Active contact tracing beyond the household in multidrug resistant tuberculosis in Vietnam: a cohort study

Drug-Resistant Tuberculosis Case-Finding Strategies: Scoping Review

BACKGROUND

Finding individuals with drug-resistant tuberculosis (DR-TB) is important to control the pandemic and improve patient clinical outcomes. To our knowledge, systematic reviews assessing the effectiveness, cost-effectiveness, acceptability, and feasibility of different DR-TB case-finding strategies to inform research, policy, and practice, have not been conducted and the scope of primary research is unknown.

OBJECTIVE

We therefore assessed the available literature on DR-TB case-finding strategies.

METHODS

We looked at systematic reviews, trials, qualitative studies, diagnostic test accuracy studies, and other primary research that sought to improve DR-TB case detection specifically. We excluded studies that included patients seeking care for tuberculosis (TB) symptoms, patients already diagnosed with TB, or were laboratory-based. We searched the academic databases of MEDLINE, Embase, The Cochrane Library, Africa-Wide Information, CINAHL (Cumulated Index to Nursing and Allied Health Literature), Epistemonikos, and PROSPERO (The International Prospective Register of Systematic Reviews) using no language or date restrictions. We screened titles, abstracts, and full-text articles in duplicate. Data extraction and analyses were carried out in Excel (Microsoft Corp).

RESULTS

We screened 3646 titles and abstracts and 236 full-text articles. We identified 6 systematic reviews and 61 primary studies. Five reviews described the yield of contact investigation and focused on household contacts, airline contacts, comparison between drug-susceptible tuberculosis and DR-TB contacts, and concordance of DR-TB profiles between index cases and contacts. One review compared universal versus selective drug resistance testing. Primary studies described (1) 34 contact investigations, (2) 17 outbreak investigations, (3) 3 airline contact investigations, (4) 5 epidemiological analyses, (5) 1 public-private partnership program, and (6) an e-registry program. Primary studies were all descriptive and included cross-sectional and retrospective reviews of program data. No trials were identified. Data extraction from contact investigations was difficult due to incomplete reporting of relevant information.

CONCLUSIONS

Existing descriptive reviews can be updated, but there is a dearth of knowledge on the effectiveness, cost-effectiveness, acceptability, and feasibility of DR-TB case-finding strategies to inform policy and practice. There is also a need for standardization of terminology, design, and reporting of DR-TB case-finding studies.

Tuberculosis contact-tracing results in childhood: a retrospective study in a tertiary-care children's hospital in Turkey

BACKGROUND

Smear-positive adults with tuberculosis are the main source of childhood tuberculosis. The evaluation of children exposed to tuberculosis and determination of the disease stages are the cornerstones of managing childhood tuberculosis.

AIM

To determine the frequency of tuberculous contact, latent tuberculosis infection and tuberculosis disease in children who were in contact with smear-positive adults.

METHODS

This is a single-centre, retrospective study. The medical records of children exposed to tuberculosis (<18 years old) between 2014 and 2018 were investigated. After diagnosing the index cases, the children were referred to the hospital. To identify the children in contact with adults with tuberculosis, a careful medical history, demographic features and physical examination, tuberculin skin test, postero-anterior and lateral chest radiographs, and, if necessary, chest computed tomography and microbiological tests were undertaken. The children's final diagnosis, treatment regimens and follow-up were documented. The sensitivity, specificity and positive and negative predictive values, tuberculin skin test and chest radiograph imaging were assessed and compared with computed tomography results.

RESULTS

A total of 150 paediatric patients were exposed to 88 index cases. These were fathers in 29.3% of cases and mothers in 10% of cases. Of the children, 131 (87.3%) were asymptomatic, and physical examination was normal in all children, apart from one who had respiratory symptoms. The tuberculin skin test results were positive in 60 (43%) patients and chest radiograph was abnormal in 100 (66%) children. Findings were consistent with tuberculosis in 34 (40%) of the 84 patients who underwent computed tomography. Fifty (38.5%) of the remaining children were defined as having been in contact with a case of tuberculosis, 41 (31.5%) had latent tuberculous infection and 39 (30%) had tuberculosis disease.

