51
|
Dahab M, van Zandvoort K, Flasche S, Warsame A, Ratnayake R, Favas C, Spiegel PB, Waldman RJ, Checchi F. COVID-19 control in low-income settings and displaced populations: what can realistically be done? Confl Health 2020; 14:54. [PMID: 32754225 PMCID: PMC7393328 DOI: 10.1186/s13031-020-00296-8] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 07/17/2020] [Indexed: 12/24/2022] Open
Abstract
COVID-19 prevention strategies in resource limited settings, modelled on the earlier response in high income countries, have thus far focused on draconian containment strategies, which impose movement restrictions on a wide scale. These restrictions are unlikely to prevent cases from surging well beyond existing hospitalisation capacity; not withstanding their likely severe social and economic costs in the long term. We suggest that in low-income countries, time limited movement restrictions should be considered primarily as an opportunity to develop sustainable and resource appropriate mitigation strategies. These mitigation strategies, if focused on reducing COVID-19 transmission through a triad of prevention activities, have the potential to mitigate bed demand and mortality by a considerable extent. This triade is based on a combination of high-uptake of community led shielding of high-risk individuals, self-isolation of mild to moderately symptomatic cases, and moderate physical distancing in the community. We outline a set of principles for communities to consider how to support the protection of the most vulnerable, by shielding them from infection within and outside their homes. We further suggest three potential shielding options, with their likely applicability to different settings, for communities to consider and that would enable them to provide access to transmission-shielded arrangements for the highest risk community members. Importantly, any shielding strategy would need to be predicated on sound, locally informed behavioural science and monitored for effectiveness and evaluating its potential under realistic modelling assumptions. Perhaps, most importantly, it is essential that these strategies not be perceived as oppressive measures and be community led in their design and implementation. This is in order that they can be sustained for an extended period of time, until COVID-19 can be controlled or vaccine and treatment options become available.
Collapse
Affiliation(s)
- Maysoon Dahab
- Conflict & Health Research Group, King’s Centre for Global Health and Health Partnerships, King’s College London, London, UK
| | - Kevin van Zandvoort
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Stefan Flasche
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Abdihamid Warsame
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Ruwan Ratnayake
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Caroline Favas
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Paul B. Spiegel
- Centre for Humanitarian Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD USA
| | - Ronald J. Waldman
- Department of Global Health, Milken Institute School of Public Health, George Washington University, Washington, DC USA
- Doctors of the World USA, New York, NY USA
| | - Francesco Checchi
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| |
Collapse
|
52
|
Walker PGT, Whittaker C, Watson OJ, Baguelin M, Winskill P, Hamlet A, Djafaara BA, Cucunubá Z, Olivera Mesa D, Green W, Thompson H, Nayagam S, Ainslie KEC, Bhatia S, Bhatt S, Boonyasiri A, Boyd O, Brazeau NF, Cattarino L, Cuomo-Dannenburg G, Dighe A, Donnelly CA, Dorigatti I, van Elsland SL, FitzJohn R, Fu H, Gaythorpe KAM, Geidelberg L, Grassly N, Haw D, Hayes S, Hinsley W, Imai N, Jorgensen D, Knock E, Laydon D, Mishra S, Nedjati-Gilani G, Okell LC, Unwin HJ, Verity R, Vollmer M, Walters CE, Wang H, Wang Y, Xi X, Lalloo DG, Ferguson NM, Ghani AC. The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries. Science 2020; 369:413-422. [PMID: 32532802 PMCID: PMC7292504 DOI: 10.1126/science.abc0035] [Citation(s) in RCA: 483] [Impact Index Per Article: 120.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 06/09/2020] [Indexed: 12/28/2022]
Abstract
The ongoing coronavirus disease 2019 (COVID-19) pandemic poses a severe threat to public health worldwide. We combine data on demography, contact patterns, disease severity, and health care capacity and quality to understand its impact and inform strategies for its control. Younger populations in lower-income countries may reduce overall risk, but limited health system capacity coupled with closer intergenerational contact largely negates this benefit. Mitigation strategies that slow but do not interrupt transmission will still lead to COVID-19 epidemics rapidly overwhelming health systems, with substantial excess deaths in lower-income countries resulting from the poorer health care available. Of countries that have undertaken suppression to date, lower-income countries have acted earlier. However, this will need to be maintained or triggered more frequently in these settings to keep below available health capacity, with associated detrimental consequences for the wider health, well-being, and economies of these countries.
Collapse
Affiliation(s)
- Patrick G T Walker
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
- Pathology and Laboratory Medicine, Warren Alpert Medical School, Brown University, Providence, RI, USA
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Peter Winskill
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Arran Hamlet
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Bimandra A Djafaara
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Zulma Cucunubá
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Daniela Olivera Mesa
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Will Green
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Hayley Thompson
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Shevanthi Nayagam
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Kylie E C Ainslie
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Adhiratha Boonyasiri
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Olivia Boyd
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Nicholas F Brazeau
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Lorenzo Cattarino
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Amy Dighe
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Christl A Donnelly
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Ilaria Dorigatti
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Sabine L van Elsland
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Rich FitzJohn
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Han Fu
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Katy A M Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Lily Geidelberg
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Nicholas Grassly
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - David Haw
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Sarah Hayes
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Wes Hinsley
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - David Jorgensen
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Edward Knock
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Daniel Laydon
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Swapnil Mishra
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Gemma Nedjati-Gilani
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Lucy C Okell
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - H Juliette Unwin
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Robert Verity
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Michaela Vollmer
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Caroline E Walters
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Haowei Wang
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Yuanrong Wang
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Xiaoyue Xi
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | | | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
| | - Azra C Ghani
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
| |
Collapse
|
53
|
Walker PGT, Whittaker C, Watson OJ, Baguelin M, Winskill P, Hamlet A, Djafaara BA, Cucunubá Z, Olivera Mesa D, Green W, Thompson H, Nayagam S, Ainslie KEC, Bhatia S, Bhatt S, Boonyasiri A, Boyd O, Brazeau NF, Cattarino L, Cuomo-Dannenburg G, Dighe A, Donnelly CA, Dorigatti I, van Elsland SL, FitzJohn R, Fu H, Gaythorpe KAM, Geidelberg L, Grassly N, Haw D, Hayes S, Hinsley W, Imai N, Jorgensen D, Knock E, Laydon D, Mishra S, Nedjati-Gilani G, Okell LC, Unwin HJ, Verity R, Vollmer M, Walters CE, Wang H, Wang Y, Xi X, Lalloo DG, Ferguson NM, Ghani AC. The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries. Science 2020; 369:413-422. [PMID: 32532802 DOI: 10.25561/77735] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 06/09/2020] [Indexed: 05/26/2023]
Abstract
The ongoing coronavirus disease 2019 (COVID-19) pandemic poses a severe threat to public health worldwide. We combine data on demography, contact patterns, disease severity, and health care capacity and quality to understand its impact and inform strategies for its control. Younger populations in lower-income countries may reduce overall risk, but limited health system capacity coupled with closer intergenerational contact largely negates this benefit. Mitigation strategies that slow but do not interrupt transmission will still lead to COVID-19 epidemics rapidly overwhelming health systems, with substantial excess deaths in lower-income countries resulting from the poorer health care available. Of countries that have undertaken suppression to date, lower-income countries have acted earlier. However, this will need to be maintained or triggered more frequently in these settings to keep below available health capacity, with associated detrimental consequences for the wider health, well-being, and economies of these countries.
Collapse
Affiliation(s)
- Patrick G T Walker
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
| | - Charles Whittaker
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
- Pathology and Laboratory Medicine, Warren Alpert Medical School, Brown University, Providence, RI, USA
| | - Marc Baguelin
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Peter Winskill
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Arran Hamlet
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Bimandra A Djafaara
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Zulma Cucunubá
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Daniela Olivera Mesa
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Will Green
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Hayley Thompson
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Shevanthi Nayagam
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Kylie E C Ainslie
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Adhiratha Boonyasiri
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Olivia Boyd
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Nicholas F Brazeau
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Lorenzo Cattarino
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Amy Dighe
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Christl A Donnelly
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Ilaria Dorigatti
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Sabine L van Elsland
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Rich FitzJohn
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Han Fu
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Katy A M Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Lily Geidelberg
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Nicholas Grassly
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - David Haw
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Sarah Hayes
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Wes Hinsley
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - David Jorgensen
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Edward Knock
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Daniel Laydon
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Swapnil Mishra
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Gemma Nedjati-Gilani
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Lucy C Okell
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - H Juliette Unwin
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Robert Verity
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Michaela Vollmer
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Caroline E Walters
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Haowei Wang
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Yuanrong Wang
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Xiaoyue Xi
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | | | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
| | - Azra C Ghani
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
| |
Collapse
|
54
|
Hilton J, Keeling MJ. Estimation of country-level basic reproductive ratios for novel Coronavirus (SARS-CoV-2/COVID-19) using synthetic contact matrices. PLoS Comput Biol 2020; 16:e1008031. [PMID: 32614817 PMCID: PMC7363110 DOI: 10.1371/journal.pcbi.1008031] [Citation(s) in RCA: 86] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 07/15/2020] [Accepted: 06/08/2020] [Indexed: 11/18/2022] Open
Abstract
The 2019-2020 pandemic of atypical pneumonia (COVID-19) caused by the virus SARS-CoV-2 has spread globally and has the potential to infect large numbers of people in every country. Estimating the country-specific basic reproductive ratio is a vital first step in public-health planning. The basic reproductive ratio (R0) is determined by both the nature of pathogen and the network of human contacts through which the disease can spread, which is itself dependent on population age structure and household composition. Here we introduce a transmission model combining age-stratified contact frequencies with age-dependent susceptibility, probability of clinical symptoms, and transmission from asymptomatic (or mild) cases, which we use to estimate the country-specific basic reproductive ratio of COVID-19 for 152 countries. Using early outbreak data from China and a synthetic contact matrix, we estimate an age-stratified transmission structure which can then be extrapolated to 151 other countries for which synthetic contact matrices also exist. This defines a set of country-specific transmission structures from which we can calculate the basic reproductive ratio for each country. Our predicted R0 is critically sensitive to the intensity of transmission from asymptomatic cases; with low asymptomatic transmission the highest values are predicted across Eastern Europe and Japan and the lowest across Africa, Central America and South-Western Asia. This pattern is largely driven by the ratio of children to older adults in each country and the observed propensity of clinical cases in the elderly. If asymptomatic cases have comparable transmission to detected cases, the pattern is reversed. Our results demonstrate the importance of age-specific heterogeneities going beyond contact structure to the spread of COVID-19. These heterogeneities give COVID-19 the capacity to spread particularly quickly in countries with older populations, and that intensive control measures are likely to be necessary to impede its progress in these countries.
Collapse
Affiliation(s)
- Joe Hilton
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
- Zeeman Institue (SBIDER), University of Warwick, Coventry, United Kingdom
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
| | - Matt J. Keeling
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
- Zeeman Institue (SBIDER), University of Warwick, Coventry, United Kingdom
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
| |
Collapse
|
55
|
Hilton J, Keeling MJ. Estimation of country-level basic reproductive ratios for novel Coronavirus (SARS-CoV-2/COVID-19) using synthetic contact matrices. PLoS Comput Biol 2020. [PMID: 32614817 DOI: 10.1101/2020.02.26.20028167v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023] Open
Abstract
The 2019-2020 pandemic of atypical pneumonia (COVID-19) caused by the virus SARS-CoV-2 has spread globally and has the potential to infect large numbers of people in every country. Estimating the country-specific basic reproductive ratio is a vital first step in public-health planning. The basic reproductive ratio (R0) is determined by both the nature of pathogen and the network of human contacts through which the disease can spread, which is itself dependent on population age structure and household composition. Here we introduce a transmission model combining age-stratified contact frequencies with age-dependent susceptibility, probability of clinical symptoms, and transmission from asymptomatic (or mild) cases, which we use to estimate the country-specific basic reproductive ratio of COVID-19 for 152 countries. Using early outbreak data from China and a synthetic contact matrix, we estimate an age-stratified transmission structure which can then be extrapolated to 151 other countries for which synthetic contact matrices also exist. This defines a set of country-specific transmission structures from which we can calculate the basic reproductive ratio for each country. Our predicted R0 is critically sensitive to the intensity of transmission from asymptomatic cases; with low asymptomatic transmission the highest values are predicted across Eastern Europe and Japan and the lowest across Africa, Central America and South-Western Asia. This pattern is largely driven by the ratio of children to older adults in each country and the observed propensity of clinical cases in the elderly. If asymptomatic cases have comparable transmission to detected cases, the pattern is reversed. Our results demonstrate the importance of age-specific heterogeneities going beyond contact structure to the spread of COVID-19. These heterogeneities give COVID-19 the capacity to spread particularly quickly in countries with older populations, and that intensive control measures are likely to be necessary to impede its progress in these countries.
