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Background Antimicrobial resistance (AMR) is one of the greatest global health challenges today, but burden assessment is hindered by uncertainty of AMR prevalence estimates. Geographical representation of AMR estimates typically pools data collected from several laboratories; however, these aggregations may introduce bias by not accounting for the heterogeneity of the population that each laboratory represents. Methods We used AMR data from up to 381 laboratories in the United States from The Surveillance Network to evaluate methods for estimating uncertainty of AMR prevalence estimates. We constructed confidence intervals for the proportion of resistant isolates using (1) methods that account for the clustered structure of the data, and (2) standard methods that assume data independence. Using samples of the full dataset with increasing facility coverage levels, we examined how likely the estimated confidence intervals were to include the population mean. Results Methods constructing 95% confidence intervals while accounting for possible within-cluster correlations (Survey and standard methods adjusted to employ cluster-robust errors), were more likely to include the sample mean than standard methods (Logit, Wilson score and Jeffreys interval) operating under the assumption of independence. While increased geographical coverage improved the probability of encompassing the mean for all methods, large samples still did not compensate for the bias introduced from the violation of the data independence assumption. Conclusion General methods for estimating the confidence intervals of AMR rates that assume data are independent, are likely to produce biased results. When feasible, the clustered structure of the data and any possible intra-cluster variation should be accounted for when calculating confidence intervals around AMR estimates, in order to better capture the uncertainty of prevalence estimates.

BackgroundThe rapid spread of COVID-19 globally has prompted policymakers to evaluate the capacity of health care infrastructure in their communities. Many hard-hit localities have witnessed a large influx of severe cases that strained existing hospitals. As COVID-19 spreads in India, it is essential to evaluate the country’s capacity to treat severe cases.MethodsWe combined data on public and private sector hospitals in India to produce state level estimates of hospital beds, ICU beds, and mechanical ventilators. Based on the number of public sector hospitals from the 2019 National Health Profile (NHP) of India and the relative proportions of public and private health care facilities from the National Sample Survey (NSS) 75th round (2017-2018), we estimated capacity in each Indian state and union territory (UT). We assumed that 5% of all hospital beds were ICU beds and that 50% of ICU beds were equipped with ventilators.ResultsWe estimated that India has approximately 1.9 million hospital beds, 95,000 ICU beds and 48,000 ventilators. Nationally, resources are concentrated in the private sector (hospital beds: 1,185,242 private vs 713,986 public; ICU beds: 59,262 private vs 35,699 public; ventilators: 29,631 private vs. 17,850 public). Our findings suggest substantial variation in available resources across states and UTs.ConclusionSome projections shave suggested a potential need for approximately 270,000 ICU beds in an optimistic scenario, over 2.8 times the estimated number of total available ICU beds in India. Additional resources will likely be required to accommodate patients with severe COVID-19 infections in India.

Diagnostic imaging evaluation of a pathologic process suspected to be located within the sellar and parasellar region requires much more than performing a routine CT or MR imaging examination of the brain. An intimate knowledge of the anatomy of the sellar and parasellar region is an essential prerequisite for imaging evaluation of this region. All neural components essential for vision and function of the eye and orbital structures converge in this region and contribute to the anatomic complexity. An understanding of the function of the cranial nerves related to vision and movement of the extraocular muscles aids in developing a more focused imaging examination of this region based on the provided clinical history. Furthermore, familiarity with the type and appearance of the pathologic processes that can be present at this location allows for a more precise and narrow differential diagnosis.

Computed tomography angiography (CTA) is a rapidly developing technology with great potential. This is particularly true for evaluating neurovascular disease. Clinical stroke because of atherosclerotic disease of the carotid and vertebral arteries is a common examination indication; areas of stenosis, and soft and calcified plaque along the entire vessel, not only at the carotid bifurcation, permit a full assessment of the patient's disease process. Other diseases including dissection, trauma, intracranial stenosis, thrombosis, and aneurysms can be readily diagnosed. Although duplex ultrasound may be a first line examination in many patients, both magnetic resonance angiography (MRA) and CTA offer distinct advantages over it. CTA and MRA are both highly accurate but CTA has several key advantages. CTA has been advanced by the development of improved multidetector CT (MDCT) and workstations that postprocess the data. Methods to obtain quality CTA images and to rapidly analyze the data for abnormalities are the subject of this chapter. In addition, evolving techniques in future CT scanners and workstations, and developing methods of vulnerable plaque and CT perfusion imaging are discussed.

Background A major DHF outbreak occurred in Delhi in 1996. Following this another outbreak was reported in the year 2003. In the years 2004 and 2005, though no outbreak was reported, a definitely higher number of samples were received in the virology laboratory of A.I.I.M.S. from suspected cases of dengue infection. This study was designed to compare the serological and virological profiles of confirmed dengue cases in the years 2003, 2004 and 2005. Results Out of 1820 serum samples received from suspected cases in all three years, 811 (44.56%) were confirmed as dengue infection serologically. Out of these confirmed dengue cases maximum cases, in all three years, were seen in the age group 21–30 years. There was an increase in the number of samples received in the post monsoon period (September to November) with a peak in the second and third week of October. More samples were received from DHF cases in the year 2005 than 2004 and 2003. All four dengue serotypes were seen co-circulating in the year 2003, followed by complete predominance of dengue serotype 3 in 2005. Conclusion Epidemiology of dengue is changing rapidly in Delhi. Dengue infections are seen every year thus making it an endemic disease. After co-circulation of all serotypes in 2003, now dengue serotype 3 is emerging as the predominant serotype.