Impact of infection control measures to control an outbreak of multidrug-resistant tuberculosis in a human immunodeficiency virus ward, Peru

Multidrug-resistant tuberculosis (MDRTB) rates in a human immunodeficiency virus (HIV) care facility increased by the year 2000-56% of TB cases, eight times the national MDRTB rate. We reported the effect of tuberculosis infection control measures that were introduced in 2001 and that consisted of 1) building a respiratory isolation ward with mechanical ventilation, 2) triage segregation of patients, 3) relocation of waiting room to outdoors, 4) rapid sputum smear microscopy, and 5) culture/drug-susceptibility testing with the microscopic-observation drug-susceptibility assay. Records pertaining to patients attending the study site between 1997 and 2004 were reviewed. Six hundred and fifty five HIV/TB-coinfected patients (mean age 33 years, 79% male) who attended the service during the study period were included. After the intervention, MDRTB rates declined to 20% of TB cases by the year 2004 (P = 0.01). Extremely limited access to antiretroviral therapy and specific MDRTB therapy did not change during this period, and concurrently, national MDRTB prevalence increased, implying that the infection control measures caused the fall in MDRTB rates. The infection control measures were estimated to have cost US$91,031 while preventing 97 MDRTB cases, potentially saving US$1,430,026. Thus, this intervention significantly reduced MDRTB within an HIV care facility in this resource-constrained setting and should be cost-effective

Tuberculosis/HIV/AIDS coinfection in Porto Alegre, RS/Brazil - invisibility and silencing of the most affected groups

OBJECTIVE: To analyze how belonging to certain social groups contributes to constituting the vulnerabilities associated with illnesses due to tuberculosis/HIV/AIDS coinfection. METHODOLOGYThis is a qualitative study carried out in the city of Porto Alegre, state of Rio Grande do Sul, in regions of high social vulnerability. Twenty coinfected people were interviewed in specialized health services between August and December 2016. The analysis was based on the frameworks The Sound of Silence and Vulnerability and Human Rights. RESULTS: Socioeconomic conditions were decisive for the constitution of the vulnerability conditions. Processes of people invisibilization, and the silencing of their voices, in a scenario marked by economic, racial and gender inequalities, contributed for their health needs not to be understood and effectively taken into account in the services actions. FINAL CONSIDERATIONS: The more effective strategies are to legitimize voices and to understand the needs of those affected by coinfection, the greater the chances that programmatic responses to the problem will be successful

Epidemiological and transmissibility analysis of influenza A(H1N1)v in a southern hemisphere setting: Peru

We present a preliminary analysis of 1,771 confirmed cases of influenza A(H1N1)v reported in Peru by 17 July including the frequency of the clinical characteristics, the spatial and age distribution of the cases and the estimate of the transmission potential. Age-specific frequency of cases was highest among school age children and young adults, with the lowest frequency of cases among seniors, a pattern that is consistent with reports from other countries. Estimates of the reproduction number lie in the range of 1.2 to 1.7, which is broadly consistent with previous estimates for this pandemic in other regions. Validation of these estimates will be possible as additional data become available

Changes in the Viral Distribution Pattern after the Appearance of the Novel Influenza A H1N1 (pH1N1) Virus in Influenza-Like Illness Patients in Peru

Background: We describe the temporal variation in viral agents detected in influenza like illness (ILI) patients before and after the appearance of the ongoing pandemic influenza A (H1N1) (pH1N1) in Peru between 4-January and 13-July 2009. Methods: At the health centers, one oropharyngeal swab was obtained for viral isolation. From epidemiological week (EW) 1 to 18, at the US Naval Medical Research Center Detachment (NMRCD) in Lima, the specimens were inoculated into four cell lines for virus isolation. In addition, from EW 19 to 28, the specimens were also analyzed by real time-polymerase-chainreaction (rRT-PCR). Results: We enrolled 2,872 patients: 1,422 cases before the appearance of the pH1N1 virus, and 1,450 during the pandemic. Non-pH1N1 influenza A virus was the predominant viral strain circulating in Peru through (EW) 18, representing 57.8% of the confirmed cases; however, this predominance shifted to pH1N1 (51.5%) from EW 19–28. During this study period, most of pH1N1 cases were diagnosed in the capital city (Lima) followed by other cities including Cusco and Trujillo. In contrast, novel influenza cases were essentially absent in the tropical rain forest (jungle) cities during our study period. The city of Iquitos (Jungle) had the highest number of influenza B cases and only one pH1N1 case. Conclusions: The viral distribution in Peru changed upon the introduction of the pH1N1 virus compared to previous months. Although influenza A viruses continue to be the predominant viral pathogen, the pH1N1 virus predominated over the other influenza A viruses

A Comparison of the Epidemiology and Clinical Presentation of Seasonal Influenza A and 2009 Pandemic Influenza A (H1N1) in Guatemala