Collapse
Affiliation(s)
- Joe Hilton
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
- Zeeman Institue (SBIDER), University of Warwick, Coventry, United Kingdom
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
| | - Matt J Keeling
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
- Zeeman Institue (SBIDER), University of Warwick, Coventry, United Kingdom
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
| |
Collapse
|
56
|
Castellanos ME, Zalwango S, Kakaire R, Ebell MH, Dobbin KK, Sekandi J, Kiwanuka N, Whalen CC. Defining adequate contact for transmission of Mycobacterium tuberculosis in an African urban environment. BMC Public Health 2020; 20:892. [PMID: 32517672 PMCID: PMC7285782 DOI: 10.1186/s12889-020-08998-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 05/27/2020] [Indexed: 01/25/2023] Open
Abstract
Background The risk of infection from respiratory pathogens increases according to the contact rate between the infectious case and susceptible contact, but the definition of adequate contact for transmission is not standard. In this study we aimed to identify factors that can explain the level of contact between tuberculosis cases and their social networks in an African urban environment. Methods This was a cross-sectional study conducted in Kampala, Uganda from 2013 to 2017. We carried out an exploratory factor analysis (EFA) in social network data from tuberculosis cases and their contacts. We evaluated the factorability of the data to EFA using the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO). We used principal axis factoring with oblique rotation to extract and rotate the factors, then we calculated factor scores for each using the weighted sum scores method. We assessed construct validity of the factors by associating the factors with other variables related to social mixing. Results Tuberculosis cases (N = 120) listed their encounters with 1154 members of their social networks. Two factors were identified, the first named “Setting” captured 61% of the variance whereas the second, named ‘Relationship’ captured 21%. Median scores for the setting and relationship factors were 10.2 (IQR 7.0, 13.6) and 7.7 (IQR 6.4, 10.1) respectively. Setting and Relationship scores varied according to the age, gender, and nature of the relationship among tuberculosis cases and their contacts. Family members had a higher median setting score (13.8, IQR 11.6, 15.7) than non-family members (7.2, IQR 6.2, 9.4). The median relationship score in family members (9.9, IQR 7.6, 11.5) was also higher than in non-family members (6.9, IQR 5.6, 8.1). For both factors, household contacts had higher scores than extra-household contacts (p < .0001). Contacts of male cases had a lower setting score as opposed to contacts of female cases. In contrast, contacts of male and female cases had similar relationship scores. Conclusions In this large cross-sectional study from an urban African setting, we identified two factors that can assess adequate contact between tuberculosis cases and their social network members. These findings also confirm the complexity and heterogeneity of social mixing.
Collapse
Affiliation(s)
- María Eugenia Castellanos
- Global Health Institute, College of Public Health, University of Georgia, Athens, Georgia. .,Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, Georgia.
| | - Sarah Zalwango
- College of Health Sciences, School of Public Health, Makerere University, Kampala, Uganda
| | - Robert Kakaire
- Global Health Institute, College of Public Health, University of Georgia, Athens, Georgia.,Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, Georgia
| | - Mark H Ebell
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, Georgia
| | - Kevin K Dobbin
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, Georgia
| | - Juliet Sekandi
- Global Health Institute, College of Public Health, University of Georgia, Athens, Georgia.,Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, Georgia
| | - Noah Kiwanuka
- College of Health Sciences, School of Public Health, Makerere University, Kampala, Uganda
| | - Christopher C Whalen
- Global Health Institute, College of Public Health, University of Georgia, Athens, Georgia.,Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, Georgia
| |
Collapse
|
57
|
Siedner MJ, Harling G, Derache A, Smit T, Khoza T, Gunda R, Mngomezulu T, Gareta D, Majozi N, Ehlers E, Dreyer J, Nxumalo S, Dayi N, Ording-Jesperson G, Ngwenya N, Wong E, Iwuji C, Shahmanesh M, Seeley J, De Oliveira T, Ndung'u T, Hanekom W, Herbst K. Protocol: Leveraging a demographic and health surveillance system for Covid-19 Surveillance in rural KwaZulu-Natal. Wellcome Open Res 2020; 5:109. [PMID: 32802963 PMCID: PMC7424917 DOI: 10.12688/wellcomeopenres.15949.1] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/22/2020] [Indexed: 12/28/2022] Open
Abstract
A coordinated system of disease surveillance will be critical to effectively control the coronavirus disease 2019 (Covid-19) pandemic. Such systems enable rapid detection and mapping of epidemics and inform allocation of scarce prevention and intervention resources. Although many lower- and middle-income settings lack infrastructure for optimal disease surveillance, health and demographic surveillance systems (HDSS) provide a unique opportunity for epidemic monitoring. This protocol describes a surveillance program at the Africa Health Research Institute's Population Intervention Platform site in northern KwaZulu-Natal. The program leverages a longstanding HDSS in a rural, resource-limited setting with very high prevalence of HIV and tuberculosis to perform Covid-19 surveillance. Our primary aims include: describing the epidemiology of the Covid-19 epidemic in rural KwaZulu-Natal; determining the impact of the Covid-19 outbreak and non-pharmaceutical control interventions (NPI) on behaviour and wellbeing; determining the impact of HIV and tuberculosis on Covid-19 susceptibility; and using collected data to support the local public-sector health response. The program involves telephone-based interviews with over 20,000 households every four months, plus a sub-study calling 750 households every two weeks. Each call asks a household representative how the epidemic and NPI are affecting the household and conducts a Covid-19 risk screen for all resident members. Any individuals screening positive are invited to a clinical screen, potential test and referral to necessary care - conducted in-person near their home following careful risk minimization procedures. In this protocol we report the details of our cohort design, questionnaires, data and reporting structures, and standard operating procedures in hopes that our project can inform similar efforts elsewhere.
Collapse
Affiliation(s)
- Mark J. Siedner
- Africa Health Research Institute, Durban, KwaZulu-Natal, South Africa
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Guy Harling
- Africa Health Research Institute, Durban, KwaZulu-Natal, South Africa
- Institute for Global Health, University College London, London, UK
- MRC/Wits Rural Public Health & Health Transitions Research Unit (Agincourt), University of the Witwatersrand, Johannesburg, South Africa
- Department of Epidemiology and Harvard Center for Population and Development Studies, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Anne Derache
- Africa Health Research Institute, Durban, KwaZulu-Natal, South Africa
| | - Theresa Smit
- Africa Health Research Institute, Durban, KwaZulu-Natal, South Africa
| | - Thandeka Khoza
- Africa Health Research Institute, Durban, KwaZulu-Natal, South Africa
| | - Resign Gunda
- Africa Health Research Institute, Durban, KwaZulu-Natal, South Africa
| | | | - Dickman Gareta
- Africa Health Research Institute, Durban, KwaZulu-Natal, South Africa
| | | | - Eugene Ehlers
- Africa Health Research Institute, Durban, KwaZulu-Natal, South Africa
| | - Jaco Dreyer
- Africa Health Research Institute, Durban, KwaZulu-Natal, South Africa
| | - Siyabonga Nxumalo
- Africa Health Research Institute, Durban, KwaZulu-Natal, South Africa
| | - Njabulo Dayi
- Africa Health Research Institute, Durban, KwaZulu-Natal, South Africa
| | | | - Nothando Ngwenya
- Africa Health Research Institute, Durban, KwaZulu-Natal, South Africa
| | - Emily Wong
- Africa Health Research Institute, Durban, KwaZulu-Natal, South Africa
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Collins Iwuji
- Africa Health Research Institute, Durban, KwaZulu-Natal, South Africa
- Department of Global Health and Infection, Brighton and Sussex Medical School, University of Sussex, Brighton, UK
| | - Maryam Shahmanesh
- Africa Health Research Institute, Durban, KwaZulu-Natal, South Africa
- Institute for Global Health, University College London, London, UK
| | - Janet Seeley
- Africa Health Research Institute, Durban, KwaZulu-Natal, South Africa
- Global Health and Development Department, London School of Hygiene & Tropical Medicine, London, UK
| | - Tulio De Oliveira
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP)), School of Laboratory Medicine and Medical Sciences, University of KwaZulu Natal, Durban, KwaZulu-Natal, South Africa
- Department of Global Health, University of Washington, Seattle, USA
| | - Thumbi Ndung'u
- Africa Health Research Institute, Durban, KwaZulu-Natal, South Africa
- Division of Infection and Immunity, University College London, London, UK
- HIV Pathogenesis Programme, The Doris Duke Medical Research Institute, University of KwaZulu Natal, Durban, KwaZulu-Natal, South Africa
- Max Planck Institute for Infection Biology, Berlin, Germany
| | - Willem Hanekom
- Africa Health Research Institute, Durban, KwaZulu-Natal, South Africa
- Division of Infection and Immunity, University College London, London, UK
| | - Kobus Herbst
- Africa Health Research Institute, Durban, KwaZulu-Natal, South Africa
- SAPRIN, South African Medical Research Council, Cape Town, South Africa
| |
Collapse
|
58
|
Nelson KN, Jenness SM, Mathema B, Lopman BA, Auld SC, Shah NS, Brust JCM, Ismail N, Omar SV, Brown TS, Allana S, Campbell A, Moodley P, Mlisana K, Gandhi NR. Social Mixing and Clinical Features Linked With Transmission in a Network of Extensively Drug-resistant Tuberculosis Cases in KwaZulu-Natal, South Africa. Clin Infect Dis 2020; 70:2396-2402. [PMID: 31342067 PMCID: PMC7245156 DOI: 10.1093/cid/ciz636] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 07/10/2019] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Tuberculosis (TB) is the leading infectious cause of death globally, and drug-resistant TB strains pose a serious threat to controlling the global TB epidemic. The clinical features, locations, and social factors driving transmission in settings with high incidences of drug-resistant TB are poorly understood. METHODS We measured a network of genomic links using Mycobacterium tuberculosis whole-genome sequences. RESULTS Patients with 2-3 months of cough or who spent time in urban locations were more likely to be linked in the network, while patients with sputum smear-positive disease were less likely to be linked than those with smear-negative disease. Associations persisted using different thresholds to define genomic links and irrespective of assumptions about the direction of transmission. CONCLUSIONS Identifying factors that lead to many transmissions, including contact with urban areas, can suggest settings instrumental in transmission and indicate optimal locations and groups to target with interventions.
Collapse
Affiliation(s)
- Kristin N Nelson
- Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Samuel M Jenness
- Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Barun Mathema
- Mailman School of Public Health, Columbia University, New York, New York
| | | | - Sara C Auld
- Rollins School of Public Health, Emory University, Atlanta, Georgia
- Emory University School of Medicine, Atlanta, Georgia
| | - N Sarita Shah
- US Centers for Disease Control and Prevention, Atlanta, Georgia
| | - James C M Brust
- Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York
| | - Nazir Ismail
- National Institute for Communicable Diseases, Johannesburg, South Africa
- University of Pretoria, South Africa
| | - Shaheed Vally Omar
- National Institute for Communicable Diseases, Johannesburg, South Africa
| | - Tyler S Brown
- Massachusetts General Hospital, Infectious Diseases Division, Boston
| | - Salim Allana
- Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Angie Campbell
- Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Pravi Moodley
- National Health Laboratory Service, Durban, South Africa
- School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Koleka Mlisana
- National Health Laboratory Service, Durban, South Africa
- School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Neel R Gandhi
- Rollins School of Public Health, Emory University, Atlanta, Georgia
- Emory University School of Medicine, Atlanta, Georgia
| |
Collapse
|
59
|
Siedner MJ, Kraemer JD, Meyer MJ, Harling G, Mngomezulu T, Gabela P, Dlamini S, Gareta D, Majozi N, Ngwenya N, Seeley J, Wong E, Iwuji C, Shahmanesh M, Hanekom W, Herbst K. Access to primary healthcare during lockdown measures for COVID-19 in rural South Africa: a longitudinal cohort study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.05.15.20103226. [PMID: 32511504 PMCID: PMC7273272 DOI: 10.1101/2020.05.15.20103226] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Objectives Public health interventions designed to interrupt COVID-19 transmission could have deleterious impacts on primary healthcare access. We sought to identify whether implementation of the nationwide lockdown (shelter-in-place) order in South Africa affected ambulatory clinic visitation in rural Kwa-Zulu Natal (KZN). Design Prospective, longitudinal cohort study Setting Data were analyzed from the Africa Health Research Institute Health and Demographic Surveillance System, which includes prospective data capture of clinic visits at eleven primary healthcare clinics in northern KwaZulu-Natal Participants A total of 36,291 individuals made 55,545 clinic visits during the observation period. Exposure of Interest We conducted an interrupted time series analysis with regression discontinuity methods to estimate changes in outpatient clinic visitation from 60 days before through 35 days after the lockdown period. Outcome Measures Daily clinic visitation at ambulatory clinics. In stratified analyses we assessed visitation for the following sub-categories: child health, perinatal care and family planning, HIV services, non-communicable diseases, and by age and sex strata. Results We found no change in total clinic visits/clinic/day from prior to and during the lockdown (-6.9 visits/clinic/day, 95%CI -17.4, 3.7) or trends in clinic visitation over time during the lockdown period (-0.2, 95%CI -3.4, 3.1). We did detect a reduction in child healthcare visits at the lockdown (-7.2 visits/clinic/day, 95%CI -9.2, -5.3), which was seen in both children <1 and children 1-5. In contrast, we found a significant increase in HIV visits immediately after the lockdown (8.4 visits/clinic/day, 95%CI 2.4, 14.4). No other differences in clinic visitation were found for perinatal care and family planning, non-communicable diseases, or among adult men and women. Conclusions In rural KZN, the ambulatory healthcare system was largely resilient during the national-wide lockdown order. A major exception was child healthcare visitation, which declined immediately after the lockdown but began to normalize in the weeks thereafter. Future work should explore efforts to decentralize chronic care for high-risk populations and whether catch-up vaccination programs might be required in the wake of these findings.
