The prevalence of tuberculosis infection among foreign-born Canadians: a modelling study.

BACKGROUND: The prevalence of tuberculosis infection is critical to the design of tuberculosis prevention strategies, yet is unknown in Canada. We estimated the prevalence of tuberculosis infection among Canadian residents born abroad. METHODS: We estimated the prevalence of tuberculosis infection by age and year of migration to Canada for people from each of 168 countries by constructing country-specific and calendar year-specific trends for annual risk of infection using a previously developed model. We combined country-specific prevalence estimates with Canadian Census data from 2001, 2006, 2011, 2016 and 2021 to estimate the overall prevalence of tuberculosis infection among foreign-born Canadian residents. RESULTS: The estimated overall prevalence of tuberculosis infection among foreign-born people in Canada was 25% (95% uncertainty interval [UI] 20%-35%) for census year 2001, 24% (95% UI 20%-33%) for 2006, 23% (95% UI 19%-30%) for 2011, 22% (95% UI 19%-28%) for 2016 and 22% (95% UI 19%-27%) for 2021. The prevalence increased with age at migration and incidence of tuberculosis in the country of origin. In 2021, the estimated prevalence of infection among foreign-born residents was lowest in Quebec (19%, 95% UI 16%-24%) and highest in Alberta (24%, 95% UI 21%-28%) and British Columbia (24%, 95% UI 20%-30%). Among all foreign-born Canadian residents with tuberculosis infection in 2021, we estimated that only 1 in 488 (95% UI 185-1039) had become infected within the 2 preceding years. INTERPRETATION: About 1 in 4 foreign-born Canadian residents has tuberculosis infection, but very few were infected within the 2 preceding years (the highest risk period for progression to tuberculosis disease). These data may inform future tuberculosis infection screening policies.

Mental health and wellbeing of health and aged care workers in Australia, May 2021 - June 2022: a longitudinal cohort study.

OBJECTIVES: To assess the mental health and wellbeing of health and aged care workers in Australia during the second and third years of the coronavirus disease 2019 (COVID-19) pandemic, overall and by occupation group. DESIGN, SETTING, PARTICIPANTS: Longitudinal cohort study of health and aged care workers (ambulance, hospitals, primary care, residential aged care) in Victoria: May-July 2021 (survey 1), October-December 2021 (survey 2), and May-June 2022 (survey 3). MAIN OUTCOME MEASURES: Proportions of respondents (adjusted for age, gender, socio-economic status) reporting moderate to severe symptoms of depression (Patient Health Questionnaire-9, PHQ-9), anxiety (Generalized Anxiety Disorder scale, GAD-7), or post-traumatic stress (Impact of Event Scale-6, IES-6), burnout (abbreviated Maslach Burnout Inventory, aMBI), or high optimism (10-point visual analogue scale); mean scores (adjusted for age, gender, socio-economic status) for wellbeing (Personal Wellbeing Index-Adult, PWI-A) and resilience (Connor Davidson Resilience Scale 2, CD-RISC-2). RESULTS: A total of 1667 people responded to at least one survey (survey 1, 989; survey 2, 1153; survey 3, 993; response rate, 3.3%). Overall, 1211 survey responses were from women (72.6%); most respondents were hospital workers (1289, 77.3%) or ambulance staff (315, 18.9%). The adjusted proportions of respondents who reported moderate to severe symptoms of depression (survey 1, 16.4%; survey 2, 22.6%; survey 3, 19.2%), anxiety (survey 1, 8.8%; survey 2, 16.0%; survey 3, 11.0%), or post-traumatic stress (survey 1, 14.6%; survey 2, 35.1%; survey 3, 14.9%) were each largest for survey 2. The adjusted proportions of participants who reported moderate to severe symptoms of burnout were higher in surveys 2 and 3 than in survey 1, and the proportions who reported high optimism were smaller in surveys 2 and 3 than in survey 1. Adjusted mean scores for wellbeing and resilience were similar at surveys 2 and 3 and lower than at survey 1. The magnitude but not the patterns of change differed by occupation group. CONCLUSION: Burnout was more frequently reported and mean wellbeing and resilience scores were lower in mid-2022 than in mid-2021 for Victorian health and aged care workers who participated in our study. Evidence-based mental health and wellbeing programs for workers in health care organisations are needed. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry: ACTRN12621000533897 (observational study; retrospective).

