The aim of the study was to explore the association between tuberculosis (TB) and common mental disorders (CMD), in an area with high prevalence of TB.

We performed a case–control study of TB patients and unmatched healthy controls, from a demographic surveillance site in Guinea-Bissau. Screening for CMD was performed once for controls and at inclusion and follow-up for TB patients. Kessler 10 (K-10) and a brief version of Hopkins Symptom Checklist 25 (SCL-8d) were used as screening instruments.

571 controls were interviewed and 416 interviews were performed for 215 TB cases. Estimated CMD prevalence at the time of diagnosis of TB was 33.6 % (SCL-8d) and 46.2 % (K-10), compared with 6.8 % (SCL-8d) and 6.7 % (K-10) among controls; adjusted OR 7.18 (95 % CI 4.07 to 12.67) and 14.52 (95 % CI 8.15 to 25.84), respectively. No significant difference in CMD prevalence rates was observed between TB patients, after 6 months of treatment, and controls.

Psychological distress and common mental disorders were more prevalent among TB patients at the time of diagnosis compared with the background population, but after completion of TB treatment no increased prevalence of psychological distress was found.

Obesity is a risk factor for various diseases and can affect the disease course. Studies have shown detrimental effects of obesity on patients affected with SARS-CoV-2 including increased hospitalization and more severe disease. This study aims to investigate the effects of obesity on symptom duration in patients with COVID-19, and also explore the possibility of using BMI as a predictor of symptom duration in outpatient settings.

Patients diagnosed with COVID-19 between June and October 2020, who had no other comorbidities, and were planned to receive treatment in the outpatient setting were enrolled in the study. Duration of the symptoms was determined based on participants’ self-report of their symptoms. Linear regression was used to create predictive models based on participants’ BMI, age, sex, disease presentation, and their self-reported symptom duration.

A total of 210 patients were included in the final analysis. Patients with higher BMI had significantly longer symptom duration. Linear regression models showed highest correlation between BMI and symptom duration compared to other covariates.

Low error in predictions and high coverage of data variability showed BMI can be used as a predictive factor for symptom duration in COVID-19 patients treated in outpatient settings.

Mass-gathering events (MGEs) are commonly associated with a higher than average rate of morbidity. Spectators, workers, and the substantial number of MGE attendees can increase the spread of communicable diseases. During an MGE, emergency departments (EDs) play an important role in offering health care services to both residents of the local community and event attendees. Syndromic indicators (SIs) are widely used in an ED surveillance system for early detection of communicable diseases.

This literature review aimed to develop an understanding of the effect of MGEs on ED patient presentations with communicable diseases and their corresponding SIs.

An integrative literature review methodology was used. Online databases were searched to retrieve relevant academic articles that focused on MGEs, EDs, and SIs. Inclusion/exclusion criteria were applied to screen articles. The Standard Quality Assessment Criteria for Evaluating Primary Research (QualSyst) assessment tool was used to assess the quality of included papers.

Eleven papers were included in this review; all discussed the impact of an MGE on patient presentations with communicable diseases at EDs/hospitals. Most included studies used the raw number of patients who presented or were admitted to EDs/hospitals to determine impact. Further, the majority of studies focused on either respiratory infections (n = 4) or gastrointestinal infections (n = 2); two articles reported on both. Eight articles mentioned SIs; however, such information was limited. The quality of evidence (using QualSyst) ranged from 50% to 90%.

Limited research exists on the impact of MGEs on ED presentations with communicable diseases and related SIs. Recommendations for future MGE studies include assessing differences in ED presentations with communicable diseases regarding demographics, clinical characteristics, and outcomes before, during, and after the event. This would benefit health care workers and researchers by offering more comprehensive knowledge for application into practice.

Communicable disease management (CDM) is an important component of disaster public health response operations. However, there is a lack of any performance assessment (PA) framework and related indicators for the PA. This study aimed to develop a PA framework and indicators in CDM in disasters.

In this study, a series of methods were used. First, a systematic literature review (SLR) was performed in order to extract the existing PA frameworks and indicators. Then, using a qualitative approach, some interviews with purposively selected experts were conducted and used in developing the PA framework and indicators. Finally, the analytical hierarchy process (AHP) was used for weighting of the developed indicators.

The input, process, products, and outcomes (IPPO) framework was found to be an appropriate framework for CDM PA. Seven main functions were revealed to CDM during disasters. Forty PA indicators were developed for the four categories.

There is a lack of any existing PA framework in CDM in disasters. Thus, in this study, a PA framework (IPPO framework) was developed for the PA of CDM in disasters through a series of methods. It can be an appropriate framework and its indicators could measure the performance of CDM in disasters.

BabaieJ , ArdalanA , VatandoostH , GoyaMM , AkbarisariA . Developing a Performance Assessment Framework and Indicators for Communicable Disease Management in Natural Disasters. Prehosp Disaster Med. 2016;31(1):27–35.

Hurricane Sandy's October 29, 2012 arrival in New York City caused flooding, power disruption, and population displacement. Infectious disease risk may have been affected by floodwater exposure, residence in emergency shelters, overcrowding, and lack of refrigeration or heating. For 42 reportable diseases that could have been affected by hurricane-related exposures, we developed methods to assess whether hurricane-affected areas had higher disease incidence than other areas of NYC.

We identified post-hurricane cases as confirmed, probable, or suspected cases with onset or diagnosis between October 30 and November 26 that were reported via routine passive surveillance. Pre-hurricane cases for the same 4-week period were identified in 5 prior years, 2007–2011. Cases were geocoded to the census tract of residence. Using data compiled by the NYC Office of Emergency Management, we determined (1) the proportion of the population in each census tract living in a flooded block and (2) the subset of flooded tracts severely “impacted”, e.g., by prolonged service outages or physical damage. A separate multivariable regression model was constructed for each disease, modeling the outcome of case counts using a negative binomial distribution. Independent variables were: neighborhood poverty; whether cases were pre- or post-hurricane (time); the proportion of the population flooded in impacted and not impacted tracts; and interaction terms between the flood/impact variables and time. Models used repeated measures to adjust for correlated observations from the same tract and an offset term of the log of the population size. Sensitivity analyses assessed the effects of case count fluctuations and accounted for variations in reporting volume by using an offset term of the log of total cases.

Only legionellosis was statistically significantly associated with increased occurrence in flooded/impacted areas post-hurricane, adjusting for baseline differences (P = .04). However, there was only 1 legionellosis case post-hurricane in a flooded/impacted area.

Hurricane Sandy did not appear to elevate reportable disease incidence in NYC. Defining and acquiring reliable data and meta-data regarding hurricane-affected areas was a challenge in the weeks post-storm. Relevant metrics could be developed during disaster preparedness planning. These methods to detect excess disease can be adapted for future emergencies. (Disaster Med Public Health Preparedness. 2013;7:513-521)