CONCLUSION

Pulmonary tuberculosis is asymptomatic in most children but with meticulous use of computed tomography it can be detected in asymptomatic children who have had close contact with tuberculosis.Abbreviation: AFB: acid-fast bacilli; AUC: area under the curve; BCG: bacillus Calmette-Guérin; CI: confidence interval; CT: computed tomography; CXR: chest radiograph; HIV: human immunodeficiency virus; ICD-10: International Classification of Diseases 10; LTBI: latent tuberculosis infection; MDR-TB: multi-drug-resistant tuberculosis; NPV: negative predictive value; PCR: polymerase chain reaction; PPV: positive predictive value; ROC: receiver operating characteristics; SD: standard deviation; TB: tuberculosis; TST: tuberculin skin test; XDR-TB: extensively drug-resistant tuberculosis.

Preventive Treatment for Household Contacts of Drug-Susceptible Tuberculosis Patients

People who live in the household of someone with infectious pulmonary tuberculosis are at a high risk of tuberculosis infection and subsequent progression to tuberculosis disease. These individuals are prioritized for contact investigation and tuberculosis preventive treatment (TPT). The treatment of TB infection is critical to prevent the progression of infection to disease and is prioritized in household contacts. Despite the availability of TPT, uptake in household contacts is poor. Multiple barriers prevent the optimal implementation of these policies. This manuscript lays out potential next steps for closing the policy-to-implementation gap in household contacts of all ages.

Projecting the criticality of COVID-19 transmission in India using GIS and machine learning methods

There is a new public health catastrophe forbidding the world. With the advent and spread of 2019 novel coronavirus (2019-nCoV). Learning from the experiences of various countries and the World Health Organization (WHO) guidelines, social distancing, use of sanitizers, thermal screening, quarantining, and provision of lockdown in the cities being the effective measure that can contain the spread of the pandemic. Though complete lockdown helps in containing the spread, it generates complexity by breaking the economic activity chain. Besides, laborers, farmers, and workers may lose their daily earnings. Owing to these detrimental effects, the government has to open the lockdown strategically. Prediction of the COVID-19 spread and analyzing when the cases would stop increasing helps in developing a strategy. An attempt is made in this paper to predict the time after which the number of new cases stops rising, considering the strong implementation of lockdown conditions using three different techniques such as Decision Tree, Support Vector Machine, and Gaussian Process Regression algorithm are used to project the number of cases. Thus, the projections are used in identifying inflection points, which would help in planning the easing of lockdown in a few of the areas strategically. The criticality in a region is evaluated using the criticality index (CI), which is proposed by authors in one of the past of research works. This research work is made available in a dashboard to enable the decision-makers to combat the pandemic.

Psychometric evaluation of a new drug-resistant tuberculosis stigma scale

OBJECTIVES

Stigma contributes to diagnostic delay, disease concealment, and reduced wellbeing for people with multidrug-resistant tuberculosis (MDR-TB) and their communities. Despite the negative effects of stigma, there are no scales to measure stigma in people with MDR-TB. This study aimed to develop and validate a scale to measure stigma in people affected by MDR-TB in Vietnam.

STUDY DESIGN AND SETTING

People with rifampicin-resistant (RR)-MDR-TB who had completed at least 3 months of treatment were invited to complete a survey containing 45 draft stigma items. Data analysis included exploratory factor analysis, internal consistency, content, criterion and construct validity, and test-retest reliability.

RESULTS

A total of 315 people with RR/MDR-TB completed the survey. Exploratory factor analysis revealed a 14 item RR/MDR-TB stigma scale with four subscales, including guilt, social exclusion, physical isolation, and blame. Internal consistency and test-retest reliability were good (Cronbach's Alpha = 0.76, ICC = 0.92). Construct validity was adequate with moderate correlations with related constructs.

CONCLUSION

Our RR/MDR-TB Scale demonstrated good psychometric properties in Vietnam. This scale will assist in the measurement of stigma in people with RR/MDR-TB. It will also aid in the evaluation of stigma reduction interventions in people with RR/MDR-TB.

Excess Risk of Tuberculous Infection among Extra-Household Contacts of Tuberculosis Cases in an African City

RATIONALE

Although households of tuberculosis cases represent a setting for intense transmission of M. tuberculosis, household exposure accounts for less than 20% of transmission within a community.

OBJECTIVES

To estimate excess risk of M. tuberculosis infection among household and extra-household contacts of index cases.

METHODS

We performed a cross-sectional study in Kampala, Uganda, to delineate social networks of tuberculosis cases and matched controls without tuberculosis. We estimated the age-stratified prevalence difference of tuberculous infection between case and control networks, partitioned as household and extra-household contacts.