Collapse
Affiliation(s)
- Mark J. Siedner
- Corresponding Author: Mark J. Siedner, Africa Health Research Institute, KwaZulu-Natal, South Africa,
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
60
|
Badu K, Thorn JP, Goonoo N, Dukhi N, Fagbamigbe AF, Kulohoma BW, Oyebola K, Abdelsalam SI, Doorsamy W, Awe O, Sylverken AA, Egeru A, Gitaka J. Africa’s response to the COVID-19 pandemic: A review of the nature of the virus, impacts and implications for preparedness. AAS Open Res 2020. [DOI: 10.12688/aasopenres.13060.1] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Background: COVID-19 continues to wreak havoc in different countries across the world, claiming thousands of lives, increasing morbidity and disrupting lifestyles. The global scientific community is in urgent need of relevant evidence, to understand the challenges and knowledge gaps, as well as the opportunities to contain the spread of the virus. Considering the unique socio-economic, demographic, political, ecological and climatic contexts in Africa, the responses which may prove to be successful in other regions may not be appropriate on the continent. This paper aims to provide insight for scientists, policy makers and international agencies to contain the virus and to mitigate its impact at all levels. Methods: The Affiliates of the African Academy of Sciences (AAS), came together to synthesize the current evidence, identify the challenges and opportunities to enhance the understanding of the disease. We assess the potential impact of this pandemic and the unique challenges of the disease on African nations. We examine the state of Africa’s preparedness and make recommendations for steps needed to win the war against this pandemic and combat potential resurgence. Results: We identified gaps and opportunities among cross-cutting issues which is recommended to be addressed or harnessed in this pandemic. Factors such as the nature of the virus and the opportunities for drug targeting, point of care diagnostics, health surveillance systems, food security, mental health, xenophobia and gender-based violence, shelter for the homeless, water and sanitation, telecommunications challenges, domestic regional coordination and financing. Conclusion: Based on our synthesis of the current evidence, while there are plans for preparedness in several African countries, there are significant limitations. Multi-sectoral efforts from the science, education, medical, technological, communication, business and industry sectors as well as local communities is required in order to win this fight.
Collapse
|
61
|
Spatiotemporal heterogeneity of social contact patterns related to infectious diseases in the Guangdong Province, China. Sci Rep 2020; 10:6119. [PMID: 32296083 PMCID: PMC7160103 DOI: 10.1038/s41598-020-63383-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 03/19/2020] [Indexed: 02/08/2023] Open
Abstract
The social contact patterns associated with the infectious disease transmitted by airborne droplets or close contact follow specific rules. Understanding these processes can improve the accuracy of disease transmission models, permitting their integration into model simulations. In this study, we performed a large-scale population-based survey to collect social contact patterns in three cities on the Pearl River Delta of China in winter and summer. A total of 5,818 participants were face-to-face interviewed and 35,542 contacts were recorded. The average number of contacts per person each day was 16.7 considering supplementary professional contacts (SPCs). Contacts that occurred on a daily basis, lasted more than 4 hours, and took place in households were more likely to involve physical contact. The seasonal characteristics of social contact were heterogeneous, such that contact in the winter was more likely to involve physical contact compared to summer months. The spatial characteristics of the contacts were similar. Social mixing patterns differed according to age, but all ages maintained regular contact with their peers. Taken together, these findings describe the spatiotemporal distribution of social contact patterns relevant to infections in the Guangdong Province of China. This information provides important parameters for mathematical models of infectious diseases.
Collapse
|
62
|
Marquez C, Atukunda M, Balzer LB, Chamie G, Kironde J, Ssemmondo E, Ruel TD, Mwangwa F, Tram KH, Clark TD, Kwarisiima D, Petersen M, Kamya MR, Charlebois ED, Havlir DV. The age-specific burden and household and school-based predictors of child and adolescent tuberculosis infection in rural Uganda. PLoS One 2020; 15:e0228102. [PMID: 31995631 PMCID: PMC6988961 DOI: 10.1371/journal.pone.0228102] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Accepted: 01/07/2020] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND The age-specific epidemiology of child and adolescent tuberculosis (TB) is poorly understood, especially in rural areas of East Africa. We sought to characterize the age-specific prevalence and predictors of TB infection among children and adolescents living in rural Uganda, and to explore the contribution of household TB exposure on TB infection. METHODS From 2015-2016 we placed and read 3,121 tuberculin skin tests (TST) in children (5-11 years old) and adolescents (12-19 years old) participating in a nested household survey in 9 rural Eastern Ugandan communities. TB infection was defined as a positive TST (induration ≥10mm or ≥5mm if living with HIV). Age-specific prevalence was estimated using inverse probability weighting to adjust for incomplete measurement. Generalized estimating equations were used to assess the association between TB infection and multi-level predictors. RESULTS The adjusted prevalence of TB infection was 8.5% (95%CI: 6.9-10.4) in children and 16.7% (95% CI:14.0-19.7) in adolescents. Nine percent of children and adolescents with a prevalent TB infection had a household TB contact. Among children, having a household TB contact was strongly associated with TB infection (aOR 5.5, 95% CI: 1.7-16.9), but the strength of this association declined among adolescents and did not meet significance (aOR 2.3, 95% CI: 0.8-7.0). The population attributable faction of TB infection due to a household TB contact was 8% for children and 4% among adolescents. Mobile children and adolescents who travel outside of their community for school had a 1.7 (95% CI 1.0-2.9) fold higher odds of TB infection than those who attended school in the community. CONCLUSION Children and adolescents in this area of rural eastern Uganda suffer a significant burden of TB. The majority of TB infections are not explained by a known household TB contact. Our findings underscore the need for community-based TB prevention interventions, especially among mobile youth.
Collapse
Affiliation(s)
- Carina Marquez
- University of California, San Francisco, Division of HIV, Infectious Diseases and Global Medicine, San Francisco, California, United States of America
| | | | - Laura B. Balzer
- University of Massachusetts, Amherst, Department of Biostatistics and Epidemiology, Amherst, Massachusetts, United States of America
| | - Gabriel Chamie
- University of California, San Francisco, Division of HIV, Infectious Diseases and Global Medicine, San Francisco, California, United States of America
| | - Joel Kironde
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | | | - Theodore D. Ruel
- University of California, San Francisco, Department of Pediatrics, San Francisco, California, United States of America
| | | | - Khai Hoan Tram
- Stanford University School of Medicine, Palo Alto, California, United States of America
| | - Tamara D. Clark
- University of California, San Francisco, Division of HIV, Infectious Diseases and Global Medicine, San Francisco, California, United States of America
| | - Dalsone Kwarisiima
- Infectious Diseases Research Collaboration, Kampala, Uganda
- Makerere University College of Health Sciences, School of Medicine, Kampala, Uganda
| | - Maya Petersen
- University of California, Berkeley School of Public Health, Berkeley, California, United States of America
| | - Moses R. Kamya
- Infectious Diseases Research Collaboration, Kampala, Uganda
- Makerere University College of Health Sciences, School of Medicine, Kampala, Uganda
| | - Edwin D. Charlebois
- Center for AIDS Prevention (CAPS), University of California, San Francisco, San Francisco, California United States of America
| | - Diane V. Havlir
- University of California, San Francisco, Division of HIV, Infectious Diseases and Global Medicine, San Francisco, California, United States of America
| |
Collapse
|
63
|
Philippin H, Knoll KM, Macleod D. COVID-19 numbers and models: misleading us, or leading us out of misery? COMMUNITY EYE HEALTH 2020; 33:43-45. [PMID: 33304055 PMCID: PMC7677793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Affiliation(s)
- Heiko Philippin
- Clinical Research Fellow: International Centre for Eye Health, London School of Hygiene & Tropical Medicine, UK, Global Advisor for Inclusive Eye Health/Research & Training: CBM, Bensheim, Germany, and Glaucoma Specialist: Eye Center, Medical Center, Faculty of Medicine, University of Freiburg, Germany
| | - Karin M Knoll
- Consultant Ophthalmologist: Kilimanjaro Christian Medical Centre, Moshi, Tanzania and CBM, Bensheim, Germany
| | - David Macleod
- Assistant Professor: MRC Tropical Epidemiology Group, London School of Hygiene & Tropical Medicine, UK
| |
Collapse
|
64
|
Infection prevention in general medical wards: A call to action. Afr J Thorac Crit Care Med 2019; 25. [PMID: 34286264 PMCID: PMC8278849 DOI: 10.7196/ajtccm.2019.v25i4.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
|
65
|
Zhang J, Klepac P, Read JM, Rosello A, Wang X, Lai S, Li M, Song Y, Wei Q, Jiang H, Yang J, Lynn H, Flasche S, Jit M, Yu H. Patterns of human social contact and contact with animals in Shanghai, China. Sci Rep 2019; 9:15141. [PMID: 31641189 PMCID: PMC6805924 DOI: 10.1038/s41598-019-51609-8] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 09/29/2019] [Indexed: 12/17/2022] Open
Abstract
East Asia is as a principal hotspot for emerging zoonotic infections. Understanding the likely pathways for their emergence and spread requires knowledge on human-human and human-animal contacts, but such studies are rare. We used self-completed and interviewer-completed contact diaries to quantify patterns of these contacts for 965 individuals in 2017/2018 in a high-income densely-populated area of China, Shanghai City. Interviewer-completed diaries recorded more social contacts (19.3 vs. 18.0) and longer social contact duration (35.0 vs. 29.1 hours) than self-reporting. Strong age-assortativity was observed in all age groups especially among young participants (aged 7-20) and middle aged participants (25-55 years). 17.7% of participants reported touching animals (15.3% (pets), 0.0% (poultry) and 0.1% (livestock)). Human-human contact was very frequent but contact with animals (especially poultry) was rare although associated with frequent human-human contact. Hence, this densely populated area is more likely to act as an accelerator for human-human spread but less likely to be at the source of a zoonosis outbreak. We also propose that telephone interview at the end of reporting day is a potential improvement of the design of future contact surveys.
Collapse
Affiliation(s)
- Juanjuan Zhang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Petra Klepac
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Jonathan M Read
- Centre for Health Informatics, Computation and Statistics, Lancaster Medical School, Lancaster University, Lancashire, UK
| | - Alicia Rosello
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Xiling Wang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Shengjie Lai
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
- Flowminder Foundation, Stockholm, Sweden
| | - Meng Li
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Yujian Song
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Qingzhen Wei
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Hao Jiang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Juan Yang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Henry Lynn
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Stefan Flasche
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Mark Jit
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, London, UK
- Modelling and Economics Unit, Public Health England, London, UK
- School of Public Health, University of Hong Kong, Hong Kong, China
| | - Hongjie Yu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.
| |
Collapse
|
66
|
Szkwarko D, Bouton TC, Rybak NR, Carter EJ, Chiang SS. Tuberculosis: An Epidemic Perpetuated by Health Inequalities. RHODE ISLAND MEDICAL JOURNAL (2013) 2019; 102:47-50. [PMID: 31480821 PMCID: PMC7024599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Tuberculosis (TB) is the leading single-agent infectious disease killer worldwide. The World Health Organization (WHO)'s End TB Strategy aims to achieve tuberculosis (TB) elimination by 2030, and in September 2018, the United Nations General Assembly held a High-Level Meeting on TB to address the urgency of the TB epidemic and the health inequalities that continue to propel it. The meeting endorsed an ambitious, comprehensive approach to the TB epidemic that incorporates universal health coverage and tackles the social determinants of this disease. In this article, we provide an overview of the key strategies promoted in this meeting and introduce work by five Rhode Island-based physicians that align with these goals.