Detecting Mycobacterium tuberculosis Infection in Children Migrating to Australia.

In 2015, Australia updated premigration screening for tuberculosis (TB) disease in children 2-10 years of age to include testing for infection with Mycobacterium tuberculosis and enable detection of latent TB infection (LTBI). We analyzed TB screening results in children <15 years of age during November 2015-June 2017. We found 45,060 child applicants were tested with interferon-gamma release assay (IGRA) (57.7% of tests) or tuberculin skin test (TST) (42.3% of tests). A total of 21 cases of TB were diagnosed: 4 without IGRA or TST, 10 with positive IGRA or TST, and 7 with negative results. LTBI was detected in 3.3% (1,473/44,709) of children, for 30 applicants screened per LTBI case detected. LTBI-associated factors included increasing age, TB contact, origin from a higher TB prevalence region, and testing by TST. Detection of TB and LTBI benefit children, but the updated screening program's effect on TB in Australia is likely to be limited.

Modeling the Cost-Effectiveness of Latent Tuberculosis Screening and Treatment Strategies in Recent Migrants to a Low-Incidence Setting.

Many tuberculosis (TB) cases in low-incidence settings are attributed to reactivation of latent TB infection (LTBI) acquired overseas. We assessed the cost-effectiveness of community-based LTBI screening and treatment strategies in recent migrants to a low-incidence setting (Australia). A decision-analytical Markov model was developed that cycled 1 migrant cohort (≥11-year-olds) annually over a lifetime from 2020. Postmigration/onshore and offshore (screening during visa application) strategies were compared with existing policy (chest x-ray during visa application). Outcomes included TB cases averted and discounted cost per quality-adjusted life-year (QALY) gained from a health-sector perspective. Most recent migrants are young adults and cost-effectiveness is limited by their relatively low LTBI prevalence, low TB mortality risks, and high emigration probability. Onshore strategies cost at least $203,188 (Australian) per QALY gained, preventing approximately 2.3%-7.0% of TB cases in the cohort. Offshore strategies (screening costs incurred by migrants) cost at least $13,907 per QALY gained, preventing 5.5%-16.9% of cases. Findings were most sensitive to the LTBI treatment quality-of-life decrement (further to severe adverse events); with a minimal decrement, all strategies caused more ill health than they prevented. Additional LTBI strategies in recent migrants could only marginally contribute to TB elimination and are unlikely to be cost-effective unless screening costs are borne by migrants and potential LTBI treatment quality-of-life decrements are ignored.

Estimating tuberculosis drug resistance amplification rates in high-burden settings.

BACKGROUND: Antimicrobial resistance develops following the accrual of mutations in the bacterial genome, and may variably impact organism fitness and hence, transmission risk. Classical representation of tuberculosis (TB) dynamics using a single or two strain (DS/MDR-TB) model typically does not capture elements of this important aspect of TB epidemiology. To understand and estimate the likelihood of resistance spreading in high drug-resistant TB incidence settings, we used epidemiological data to develop a mathematical model of Mycobacterium tuberculosis (Mtb) transmission. METHODS: A four-strain (drug-susceptible (DS), isoniazid mono-resistant (INH-R), rifampicin mono-resistant (RIF-R) and multidrug-resistant (MDR)) compartmental deterministic Mtb transmission model was developed to explore the progression from DS- to MDR-TB in The Philippines and Viet Nam. The models were calibrated using data from national tuberculosis prevalence (NTP) surveys and drug resistance surveys (DRS). An adaptive Metropolis algorithm was used to estimate the risks of drug resistance amplification among unsuccessfully treated individuals. RESULTS: The estimated proportion of INH-R amplification among failing treatments was 0.84 (95% CI 0.79-0.89) for The Philippines and 0.77 (95% CI 0.71-0.84) for Viet Nam. The proportion of RIF-R amplification among failing treatments was 0.05 (95% CI 0.04-0.07) for The Philippines and 0.011 (95% CI 0.010-0.012) for Viet Nam. CONCLUSION: The risk of resistance amplification due to treatment failure for INH was dramatically higher than RIF. We observed RIF-R strains were more likely to be transmitted than acquired through amplification, while both mechanisms of acquisition were important contributors in the case of INH-R. These findings highlight the complexity of drug resistance dynamics in high-incidence settings, and emphasize the importance of prioritizing testing algorithms which allow for early detection of INH-R.

