Cholera Outbreak Linked with Lack of Safe Water Supply Following a Tropical Cyclone in Pondicherry, India, 2012

In the aftermath of a severe cyclonic storm on 7 January 2012, a cluster of acute diarrhoea cases was reported from two localities in Pondicherry, Southern India. We investigated the outbreak to identify causes and recommend control measures. We defined a case as occurrence of diarrhoea of more than three loose stools per day with or without vomiting in a resident of affected areas during 6-18 January 2012. We used active (door-to-door survey) and stimulated passive (healthy facility-based) surveillance to identify cases. We described the outbreak by time, place, and person. We compared the case-patients with up to three controls without any apparent signs and symptoms of diarrhoea and matched for age, gender, and neighbourhood. We calculated matched odds ratio (MOR), 95% confidence intervals (CI), and population attributable fractions (PAF). We collected rectal swabs and water samples for laboratory diagnosis and tested water samples for microbiological quality. We identified 921 cases and one death among 8,367 residents (attack rate: 11%, case-fatality: 0.1%). The attack rate was the highest among persons of 50 years and above (14%) and females (12%). The outbreak started on 6 January and peaked on the 9th and lasted till 14 January. Cases were clustered around two major leakages in water supply system. Nine of the 16 stool samples yielded V. cholerae O1 Ogawa. We identified that consumption of water from the public distribution system (MOR=37, 95% CI 4.9-285, PAF: 97%), drinking unboiled water (MOR=35, 95% CI 4.5-269, PAF: 97%), and a common latrine used by two or more households (MOR=2.7, 95% CI 1.3-5.6) were independently associated with cholera. Epidemiological evidence suggested that this outbreak was due to ingestion of water contaminated by drainage following rains during cyclone. We recommended repair of the water supply lines, cleaning-up of the drains, handwashing, and drinking of boiled water

A single weighting approach to analyze respondent-driven sampling data

Background and objectives: Respondent-driven sampling (RDS) is widely used to sample hidden populations and RDS data are analyzed using specially designed RDS analysis tool (RDSAT). RDSAT estimates parameters such as proportions. Analysis with RDSAT requires separate weight assignment for individual variables even in a single individual; hence, regression analysis is a problem. RDS-analyst is another advanced software that can perform three methods of estimates, namely, successive sampling method, RDS I and RDS II. All of these are in the process of refinement and need special skill to perform analysis. We propose a simple approach to analyze RDS data for comprehensive statistical analysis using any standard statistical software. Methods: We proposed an approach (RDS-MOD - respondent driven sampling-modified) that determines a single normalized weight (similar to RDS II of Volz-Heckathorn) for each participant. This approach converts the RDS data into clustered data to account the pre-existing relationship between recruits and the recruiters. Further, Taylor's linearization method was proposed for calculating confidence intervals for the estimates. Generalized estimating equation approach was used for regression analysis and parameter estimates of different software were compared. Results: The parameter estimates such as proportions obtained by our approach were matched with those from currently available special software for RDS data. Interpretation & conclusions: The proposed weight was comparable to different weights generated by RDSAT. The estimates were comparable to that by RDS II approach. RDS-MOD provided an efficient and easy-to-use method of estimation and regression accounting inter-individual recruits' dependence

Factors associated with recovery among C-CHIKV patients after five months following outbreak investigation.

*<p>In the multiple regression analysis.</p

Median HRQoL scores by group.

<p>Each domain’s score was significantly different at 5% level (p<0.001).</p>*<p>Physical functioning; Role physical; Bodily pain & General health^10.</p>**<p>Role emotional; Social functioning; Vitality & Mental health^10.</p

HRQoL scores of clinically recovered C-CHIKV patients according to duration of disease free days (healthy days) at five months of follow-up.

<p>HRQoL scores of clinically recovered C-CHIKV patients according to duration of disease free days (healthy days) at five months of follow-up.</p

Signs and symptoms reported by C-CHIKV patients^* according to clinical status.

*<p>Includes early and late symptoms experienced by the patients.</p

Selection of study participants.

<p>Selection of study participants.</p

Socio-demographic and economic characteristics of study population.

<p>Socio-demographic and economic characteristics of study population.</p