Collapse
Affiliation(s)
- Daria Szkwarko
- Warren Alpert Medical School of Brown University, Providence, RI; University of Massachusetts Medical School, Worcester, MA
| | - Tara C Bouton
- Warren Alpert Medical School of Brown University, Providence, RI; Boston University, Boston, MA
| | - Natasha R Rybak
- Warren Alpert Medical School of Brown University, Providence, RI
| | - E Jane Carter
- Warren Alpert Medical School of Brown University, Providence, RI
| | - Silvia S Chiang
- Warren Alpert Medical School of Brown University, Providence, RI
| |
Collapse
|
67
|
Kiti MC, Melegaro A, Cattuto C, Nokes DJ. Study design and protocol for investigating social network patterns in rural and urban schools and households in a coastal setting in Kenya using wearable proximity sensors. Wellcome Open Res 2019; 4:84. [PMID: 31489381 PMCID: PMC6719676 DOI: 10.12688/wellcomeopenres.15268.2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/19/2019] [Indexed: 11/28/2022] Open
Abstract
Background: Social contact patterns shape the transmission of respiratory infections spread via close interactions. There is a paucity of observational data from schools and households, particularly in developing countries. Portable wireless sensors can record unbiased proximity events between individuals facing each other, shedding light on pathways of infection transmission. Design and methods: The aim is to characterize face-to-face contact patterns that may shape the transmission of respiratory infections in schools and households in Kilifi, Kenya. Two schools, one each from a rural and urban area, will be purposively selected. From each school, 350 students will be randomly selected proportional to class size and gender to participate. Nine index students from each school will be randomly selected and followed-up to their households. All index household residents will be recruited into the study. A further 3-5 neighbouring households will also be recruited to give a maximum of 350 participants per household setting. The sample size per site is limited by the number of sensors available for data collection. Each participant will wear a wireless proximity sensor lying on their chest area for 7 consecutive days. Data on proximal dyadic interactions will be collected automatically by the sensors only for participants who are face-to-face. Key characteristics of interest include the distribution of degree and the frequency and duration of contacts and their variation in rural and urban areas. These will be stratified by age, gender, role, and day of the week. Expected results: Resultant data will inform on social contact patterns in rural and urban areas of a previously unstudied population. Ensuing data will be used to parameterize mathematical simulation models of transmission of a range of respiratory viruses, including respiratory syncytial virus, and used to explore the impact of intervention measures such as vaccination and social distancing.
Collapse
Affiliation(s)
- Moses Chapa Kiti
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Kilifi, 80108, Kenya
| | - Alessia Melegaro
- Department of Social and Political Sciences, Bocconi University, Milan, Italy
| | - Ciro Cattuto
- Data Science Laboratory, Institute for Scientific Interchange Foundation, Turin, Italy
| | - David James Nokes
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Kilifi, 80108, Kenya.,Zeeman Institute of Systems Biology and Infectious Disease Research, University of Warwick, Coventry, UK.,School of Life Sciences, University of Warwick, Coventry, UK
| |
Collapse
|
68
|
Potter GE, Wong J, Sugimoto J, Diallo A, Victor JC, Neuzil K, Halloran ME. Networks of face-to-face social contacts in Niakhar, Senegal. PLoS One 2019; 14:e0220443. [PMID: 31386686 PMCID: PMC6684077 DOI: 10.1371/journal.pone.0220443] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Accepted: 07/15/2019] [Indexed: 11/30/2022] Open
Abstract
We present the first analysis of face-to-face contact network data from Niakhar, Senegal. Participants in a cluster-randomized influenza vaccine trial were interviewed about their contact patterns when they reported symptoms during their weekly household surveillance visit. We employ a negative binomial model to estimate effects of covariates on contact degree. We estimate the mean contact degree for asymptomatic Niakhar residents to be 16.5 (95% C.I. 14.3, 18.7) in the morning and 14.8 in the afternoon (95% C.I. 12.7, 16.9). We estimate that symptomatic people make 10% fewer contacts than asymptomatic people (95% C.I. 5%, 16%; p = 0.006), and those aged 0-5 make 33% fewer contacts than adults (95% C.I. 29%, 37%; p < 0.001). By explicitly modelling the partial rounding pattern observed in our data, we make inference for both the underlying (true) distribution of contacts as well as for the reported distribution. We created an estimator for homophily by compound (household) membership and estimate that 48% of contacts by symptomatic people are made to their own compound members in the morning (95% CI, 45%, 52%) and 60% in the afternoon/evening (95% CI, 56%, 64%). We did not find a significant effect of symptom status on compound homophily. We compare our findings to those from other countries and make design recommendations for future surveys.
Collapse
Affiliation(s)
- Gail E. Potter
- The Emmes Company, Rockville, MD, United States of America
- California Polytechnic State University, San Luis Obispo, CA, United States of America
| | - Jimmy Wong
- California Polytechnic State University, San Luis Obispo, CA, United States of America
| | - Jonathan Sugimoto
- Fred Hutchinson Cancer Research Center, Seattle, WA, United States of America
| | - Aldiouma Diallo
- Institut de Recherche pour le Développement, Niakhar, Senegal
| | | | - Kathleen Neuzil
- University of Maryland Center for Vaccine Development, Baltimore, MD, United States of America
| | - M. Elizabeth Halloran
- Fred Hutchinson Cancer Research Center, Seattle, WA, United States of America
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| |
Collapse
|
69
|
Martinez L, Verma R, Croda J, Horsburgh CR, Walter KS, Degner N, Middelkoop K, Koch A, Hermans S, Warner DF, Wood R, Cobelens F, Andrews JR. Detection, survival and infectious potential of Mycobacterium tuberculosis in the environment: a review of the evidence and epidemiological implications. Eur Respir J 2019; 53:1802302. [PMID: 31048345 PMCID: PMC6753378 DOI: 10.1183/13993003.02302-2018] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Accepted: 04/18/2019] [Indexed: 11/05/2022]
Abstract
Much remains unknown about Mycobacterium tuberculosis transmission. Seminal experimental studies from the 1950s demonstrated that airborne expulsion of droplet nuclei from an infectious tuberculosis (TB) patient is the primary route of transmission. However, these findings did not rule out other routes of M. tuberculosis transmission. We reviewed historical scientific evidence from the late 19th/early 20th century and contemporary studies investigating the presence, persistence and infectiousness of environmental M. tuberculosis We found both experimental and epidemiological evidence supporting the presence and viability of M. tuberculosis in multiple natural and built environments for months to years, presumably following contamination by a human source. Furthermore, several studies confirm M. tuberculosis viability and virulence in the environment using guinea pig and mouse models. Most of this evidence was historical; however, several recent studies have reported consistent findings of M. tuberculosis detection and viability in the environment using modern methods. Whether M. tuberculosis in environments represents an infectious threat to humans requires further investigation; this may represent an untapped source of data with which to further understand M. tuberculosis transmission. We discuss potential opportunities for harnessing these data to generate new insights into TB transmission in congregate settings.
Collapse
Affiliation(s)
- Leonardo Martinez
- Division of Infectious Diseases and Geographic Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Renu Verma
- Division of Infectious Diseases and Geographic Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Julio Croda
- Oswaldo Cruz Foundation, Campo Grande and Salvador, Brazil
- School of Medicine, Federal University of Mato Grosso do Sul, Campo Grande, Brazil
| | - C Robert Horsburgh
- Dept of Medicine, Boston University School of Medicine, Boston, MA, USA
- Dept of Epidemiology, Biostatistics and Global Health, Boston University School of Public Health, Boston, MA, USA
| | - Katharine S Walter
- Division of Infectious Diseases and Geographic Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Nicholas Degner
- Division of Infectious Diseases and Geographic Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Keren Middelkoop
- The Desmond Tutu HIV Centre, Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Dept of Medicine, University of Cape Town, Cape Town, South Africa
| | - Anastasia Koch
- SAMRC/NHLS/UCT Molecular Mycobacteriology Research Unit, Dept of Pathology and Institute of Infectious Diseases and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Sabine Hermans
- The Desmond Tutu HIV Centre, Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Dept of Global Health, Amsterdam Institute for Global Health and Development, Academic Medical Center, Amsterdam, The Netherlands
| | - Digby F Warner
- SAMRC/NHLS/UCT Molecular Mycobacteriology Research Unit, Dept of Pathology and Institute of Infectious Diseases and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Wellcome Center for Infectious Diseases Research in Africa, Institute of Infectious Diseases and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Robin Wood
- The Desmond Tutu HIV Centre, Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Frank Cobelens
- Dept of Global Health, Amsterdam Institute for Global Health and Development, Academic Medical Center, Amsterdam, The Netherlands
| | - Jason R Andrews
- Division of Infectious Diseases and Geographic Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| |
Collapse
|
70
|
Kiti MC, Melegaro A, Cattuto C, Nokes DJ. Study design and protocol for investigating social network patterns in rural and urban schools and households in a coastal setting in Kenya using wearable proximity sensors. Wellcome Open Res 2019; 4:84. [DOI: 10.12688/wellcomeopenres.15268.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/16/2019] [Indexed: 11/20/2022] Open
Abstract
Background: Social contact patterns shape the transmission of respiratory infections spread via close interactions. There is a paucity of observational data from schools and households, particularly in developing countries. Portable wireless sensors can record unbiased proximity events between individuals facing each other, shedding light on pathways of infection transmission. Design and methods: The aim is to characterize face-to-face contact patterns that may shape the transmission of respiratory infections in schools and households in Kilifi, Kenya. Two schools, one each from a rural and urban area, will be purposively selected. From each school, 350 students will be randomly selected proportional to class size and gender to participate. Nine index students from each school will be randomly selected and followed-up to their households. All index household residents will be recruited into the study. A further 3-5 neighbouring households will also be recruited to give a maximum of 350 participants per household setting. The sample size per site is limited by the number of sensors available for data collection. Each participant will wear a wireless proximity sensor lying on their chest area for 7 consecutive days. Data on proximal dyadic interactions will be collected automatically by the sensors only for participants who are face-to-face. Key characteristics of interest include the distribution of degree and the frequency and duration of contacts and their variation in rural and urban areas. These will be stratified by age, gender, role, and day of the week. Expected results: Resultant data will inform on social contact patterns in rural and urban areas of a previously unstudied population. Ensuing data will be used to parameterize mathematical simulation models of transmission of a range of respiratory viruses, including respiratory syncytial virus, and used to explore the impact of intervention measures such as vaccination and social distancing.
Collapse
|
71
|
Martinez L, Lo NC, Cords O, Hill PC, Khan P, Hatherill M, Mandalakas A, Kay A, Croda J, Horsburgh CR, Zar HJ, Andrews JR. Paediatric tuberculosis transmission outside the household: challenging historical paradigms to inform future public health strategies. THE LANCET RESPIRATORY MEDICINE 2019; 7:544-552. [PMID: 31078497 DOI: 10.1016/s2213-2600(19)30137-7] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 03/15/2019] [Accepted: 03/19/2019] [Indexed: 01/01/2023]
Abstract
Tuberculosis is a major cause of death and disability among children globally, yet children have been neglected in global tuberculosis control efforts. Historically, tuberculosis in children has been thought of as a family disease, and because of this, household contact tracing of children after identification of an adult tuberculosis case has been emphasised as the principal public health intervention. However, the population-level effect of household contact tracing is predicated on the assumption that most paediatric tuberculosis infections are acquired within the household. In this Personal View, we focus on accumulating scientific evidence indicating that the majority of Mycobacterium tuberculosis transmission to children in high-burden settings occurs in the community, outside of households in which a person has tuberculosis. We estimate the population-attributable fraction of M tuberculosis transmission to children due to household exposures to be between 10% and 30%. M tuberculosis transmission from the household was low (<30%) even in children younger than age 5 years. We propose that an effective public health response to childhood tuberculosis requires comprehensive, community-based interventions, such as active surveillance in select settings, rather than contact tracing alone. Importantly, the historical paradigm that most paediatric transmission occurs in households should be reconsidered on the basis of the scientific knowledge presented.
Collapse
Affiliation(s)
- Leonardo Martinez
- Division of Infectious Diseases and Geographic Medicine, School of Medicine, Stanford University, Stanford, CA, USA.
| | - Nathan C Lo
- Division of Infectious Diseases and Geographic Medicine, School of Medicine, Stanford University, Stanford, CA, USA; Division of Epidemiology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Olivia Cords
- Division of Infectious Diseases and Geographic Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Philip C Hill
- Centre for International Health, University of Otago Medical School, Dunedin, New Zealand
| | - Palwasha Khan
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Mark Hatherill
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine and Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Anna Mandalakas
- The Global Tuberculosis Program, Texas Children's Hospital and the Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA
| | - Alexander Kay
- The Global Tuberculosis Program, Texas Children's Hospital and the Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA; The Baylor Children's Foundation, Mbabane, Swaziland
| | - Julio Croda
- Universidade Federal de Mato Grosso do Sul, Faculdade de Medicina, Campo Grande, Mato Grosso do Sul, Brazil; Fundação Oswaldo Cruz, Campo Grande, Mato Grosso do Sul, Brazil
| | - C Robert Horsburgh
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA; Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Heather J Zar
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital and South Africa Medical Research Council Unit on Child and Adolescent Health, Cape Town, South Africa
| | - Jason R Andrews
- Division of Infectious Diseases and Geographic Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| |
Collapse
|
72
|
Estimating age-mixing patterns relevant for the transmission of airborne infections. Epidemics 2019; 28:100339. [PMID: 30910644 PMCID: PMC6731521 DOI: 10.1016/j.epidem.2019.03.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Revised: 03/19/2019] [Accepted: 03/19/2019] [Indexed: 12/03/2022] Open
Abstract
Airborne infection transmission can occur between anybody sharing indoor space. We demonstrate a method for calculating age-mixing patterns for these contacts. It only requires data that can be easily collected during social contact surveys. Age-mixing patterns for these contacts may vary from those typically used in models.
