A Retrospective Audit of Widal Testing For Enteric Fever in the City Of Ahmedabad

Introduction: Widal test has been used extensively for the sero-diagnosis of Enteric fever in India, however, its accuracy and reliability are debatable. We studied widal testing and widal positivity rates in the entire city of Ahmedabad for the diagnosis of Enteric Fever. Methods We screened all 1700 possible diagnostic laboratory facilities, in Ahmedabad, in the public and private sector. We performed telephonic surveys for the initial filtering of facilities that could be conducting widal testing. It was followed by physical visits to probable facilities to confirm testing methods and preservation of reports of widal testing. We followed a systematic process for screening and selection of 23 laboratories, which conducted widal tests and had reliable data. While 14 laboratories refused to share data, data provided by three of them were inappropriate and couldn’t be used.  We finally analyzed data from four large public hospitals, one private trust hospital and one corporate laboratory for variable periods in a span of 15 years (2000 – 2015). Result: The Widal testing rate was found to be 8.7% and widal positivity as 12.5% in a sample of 1.2 million clinically suspected in-patients. In 15 years, the private hospital had admitted 1/10th as many cases as all the public hospitals together. However, the widal testing and positivity rates were similar in both. We observed a lower proportion of widal positivity among children below 12 years and a disproportionate, but insignificant, gender distribution of widal positivity. Conclusion: This study indicates that the widal test, which is meant to be an initial screening test, is widely used in the city. We propose linkage of testing and reporting of widal with other more reliable and accurate tests such as Typhidot and blood culture in order to strengthen our knowledge of enteric fever epidemiology in India