A new influenza A (H1N1) virus was first found in April 2009 and proceeded to cause a global pandemic. We compare the epidemiology and clinical presentation of seasonal influenza A (H1N1 and H3N2) and 2009 pandemic influenza A (H1N1) (pH1N1) using a prospective surveillance system for acute respiratory disease in Guatemala.Patients admitted to two public hospitals in Guatemala in 2008-2009 who met a pneumonia case definition, and ambulatory patients with influenza-like illness (ILI) at 10 ambulatory clinics were invited to participate. Data were collected through patient interview, chart abstraction and standardized physical and radiological exams. Nasopharyngeal swabs were taken from all enrolled patients for laboratory diagnosis of influenza A virus infection with real-time reverse transcription polymerase chain reaction. We identified 1,744 eligible, hospitalized pneumonia patients, enrolled 1,666 (96%) and tested samples from 1,601 (96%); 138 (9%) had influenza A virus infection. Surveillance for ILI found 899 eligible patients, enrolled 801 (89%) and tested samples from 793 (99%); influenza A virus infection was identified in 246 (31%). The age distribution of hospitalized pneumonia patients was similar between seasonal H1N1 and pH1N1 (P = 0.21); the proportion of pneumonia patients <1 year old with seasonal H1N1 (39%) and pH1N1 (37%) were similar (P = 0.42). The clinical presentation of pH1N1 and seasonal influenza A was similar for both hospitalized pneumonia and ILI patients. Although signs of severity (admission to an intensive care unit, mechanical ventilation and death) were higher among cases of pH1N1 than seasonal H1N1, none of the differences was statistically significant.Small sample sizes may limit the power of this study to find significant differences between seasonal influenza A and pH1N1. In Guatemala, influenza, whether seasonal or pH1N1, appears to cause severe disease mainly in infants; targeted vaccination of children should be considered

How to Minimize the Attack Rate during Multiple Influenza Outbreaks in a Heterogeneous Population

<div><h3>Background</h3><p>If repeated interventions against multiple outbreaks are not feasible, there is an optimal level of control during the first outbreak. Any control measures above that optimal level will lead to an outcome that may be as sub-optimal as that achieved by an intervention that is too weak. We studied this scenario in more detail.</p> <h3>Method</h3><p>An age-stratified ordinary-differential-equation model was constructed to study infectious disease outbreaks and control in a population made up of two groups, adults and children. The model was parameterized using influenza as an example. This model was used to simulate two consecutive outbreaks of the same infectious disease, with an intervention applied only during the first outbreak, and to study how cumulative attack rates were influenced by population composition, strength of inter-group transmission, and different ways of triggering and implementing the interventions. We assumed that recovered individuals are fully immune and the intervention does not confer immunity.</p> <h3>Results/Conclusion</h3><p>The optimal intervention depended on coupling between the two population sub-groups, the length, strength and timing of the intervention, and the population composition. Population heterogeneity affected intervention strategies only for very low cross-transmission between groups. At more realistic values, coupling between the groups led to synchronization of outbreaks and therefore intervention strategies that were optimal in reducing the attack rates for each subgroup and the population overall coincided. For a sustained intervention of low efficacy, early intervention was found to be best, while at high efficacies, a delayed start was better. For short interventions, a delayed start was always advantageous, independent of the intervention efficacy. For most scenarios, starting the intervention after a certain cumulative proportion of children were infected seemed more robust in achieving close to optimal outcomes compared to a strategy that used a specified duration after an outbreak’s beginning as the trigger.</p> </div

Pros and cons of estimating the reproduction number from early epidemic growth rate of influenza A (H1N1) 2009

<p>Abstract</p> <p>Background</p> <p>In many parts of the world, the exponential growth rate of infections during the initial epidemic phase has been used to make statistical inferences on the reproduction number, <it>R</it>, a summary measure of the transmission potential for the novel influenza A (H1N1) 2009. The growth rate at the initial stage of the epidemic in Japan led to estimates for <it>R </it>in the range 2.0 to 2.6, capturing the intensity of the initial outbreak among school-age children in May 2009.</p> <p>Methods</p> <p>An updated estimate of <it>R </it>that takes into account the epidemic data from 29 May to 14 July is provided. An age-structured renewal process is employed to capture the age-dependent transmission dynamics, jointly estimating the reproduction number, the age-dependent susceptibility and the relative contribution of imported cases to secondary transmission. Pitfalls in estimating epidemic growth rates are identified and used for scrutinizing and re-assessing the results of our earlier estimate of <it>R</it>.</p> <p>Results</p> <p>Maximum likelihood estimates of <it>R </it>using the data from 29 May to 14 July ranged from 1.21 to 1.35. The next-generation matrix, based on our age-structured model, predicts that only 17.5% of the population will experience infection by the end of the first pandemic wave. Our earlier estimate of <it>R </it>did not fully capture the population-wide epidemic in quantifying the next-generation matrix from the estimated growth rate during the initial stage of the pandemic in Japan.</p> <p>Conclusions</p> <p>In order to quantify <it>R </it>from the growth rate of cases, it is essential that the selected model captures the underlying transmission dynamics embedded in the data. Exploring additional epidemiological information will be useful for assessing the temporal dynamics. Although the simple concept of <it>R </it>is more easily grasped by the general public than that of the next-generation matrix, the matrix incorporating detailed information (e.g., age-specificity) is essential for reducing the levels of uncertainty in predictions and for assisting public health policymaking. Model-based prediction and policymaking are best described by sharing fundamental notions of heterogeneous risks of infection and death with non-experts to avoid potential confusion and/or possible misuse of modelling results.</p

Characterizing the Epidemiology of the 2009 Influenza A/H1N1 Pandemic in Mexico

Gerardo Chowell and colleagues address whether school closures and other social distancing strategies were successful in reducing pandemic flu transmission in Mexico by analyzing the age- and state-specific incidence of influenza morbidity and mortality in 32 Mexican states