Asian Pacific Journal of Tropical Medicine

141-142

Authors: Saurabh Bhargava, Kiran Kumari, R.K. Sarin, Rajvinder Singh

After a prolonged debate and discussion, the World Health Organization (WHO) relisted ‘snakebite’ under Category ‘A’ of ‘Neglected Tropical Diseases’ (NTDs) in June 2017 hoping to diminish its burden on people. Earlier, in March 2009, WHO added snakebite to its official list of NTDs but later downgraded to ‘Other Neglected Conditions’, a list reduced in importance[1,2]. Obviously, the recent inclusion of snakebite in NTDs was due to the estimation of huge toll of deaths due to snake envenomation in tropical countries as supported by the multiple reports. This inclusion will expedite the process of developing better management protocols and infusing funds for such affected countries. However, a significant question here is whether the estimation of snakebite cases is accurate enough to publicise the authentic figures. Snakebite is a problem of resource poor tropical countries which usually lacks a solid central registry system of record keeping. Even when such record keeping is in place, yet people out there may not be aware enough for such measures and they don’t bother to register such cases. Snakebite incidences in such circumstances could definitely affect the national statistical figures of snakebite in any country. Many scientific reports and WHO reports have suggested that India leads the chart of mortality and morbidity caused by snakebites and envenomations contributing almost 50% of world’s burden by snakebites[3,4]. However, true figures remained elusive and the estimates range from 10 000 to 50 000 annual deaths in India. These estimates are usually based on extrapolation of smaller and regional studies. Nationally representative “Million Death Study” conducted by the Registrar General of India projected 45 900 annual deaths in India due to snake envenomation on the basis of statistical data generated from randomly selected 6 671 areas from 28 states and 7 Union territories of India between 2001 and 2003[5]. The WHO in 2005 has quoted similar figures about snake envenomations in India[3]. In 2008, an international study funded by the WHO estimated the global burden of snakebites to be around 1 841 000 snakebite incidences with around 94 000 annual deaths across the world[4]. They reported around 81 000 envenoming and nearly 11 000 annual deaths due to snakebite in India. On the other hand, the National Health Profile (NHP) maintained by the Central Bureau of Health Intelligence, Government of India reported average annual snakebite mortality of just 1 215 across India from 2010 to 2016[6]. This is the data that is quoted by the government for various purposes including policy making. The National Crime Records Bureau (NCRB) working under the Ministry of Home Affairs of Government of India reported an average 8 507 annual deaths due to snake envenomation across India during 2010-2016[7]. Data published by both, NHP and NCRB depict a huge difference in official figures of snakebites and is quite underreported than other scientific epidemiological studies. However, a well designed field survey conducted in Tamil Nadu in India suggested that even 50 000 snake envenomed mortality is underreported, and the actual burden of snake envenomation could be even more. This extensive field survey of 28 494 people from thirty villages in state of Tamil Nadu suggested around 10 000 annual deaths from snake envenomation[8] and it was in contrast with around 3 100 snakebite deaths suggested by nationally representative “Million Death Study” conducted by the Registrar General of India, average 44 annual deaths by NHP reports during 2010-2017, and average 638 annual deaths by NCRB from 2011 to 2015. Similarly, NCRB reported 368 deaths due to snake envenomation during 2011-2015 in the state of Haryana, whereas NHP reported only 32 deaths over same period in same region. However, another independently conducted government hospital based epidemiological profiling of the same region reported at least 130 deaths due snake envenomation in Haryana during 2011-2015[9]. Similar disparity between government records of snakebite and independently conducted surveys have been reported by researchers from West Bengal[10].

152-161

Authors: Banghan Ding, Bangjiang Fang, Jun Li, Qingquan Liu, Chuanzhu Lv, Xuezhong Yu, Xiaodong Zhao

Xiyanping injection, a traditional Chinese medicine injection made of andrographolide sulfonate, consisting of well-defined ingredients with antiviral, antibacterial, anti-inflammatory and antipyretic efficacy, has been widely used for treating infectious diseases of respiratory and digestive systems. However, its wide applications may easily lead to unreasonable clinical medication. In order to guide the precise clinical application and rational use of Xiyanping injection, experts in related fields conducted systematically literature review, evaluated and deliberated the application of Xiyanping injection in treating acute infectious diseases using evidence-based medicine method, and jointly drafted the consensus to summarize types of acute infectious diseases in children and adults that can be treated with Xiyanping injection, and recommend the intervention time, usage and dosage, course of treatment and combined medication of the injection. Besides, the consensus elucidates the safety, precautions and contraindications of the injection, so as to provide guidance for clinical use.