Anti-microbial Resistance surveillance in typhoidal Salmonella in Ahmedabad

Objective To report on (i) the health care eco-system that produces data on AMR, and (ii) pattern of resistance in typhoidal Salmonellae isolates in the city of Ahmedabad in western India. Introduction India carries the highest burden of Enteric Fever in the world. This is further aggravated by the high prevalence of antimicrobial resistance (AMR) in typhoidal Salmonellae. The strategy to combat resistance has been to combine and cycle anti-microbials based on the regional AMR pattern of the organism. But this requires that resistance patterns and genetic mechanisms are mapped at a regional level and regularly recorded and disseminated by a national surveillance system. Methods Through municipality records and internet searches we identified 1696 private and 83 public labs. Our screening of these yielded 4 public medical colleges, 4 private healthcare-institution-attached labs, and 4 corporate labs which were probably performing culture and antibiotic sensitivity testing (AST). Only 2 public medical colleges and 1 corporate lab shared their data with us (Fig 1). There was considerable variation in culturing and sensitivity testing methodology across labs. Results Out of 51,260 blood cultures, Salmonellae isolates were detected in only 146 (0.28%); 67 (54%) of these were resistant. Multi-drug resistance was absent. The extremely low isolation rates in our three facilities may be indicative of lower referral rates of suspected patients for blood culture or, possibly, lower incidence of Salmonella infection in Ahmedabad. Anti-microbial susceptibility testing (AST) was conducted on 124 isolates, of which 67 (54%) were found resistant. Multi-drug resistance was absent, but ciprofloxacin resistance varied widely between the private and public sector labs. The minimal resistance to 3rd generation cephalosporins probably indicates initiation of resistance to this important group of antibiotics in the city's typhoidal salmonella. Notably, isolates from the private sector lab showed complete resistance to azithromycin. Concurrent resistance to more than 1 antibiotic was very high, 88%, amongst the 67 resistant isolates. Although we were unable to estimate the true size of salmonella positivity against total blood cultures in our city, the difference in proportion of AMR isolates reported in our public and private samples, 30% vs 100%, is important because it may be indicative of high levels of AMR in the private. Notably, isolates from the public sector showed higher resistance to Ciprofloxacin and from private sector showed complete resistance to Azithromycin. The higher Ciprofloxacin resistance in the public sector may be indicative of more usage of the relatively cheaper ciprofloxacin among public hospital clientele. The 100% resistance to azithromycin seen in our private sample is a significant finding, and has also been reported in another recent study from Ahmedabad [1]. Out of approximately 1779 big and small facilities in Ahmedabad, we identified 12 (4 public and 8 private) laboratories which had the ability to report AMR in typhoidal salmonella. 2 public and 4 private refused to share data with us. Based on data shared by 3 medium-sized private facilities, we believe that salmonella isolation and testing in private health-institution-attached laboratories is negligible. Our data collection efforts over one year led to reasonable volume of data from only 2 publicly funded teaching hospitals and 1 private standalone lab. Although all facilities claimed to follow CLSI guidelines, the total number of antibiotics tested at each facility varied. Minimum inhibitory concentration to assess extent of resistance was not reported by any of the labs. The publicly-funded teaching hospitals in the city have the largest concentration of microbiologists and the motivation to test for AMR in indoor patients. But they did not consistently test all isolates against all antibiotics in their list. The proportion of private hospitals and laboratories that conduct ASTs in Ahmedabad is relatively small. For individual labs, both private and public, there is no inherent incentive to detect city-level AMR patterns or subsequent molecular level mechanisms of transmission of resistance. This lack of enthusiasm among microbiologists to further process their samples through more specialized lab testing and analysis is an issue in other parts of the world too [2]. Thus patchy performance of AST and incomparability of sensitivity across labs results in poor surveillance [3]. The antibiotic regimen currently recommended by ICMR for treatment of Enteric Fever in the entire country is based on 209 Salmonella isolates from only four public institutes [4]. Across India’s cities and towns, there are several hundreds of public and private hospitals and laboratories undertaking ASTs, just like the ones in Ahmedabad presented in this study [5]. Unless practitioners are guided by regional data on resistance in endemic organisms, uninformed prescription practices will worsen existing microbial resistance. Drawing these varied facilities, or at least a representative sample of them into a cohesive network is essential for surveillance of antimicrobial resistance in all major bacterial pathogens; particularly so for typhoidal Salmonella which are endemic in our part of the world and are primarily exposed to antibiotics consumed by humans since they are obligate human parasites. Only a representative network of labs will provide the contextualized and stratified data necessary for development of the most accurate strategy to formulate regional prescription guidelines. However, this is an enormous challenge in our setting. Conclusions High resistance to Ciprofloxacin and Azithromycin in Ahmedabad may be due to increased use of these two antibiotics in the public and private sectors respectively. But they are in need of further molecular characterization. Clinical microbiological methods lack uniformity and laboratory referral networks are not developed even in large cities of India. Although some useful data is produced by a few individual labs, the crucial exercise of meaningful networking for effective surveillance remains. As we enter an era of internationally linked anti-microbial resistance surveillance systems, the biggest challenge lies in selecting performing laboratories and inducing them to integrate with it. References 1)   Jeeyani HN, Mod HK, Tolani JN. Current perspectives of enteric fever : a hospital based study of 185 culture positive cases from Ahmedabad , India. 2017;4: 4–9. 2) Petti CA, Polage CR, Quinn TC, Ronald AR, Sande MA. Laboratory Medicine in Africa : A Barrier to Effective Health Care. Clin Infect Dis. 2006;42: 377–382. 3) Masterton RG. Surveillance studies: how can they help the management of infection? J Antimicrob Chemother. 2000;46: 53–58. doi:10.1093/jac/46.suppl_2.53 4) ICMR. Treatment Guidelines for Antimicrobial Use in Common Syndromes Indian Council of Medical Research Department of Health Research New Delhi , India. 2017; 5) Gandra S, Merchant AT, Laxminarayan R. A role for private sector laboratories in public health surveillance of antimicrobial resistance. Future Microbiol. 2016;11: 709–712. doi:10.2217/fmb.16.1