RESULTS

We enrolled 123 index cases, 124 index controls, and 2415 first-degree network contacts. The prevalence of infection was highest among household contacts of cases (61.5%), lowest among household contacts of controls (25.2%), and intermediary among extra-household tuberculosis contacts (44.9%) and extra-household control contacts (41.2%). The age-adjusted prevalence difference between extra-household contacts of cases and their controls was 5.4%. The prevalence of infection was similar among the majority of extra-household case contacts and corresponding controls (47%).

CONCLUSIONS

Most first-degree social network members of tuberculosis cases do not have adequate contact with the index case to experience additional risk for infection but appear instead to acquire infection through unrecognized exposures with infectious cases in the community.

Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID

This study has two goals. The first is to explain the geo-environmental determinants of the accelerated diffusion of COVID-19 that is generating a high level of deaths. The second is to suggest a strategy to cope with future epidemic threats similar to COVID-19 having an accelerated viral infectivity in society. Using data on sample of N = 55 Italian province capitals, and data of infected individuals at as of April 7th, 2020, results reveal that the accelerate and vast diffusion of COVID-19 in North Italy has a high association with air pollution of cities measured with days exceeding the limits set for PM10 (particulate matter 10 μm or less in diameter) or ozone. In particular, hinterland cities with average high number of days exceeding the limits set for PM10 (and also having a low wind speed) have a very high number of infected people on 7th April 2020 (arithmetic mean is about 2200 infected individuals, with average polluted days greater than 80 days per year), whereas coastal cities also having days exceeding the limits set for PM10 or ozone but with high wind speed have about 944.70 average infected individuals, with about 60 average polluted days per year; moreover, cities having more than 100 days of air pollution (exceeding the limits set for PM10), they have a very high average number of infected people (about 3350 infected individuals, 7th April 2020), whereas cities having less than 100 days of air pollution per year, they have a lower average number of infected people (about 1014 individuals). The findings here also suggest that to minimize the impact of future epidemics similar to COVID-19, the max number of days per year that Italian provincial capitals or similar industrialized cities can exceed the limits set for PM10 or for ozone, considering their meteorological conditions, is about 48 days. Moreover, results here reveal that the explanatory variable of air pollution in cities seems to be a more important predictor in the initial phase of diffusion of viral infectivity (on 17th March 2020, b1 = 1.27, p < 0.001) than interpersonal contacts (b2 = 0.31, p < 0.05). In the second phase of maturity of the transmission dynamics of COVID-19, air pollution reduces intensity (on 7th April 2020 with b'1 = 0.81, p < 0.001) also because of the indirect effect of lockdown, whereas regression coefficient of transmission based on interpersonal contacts has a stable level (b'2 = 0.31, p < 0.01). This result reveals that accelerated transmission dynamics of COVID-19 is due to mainly to the mechanism of "air pollution-to-human transmission" (airborne viral infectivity) rather than "human-to-human transmission". Overall, then, transmission dynamics of viral infectivity, such as COVID-19, is due to systemic causes: general factors that are the same for all regions (e.g., biological characteristics of virus, incubation period, etc.) and specific factors which are different for each region and/or city (e.g., complex interaction between air pollution, meteorological conditions and biological characteristics of viral infectivity) and health level of individuals (habits, immune system, age, sex, etc.). Lessons learned for COVID-19 in the case study here suggest that a proactive strategy to cope with future epidemics is also to apply especially an environmental and sustainable policy based on reduction of levels of air pollution mainly in hinterland and polluting cities- (having low wind speed, high percentage of moisture and number of fog days) -that seem to have an environment that foster a fast transmission dynamics of viral infectivity in society. Hence, in the presence of polluting industrialization in regions that can trigger the mechanism of air pollution-to-human transmission dynamics of viral infectivity, this study must conclude that a comprehensive strategy to prevent future epidemics similar to COVID-19 has to be also designed in environmental and socioeconomic terms, that is also based on sustainability science and environmental science, and not only in terms of biology, medicine, healthcare and health sector.