Revisiting the Natural History of Pulmonary Tuberculosis: A Bayesian Estimation of Natural Recovery and Mortality Rates.

BACKGROUND: Tuberculosis (TB) natural history remains poorly characterized, and new investigations are impossible as it would be unethical to follow up TB patients without treatment. METHODS: We considered the reports identified in a previous systematic review of studies from the prechemotherapy era, and extracted detailed data on mortality over time. We used a Bayesian framework to estimate the rates of TB-induced mortality and self-cure. A hierarchical model was employed to allow estimates to vary by cohort. Inference was performed separately for smear-positive TB (SP-TB) and smear-negative TB (SN-TB). RESULTS: We included 41 cohorts of SP-TB patients and 19 cohorts of pulmonary SN-TB patients in the analysis. The median estimates of the TB-specific mortality rates were 0.389 year-1 (95% credible interval [CrI], .335-.449) and 0.025 year-1 (95% CrI, .017-.035) for SP-TB and SN-TB patients, respectively. The estimates for self-recovery rates were 0.231 year-1 (95% CrI, .177-.288) and 0.130 year-1 (95% CrI, .073-.209) for SP-TB and SN-TB patients, respectively. These rates correspond to average durations of untreated TB of 1.57 years (95% CrI, 1.37-1.81) and 5.35 years (95% CrI, 3.42-8.23) for SP-TB and SN-TB, respectively, when assuming a non-TB-related mortality rate of 0.014 year-1 (ie, a 70-year life expectancy). CONCLUSIONS: TB-specific mortality rates are around 15 times higher for SP-TB than for SN-TB patients. This difference was underestimated dramatically in previous TB modeling studies, raising concerns about the accuracy of the associated predictions. Despite being less infectious, SN-TB may be responsible for equivalent numbers of secondary infections as SP-TB due to its much longer duration.

Timing of Mycobacterium tuberculosis exposure explains variation in BCG effectiveness: a systematic review and meta-analysis.

RATIONALE: The heterogeneity in efficacy observed in studies of BCG vaccination is not fully explained by currently accepted hypotheses, such as latitudinal gradient in non-tuberculous mycobacteria exposure. METHODS: We updated previous systematic reviews of the effectiveness of BCG vaccination to 31 December 2020. We employed an identical search strategy and inclusion/exclusion criteria to these earlier reviews, but reclassified several studies, developed an alternative classification system and considered study demography, diagnostic approach and tuberculosis (TB)-related epidemiological context. MAIN RESULTS: Of 21 included trials, those recruiting neonates and children aged under 5 were consistent in demonstrating considerable protection against TB for several years. Trials in high-burden settings with shorter follow-up also showed considerable protection, as did most trials in settings of declining burden with longer follow-up. However, the few trials performed in high-burden settings with longer follow-up showed no protection, sometimes with higher case rates in the vaccinated than the controls in the later follow-up period. CONCLUSIONS: The most plausible explanatory hypothesis for these results is that BCG protects against TB that results from exposure shortly after vaccination. However, we found no evidence of protection when exposure occurs later from vaccination, which would be of greater importance in trials in high-burden settings with longer follow-up. In settings of declining burden, most exposure occurs shortly following vaccination and the sustained protection observed for many years thereafter represents continued protection against this early exposure. By contrast, in settings of continued intense transmission, initial protection subsequently declines with repeated exposure to Mycobacterium tuberculosis or other pathogens.

Modelling direct and herd protection effects of vaccination against the SARS-CoV-2 Delta variant in Australia.

OBJECTIVES: To analyse the outcomes of COVID-19 vaccination by vaccine type, age group eligibility, vaccination strategy, and population coverage. DESIGN: Epidemiologic modelling to assess the final size of a COVID-19 epidemic in Australia, with vaccination program (Pfizer, AstraZeneca, mixed), vaccination strategy (vulnerable first, transmitters first, untargeted), age group eligibility threshold (5 or 15 years), population coverage, and pre-vaccination effective reproduction number ( Reffv¯ ) for the SARS-CoV-2 Delta variant as factors. MAIN OUTCOME MEASURES: Numbers of SARS-CoV-2 infections; cumulative hospitalisations, deaths, and years of life lost. RESULTS: Assuming Reffv¯ = 5, the current mixed vaccination program (vaccinating people aged 60 or more with the AstraZeneca vaccine and people under 60 with the Pfizer vaccine) will not achieve herd protection unless population vaccination coverage reaches 85% by lowering the vaccination eligibility age to 5 years. At Reffv¯ = 3, the mixed program could achieve herd protection at 60-70% population coverage and without vaccinating 5-15-year-old children. At Reffv¯ = 7, herd protection is unlikely to be achieved with currently available vaccines, but they would still reduce the number of COVID-19-related deaths by 85%. CONCLUSION: Vaccinating vulnerable people first is the optimal policy when population vaccination coverage is low, but vaccinating more socially active people becomes more important as the Reffv¯ declines and vaccination coverage increases. Assuming the most plausible Reffv¯ of 5, vaccinating more than 85% of the population, including children, would be needed to achieve herd protection. Even without herd protection, vaccines are highly effective in reducing the number of deaths.

