Estimating age-mixing patterns relevant for the transmission of airborne infections

Contact screening and management in a high-transmission MDR-TB setting in Papua New Guinea: Progress, challenges and future directions

Daru, South Fly District, Papua New Guinea is a high transmission setting for multidrug-resistant tuberculosis (MDR-TB). An emergency response by the Government in 2014 established a high-quality model for treatment and care. Household contact screening and management commenced in 2016 with TB preventive treatment (TPT) for well young child (<5 years) contacts of people with drug-susceptible TB and later expanded to young child contacts of MDR-TB. The model of care is community-based and led by non-specialist health workers, under supervision. An electronic medical record system supports care, reporting and operational research. Community engagement and education has been central, with a concerted focus on peer-led counselling and patient-centred services to improve TPT uptake and completion. Challenges include the application of households as the unit of intervention for detection of active TB and TPT provision. Our implementation experience in Daru has highlighted significant population mixing dynamics with most transmission likely occurring outside the household. We propose a community-wide screening approach with the provision of TPT based on testing to include older children, adolescents, and young adults. As there is the possibility of MDR-TB infection irrespective of the drug susceptibility of the household index case, a novel option is a combination TPT regimen of 6 months of daily isoniazid and levofloxacin (6HLfx). A sensitive aged-related algorithm to detect and exclude active TB is being developed. Ongoing community engagement, quality data systems with operational research to evaluate approaches are critical in high transmission MDR-TB settings.

Exploring human mixing patterns based on time use and social contact data and their implications for infectious disease transmission models

BACKGROUND

The increasing availability of data on social contact patterns and time use provides invaluable information for studying transmission dynamics of infectious diseases. Social contact data provide information on the interaction of people in a population whereas the value of time use data lies in the quantification of exposure patterns. Both have been used as proxies for transmission risks within in a population and the combination of both sources has led to investigate which contacts are more suitable to describe these transmission risks.

METHODS

We used social contact and time use data from 1707 participants from a survey conducted in Flanders, Belgium in 2010-2011. We calculated weighted exposure time and social contact matrices to analyze age- and gender-specific mixing patterns and to quantify behavioral changes by distance from home. We compared the value of both separate and combined data sources for explaining seroprevalence and incidence data on parvovirus-B19, Varicella-Zoster virus (VZV) and influenza like illnesses (ILI), respectively.

RESULTS

Assortative mixing and inter-generational interaction is more pronounced in the exposure matrix due to the high proportion of time spent at home. This pattern is less pronounced in the social contact matrix, which is more impacted by the reported contacts at school and work. The average number of contacts declined with distance. On the individual-level, we observed an increase in the number of contacts and the transmission potential by distance when travelling. We found that both social contact data and time use data provide a good match with the seroprevalence and incidence data at hand. When comparing the use of different combinations of both data sources, we found that the social contact matrix based on close contacts of at least 4 h appeared to be the best proxy for parvovirus-B19 transmission. Social contacts and exposure time were both on their own able to explain VZV seroprevalence data though combining both scored best. Compared with the contact approach, the time use approach provided the better fit to the ILI incidence data.

CONCLUSIONS

Our work emphasises the common and complementary value of time use and social contact data for analysing mixing behavior and analysing infectious disease transmission. We derived spatial, temporal, age-, gender- and distance-specific mixing patterns, which are informative for future modelling studies.

Improving Estimates of Social Contact Patterns for Airborne Transmission of Respiratory Pathogens

Data on social contact patterns are widely used to parameterize age-mixing matrices in mathematical models of infectious diseases. Most studies focus on close contacts only (i.e., persons spoken with face-to-face). This focus may be appropriate for studies of droplet and short-range aerosol transmission but neglects casual or shared air contacts, who may be at risk from airborne transmission. Using data from 2 provinces in South Africa, we estimated age mixing patterns relevant for droplet transmission, nonsaturating airborne transmission, and Mycobacterium tuberculosis transmission, an airborne infection where saturation of household contacts occurs. Estimated contact patterns by age did not vary greatly between the infection types, indicating that widespread use of close contact data may not be resulting in major inaccuracies. However, contact in persons >50 years of age was lower when we considered casual contacts, and therefore the contribution of older age groups to airborne transmission may be overestimated.

Are we underestimating the annual risk of infection with Mycobacterium tuberculosis in high-burden settings?

