Geographical distribution of typhoid risk factors in low and middle income countries

Overdispersion. Appendix 2. Full specification of regression outputs. Appendix 3. TRF index (type 5) by sub-national boundary. Appendix 4. Final TRF index values by sub-national boundary. Appendix 5. TRF index in Bangladesh. (ZIP 1995 kb

A descriptive analysis of antimicrobial resistance patterns of WHO priority pathogens isolated in children from a tertiary care hospital in India.

The World Health Organization (WHO) has articulated a priority pathogens list (PPL) to provide strategic direction to research and develop new antimicrobials. Antimicrobial resistance (AMR) patterns of WHO PPL in a tertiary health care facility in Southern India were explored to understand the local priority pathogens. Culture reports of laboratory specimens collected between 1st January 2014 and 31st October 2019 from paediatric patients were extracted. The antimicrobial susceptibility patterns for selected antimicrobials on the WHO PPL were analysed and reported. Of 12,256 culture specimens screened, 2335 (19%) showed culture positivity, of which 1556 (66.6%) were organisms from the WHO-PPL. E. coli was the most common organism isolated (37%), followed by Staphylococcus aureus (16%). Total of 72% of E. coli were extended-spectrum beta-lactamases (ESBL) producers, 55% of Enterobacteriaceae were resistant to 3rd generation cephalosporins due to ESBL, and 53% of Staph. aureus were Methicillin-resistant. The analysis showed AMR trends and prevalence patterns in the study setting and the WHO-PPL document are not fully comparable. This kind of local priority difference needs to be recognised in local policies and practices

Burden of typhoid fever in low-income and middle-income countries: a systematic, literature-based update with risk-factor adjustment

Background No access to safe water is an important risk factor for typhoid fever, yet risk-level heterogeneity is unaccounted for in previous global burden estimates. Since WHO has recommended risk-based use of typhoid polysaccharide vaccine, we revisited the burden of typhoid fever in low-income and middle-income countries (LMICs) after adjusting for water-related risk. Methods We estimated the typhoid disease burden from studies done in LMICs based on blood-culture-confi rmed incidence rates applied to the 2010 population, after correcting for operational issues related to surveillance, limitations of diagnostic tests, and water-related risk. We derived incidence estimates, correction factors, and mortality estimates from systematic literature reviews. We did scenario analyses for risk factors, diagnostic sensitivity, and case fatality rates, accounting for the uncertainty in these estimates and we compared them with previous disease burden estimates. Findings The estimated number of typhoid fever cases in LMICs in 2010 after adjusting for water-related risk was 11·9 million (95% CI 9·9–14·7) cases with 129 000 (75 000–208 000) deaths. By comparison, the estimated riskunadjusted burden was 20·6 million (17·5–24·2) cases and 223 000 (131 000–344 000) deaths. Scenario analyses indicated that the risk-factor adjustment and updated diagnostic test correction factor derived from systematic literature reviews were the drivers of diff erences between the current estimate and past estimates. Interpretation The risk-adjusted typhoid fever burden estimate was more conservative than previous estimates. However, by distinguishing the risk diff erences, it will allow assessment of the eff ect at the population level and will facilitate cost-eff ectiveness calculations for risk-based vaccination strategies for future typhoid conjugate vaccine

Model-based estimation of the economic burden of cholera in Africa

Objectives To estimate the economic burden of cholera in Africa.Settings Cholera affected 44 countries in Africa.Participants The analysis used data from public sources in Africa published until September 2019.Methods Based on existing data from field-based cost-of-illness studies, estimated cholera incidence rates, and reported cholera cases to WHO, this research estimates the economic burden of cholera in Africa from a societal perspective with 2015 as the base year. The estimate included out-of-pocket costs, public health system costs, productivity loss related to illness and an optional productivity loss related to premature deaths valued by the human capital approach. As various input data such as cholera incidence, hospitalisation rates and the number of workdays lost were not well defined, a series of scenario analyses and uncertainty analyses, accounting for unknowns and data variability, was conducted. Similarly, the value of time lost due to illness and deaths using the human capital approach was explored through scenario analyses.Results In 2015, an estimated 1 008 642 cases in 44 African countries resulted in an economic burden of US$130 million from cholera-related illness and its treatment. When the estimated 38 104 cholera deaths were included in the analysis, the economic burden increased to US$1 billion or international $2.4 billion for the same year. At the same time, when only the 71 126 cases and 937 deaths reported to the WHO are considered, the economic burden was only US$68 million for the year 2015. The estimates of economic burden are thus heavily dependent on the cholera incidence rate, how time lost due to illness and deaths are calculated, hospitalisation rates and hospitalisation costs.Conclusion The findings can be used as an economic justification for cholera control in Africa and for generating value-for-money evidence to underpin Ending Cholera—A Global Roadmap to 2030 with considerations to study limitations