Vaccines and variants: Modelling insights into emerging issues in COVID-19 epidemiology.

Mathematical modelling has played a pivotal role in understanding the epidemiology of and guiding public health responses to the ongoing coronavirus disease of 2019 (COVID-19) pandemic. Here, we review the role of epidemiological models in understanding evolving epidemic characteristics, including the effects of vaccination and Variants of Concern (VoC). We highlight ways in which models continue to provide important insights, including (1) calculating the herd immunity threshold and evaluating its limitations; (2) verifying that nascent vaccines can prevent severe disease, infection, and transmission but may be less efficacious against VoC; (3) determining optimal vaccine allocation strategies under efficacy and supply constraints; and (4) determining that VoC are more transmissible and lethal than previously circulating strains, and that immune escape may jeopardize vaccine-induced herd immunity. Finally, we explore how models can help us anticipate and prepare for future stages of COVID-19 epidemiology (and that of other diseases) through forecasts and scenario projections, given current uncertainties and data limitations.

Estimating Long-term Tuberculosis Reactivation Rates in Australian Migrants.

BACKGROUND: The risk of progression to tuberculosis (TB) disease is greatest soon after infection, yet disease may occur many years or decades later. However, rates of TB reactivation long after infection remain poorly quantified. Australia has a low incidence of TB and most cases occur among migrants. We explored how TB rates in Australian migrants varied with time from migration, age, and gender. METHODS: We combined TB notifications in census years 2006, 2011, and 2016 with time- and country-specific estimates of latent TB prevalences in migrant cohorts to quantify postmigration reactivation rates. RESULTS: During the census years, 3246 TB cases occurred among an estimated 2 084 000 migrants with latent TB. There were consistent trends in postmigration reactivation rates, which appeared to be dependent on both time from migration and age. Rates were lower in cohorts with increasing time, until at least 20 years from migration, and on this background there also appeared to be increasing rates during youth (15-24 years of age) and in those aged 70 years and above. Within 5 years of migration, annual reactivation rates were approximately 400 per 100 000 (uncertainty interval [UI] 320-480), dropping to 170 (UI 130-220) from 5 to 10 years and 110 (UI 70-160) from 10 to 20 years, then sustaining at 60-70 per 100 000 up to 60 years from migration. Rates varied depending on age at migration. CONCLUSIONS: Postmigration reactivation rates appeared to show dependency on both time from migration and age. This approach to quantifying reactivation risks will enable evaluations of the potential impacts of TB control and elimination strategies.

Role of modelling in COVID-19 policy development.

Models have played an important role in policy development to address the COVID-19 outbreak from its emergence in China to the current global pandemic. Early projections of international spread influenced travel restrictions and border closures. Model projections based on the virus's infectiousness demonstrated its pandemic potential, which guided the global response to and prepared countries for increases in hospitalisations and deaths. Tracking the impact of distancing and movement policies and behaviour changes has been critical in evaluating these decisions. Models have provided insights into the epidemiological differences between higher and lower income countries, as well as vulnerable population groups within countries to help design fit-for-purpose policies. Economic evaluation and policies have combined epidemic models and traditional economic models to address the economic consequences of COVID-19, which have informed policy calls for easing restrictions. Social contact and mobility models have allowed evaluation of the pathways to safely relax mobility restrictions and distancing measures. Finally, models can consider future end-game scenarios, including how suppression can be achieved and the impact of different vaccination strategies.

Modelling insights into the COVID-19 pandemic.