The annual risk of infection with Mycobacterium tuberculosis determines a population's exposure level and thus the fraction of incident tuberculosis resulting from recent infection (often considered as having occurred within the past 2 years). Contemporary annual risk of infection estimates centre around 1% in most high-burden countries. We present three arguments why these estimates-primarily derived from cross-sectional tuberculin surveys in young school children (aged 5-12 years)-might underrepresent the true annual risk of infection. First, young children are expected to have lower risk of infection than older adolescents and adults (ie, those aged 15 years and older). Second, exposure might not lead to a positive test result in some individuals. Third, cross-sectional surveys might overlook transient immune responses. Accounting for these biases, the true annual risk of infection among adults in high-burden settings is probably closer to 5-10%. Consequently, most tuberculosis in those settings should reflect infection within the past 2 years rather than remote infection occurring many years ago. Under this reframing, major reductions in tuberculosis incidence could be achievable by focusing on the minority of people who have been recently infected.

Updating age-specific contact structures to match evolving demography in a dynamic mathematical model of tuberculosis vaccination

We investigated the effects of updating age-specific social contact matrices to match evolving demography on vaccine impact estimates. We used a dynamic transmission model of tuberculosis in India as a case study. We modelled four incremental methods to update contact matrices over time, where each method incorporated its predecessor: fixed contact matrix (M0), preserved contact reciprocity (M1), preserved contact assortativity (M2), and preserved average contacts per individual (M3). We updated the contact matrices of a deterministic compartmental model of tuberculosis transmission, calibrated to epidemiologic data between 2000 and 2019 derived from India. We additionally calibrated the M0, M2, and M3 models to the 2050 TB incidence rate projected by the calibrated M1 model. We stratified age into three groups, children (<15y), adults (≥15y, <65y), and the elderly (≥65y), using World Population Prospects demographic data, between which we applied POLYMOD-derived social contact matrices. We simulated an M72-AS01_E-like tuberculosis vaccine delivered from 2027 and estimated the per cent TB incidence rate reduction (IRR) in 2050 under each update method. We found that vaccine impact estimates in all age groups remained relatively stable between the M0–M3 models, irrespective of vaccine-targeting by age group. The maximum difference in impact, observed following adult-targeted vaccination, was 7% in the elderly, in whom we observed IRRs of 19% (uncertainty range 13–32), 20% (UR 13–31), 22% (UR 14–37), and 26% (UR 18–38) following M0, M1, M2 and M3 updates, respectively. We found that model-based TB vaccine impact estimates were relatively insensitive to demography-matched contact matrix updates in an India-like demographic and epidemiologic scenario. Current model-based TB vaccine impact estimates may be reasonably robust to the lack of contact matrix updates, but further research is needed to confirm and generalise this finding.

Mathematical models are increasingly used to predict the impact of new and existing tools, e.g., vaccines, that aim to control the transmission of infectious diseases. Within these models, investigators often assume that individuals contact each other according to specific patterns, particularly between and within different age groups. These patterns are typically derived from surveys of social contact or other models and reflect the particular age composition of their source population. However, when models are set over long time scales, e.g., decades, population age composition is likely to change. Despite this reality, few models update their contact patterns to match changing age composition. Furthermore, none have assessed whether their final estimates of disease-control intervention impact are affected by updating contact patterns. We measured whether different techniques to update social contact patterns to match evolving demography produce different vaccine impact estimates, using a mathematical model of tuberculosis set in an India-like scenario between 2025–2050. We found that vaccine impact was stable across a range of different update methods. Thus, existing model-based vaccine impact estimates may be stable to a lack of these updates, but further work is required to confirm these findings.

Prevalence and Risk Factors for Mycobacterium tuberculosis Infection Among Adolescents in Rural South Africa

BACKGROUND

We aimed to estimate the prevalence of and explore risk factors for Mycobacterium tuberculosis infection among adolescents in a high tuberculosis (TB) and human immunodeficiency virus (HIV) prevalence setting.

METHODS

A cross-sectional study of adolescents (10-19 years) randomly selected from a demographic surveillance area (DSA) in rural KwaZulu-Natal, South Africa. We determined M tuberculosis infection status using the QuantiFERON-TB Gold-plus assay. We used HIV data from the DSA to estimate community-level adult HIV prevalence and random-effects logistic regression to identify risk factors for TB infection.

RESULTS

We enrolled 1094 adolescents (548 [50.1%] female); M tuberculosis infection prevalence (weighted for nonresponse by age, sex, and urban/rural residence) was 23.0% (95% confidence interval [CI], 20.6-25.6%). Mycobacterium tuberculosis infection was associated with older age (adjusted odds ratio [aOR], 1.37; 95% CI, 1.10-1.71, for increasing age-group [12-14, 15-17, and 18-19 vs 10-11 years]), ever (vs never) having a household TB contact (aOR, 2.13; 95% CI, 1.25-3.64), and increasing community-level HIV prevalence (aOR, 1.43 and 95% CI, 1.07-1.92, for increasing HIV prevalence category [25%-34.9%, 35%-44.9%, ≥45% vs <25%]).

CONCLUSIONS

Our data support prioritizing TB prevention and care activities in TB-affected households and high HIV prevalence communities.