Introduction Age-mixing patterns can have substantial effects on infectious disease dynamics and intervention effects. Data on close contacts (people spoken to and/or touched) are often used to estimate age-mixing. These are not the only relevant contacts for airborne infections such as tuberculosis, where transmission can occur between anybody ‘sharing air’ indoors. Directly collecting data on age-mixing patterns between casual contacts (shared indoor space, but not ‘close’) is difficult however. We demonstrate a method for indirectly estimating age-mixing patterns between casual indoor contacts from social contact data. Methods We estimated age-mixing patterns between close, casual, and all contacts using data from a social contact survey in South Africa. The age distribution of casual contacts in different types of location was estimated from the reported time spent in the location type by respondents in each age group. Results Patterns of age-mixing calculated from contact numbers were similar between close and all contacts, however patterns of age-mixing calculated from contact time were more age-assortative in all contacts than in close contacts. There was also more variation by age group in total numbers of casual and all contacts, than in total numbers of close contacts. Estimates were robust to sensitivity analyses. Conclusions Patterns of age-mixing can be estimated for all contacts using data that can be easily collected as part of social contact surveys or time-use surveys, and may differ from patterns between close contacts.
Collapse
|
73
|
Munasinghe L, Asai Y, Nishiura H. Quantifying heterogeneous contact patterns in Japan: a social contact survey. Theor Biol Med Model 2019; 16:6. [PMID: 30890153 PMCID: PMC6425701 DOI: 10.1186/s12976-019-0102-8] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 03/05/2019] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND Social contact surveys can greatly help in quantifying the heterogeneous patterns of infectious disease transmission. The present study aimed to conduct a contact survey in Japan, offering estimates of contact by age and location and validating a social contact matrix using a seroepidemiological dataset of influenza. METHODS An internet-based questionnaire survey was conducted, covering all 47 prefectures in Japan and including a total of 1476 households. The social contact matrix was quantified assuming reciprocity and using the maximum likelihood method. By imposing several parametric assumptions for the next-generation matrix, the empirical seroepidemiological data of influenza A (H1N1) 2009 was analysed and we estimated the basic reproduction number, R0. RESULTS In total, the reported number of contacts on weekdays was 10,682 whereas that on weekend days was 8867. Strong age-dependent assortativity was identified. Forty percent of weekday contacts took place at schools or workplaces, but that declined to 14% on weekends. Accounting for the age-dependent heterogeneity with the known social contact matrix, the minimum value of the Akaike information criterion was obtained and R0 was estimated at 1.45 (95% confidence interval: 1.42, 1.49). CONCLUSIONS Survey datasets will be useful for parameterizing the heterogeneous transmission model of various directly transmitted infectious diseases in Japan. Age-dependent assortativity, especially among children, along with numerous contacts in school settings on weekdays implies the potential effectiveness of school closure.
Collapse
Affiliation(s)
- Lankeshwara Munasinghe
- Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Sapporo, Japan
| | - Yusuke Asai
- Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Sapporo, Japan
| | - Hiroshi Nishiura
- Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Sapporo, Japan
| |
Collapse
|
74
|
Seddon JA, Chiang SS, Esmail H, Coussens AK. The Wonder Years: What Can Primary School Children Teach Us About Immunity to Mycobacterium tuberculosis? Front Immunol 2018; 9:2946. [PMID: 30619306 PMCID: PMC6300506 DOI: 10.3389/fimmu.2018.02946] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 11/30/2018] [Indexed: 12/22/2022] Open
Abstract
In high burden settings, the risk of infection with Mycobacterium tuberculosis increases throughout childhood due to cumulative exposure. However, the risk of progressing from tuberculosis (TB) infection to disease varies by age. Young children (<5 years) have high risk of disease progression following infection. The risk falls in primary school children (5 to <10 years), but rises again during puberty. TB disease phenotype also varies by age: generally, young children have intrathoracic lymph node disease or disseminated disease, while adolescents (10 to <20 years) have adult-type pulmonary disease. TB risk also exhibits a gender difference: compared to adolescent boys, adolescent girls have an earlier rise in disease progression risk and higher TB incidence until early adulthood. Understanding why primary school children, during what we term the "Wonder Years," have low TB risk has implications for vaccine development, therapeutic interventions, and diagnostics. To understand why this group is at low risk, we need a better comprehension of why younger children and adolescents have higher risks, and why risk varies by gender. Immunological response to M. tuberculosis is central to these issues. Host response at key stages in the immunopathological interaction with M. tuberculosis influences risk and disease phenotype. Cell numbers and function change dramatically with age and sexual maturation. Young children have poorly functioning innate cells and a Th2 skew. During the "Wonder Years," there is a lymphocyte predominance and a Th1 skew. During puberty, neutrophils become more central to host response, and CD4+ T cells increase in number. Sex hormones (dehydroepiandrosterone, adiponectin, leptin, oestradiol, progesterone, and testosterone) profoundly affect immunity. Compared to girls, boys have a stronger Th1 profile and increased numbers of CD8+ T cells and NK cells. Girls are more Th2-skewed and elicit more enhanced inflammatory responses. Non-immunological factors (including exposure intensity, behavior, and co-infections) may impact disease. However, given the consistent patterns seen across time and geography, these factors likely are less central. Strategies to protect children and adolescents from TB may need to differ by age and sex. Further work is required to better understand the contribution of age and sex to M. tuberculosis immunity.
Collapse
Affiliation(s)
- James A. Seddon
- Department of Paediatrics, Imperial College London, London, United Kingdom
- Desmond Tutu TB Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Silvia S. Chiang
- Department of Pediatrics, Warren Alpert Medical School of Brown University, Providence, RI, United States
- Center for International Health Research, Rhode Island Hospital, Providence, RI, United States
| | - Hanif Esmail
- Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Anna K. Coussens
- Wellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
- Infection and Immunity Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
- Division of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC, Australia
- Division of Medical Microbiology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| |
Collapse
|
75
|
le Polain de Waroux O, Flasche S, Kucharski AJ, Langendorf C, Ndazima D, Mwanga-Amumpaire J, Grais RF, Cohuet S, Edmunds WJ. Identifying human encounters that shape the transmission of Streptococcus pneumoniae and other acute respiratory infections. Epidemics 2018; 25:72-79. [PMID: 30054196 PMCID: PMC6227246 DOI: 10.1016/j.epidem.2018.05.008] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Revised: 05/17/2018] [Accepted: 05/17/2018] [Indexed: 01/23/2023] Open
Abstract
Although patterns of social contacts are believed to be an important determinant of infectious disease transmission, it remains unclear how the frequency and nature of human interactions shape an individual's risk of infection. We analysed data on daily social encounters individually matched to data on S. pneumoniae carriage and acute respiratory symptoms (ARS), from 566 individuals who took part in a survey in South-West Uganda. We found that the frequency of physical (i.e. skin-to-skin), long (≥1 h) and household contacts - which capture some measure of close (i.e. relatively intimate) contact - was higher among pneumococcal carriers than non-carriers, and among people with ARS compared to those without, irrespective of their age. With each additional physical encounter the age-adjusted risk of carriage and ARS increased by 6% (95%CI 2-9%) and 7% (2-13%) respectively. In contrast, the number of casual contacts (<5 min long) was not associated with either pneumococcal carriage or ARS. A detailed analysis by age of contacts showed that the number of close contacts with young children (<5 years) was particularly higher among older children and adult carriers than non-carriers, while the higher number of contacts among people suffering from ARS was more homogeneous across contacts of all ages. Our findings provide key evidence that the frequency of close interpersonal contact is important for transmission of respiratory infections, but not that of casual contacts. Those results are essential for both improving disease prevention and control efforts as well as informing research on infectious disease dynamics and transmission models, and more studies should be undertaken to further validate our results.
Collapse
Affiliation(s)
- Olivier le Polain de Waroux
- Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, WC1E 7HT, London, United Kingdom.
| | - Stefan Flasche
- Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, WC1E 7HT, London, United Kingdom
| | - Adam J Kucharski
- Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, WC1E 7HT, London, United Kingdom
| | - Celine Langendorf
- Department of Research, Epicentre, 8 Rue Saint-Sabin, 75011, Paris, France
| | - Donny Ndazima
- Epicentre Mbarara Research Centre, PO Box 1956, Mbarara, Uganda
| | - Juliet Mwanga-Amumpaire
- Epicentre Mbarara Research Centre, PO Box 1956, Mbarara, Uganda; Mbarara Universityof Science and Technology, Mbarara University, PO Box 1410, Mbarara, Uganda
| | - Rebecca F Grais
- Department of Research, Epicentre, 8 Rue Saint-Sabin, 75011, Paris, France
| | - Sandra Cohuet
- Department of Field Epidemiology, Epicentre, 8 Rue Saint-Sabin, 75011, Paris, France
| | - W John Edmunds
- Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, WC1E 7HT, London, United Kingdom
| |
Collapse
|
76
|
Viney K, Wingfield T, Kuksa L, Lönnroth K. Access and adherence to tuberculosis prevention and care for hard-to-reach groups. Tuberculosis (Edinb) 2018. [DOI: 10.1183/2312508x.10022117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
77
|
Nelson KN, Shah NS, Mathema B, Ismail N, Brust JCM, Brown TS, Auld SC, Omar SV, Morris N, Campbell A, Allana S, Moodley P, Mlisana K, Gandhi NR. Spatial Patterns of Extensively Drug-Resistant Tuberculosis Transmission in KwaZulu-Natal, South Africa. J Infect Dis 2018; 218:1964-1973. [PMID: 29961879 PMCID: PMC6217717 DOI: 10.1093/infdis/jiy394] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2018] [Accepted: 06/26/2018] [Indexed: 12/29/2022] Open
Abstract
Background Transmission is driving the global drug-resistant tuberculosis (TB) epidemic; nearly three-quarters of drug-resistant TB cases are attributable to transmission. Geographic patterns of disease incidence, combined with information on probable transmission links, can define the spatial scale of transmission and generate hypotheses about factors driving transmission patterns. Methods We combined whole-genome sequencing data with home Global Positioning System coordinates from 344 participants with extensively drug-resistant (XDR) TB in KwaZulu-Natal, South Africa, diagnosed from 2011 to 2014. We aimed to determine if genomically linked (difference of ≤5 single-nucleotide polymorphisms) cases lived close to one another, which would suggest a role for local community settings in transmission. Results One hundred eighty-two study participants were genomically linked, comprising 1084 case-pairs. The median distance between case-pairs' homes was 108 km (interquartile range, 64-162 km). Between-district, as compared to within-district, links accounted for the majority (912/1084 [84%]) of genomic links. Half (526 [49%]) of genomic links involved a case from Durban, the urban center of KwaZulu-Natal. Conclusions The high proportions of between-district links with Durban provide insight into possible drivers of province-wide XDR-TB transmission, including urban-rural migration. Further research should focus on characterizing the contribution of these drivers to overall XDR-TB transmission in KwaZulu-Natal to inform design of targeted strategies to curb the drug-resistant TB epidemic.