Coronavirus disease 2019 (COVID-19) is a newly emerged infectious disease caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) that was declared a pandemic by the World Health Organization on 11th March, 2020. Response to this ongoing pandemic requires extensive collaboration across the scientific community in an attempt to contain its impact and limit further transmission. Mathematical modelling has been at the forefront of these response efforts by: (1) providing initial estimates of the SARS-CoV-2 reproduction rate, R0 (of approximately 2-3); (2) updating these estimates following the implementation of various interventions (with significantly reduced, often sub-critical, transmission rates); (3) assessing the potential for global spread before significant case numbers had been reported internationally; and (4) quantifying the expected disease severity and burden of COVID-19, indicating that the likely true infection rate is often orders of magnitude greater than estimates based on confirmed case counts alone. In this review, we highlight the critical role played by mathematical modelling to understand COVID-19 thus far, the challenges posed by data availability and uncertainty, and the continuing utility of modelling-based approaches to guide decision making and inform the public health response. †Unless otherwise stated, all bracketed error margins correspond to the 95% credible interval (CrI) for reported estimates.

The Importance of Heterogeneity to the Epidemiology of Tuberculosis.

Although less well-recognized than for other infectious diseases, heterogeneity is a defining feature of tuberculosis (TB) epidemiology. To advance toward TB elimination, this heterogeneity must be better understood and addressed. Drivers of heterogeneity in TB epidemiology act at the level of the infectious host, organism, susceptible host, environment, and distal determinants. These effects may be amplified by social mixing patterns, while the variable latent period between infection and disease may mask heterogeneity in transmission. Reliance on notified cases may lead to misidentification of the most affected groups, as case detection is often poorest where prevalence is highest. Assuming that average rates apply across diverse groups and ignoring the effects of cohort selection may result in misunderstanding of the epidemic and the anticipated effects of control measures. Given this substantial heterogeneity, interventions targeting high-risk groups based on location, social determinants, or comorbidities could improve efficiency, but raise ethical and equity considerations.

Profiling Mycobacterium tuberculosis transmission and the resulting disease burden in the five highest tuberculosis burden countries.

BACKGROUND: Tuberculosis (TB) control efforts are hampered by an imperfect understanding of TB epidemiology. The true age distribution of disease is unknown because a large proportion of individuals with active TB remain undetected. Understanding of transmission is limited by the asymptomatic nature of latent infection and the pathogen's capacity for late reactivation. A better understanding of TB epidemiology is critically needed to ensure effective use of existing and future control tools. METHODS: We use an agent-based model to simulate TB epidemiology in the five highest TB burden countries-India, Indonesia, China, the Philippines and Pakistan-providing unique insights into patterns of transmission and disease. Our model replicates demographically realistic populations, explicitly capturing social contacts between individuals based on local estimates of age-specific contact in household, school and workplace settings. Time-varying programmatic parameters are incorporated to account for the local history of TB control. RESULTS: We estimate that the 15-19-year-old age group is involved in more than 20% of transmission events in India, Indonesia, the Philippines and Pakistan, despite representing only 5% of the local TB incidence. According to our model, childhood TB represents around one fifth of the incident TB cases in these four countries. In China, three quarters of incident TB were estimated to occur in the ≥ 45-year-old population. The calibrated per-contact transmission risk was found to be similar in each of the five countries despite their very different TB burdens. CONCLUSIONS: Adolescents and young adults are a major driver of TB in high-incidence settings. Relying only on the observed distribution of disease to understand the age profile of transmission is potentially misleading.

Cost-effectiveness of 3 months of weekly rifapentine and isoniazid compared with other standard treatment regimens for latent tuberculosis infection: a decision analysis study.

Background: Latent tuberculosis infection (LTBI) is a critical driver of the global burden of active TB, and therefore LTBI treatment is key for TB elimination. Treatment regimens for LTBI include self-administered daily isoniazid for 6 (6H) or 9 (9H) months, self-administered daily rifampicin plus isoniazid for 3 months (3RH), self-administered daily rifampicin for 4 months (4R) and weekly rifapentine plus isoniazid for 3 months self-administered (3HP-SAT) or administered by a healthcare worker as directly observed therapy (3HP-DOT). Data on the relative cost-effectiveness of these regimens are needed to assist policymakers and clinicians in selecting an LTBI regimen. Objectives: To evaluate the cost-effectiveness of all regimens for treating LTBI. Methods: We developed a Markov model to investigate the cost-effectiveness of 3HP-DOT, 3HP-SAT, 4R, 3RH, 9H and 6H for LTBI treatment in a cohort of 10000 adults with LTBI. Cost-effectiveness was evaluated from a health system perspective over a 20 year time horizon. Results: Compared with no preventive treatment, 3HP-DOT, 3HP-SAT, 4R, 3RH, 9H and 6H prevented 496, 470, 442, 418, 370 and 276 additional cases of active TB per 10000 patients, respectively. All regimens reduced costs and increased QALYs compared with no preventive treatment. 3HP was more cost-effective under DOT than under SAT at a cost of US$27948 per QALY gained. Conclusions: Three months of weekly rifapentine plus isoniazid is more cost-effective than other regimens. Greater recognition of the benefits of short-course regimens can contribute to the scale-up of prevention and achieving the 'End TB' targets.