169-175

Authors: Afshar Etemadi, Rezvan Moniri, Mahmood Saffari, Hossein Akbari, Saeed Alamian, Ali Mohammad Behrozikhah

Objective: To determine epidemiological, molecular characterization, and potential risk factors of human brucellosis. Methods: This descriptive study was carried out in the clinical setting in Iran between 2017 and 2018. A total of 297 participants enrolled in the study. The sample size was calculated based on the occurrence rate of brucellosis in different areas. Patients were assessed using serological tests and conventional culture methods. Phage and multiplex PCR methods typed all of Brucella isolates. Potential risk factors of disease were determined. Results: A total of 141 of 297 (47.5%) Brucella strains were isolated and all of them were detected as Brucella melitensis biovar 1. Based on serologic titers, high culture positivity was recorded at 1/640 titer (P<0.006). The risk factors for brucellosis were patients older than 40 years (OR=2.23, 95%CI: 1.4-3.55, P=0.001), animal keeper (OR=7, 95%CI: 1.51-32.41, P=0.005), housewife (OR=8.76, 95%CI: 1.85-41.37, P=0.002), farmer (OR=6.42, 95%CI: 1.21-33.97, P=0.019), and contact with animal (OR=1.31, 95%CI: 0.60-2.85, P=0.005). Conclusions: To the best of our knowledge, this is the first comprehensive report from Iran presenting the detection of Brucella species by the multiplex PCR. Brucella melitensis biovar 1 is still the dominant causative agent in Iran. The consumption of unpasteurized dairy products, living in rural areas, and animal contact were risk factors of brucellosis.

185-186

Authors: Hamid Reza Niazkar, Majid Niazkar

COVID-19 due to infections with the 2019 novel coronavirus (SARS-CoV-2) was first reported in Wuhan, Hubei Province, China in December 2019. Up to 2 March 2020, it has already spread to more than 55 countries, infected more than 85 000 individuals, and yielded to about 3 000 death cases[1]. Due to the vigorous outbreak of COVID-19, it has become a global concern. Further management and controlling of this worldwide threat inevitably relies on the preparedness and precise risk assessment of the future, particularly in countries that new cases have been observed. In this regard, estimation of new probable confirmed and death cases in the near future is crucial to health care systems of each country. As a result, some researches have been conducted to address this new concern[2-4]. In one of the most recent studies focusing on the first month of COVID-19 outbreak in Thailand, Sookaromdee and Wiwanitkit[2] proposed a second-order polynomial function, which relates imported infected cases (I) with time (t). The relation is given as I=2AT2+AT+C, where A is the acceleration rate of disease spreading and C is a constant. This relation was derived by integrating V=AT, where V is the average velocity of the spreading disease[2]. The authors sincerely appreciate Sookaromdee and Wiwanitkit for their valuable contribution. However, several issues are presented in accordance with their mathematical model: First of all, COVID-19 is highly contagious, and infected individuals can spread the disease during the incubation period, which is estimated to be about 2-14 days[5]. Due to the incubation period of COVID-19, imported infected cases may underestimate the outbreak, and may not be a suitable index for prediction purposes. In contrast, a forecasting model based on either total confirmed or death cases may give more accurate estimations. In the suggested model by Sookaromdee and Wiwanitkit[2], it was basically assumed that the spreading acceleration rate of COVID-19 outbreak is constant in respect with time. This assumption may not be compatible with the natural course of the outbreak. According to COVID-19 outbreak reported from 20 January to 24 February 2020 in Thailand[1], the accumulative number of confirmed cases not only has a highly nonlinear relationship with time but also demonstrates a repetitive pattern comprising of a steep rise followed by a plateau, as shown in Figure 1.