Collapse
Affiliation(s)
- Kristin N Nelson
- Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - N Sarita Shah
- Rollins School of Public Health, Emory University, Atlanta, Georgia
- Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Barun Mathema
- Mailman School of Public Health, Columbia University, New York, New York
| | - Nazir Ismail
- National Institute for Communicable Diseases, Johannesburg
- University of Pretoria, South Africa
| | - James C M Brust
- Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York
| | - Tyler S Brown
- Infectious Diseases Division, Massachusetts General Hospital, Boston
| | - Sara C Auld
- Rollins School of Public Health, Emory University, Atlanta, Georgia
- Emory University School of Medicine, Atlanta, Georgia
| | | | - Natashia Morris
- Environment and Health Research Unit, South African Medical Research Council, Johannesburg
| | - Angie Campbell
- Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Salim Allana
- Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Pravi Moodley
- National Health Laboratory Service, University of KwaZulu-Natal, Durban, South Africa
- School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Koleka Mlisana
- National Health Laboratory Service, University of KwaZulu-Natal, Durban, South Africa
- School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Neel R Gandhi
- Rollins School of Public Health, Emory University, Atlanta, Georgia
- Emory University School of Medicine, Atlanta, Georgia
| |
Collapse
|
78
|
Brinkhues S, Schram MT, Hoebe CJPA, Kretzschmar MEE, Koster A, Dagnelie PC, Sep SJS, van Kuijk SMJ, Savelkoul PHM, Dukers-Muijrers NHTM. Social networks in relation to self-reported symptomatic infections in individuals aged 40-75 - the Maastricht study. BMC Infect Dis 2018; 18:300. [PMID: 29973154 PMCID: PMC6030801 DOI: 10.1186/s12879-018-3197-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2017] [Accepted: 06/18/2018] [Indexed: 01/28/2023] Open
Abstract
Background Most infections are spread through social networks (detrimental effect). However, social networks may also lower infection acquisition (beneficial effect). This study aimed to examine associations between social network parameters and prevalence of self-reported upper and lower respiratory, gastrointestinal and urinary tract infections in a population aged 40–75. Methods In this population-based cross-sectional cohort study (N = 3004, mean age 60.0 ± 8.2 years, 49% women), infections within the past two months were assessed by self-administered questionnaires. Social network parameters were assessed using a name generator questionnaire. To examine the associated beneficial and detrimental network parameters, univariable and multivariable logistic regression was used. Results Participants reported an average of 10 people (alters) with whom they had 231 contacts per half year. Prevalences were 31.1% for upper respiratory, 11.5% for lower respiratory, 12.5% for gastrointestinal, and 5.7% for urinary tract infections. Larger network size, and a higher percentage of alters that were friends or acquaintances were associated with higher odds of upper respiratory, lower respiratory and/or gastrointestinal infections (detrimental). A higher total number of contacts, higher percentages of alters of the same age, and higher percentages of family members/acquaintances were associated with lower odds of upper respiratory, lower respiratory and/or gastrointestinal infections (beneficial). Conclusion We identified both detrimental and beneficial associations of social network parameters with the prevalence of infections. Our findings can be used to complement mathematical models on infection spread, as well as to optimize current infectious disease control. Electronic supplementary material The online version of this article (10.1186/s12879-018-3197-3) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Stephanie Brinkhues
- Department of Medical Microbiology, Maastricht University Medical Centre (MUMC+); CAPHRI, Care and Public Health Research Institute, Maastricht University, P.O. Box 616, 6200, MD, Maastricht, The Netherlands.,Department of Sexual Health, Infectious Diseases and Environmental Health, Public Health Service South Limburg, Postbus 33, 6400AA, Heerlen, The Netherlands
| | - Miranda T Schram
- Department of Medicine, Maastricht University Medical Centre (MUMC+); CARIM, Cardiovascular Research Institute Maastricht, Maastricht University, P.O. Box 616, 6200, MD, Maastricht, The Netherlands
| | - Christian J P A Hoebe
- Department of Medical Microbiology, Maastricht University Medical Centre (MUMC+); CAPHRI, Care and Public Health Research Institute, Maastricht University, P.O. Box 616, 6200, MD, Maastricht, The Netherlands.,Department of Sexual Health, Infectious Diseases and Environmental Health, Public Health Service South Limburg, Postbus 33, 6400AA, Heerlen, The Netherlands
| | - Mirjam E E Kretzschmar
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, University Medical Centre Utrecht, Julius Centre for Health Sciences and Primary Care, Utrecht, The Netherlands
| | - Annemarie Koster
- Department of Social Medicine; CAPHRI, School for Public Health and Primary Care, Maastricht University, P.O. Box 616, 6200, MD, Maastricht, The Netherlands
| | - Pieter C Dagnelie
- Department of Epidemiology, CARIM, Cardiovascular Research Institute Maastricht; CAPHRI, Care and Public Health Research Institute, Maastricht University, P.O. Box 616, 6200, MD, Maastricht, The Netherlands
| | - Simone J S Sep
- Department of Medicine, Maastricht University Medical Centre (MUMC+); CARIM, Cardiovascular Research Institute Maastricht, Maastricht University, P.O. Box 616, 6200, MD, Maastricht, The Netherlands
| | - Sander M J van Kuijk
- Department of Clinical Epidemiology and Medical Technology Assessment (KEMTA), Maastricht University Medical Centre (MUMC+), P.O. Box 616, 6200, MD, Maastricht, The Netherlands
| | - Paul H M Savelkoul
- Department of Medical Microbiology, Maastricht University Medical Centre (MUMC+); CAPHRI, Care and Public Health Research Institute, Maastricht University, P.O. Box 616, 6200, MD, Maastricht, The Netherlands.,Department of Medical Microbiology & Infection Control, VU University Medical Center, Amsterdam, The Netherlands
| | - Nicole H T M Dukers-Muijrers
- Department of Medical Microbiology, Maastricht University Medical Centre (MUMC+); CAPHRI, Care and Public Health Research Institute, Maastricht University, P.O. Box 616, 6200, MD, Maastricht, The Netherlands. .,Department of Sexual Health, Infectious Diseases and Environmental Health, Public Health Service South Limburg, Postbus 33, 6400AA, Heerlen, The Netherlands.
| |
Collapse
|
79
|
Auld SC, Shah NS, Cohen T, Martinson NA, Gandhi NR. Where is tuberculosis transmission happening? Insights from the literature, new tools to study transmission and implications for the elimination of tuberculosis. Respirology 2018; 23:10.1111/resp.13333. [PMID: 29869818 PMCID: PMC6281783 DOI: 10.1111/resp.13333] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2018] [Revised: 05/14/2018] [Accepted: 05/20/2018] [Indexed: 12/12/2022]
Abstract
More than 10 million new cases of tuberculosis (TB) are diagnosed worldwide each year. The majority of these cases occur in low- and middle-income countries where the TB epidemic is predominantly driven by transmission. Efforts to 'end TB' will depend upon our ability to halt ongoing transmission. However, recent studies of new approaches to interrupt transmission have demonstrated inconsistent effects on reducing population-level TB incidence. TB transmission occurs across a wide range of settings, that include households and hospitals, but also community-based settings. While home-based contact investigations and infection control programmes in hospitals and clinics have a successful track record as TB control activities, there is a gap in our knowledge of where, and between whom, community-based transmission of TB occurs. Novel tools, including molecular epidemiology, geospatial analyses and ventilation studies, provide hope for improving our understanding of transmission in countries where the burden of TB is greatest. By integrating these diverse and innovative tools, we can enhance our ability to identify transmission events by documenting the opportunity for transmission-through either an epidemiologic or geospatial connection-alongside genomic evidence for transmission, based upon genetically similar TB strains. A greater understanding of locations and patterns of transmission will translate into meaningful improvements in our current TB control activities by informing targeted, evidence-based public health interventions.
Collapse
Affiliation(s)
- Sara C Auld
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - N Sarita Shah
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Division of Global HIV and TB, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | - Neil A Martinson
- Perinatal HIV Research Unit, University of the Witwatersrand, Johannesburg, South Africa
- Center for TB Research, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Neel R Gandhi
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Department of Global Health, Emory University Rollins School of Public Health, Atlanta, GA, USA
| |
Collapse
|
80
|
le Polain de Waroux O, Cohuet S, Ndazima D, Kucharski AJ, Juan-Giner A, Flasche S, Tumwesigye E, Arinaitwe R, Mwanga-Amumpaire J, Boum Y, Nackers F, Checchi F, Grais RF, Edmunds WJ. Characteristics of human encounters and social mixing patterns relevant to infectious diseases spread by close contact: a survey in Southwest Uganda. BMC Infect Dis 2018; 18:172. [PMID: 29642869 PMCID: PMC5896105 DOI: 10.1186/s12879-018-3073-1] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Accepted: 03/27/2018] [Indexed: 11/24/2022] Open
Abstract
Background Quantification of human interactions relevant to infectious disease transmission through social contact is central to predict disease dynamics, yet data from low-resource settings remain scarce. Methods We undertook a social contact survey in rural Uganda, whereby participants were asked to recall details about the frequency, type, and socio-demographic characteristics of any conversational encounter that lasted for ≥5 min (henceforth defined as ‘contacts’) during the previous day. An estimate of the number of ‘casual contacts’ (i.e. < 5 min) was also obtained. Results In total, 566 individuals were included in the study. On average participants reported having routine contact with 7.2 individuals (range 1-25). Children aged 5-14 years had the highest frequency of contacts and the elderly (≥65 years) the fewest (P < 0.001). A strong age-assortative pattern was seen, particularly outside the household and increasingly so for contacts occurring further away from home. Adults aged 25-64 years tended to travel more often and further than others, and males travelled more frequently than females. Conclusion Our study provides detailed information on contact patterns and their spatial characteristics in an African setting. It therefore fills an important knowledge gap that will help more accurately predict transmission dynamics and the impact of control strategies in such areas. Electronic supplementary material The online version of this article (10.1186/s12879-018-3073-1) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- O le Polain de Waroux
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
| | | | - D Ndazima
- Epicentre, Uganda Research Centre, Mbarara, Uganda
| | - A J Kucharski
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | | | - S Flasche
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - E Tumwesigye
- Kabwohe Medical Research Centre, Kabwohe, Uganda
| | - R Arinaitwe
- Epicentre, Uganda Research Centre, Mbarara, Uganda
| | - J Mwanga-Amumpaire
- Epicentre, Uganda Research Centre, Mbarara, Uganda.,Mbarara University Of Science and Technology (MUST), Mbarara, Uganda
| | - Y Boum
- Epicentre, Uganda Research Centre, Mbarara, Uganda
| | | | - F Checchi
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | | | - W J Edmunds
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| |
Collapse
|
81
|
McCreesh N, White RG. An explanation for the low proportion of tuberculosis that results from transmission between household and known social contacts. Sci Rep 2018; 8:5382. [PMID: 29599463 PMCID: PMC5876383 DOI: 10.1038/s41598-018-23797-2] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Accepted: 03/14/2018] [Indexed: 02/05/2023] Open
Abstract
We currently have little idea where Mycobacterium tuberculosis (Mtb) transmission occurs in high incidence settings. Molecular studies suggest that only around 8-19% of transmission to adults occurs within-household, or between known social-contacts. This contrasts with findings from social-contact studies, which show that substantial proportions of contact time occur in households, workplaces and schools. A mathematical model of social-contact behaviour and Mtb transmission was developed, incorporating variation in susceptibility and infectiousness. Three types of contact were simulated: household, repeated (individuals outside household contacted repeatedly with daily-monthly frequency) and non-repeated. The model was parameterised using data from Cape Town, South Africa, on mean and variance in contact numbers and contact durations, by contact type, and fitted to an estimate of overdispersion in numbers of secondary cases ('superspreading') in Cape Town. Household, repeated, and non-repeated contacts contributed 36%, 13%, and 51% of contact time, and 13%, 8%, and 79% of disease, respectively. Results suggest contact saturation, exacerbated by long disease durations and superspreading, cause the high proportion of transmission between non-repeated contacts. Household and social-contact tracing is therefore unlikely to reach most tuberculosis cases. A better understanding of transmission locations, and methods to identify superspreaders, are urgently required to improve tuberculosis prevention strategies.
Collapse
Affiliation(s)
- Nicky McCreesh
- London School of Hygiene and Tropical Medicine, London, UK.
| | | |
Collapse
|
82
|
Kwok KO, Cowling B, Wei V, Riley S, Read JM. Temporal variation of human encounters and the number of locations in which they occur: a longitudinal study of Hong Kong residents. J R Soc Interface 2018; 15:20170838. [PMID: 29367241 PMCID: PMC5805989 DOI: 10.1098/rsif.2017.0838] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Accepted: 01/02/2018] [Indexed: 01/30/2023] Open
Abstract
Patterns of social contact between individuals are important for the transmission of many pathogens and shaping patterns of immunity at the population scale. To refine our understanding of how human social behaviour may change over time, we conducted a longitudinal study of Hong Kong residents. We recorded the social contact patterns for 1450 individuals, up to four times each between May 2012 and September 2013. We found individuals made contact with an average of 12.5 people within 2.9 geographical locations, and spent an average estimated total duration of 9.1 h in contact with others during a day. Distributions of the number of contacts and locations in which contacts were made were not significantly different between study waves. Encounters were assortative by age, and the age mixing pattern was broadly consistent across study waves. Fitting regression models, we examined the association of contact rates (number of contacts, total duration of contact, number of locations) with covariates and calculated the inter- and intra-participant variation in contact rates. Participant age was significantly associated with the number of contacts made, the total duration of contact and the number of locations in which contact occurred, with children and parental-age adults having the highest rates of contact. The number of contacts and contact duration increased with the number of contact locations. Intra-individual variation in contact rate was consistently greater than inter-individual variation. Despite substantial individual-level variation, remarkable consistency was observed in contact mixing at the population scale. This suggests that aggregate measures of mixing behaviour derived from cross-sectional information may be appropriate for population-scale modelling purposes, and that if more detailed models of social interactions are required for improved public health modelling, further studies are needed to understand the social processes driving intra-individual variation.
