Geography and prevalence of rickettsial infections in Northern Tamil Nadu, India: a cross-sectional study.

Rickettsial infections and Q fever are a common cause of acute febrile illness globally. Data on the role of climate and altitude on the prevalence of these infections in lacking from Southern India. In this study, we determined the sero-prevalence of scrub typhus (ST), spotted fever (SF), murine typhus (MT) and Q Fever (QF) in 8 eight geographical regions of North Tamil Nadu by detecting IgG antibodies using ELISA. Totally we tested 2565 people from 86 localities. Among the 27.3% positives, approximately 5% were IgG positive for two or more infections. Sero-prevalence to rickettsioses and Q fever was highest for individuals from rural areas and increased with age (> 30 years). Those in the Nilgiris highlands (wetter and cooler) and Erode, which has the most land under irrigation, demonstrated the least exposure to rickettsioses and Q fever. Lowland plains (AOR: 8.4-22.9; 95% CI 3.1-55.3) and highland areas up to 1000 m (AOR: 6.1-10.3; 95% CI 2.4-23.9) showed the highest risk of exposure to scrub typhus. For spotted fever, the risk of exposure was highest in Jawadhi (AOR:10.8; 95% CI 2.6-44.3) and Kalrayan (AOR:16.6; 95% CI 4.1-66.2). Q fever positivity was most likely to be encountered in Salem (AOR: 5.60; 95% CI 1.01-31.08) and Kalrayan hills (AOR:12.3; 95% CI 2.9-51.6). Murine typhus risk was significant only in Tiruvannamalai (AOR:24.2; 95% CI 3.3-178.6). Our study suggests that prevalence of rickettsial infections and Q fever is low in areas which receive rainfall of ≥ 150 cm/year, with average minimum and maximum temperatures between 15 and 25 °C and elevation in excess of 2000 m. It is also less in well irrigated lowlands with dry climate. These preliminary findings need confirmation by active surveillance in these areas.

Using global positioning system technology and Google My Maps in follow-up studies-An experience from influenza surveillance study, Chennai, India.

A multi-centric influenza surveillance conducted among 1500 elderly participants in Chennai, India, required weekly visits to the participants regularly for three years. Difficulties were faced in locating and navigating to households of the participants due to vast study area, adverse weather conditions and staff attrition, which affected data quality. To overcome these difficulties, we devised a new way of using the 'Global Position System' (GPS) and 'Google My Maps'. GPS coordinates of all participants' households were collected and merged with their demographic data using 'Microsoft excel'. Dataset was uploaded to 'Google My Maps' in appropriate layers. This map was used to locate and navigate to households of the participants and the average working hours in the field reduced by18% even in difficult circumstances. The average number of supervisory visits increased by 150%. This method will greatly facilitate the data collection in cohort based research studies.