Effect of climate on Enteric Fever incidence in Ahmedabad, India

ObjectiveThis study is an attempt to explore the relationship of EF incidence with climate variables and ENSO events in the seventh most populous city in India.IntroductionEnteric fever (EF) is a grave systemic infection, which has been controlled quite effectively in developed countries, but continues to be a grave public health concern for India. Environmental drivers such as rainfall, temperature, relative humidity and El Niño-Southern Oscillations (ENSO) are known to influence the transmission of Salmonella typhi and paratyphi. India possesses the largest population burden of EF, yet very few studies have explored its climatic associations.MethodsWe analyzed address-confirmed widal positive, monthly EF cases reported by Ahmedabad Municipal Corporation and local climate data recorded by the Meteorology Office from 1986-2017. EF incidence trend in the city was cross validated using EF monthly reports from one large public hospital and from private reports. We also collected data for Temperature, Humidity and Rainfall from Meteorological Centre of Ahmedabad, population data from Census department, and identified IOD and ENSO events from National Oceanic and Atmospheric Administration (NOAA) for the same period.ResultsOur study recorded 29,808 Widal positive cases for 32 years. EF incidence trend over last 32 years showed a decadal pattern. Initial study period (1986-1995) showed higher and erratic case rates, while cases were more restrained during the last decade (1995-2005), although a steady rise is persisting. We also observed a consistent rise in EF cases in the last 8 years (Fig 1).Analysis of annual pattern of monthly-normalized EF cases revealed a bimodal distribution of peaks, in the month of June and September. Peaks of EF cases showed a lag and lead of one month with Tmax and Tmin. The first EF peak in June lagged the Tmax peak in May by a month and the second EF peak in September led the Tmax peak in October by a month. The second peak of EF cases in September coincided with the peak humidity in the same month. The dip between the two EF peaks coincided with maximum rainfall peak in July (Fig 2 a,b,c). Spearman’s rank correlation showed a small positive but significant correlation between monthly EF case rates and climate variables (Tab 1). A Poisson model showed significant but weak association between EF incidence and all climate variables - Tmin, RH and Rainfall. In our study T max had the strongest association with EF cases, wherein an increase of one case was accompanied by a 0.1°C increase of the Tmax (Tab 2).Over the 32 years, there were 4 strong and 4 moderate El Nino years, 5 strong and 2 moderate La Nina years and 17 neutral years. Figure 3 shows that except for the two El Nino years which coincided with positive IOD events, the remaining six El Nino years experienced a subdued rainfall. Six out of seven La Nina years experienced high rainfall. The early El Nino events of 1986, 1987, 1991 and the most recent one of 2015 exhibit a trend of low rainfall and high cases. This trend is diluted in the middle El Nino years, 1994, 1997, 2002 and 2009 showing high and low rainfall and relatively lesser annual case rates. Although the highest case rate was recorded in a La Nina year - 59/100,000 in 1988, average case rates were highest for El Nino years (25.5), lower for La Nina (20.5) and lowest for Neutral years (17.6). However, we were unable to establish any statistical significance between average EF case rates during each of these periods. A spearman correlation between EF cases and rainfall was small but significant for El Nino (rs= 0.35, p=0.001) and for neutral years (rs= 0.20, p= 0.004), but not for La Nina years. A repeated measures ANOVA analysis showed no significant difference in average EF cases during the three ENSO categories, however visual profile plot (Fig 4) of estimated marginal monthly means over the year showed distinct differences – early rise and peaking of cases in the El Nino and La Nina years, and a much more restrained rise without conspicuous peaks in Neutral years.The 2 positive IOD events that occurred along with the strong El Nino events in 1994 and 1997 may have led to lowering of case rates during El Nino years, and thus the lack of a significant increase in EF incidence rates. But this could also be due to the fact that our analysis, unlike a time series analysis, has used an El Nino year as a variable, which does not accommodate the fact that El Nino does not run by a calendar year. We were unable to conduct a geospatial analysis which may have better correlated our data with temperature and rainfall intensity during the three ENSO phases in our region. Uneven development of urban infrastructure would also influence rates of illness. Furthermore, the cases reported to the epidemic cell were based on Slide and/or Tube Widal positive tests which is considered a poor diagnostic test. Despite these numerous and at times opposing factors influencing trends of EF, the upswing in case incidence rate early in the El Nino and La Nina years, when the weather is still balmy and water shortages haven’t yet begun in the city, merits deeper investigation.ConclusionsFuture control strategies for EF need to consider the influence of local environment, geographical climate variation and seasonal patterns. This relationship between ENSO events and EF cases needs to be investigated with larger and longer data sets from different cities and towns in the sub-continent. One of the limitation of our study is we need longer and larger, spatially distributed dataset of EF incidences to associate it better with climate phenomena