Collapse
Affiliation(s)
- Kin On Kwok
- The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, People's Republic of China
- Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Shatin, Hong Kong, Hong Kong Special Administrative Region, People's Republic of China
- Shenzhen Research Institute, Chinese University of Hong Kong, Shenzhen, People's Republic of China
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, People's Republic of China
| | - Ben Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, People's Republic of China
| | - Vivian Wei
- The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, People's Republic of China
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, People's Republic of China
| | - Steven Riley
- MRC Centre for Outbreak Analysis and Modelling, Department for Infectious Disease Epidemiology, Imperial College, London, UK
| | - Jonathan M Read
- Centre for Health Informatics, Computation and Statistics, Lancaster Medical School, Faculty of Health and Medicine, Lancaster University, Lancashire, UK
- Institute of Infection and Global Health, The Farr Institute@HeRC, University of Liverpool, Liverpool, UK
| |
Collapse
|
83
|
Jiang L, Ng IHL, Hou Y, Li D, Tan LWL, Ho HJA, Chen MIC. Infectious disease transmission: survey of contacts between hospital-based healthcare workers and working adults from the general population. J Hosp Infect 2017; 98:404-411. [PMID: 29097147 PMCID: PMC7114670 DOI: 10.1016/j.jhin.2017.10.020] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Accepted: 10/25/2017] [Indexed: 10/29/2022]
Abstract
BACKGROUND Healthcare workers (HCWs) may be the inadvertent interface between the healthcare setting and the community for infectious diseases transmission. AIM To investigate HCWs' contacts during a work day and compare these against working adults from the general population. METHODS Prospective survey of contacts through 24 h self-reported diary in three public sector tertiary care hospitals and community-based working adults in Singapore. Participants were HCWs and working adults from the community. FINDINGS In all, 211 HCWs and 1028 working adults reported a total of 4066 and 9206 contacts. HCWs reported more work-related contacts than community-based working adults (median of 13 versus 4), and more contacts that were neither household nor work-related (1 versus 0) but fewer household contacts (2 versus 3). HCWs reported more work-related contacts involving physical contacts, and more new contacts particularly with short duration (≤15 min) compared to community-based working adults. Among different HCW types, doctors reported the highest whereas ward-based nurses reported the lowest total work-related contacts. Around half of ward-based and clinic-based nurses' contacts involved physical touch. Work-related contacts reported by clinic-based nurses, doctors, and assorted HCWs were shorter than in ward-based nurses, with a substantial number effectively occurring with new contacts. Institutional effects significant on univariate analyses were much reduced and non-significant after adjusting for confounding by HCW type. CONCLUSION HCWs' contacts differ substantially from those of community-based working adults. HCWs may thus be at higher risk of acquiring and spreading contact-transmissible and respiratory infections due to the nature of their work. Whereas total number of contacts was fairly similar between HCW types, the characteristics of their contacts differed substantively.
Collapse
Affiliation(s)
- Lili Jiang
- Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore, Singapore
| | | | - Yan'an Hou
- Infectious Diseases - Epidemiology Unit, National University Hospital, Singapore, Singapore
| | - Dunli Li
- Department of Infection Control, Khoo Teck Puat Hospital, Singapore, Singapore
| | - Linda Wei Lin Tan
- Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore, Singapore
| | - Hanley Jian An Ho
- Department of Clinical Epidemiology, Tan Tock Seng Hospital, Singapore, Singapore
| | - Mark I-Cheng Chen
- Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore, Singapore; Department of Clinical Epidemiology, Tan Tock Seng Hospital, Singapore, Singapore.
| |
Collapse
|
84
|
Prem K, Cook AR, Jit M. Projecting social contact matrices in 152 countries using contact surveys and demographic data. PLoS Comput Biol 2017; 13:e1005697. [PMID: 28898249 PMCID: PMC5609774 DOI: 10.1371/journal.pcbi.1005697] [Citation(s) in RCA: 454] [Impact Index Per Article: 64.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Revised: 09/22/2017] [Accepted: 07/25/2017] [Indexed: 11/19/2022] Open
Abstract
Heterogeneities in contact networks have a major effect in determining whether a pathogen can become epidemic or persist at endemic levels. Epidemic models that determine which interventions can successfully prevent an outbreak need to account for social structure and mixing patterns. Contact patterns vary across age and locations (e.g. home, work, and school), and including them as predictors in transmission dynamic models of pathogens that spread socially will improve the models' realism. Data from population-based contact diaries in eight European countries from the POLYMOD study were projected to 144 other countries using a Bayesian hierarchical model that estimated the proclivity of age-and-location-specific contact patterns for the countries, using Markov chain Monte Carlo simulation. Household level data from the Demographic and Health Surveys for nine lower-income countries and socio-demographic factors from several on-line databases for 152 countries were used to quantify similarity of countries to estimate contact patterns in the home, work, school and other locations for countries for which no contact data are available, accounting for demographic structure, household structure where known, and a variety of metrics including workforce participation and school enrolment. Contacts are highly assortative with age across all countries considered, but pronounced regional differences in the age-specific contacts at home were noticeable, with more inter-generational contacts in Asian countries than in other settings. Moreover, there were variations in contact patterns by location, with work-place contacts being least assortative. These variations led to differences in the effect of social distancing measures in an age structured epidemic model. Contacts have an important role in transmission dynamic models that use contact rates to characterize the spread of contact-transmissible diseases. This study provides estimates of mixing patterns for societies for which contact data such as POLYMOD are not yet available.
Collapse
Affiliation(s)
- Kiesha Prem
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Alex R. Cook
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Program in Health Services and Systems Research, Duke-NUS Graduate Medical School, Singapore, Singapore
- Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore
| | - Mark Jit
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Modelling and Economics Unit, Health Protection Agency Centre for Infections, London, United Kingdom
| |
Collapse
|
85
|
Jones-López EC, Acuña-Villaorduña C, Fregona G, Marques-Rodrigues P, White LF, Hadad DJ, Dutra-Molina LP, Vinhas S, McIntosh AI, Gaeddert M, Ribeiro-Rodrigues R, Salgame P, Palaci M, Alland D, Ellner JJ, Dietze R. Incident Mycobacterium tuberculosis infection in household contacts of infectious tuberculosis patients in Brazil. BMC Infect Dis 2017; 17:576. [PMID: 28821234 PMCID: PMC5563014 DOI: 10.1186/s12879-017-2675-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Accepted: 08/08/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In household contact investigations of tuberculosis (TB), a second tuberculin skin test (TST) obtained several weeks after a first negative result consistently identifies individuals that undergo TST conversion. It remains unclear whether this delay in M. tuberculosis infection is related to differences in the infectious exposure, TST boosting, partial host resistance, or some other factor. METHODS We conducted a household contact study Vitória, Brazil. Between 2008 and 2013, we identified culture-positive pulmonary TB patients and evaluated their household contacts with both a TST and interferon gamma release assay (IGRA), and identified TST converters at 8-12 weeks post study enrollment. Contacts were classified as TST-positive (≥10 mm) at baseline, TST converters, or persistently TST-negative. We compared TST converters to TST-positive and to TST-negative contacts separately, using generalized estimating equations. RESULTS We enrolled 160 index patients and 838 contacts; 523 (62.4%) were TST+, 62 (7.4%) TST converters, and 253 (30.2%) TST-. TST converters were frequently IGRA- at 8-12 weeks. In adjusted analyses, characteristics distinguishing TST converters from TST+ contacts (no contact with another TB patient and residence ownership) were different than those differentiating them from TST- contacts (stronger cough in index patient and contact BCG scar). CONCLUSIONS The individual risk and timing of M. tuberculosis infection within households is variable and dependent on index patient, contact and environmental factors within the household, and the surrounding community. Our findings suggest a threshold effect in the risk of infection in humans.
Collapse
Affiliation(s)
- Edward C Jones-López
- Section of Infectious Diseases, Department of Medicine, Boston Medical Center and Boston University School of Medicine, 650 Albany Street, Room 605, Boston, MA, 02118, USA.
| | - Carlos Acuña-Villaorduña
- Section of Infectious Diseases, Department of Medicine, Boston Medical Center and Boston University School of Medicine, 650 Albany Street, Room 605, Boston, MA, 02118, USA.
| | - Geisa Fregona
- Núcleo de Doenças Infecciosas, Universidade Federal do Espírito Santo (UFES), Vitória, Brazil
| | | | - Laura F White
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - David Jamil Hadad
- Núcleo de Doenças Infecciosas, Universidade Federal do Espírito Santo (UFES), Vitória, Brazil
| | | | - Solange Vinhas
- Mycobacteriology Laboratory, Núcleo de Doenças Infecciosas, UFES, Vitória, Brazil
| | - Avery I McIntosh
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Mary Gaeddert
- Section of Infectious Diseases, Department of Medicine, Boston Medical Center and Boston University School of Medicine, 650 Albany Street, Room 605, Boston, MA, 02118, USA
| | | | - Padmini Salgame
- Division of Infectious Diseases, Department of Medicine, New Jersey Medical School- Rutgers University, Newark, NJ, USA
| | - Moises Palaci
- Mycobacteriology Laboratory, Núcleo de Doenças Infecciosas, UFES, Vitória, Brazil
| | - David Alland
- Division of Infectious Diseases, Department of Medicine, New Jersey Medical School- Rutgers University, Newark, NJ, USA
| | - Jerrold J Ellner
- Section of Infectious Diseases, Department of Medicine, Boston Medical Center and Boston University School of Medicine, 650 Albany Street, Room 605, Boston, MA, 02118, USA
| | - Reynaldo Dietze
- Núcleo de Doenças Infecciosas, Universidade Federal do Espírito Santo (UFES), Vitória, Brazil
| |
Collapse
|
86
|
Leung K, Jit M, Lau EHY, Wu JT. Social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong. Sci Rep 2017. [PMID: 28801623 DOI: 10.5281/zenodo.3874808] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2023] Open
Abstract
The spread of many respiratory infections is determined by contact patterns between infectious and susceptible individuals in the population. There are no published data for quantifying social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong which is a hotspot for emerging infectious diseases due to its high population density and connectivity in the air transportation network. We adopted a commonly used diary-based design to conduct a social contact survey in Hong Kong in 2015/16 using both paper and online questionnaires. Participants using paper questionnaires reported more contacts and longer contact duration than those using online questionnaires. Participants reported 13 person-hours of contact and 8 contacts per day on average, which decreased over age but increased with household size, years of education and income level. Prolonged and frequent contacts, and contacts at home, school and work were more likely to involve physical contacts. Strong age-assortativity was observed in all age groups. We evaluated the characteristics of social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong. Our findings could help to improve the design of future social contact surveys, parameterize transmission models of respiratory infectious diseases, and inform intervention strategies based on model outputs.
Collapse
Affiliation(s)
- Kathy Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Mark Jit
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People's Republic of China
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Modelling and Economics Unit, Public Health England, London, United Kingdom
| | - Eric H Y Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Joseph T Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People's Republic of China.
| |
Collapse
|
87
|
Social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong. Sci Rep 2017; 7:7974. [PMID: 28801623 PMCID: PMC5554254 DOI: 10.1038/s41598-017-08241-1] [Citation(s) in RCA: 82] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Accepted: 07/10/2017] [Indexed: 11/08/2022] Open
Abstract
The spread of many respiratory infections is determined by contact patterns between infectious and susceptible individuals in the population. There are no published data for quantifying social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong which is a hotspot for emerging infectious diseases due to its high population density and connectivity in the air transportation network. We adopted a commonly used diary-based design to conduct a social contact survey in Hong Kong in 2015/16 using both paper and online questionnaires. Participants using paper questionnaires reported more contacts and longer contact duration than those using online questionnaires. Participants reported 13 person-hours of contact and 8 contacts per day on average, which decreased over age but increased with household size, years of education and income level. Prolonged and frequent contacts, and contacts at home, school and work were more likely to involve physical contacts. Strong age-assortativity was observed in all age groups. We evaluated the characteristics of social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong. Our findings could help to improve the design of future social contact surveys, parameterize transmission models of respiratory infectious diseases, and inform intervention strategies based on model outputs.