The role of super-spreading events in Mycobacterium tuberculosis transmission: evidence from contact tracing.

BACKGROUND: In current epidemiology of tuberculosis (TB), heterogeneity in infectiousness among TB patients is a challenge, which is not well studied. We aimed to quantify this heterogeneity and the presence of "super-spreading" events that can assist in designing optimal public health interventions. METHODS: TB epidemiologic investigation data notified between 1 January 2005 and 31 December 2015 from Victoria, Australia were used to quantify TB patients' heterogeneity in infectiousness and super-spreading events. We fitted a negative binomial offspring distribution (NBD) for the number of secondary infections and secondary active TB disease each TB patient produced. The dispersion parameter, k, of the NBD measures the level of heterogeneity, where low values of k (e.g. k < 1) indicate over-dispersion. Super-spreading was defined as patients causing as many or more secondary infections as the 99th centile of an equivalent homogeneous distribution. Contact infection was determined based on a tuberculin skin test (TST) result of ≥10 mm. A NBD model was fitted to identify index characteristics that were associated with the number of contacts infected and risk ratios (RRs) were used to quantify the strength of this association. RESULTS: There were 4190 (2312 pulmonary and 1878 extrapulmonary) index TB patients and 18,030 contacts. A total of 15,522 contacts were tested with TST, of whom 3213 had a result of ≥10 mm. The dispersion parameter, k for secondary infections was estimated at 0.16 (95%CI 0.14-0.17) and there were 414 (9.9%) super-spreading events. From the 3213 secondary infections, 2415 (75.2%) were due to super-spreading events. There were 226 contacts who developed active TB disease and a higher level of heterogeneity was found for this outcome than for secondary infection, with k estimated at 0.036 (95%CI 0.025-0.046). In regression analyses, we found that infectiousness was greater among index patients found by clinical presentation and those with bacteriological confirmation. CONCLUSION: TB transmission is highly over dispersed and super-spreading events are responsible for a substantial majority of secondary infections. Heterogeneity of transmission and super-spreading are critical issues to consider in the design of interventions and models of TB transmission dynamics.

Mycobacterium tuberculosis: Active disease and latent infection in a renal transplant cohort.

AIM: Our aim was threefold: first, to determine the incidence of active TB in our cohort, second to investigate the risk factors for active TB and third, to understand current screening practices. The ultimate goal was to use our findings to inform development of local and national guidelines. METHODS: The records of all adult patients who underwent renal transplantation at our centre from 2005 to 2014 were retrospectively reviewed to assess current screening practices, the risks for and burden of active TB. RESULTS: A total of 660 individuals underwent renal transplantation during this period, totalling 3647 person years of follow up. Two patients were diagnosed with active TB after renal transplant, resulting in an incidence of 55 per 100 000 person-years. Of 656 transplant recipients, 102 (15.5%) were born in high TB incidence countries and 89 (13.5%) had an interferon gamma release assay (IGRA) at any point. Individuals born in high TB risk countries had a much higher incidence of active TB (353 per 100 000 person-years). Ten individuals had positive IGRA tests, of whom two were treated for active TB, two received chemoprophylaxis and six were not treated. CONCLUSIONS: In the absence of formal guidelines, IGRA-based screening for LTBI was infrequently performed. Our data suggest that screening and treatment of renal transplant recipients born in high incidence countries is an important preventive measure.

Controlled fire use in early humans might have triggered the evolutionary emergence of tuberculosis.

