technical administrative, and managerial occupations
bIncludes skilled and unskilled manual occupations
Prevalence odd ratio (POR) and 95% confidence intervals for seropositivity and potential risk factors, multiple logistic regression models.
| Risk factor | Gender | Wash clothes in river | Gardening | Religion | Age |
|---|---|---|---|---|---|
| 2.51 | |||||
| POR | 1.70–3.53 | ||||
| 2.61 | 3.3×109 | ||||
| POR | 1.07–6.36 | 0-∞ | |||
| 2.28 | 3.3×109 | 1.21 | |||
| POR | 1.57–3.30 | 0-∞ | 0.83–1.75 | ||
| 2.01 | 3.02×109 | 1.29 | 0.53 | ||
| POR | 1.37–2.95 | 0-∞ | 0.88–1.88 | 0.33–0.85 | |
| 2.17 | 2.91×109 | 1.41 | 0.50 | Agecat 1: 1 | |
| POR | 1.46–3.23 | 0-∞ | 0.95–2.08 | 0.31–0.81 | Agecat 2: 0.68 (0.44–1.07) |
| Agecat 3: 0.69 (0.42–1.12) | |||||
| Agecat 4: 0.51 (0.29–0.87) |
#Age categories: Category 1: ≤30 years, category 2: 31–40 years, category 3: 41–50 years, category 4: >50 years.
*POR for a risk factor adjusted for all other variables in the model.
Fig 1Mapping of proportion of seropositivity from sampled population in Udupi District in Karnataka India