Baseline Assessment of Risk Factors of Presumptive Tuberculosis among under Five Children Living with an Index Client under Treatment in Mbale District, Eastern Uganda

BACKGROUND AND AIMS

Children in contact with adults having pulmonary Tuberculosis (TB) are vulnerable to TB infection and hence contact tracing and screening is important for early detection of infection. However, there are few contacts traced and the prevalence and risk factors for transmission are not well studied. The objective of this study was to determine the prevalence of infection and risk factors associated with TB transmission among under five children in household contact with adult pulmonary TB patients.

MATERIALS AND METHODS

A cross sectional study was carried out in three health facilities with a high TB burden in Mbale District, Eastern Uganda involving all under five household contacts of adults with pulmonary tuberculosis recorded in the TB register from October 2018 to March 2019 and still on treatment. Structured questionnaires were administered to the index clients to obtain their demographic and clinical data about TB, HIV as well as information on the children. Children were screened using the intensive case finding forms to identify presumptive cases.

RESULTS

The total number of index TB Clients line listed were 70. Number of clients traced was 38, 21 (%) of whom had children under five years and a total of 33 children were identified. The number of presumptive cases was 9/33 (27.27%). 77.8% of the presumptive cases were living in poorly ventilated houses.

CONCLUSION

The study identified children with presumptive TB and various risk factors for TB transmission. Intensive contact tracing can therefore help reduce TB transmission within the communities. It is recommended to undertake studies aiming at improving contact tracing and strategies to eliminate the risk factors to TB transmission.

Two mechanisms for accelerated diffusion of COVID-19 outbreaks in regions with high intensity of population and polluting industrialization: the air pollution-to-human and human-to-human transmission dynamics (Preprint).. [DOI: 10.2196/preprints.19331]

BACKGROUND

Coronavirus disease 2019 (COVID-19) is viral infection that generates a severe acute respiratory syndrome with serious pneumonia that may result in progressive respiratory failure and death.

OBJECTIVE

This study has two goals. The first is to explain the main factors determining the diffusion of COVID-19 that is generating a high level of deaths. The second is to suggest a strategy to cope with future epidemic threats with of accelerated viral infectivity in society.

METHODS

Correlation and regression analyses on on data of N=55 Italian province capitals, and data of infected individuals at as of April 2020.

RESULTS

The main results are: o The accelerate and vast diffusion of COVID-19 in North Italy has a high association with air pollution. o Hinterland cities have average days of exceeding the limits set for PM10 (particulate matter 10 micrometers or less in diameter) equal to 80 days, and an average number of infected more than 2,000 individuals as of April 1st, 2020, coastal cities have days of exceeding the limits set for PM10 equal to 60 days and have about 700 infected in average. o Cities that average number of 125 days exceeding the limits set for PM10, last year, they have an average number of infected individual higher than 3,200 units, whereas cities having less than 100 days (average number of 48 days) exceeding the limits set for PM10, they have an average number of about 900 infected individuals. o The results reveal that accelerated transmission dynamics of COVID-19 in specific environments is due to two mechanisms given by: air pollution-to-human transmission and human-to-human transmission; in particular, the mechanisms of air pollution-to-human transmission play a critical role rather than human-to-human transmission. o The finding here suggests that to minimize future epidemic similar to COVID-19, the max number of days per year in which cities can exceed the limits set for PM10 or for ozone, considering their meteorological condition, is less than 50 days. After this critical threshold, the analytical output here suggests that environmental inconsistencies because of the combination between air pollution and meteorological conditions (with high moisture%, low wind speed and fog) trigger a take-off of viral infectivity (accelerated epidemic diffusion) with damages for health of population, economy and society.

CONCLUSIONS

Considering the complex interaction between air pollution, meteorological conditions and biological characteristics of viral infectivity, lessons learned for COVID-19 have to be applied for a proactive socioeconomic strategy to cope with future epidemics, especially an environmental policy based on reduction of air pollution mainly in hinterland zones of countries, having low wind speed, high percentage of moisture and fog that create an environment that can damage immune system of people and foster a fast transmission of viral infectivity similar to the COVID-19.

CLINICALTRIAL

not applicable

Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts

BACKGROUND

Isolation of cases and contact tracing is used to control outbreaks of infectious diseases, and has been used for coronavirus disease 2019 (COVID-19). Whether this strategy will achieve control depends on characteristics of both the pathogen and the response. Here we use a mathematical model to assess if isolation and contact tracing are able to control onwards transmission from imported cases of COVID-19.