An overview of VaxchoraTM, a live attenuated oral cholera vaccine

Cholera remains a public health threat among the least privileged populations and regions affected by conflicts and natural disasters. Together with Water, Sanitation and Hygiene practices, use of oral cholera vaccines (OCVs) is a key tool to prevent cholera. Bivalent whole-cell killed OCVs have been extensively used worldwide and found effective in protecting populations against cholera in endemic and outbreak settings. No cholera vaccine had been available for United States (US) travelers at risk for decades until 2016 when CVD 103-HgR (Vaxchora™), an oral live attenuated vaccine, was licensed by the US FDA. A single dose of Vaxchora™ protected US volunteers against experimental challenge 10 days and 3 months after vaccination. However, use of Vaxchora™ poses several challenges in resource poor settings as it requires reconstitution, is age-restricted to 18 to 64 years, has no data in populations endemic for cholera, and faces challenges related to cold chain and cost

Estimating Typhoid Fever Risk Associated with Lack of Access to Safe Water: A Systematic Literature Review

Background. Unsafe water is a well-known risk for typhoid fever, but a pooled estimate of the population-level risk of typhoid fever resulting from exposure to unsafe water has not been quantified. An accurate estimation of the risk from unsafe water will be useful in demarcating high-risk populations, modeling typhoid disease burden, and targeting prevention and control activities. Methods. We conducted a systematic literature review and meta-analysis of observational studies that measured the risk of typhoid fever associated with drinking unimproved water as per WHO-UNICEF’s definition or drinking microbiologically unsafe water. The mean value for the pooled odds ratio from case-control studies was calculated using a random effects model. In addition to unimproved water and unsafe water, we also listed categories of other risk factors from the selected studies. Results. The search of published studies from January 1, 1990, to December 31, 2013 in PubMed, Embase, and World Health Organization databases provided 779 publications, of which 12 case-control studies presented the odds of having typhoid fever for those exposed to unimproved or unsafe versus improved drinking water sources. The odds of typhoid fever among those exposed to unimproved or unsafe water ranged from 1.06 to 9.26 with case weighted mean of 2.44 (95% CI: 1.65–3.59). Besides water-related risk, the studies also identified other risk factors related to socioeconomic aspects, type of food consumption, knowledge and awareness about typhoid fever, and hygiene practices. Conclusions. In this meta-analysis, we have quantified the pooled risk of typhoid fever among people exposed to unimproved or unsafe water which is almost two and a half times more than people who were not exposed to unimproved or unsafe water. However, caution should be exercised in applying the findings from this study in modeling typhoid fever disease burden at country, regional, and global levels as improved water does not always equate to safe water

Hepatitis E infection in Odisha, India: A descriptive analysis

Up to 25% of hepatitis E virus (HEV)-infected pregnant women in their third trimester die. Despite HEV being an important cause of viral hepatitis, no robust surveillance exists in India. We reviewed jaundice outbreaks records and hospital records from jaundiced individuals seeking treatment and linked those records to laboratory results (HEV immunoglobulin M enzyme-linked immunosorbent assay) for January 2012 to September 2013 in Odisha state. A total of 14 HEV confirmed outbreaks were identified, of which 33% of 139 jaundiced cases were HEV positive. There were two deaths. An additional 495 jaundiced cases were identified through hospital records, of which 18% were HEV positive. Among HEV-positive women (n = 35), 34% were of childbearing age. While one may not be able to generalize our results, this finding suggests HE is widespread in Odisha and may represent hidden disease burden in this region. The policymakers should monitor HEV infections in similar geographical areas, especially among population of childbearing age women to initiate evidence-based control measures

Mean values for weighted case fatality rate, male to female ratio, age and length of hospital stay by region in studies published from 1991 to 2011.

<p>N = sample size, NA = not applicable,</p><p>* weighted mean,</p>§<p>region comparison by Kruskal-Wallis test.</p