Tuberculosis (TB) is caused by the Mycobacterium tuberculosis complex (MTBC), a wildly successful group of organisms and the leading cause of death resulting from a single bacterial pathogen worldwide. It is generally accepted that MTBC established itself in human populations in Africa and that animal-infecting strains diverged from human strains. However, the precise causal factors of TB emergence remain unknown. Here, we propose that the advent of controlled fire use in early humans created the ideal conditions for the emergence of TB as a transmissible disease. This hypothesis is supported by mathematical modeling together with a synthesis of evidence from epidemiology, evolutionary genetics, and paleoanthropology.

Methods used in the spatial analysis of tuberculosis epidemiology: a systematic review.

BACKGROUND: Tuberculosis (TB) transmission often occurs within a household or community, leading to heterogeneous spatial patterns. However, apparent spatial clustering of TB could reflect ongoing transmission or co-location of risk factors and can vary considerably depending on the type of data available, the analysis methods employed and the dynamics of the underlying population. Thus, we aimed to review methodological approaches used in the spatial analysis of TB burden. METHODS: We conducted a systematic literature search of spatial studies of TB published in English using Medline, Embase, PsycInfo, Scopus and Web of Science databases with no date restriction from inception to 15 February 2017. The protocol for this systematic review was prospectively registered with PROSPERO ( CRD42016036655 ). RESULTS: We identified 168 eligible studies with spatial methods used to describe the spatial distribution (n = 154), spatial clusters (n = 73), predictors of spatial patterns (n = 64), the role of congregate settings (n = 3) and the household (n = 2) on TB transmission. Molecular techniques combined with geospatial methods were used by 25 studies to compare the role of transmission to reactivation as a driver of TB spatial distribution, finding that geospatial hotspots are not necessarily areas of recent transmission. Almost all studies used notification data for spatial analysis (161 of 168), although none accounted for undetected cases. The most common data visualisation technique was notification rate mapping, and the use of smoothing techniques was uncommon. Spatial clusters were identified using a range of methods, with the most commonly employed being Kulldorff's spatial scan statistic followed by local Moran's I and Getis and Ord's local Gi(d) tests. In the 11 papers that compared two such methods using a single dataset, the clustering patterns identified were often inconsistent. Classical regression models that did not account for spatial dependence were commonly used to predict spatial TB risk. In all included studies, TB showed a heterogeneous spatial pattern at each geographic resolution level examined. CONCLUSIONS: A range of spatial analysis methodologies has been employed in divergent contexts, with all studies demonstrating significant heterogeneity in spatial TB distribution. Future studies are needed to define the optimal method for each context and should account for unreported cases when using notification data where possible. Future studies combining genotypic and geospatial techniques with epidemiologically linked cases have the potential to provide further insights and improve TB control.

Agent-based models of malaria transmission: a systematic review.

BACKGROUND: Much of the extensive research regarding transmission of malaria is underpinned by mathematical modelling. Compartmental models, which focus on interactions and transitions between population strata, have been a mainstay of such modelling for more than a century. However, modellers are increasingly adopting agent-based approaches, which model hosts, vectors and/or their interactions on an individual level. One reason for the increasing popularity of such models is their potential to provide enhanced realism by allowing system-level behaviours to emerge as a consequence of accumulated individual-level interactions, as occurs in real populations. METHODS: A systematic review of 90 articles published between 1998 and May 2018 was performed, characterizing agent-based models (ABMs) relevant to malaria transmission. The review provides an overview of approaches used to date, determines the advantages of these approaches, and proposes ideas for progressing the field. RESULTS: The rationale for ABM use over other modelling approaches centres around three points: the need to accurately represent increased stochasticity in low-transmission settings; the benefits of high-resolution spatial simulations; and heterogeneities in drug and vaccine efficacies due to individual patient characteristics. The success of these approaches provides avenues for further exploration of agent-based techniques for modelling malaria transmission. Potential extensions include varying elimination strategies across spatial landscapes, extending the size of spatial models, incorporating human movement dynamics, and developing increasingly comprehensive parameter estimation and optimization techniques. CONCLUSION: Collectively, the literature covers an extensive array of topics, including the full spectrum of transmission and intervention regimes. Bringing these elements together under a common framework may enhance knowledge of, and guide policies towards, malaria elimination. However, because of the diversity of available models, endorsing a standardized approach to ABM implementation may not be possible. Instead it is recommended that model frameworks be contextually appropriate and sufficiently described. One key recommendation is to develop enhanced parameter estimation and optimization techniques. Extensions of current techniques will provide the robust results required to enhance current elimination efforts.