METHODS

We developed a stochastic transmission model, parameterised to the COVID-19 outbreak. We used the model to quantify the potential effectiveness of contact tracing and isolation of cases at controlling a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-like pathogen. We considered scenarios that varied in the number of initial cases, the basic reproduction number (R₀), the delay from symptom onset to isolation, the probability that contacts were traced, the proportion of transmission that occurred before symptom onset, and the proportion of subclinical infections. We assumed isolation prevented all further transmission in the model. Outbreaks were deemed controlled if transmission ended within 12 weeks or before 5000 cases in total. We measured the success of controlling outbreaks using isolation and contact tracing, and quantified the weekly maximum number of cases traced to measure feasibility of public health effort.

FINDINGS

Simulated outbreaks starting with five initial cases, an R₀ of 1·5, and 0% transmission before symptom onset could be controlled even with low contact tracing probability; however, the probability of controlling an outbreak decreased with the number of initial cases, when R₀ was 2·5 or 3·5 and with more transmission before symptom onset. Across different initial numbers of cases, the majority of scenarios with an R₀ of 1·5 were controllable with less than 50% of contacts successfully traced. To control the majority of outbreaks, for R₀ of 2·5 more than 70% of contacts had to be traced, and for an R₀ of 3·5 more than 90% of contacts had to be traced. The delay between symptom onset and isolation had the largest role in determining whether an outbreak was controllable when R₀ was 1·5. For R₀ values of 2·5 or 3·5, if there were 40 initial cases, contact tracing and isolation were only potentially feasible when less than 1% of transmission occurred before symptom onset.

INTERPRETATION

In most scenarios, highly effective contact tracing and case isolation is enough to control a new outbreak of COVID-19 within 3 months. The probability of control decreases with long delays from symptom onset to isolation, fewer cases ascertained by contact tracing, and increasing transmission before symptoms. This model can be modified to reflect updated transmission characteristics and more specific definitions of outbreak control to assess the potential success of local response efforts.

FUNDING

Wellcome Trust, Global Challenges Research Fund, and Health Data Research UK.

Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts

BACKGROUND

Isolation of cases and contact tracing is used to control outbreaks of infectious diseases, and has been used for coronavirus disease 2019 (COVID-19). Whether this strategy will achieve control depends on characteristics of both the pathogen and the response. Here we use a mathematical model to assess if isolation and contact tracing are able to control onwards transmission from imported cases of COVID-19.

METHODS

We developed a stochastic transmission model, parameterised to the COVID-19 outbreak. We used the model to quantify the potential effectiveness of contact tracing and isolation of cases at controlling a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-like pathogen. We considered scenarios that varied in the number of initial cases, the basic reproduction number (R0), the delay from symptom onset to isolation, the probability that contacts were traced, the proportion of transmission that occurred before symptom onset, and the proportion of subclinical infections. We assumed isolation prevented all further transmission in the model. Outbreaks were deemed controlled if transmission ended within 12 weeks or before 5000 cases in total. We measured the success of controlling outbreaks using isolation and contact tracing, and quantified the weekly maximum number of cases traced to measure feasibility of public health effort.

FINDINGS

Simulated outbreaks starting with five initial cases, an R0 of 1·5, and 0% transmission before symptom onset could be controlled even with low contact tracing probability; however, the probability of controlling an outbreak decreased with the number of initial cases, when R0 was 2·5 or 3·5 and with more transmission before symptom onset. Across different initial numbers of cases, the majority of scenarios with an R0 of 1·5 were controllable with less than 50% of contacts successfully traced. To control the majority of outbreaks, for R0 of 2·5 more than 70% of contacts had to be traced, and for an R0 of 3·5 more than 90% of contacts had to be traced. The delay between symptom onset and isolation had the largest role in determining whether an outbreak was controllable when R0 was 1·5. For R0 values of 2·5 or 3·5, if there were 40 initial cases, contact tracing and isolation were only potentially feasible when less than 1% of transmission occurred before symptom onset.

INTERPRETATION

In most scenarios, highly effective contact tracing and case isolation is enough to control a new outbreak of COVID-19 within 3 months. The probability of control decreases with long delays from symptom onset to isolation, fewer cases ascertained by contact tracing, and increasing transmission before symptoms. This model can be modified to reflect updated transmission characteristics and more specific definitions of outbreak control to assess the potential success of local response efforts.

FUNDING

Wellcome Trust, Global Challenges Research Fund, and Health Data Research UK.