Collapse
|
88
|
Melegaro A, Del Fava E, Poletti P, Merler S, Nyamukapa C, Williams J, Gregson S, Manfredi P. Social Contact Structures and Time Use Patterns in the Manicaland Province of Zimbabwe. PLoS One 2017; 12:e0170459. [PMID: 28099479 PMCID: PMC5242544 DOI: 10.1371/journal.pone.0170459] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2016] [Accepted: 01/05/2017] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Patterns of person-to-person contacts relevant for infectious diseases transmission are still poorly quantified in Sub-Saharan Africa (SSA), where socio-demographic structures and behavioral attitudes are expected to be different from those of more developed countries. METHODS AND FINDINGS We conducted a diary-based survey on daily contacts and time-use of individuals of different ages in one rural and one peri-urban site of Manicaland, Zimbabwe. A total of 2,490 diaries were collected and used to derive age-structured contact matrices, to analyze time spent by individuals in different settings, and to identify the key determinants of individuals' mixing patterns. Overall 10.8 contacts per person/day were reported, with a significant difference between the peri-urban and the rural site (11.6 versus 10.2). A strong age-assortativeness characterized contacts of school-aged children, whereas the high proportion of extended families and the young population age-structure led to a significant intergenerational mixing at older ages. Individuals spent on average 67% of daytime at home, 2% at work, and 9% at school. Active participation in school and work resulted the key drivers of the number of contacts and, similarly, household size, class size, and time spent at work influenced the number of home, school, and work contacts, respectively. We found that the heterogeneous nature of home contacts is critical for an epidemic transmission chain. In particular, our results suggest that, during the initial phase of an epidemic, about 50% of infections are expected to occur among individuals younger than 12 years and less than 20% among individuals older than 35 years. CONCLUSIONS With the current work, we have gathered data and information on the ways through which individuals in SSA interact, and on the factors that mostly facilitate this interaction. Monitoring these processes is critical to realistically predict the effects of interventions on infectious diseases dynamics.
Collapse
Affiliation(s)
- Alessia Melegaro
- Carlo F. Dondena Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milano, Italy
- Department of Policy Analysis and Public Management, Bocconi University, Milano, Italy
| | - Emanuele Del Fava
- Carlo F. Dondena Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milano, Italy
| | - Piero Poletti
- Carlo F. Dondena Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milano, Italy
- Center for Information Technology, Bruno Kessler Foundation, Trento, Italy
| | - Stefano Merler
- Center for Information Technology, Bruno Kessler Foundation, Trento, Italy
| | - Constance Nyamukapa
- Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
- Biomedical Research and Training Institute, Harare, Zimbabwe
| | - John Williams
- Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Simon Gregson
- Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
- Biomedical Research and Training Institute, Harare, Zimbabwe
| | - Piero Manfredi
- Department of Economics and Management, University of Pisa, Pisa, Italy
| |
Collapse
|
89
|
Watson CH, Coriakula J, Ngoc DTT, Flasche S, Kucharski AJ, Lau CL, Thieu NTV, le Polain de Waroux O, Rawalai K, Van TT, Taufa M, Baker S, Nilles EJ, Kama M, Edmunds WJ. Social mixing in Fiji: Who-eats-with-whom contact patterns and the implications of age and ethnic heterogeneity for disease dynamics in the Pacific Islands. PLoS One 2017; 12:e0186911. [PMID: 29211731 PMCID: PMC5718486 DOI: 10.1371/journal.pone.0186911] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2017] [Accepted: 10/10/2017] [Indexed: 11/17/2022] Open
Abstract
Empirical data on contact patterns can inform dynamic models of infectious disease transmission. Such information has not been widely reported from Pacific islands, nor strongly multi-ethnic settings, and few attempts have been made to quantify contact patterns relevant for the spread of gastrointestinal infections. As part of enteric fever investigations, we conducted a cross-sectional survey of the general public in Fiji, finding that within the 9,650 mealtime contacts reported by 1,814 participants, there was strong like-with-like mixing by age and ethnicity, with higher contact rates amongst iTaukei than non-iTaukei Fijians. Extra-domiciliary lunchtime contacts follow these mixing patterns, indicating the overall data do not simply reflect household structures. Inter-ethnic mixing was most common amongst school-age children. Serological responses indicative of recent Salmonella Typhi infection were found to be associated, after adjusting for age, with increased contact rates between meal-sharing iTaukei, with no association observed for other contact groups. Animal ownership and travel within the geographical division were common. These are novel data that identify ethnicity as an important social mixing variable, and use retrospective mealtime contacts as a socially acceptable metric of relevance to enteric, contact and respiratory diseases that can be collected in a single visit to participants. Application of these data to other island settings will enable communicable disease models to incorporate locally relevant mixing patterns in parameterisation.
Collapse
Affiliation(s)
- Conall H Watson
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | | | - Dung Tran Thi Ngoc
- The Hospital for Tropical Diseases, Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit-Vietnam, Ho Chi Minh City, Vietnam
| | - Stefan Flasche
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Adam J Kucharski
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Colleen L Lau
- Department of Global Health, Research School of Population Health, The Australian National University, Canberra, Australia
| | - Nga Tran Vu Thieu
- The Hospital for Tropical Diseases, Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit-Vietnam, Ho Chi Minh City, Vietnam
| | - Olivier le Polain de Waroux
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | | | - Tan Trinh Van
- School of Medicine, Fiji National University, Suva, Fiji
| | - Mere Taufa
- Fiji Centre for Communicable Disease Control, Ministry of Health and Medical Services, Suva, Fiji
| | - Stephen Baker
- The Hospital for Tropical Diseases, Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit-Vietnam, Ho Chi Minh City, Vietnam.,Centre for Tropical Medicine and Global Health, Oxford University, Oxford, United Kingdom.,Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Eric J Nilles
- Division of Pacific Technical Support, World Health Organization-Western Pacific Region, Suva, Fiji
| | - Mike Kama
- Fiji Centre for Communicable Disease Control, Ministry of Health and Medical Services, Suva, Fiji
| | - W John Edmunds
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| |
Collapse
|
90
|
Ncayiyana JR, Bassett J, West N, Westreich D, Musenge E, Emch M, Pettifor A, Hanrahan CF, Schwartz SR, Sanne I, van Rie A. Prevalence of latent tuberculosis infection and predictive factors in an urban informal settlement in Johannesburg, South Africa: a cross-sectional study. BMC Infect Dis 2016; 16:661. [PMID: 27825307 PMCID: PMC5101651 DOI: 10.1186/s12879-016-1989-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2016] [Accepted: 10/28/2016] [Indexed: 01/18/2023] Open
Abstract
Background South Africa has one of the highest burdens of latent tuberculosis infection (LTBI) in high-risk populations such as young children, adolescents, household contacts of TB cases, people living with HIV, gold miners and health care workers, but little is known about the burden of LTBI in its general population. Methods Using a community-based survey with random sampling, we examined the burden of LTBI in an urban township of Johannesburg and investigated factors associated with LTBI. The outcome of LTBI was based on TST positivity, with a TST considered positive if the induration was ≥5 mm in people living with HIV or ≥10 mm in those with unknown or HIV negative status. We used bivariate and multivariable logistic regression to identify factors associated with LTBI Results The overall prevalence of LTBI was 34.3 (95 % CI 30.0, 38.8 %), the annual risk of infection among children age 0–14 years was 3.1 % (95 % CI 2.1, 5.2). LTBI was not associated with HIV status. In multivariable logistic regression analysis, LTBI was associated with age (OR = 1.03 for every year increase in age, 95 % CI = 1.01–1.05), male gender (OR = 2.70, 95 % CI = 1.55–4.70), marital status (OR = 2.00, 95 % CI = 1.31–3.54), and higher socio-economic status (OR = 2.11, 95 % CI = 1.04–4.31). Conclusions The prevalence of LTBI and the annual risk of infection with M. tuberculosis is high in urban populations, especially in men, but independent of HIV infection status. This study suggests that LTBI may be associated with higher SES, in contrast to the well-established association between TB disease and poverty.
Collapse
Affiliation(s)
- Jabulani R Ncayiyana
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA. .,Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, 29 Princess of Wales Terrace, Johannesburg, 2193, South Africa.
| | - Jean Bassett
- Witkoppen Health and Welfare Centre, 105 William Nicol Drive, Fourways, Johannesburg, 2055, South Africa
| | - Nora West
- Witkoppen Health and Welfare Centre, 105 William Nicol Drive, Fourways, Johannesburg, 2055, South Africa
| | - Daniel Westreich
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Eustasius Musenge
- Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, 29 Princess of Wales Terrace, Johannesburg, 2193, South Africa
| | - Michael Emch
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Audrey Pettifor
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Colleen F Hanrahan
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, 615 N. Wolfe Street, Baltimore, MD, 21205, USA
| | - Sheree R Schwartz
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, 615 N. Wolfe Street, Baltimore, MD, 21205, USA
| | - Ian Sanne
- Clinical HIV Research Unit, Department of Medicine, University of the Witwatersrand, Perth Road, Auckland Park, Johannesburg, 2092, South Africa
| | - Annelies van Rie
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.,Department of Epidemiology and Social Medicine, Faculty of Medicine and Health Sciences, University of Antwerp, Campus Drie Eiken, University Square, Wilrijk, Antwerp, 2610, Belgium
| |
Collapse
|
91
|
Moser CB, White LF. Estimating age-specific reproductive numbers-A comparison of methods. Stat Methods Med Res 2016; 27:2050-2059. [PMID: 28571521 DOI: 10.1177/0962280216673676] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Large outbreaks, such as those caused by influenza, put a strain on resources necessary for their control. In particular, children have been shown to play a key role in influenza transmission during recent outbreaks, and targeted interventions, such as school closures, could positively impact the course of emerging epidemics. As an outbreak is unfolding, it is important to be able to estimate reproductive numbers that incorporate this heterogeneity and to use surveillance data that is routinely collected to more effectively target interventions and obtain an accurate understanding of transmission dynamics. There are a growing number of methods that estimate age-group specific reproductive numbers with limited data that build on methods assuming a homogenously mixing population. In this article, we introduce a new approach that is flexible and improves on many aspects of existing methods. We apply this method to influenza data from two outbreaks, the 2009 H1N1 outbreaks in South Africa and Japan, to estimate age-group specific reproductive numbers and compare it to three other methods that also use existing data from social mixing surveys to quantify contact rates among different age groups. In this exercise, all estimates of the reproductive numbers for children exceeded the critical threshold of one and in most cases exceeded those of adults. We introduce a flexible new method to estimate reproductive numbers that describe heterogeneity in the population.
Collapse
Affiliation(s)
- Carlee B Moser
- 1 1Center for Biostatistics in AIDS Research, Harvard T. H. Chan School of Public Health, Boston, MA, USA.,2 Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Laura F White
- 2 Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| |
Collapse
|
92
|
Spatial and Temporal Spread of Acute Viral Respiratory Infections in Young Children Living in High-altitude Rural Communities: A Prospective Household-based Study. Pediatr Infect Dis J 2016; 35:1057-61. [PMID: 27404599 PMCID: PMC5021582 DOI: 10.1097/inf.0000000000001234] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Few studies have described patterns of transmission of viral acute respiratory infections (ARI) in children in developing countries. We examined the spatial and temporal spread of viral ARI among young children in rural Peruvian highland communities. Previous studies have described intense social interactions in those communities, which could influence the transmission of viral infections. METHODS We enrolled and followed children <3 years of age for detection of ARI during the 2009 to 2011 respiratory seasons in a rural setting with relatively wide geographic dispersion of households and communities. Viruses detected included influenza, respiratory syncytial virus (RSV), human metapneumovirus and parainfluenza 2 and 3 viruses (PIV2, PIV3). We used geospatial analyses to identify specific viral infection hot spots with high ARI incidence. We also explored the local spread of ARI from index cases using standard deviational ellipses. RESULTS Geospatial analyses revealed hot spots of high ARI incidence around the index cases of influenza outbreaks and RSV outbreak in 2010. Although PIV3 in 2009 and PIV2 in 2010 showed distinct spatial hot spots, clustering was not in proximity to their respective index cases. No significant aggregation around index cases was noted for other viruses. Standard deviational ellipse analyses suggested that influenza B and RSV in 2010, and human metapneumovirus in 2011 spread temporally in alignment with the major road network. CONCLUSIONS Despite the geographic dispersion of communities in this rural setting, we observed a rapid spread of viral ARI among young children. Influenza strains and RSV in 2010 had distinctive outbreaks arising from their index cases.
Collapse
|
93
|
Kiti MC, Tizzoni M, Kinyanjui TM, Koech DC, Munywoki PK, Meriac M, Cappa L, Panisson A, Barrat A, Cattuto C, Nokes DJ. Quantifying social contacts in a household setting of rural Kenya using wearable proximity sensors. EPJ DATA SCIENCE 2016; 5:21. [PMID: 27471661 PMCID: PMC4944592 DOI: 10.1140/epjds/s13688-016-0084-2] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2015] [Accepted: 06/06/2016] [Indexed: 06/06/2023]
Abstract
UNLABELLED Close proximity interactions between individuals influence how infections spread. Quantifying close contacts in developing world settings, where such data is sparse yet disease burden is high, can provide insights into the design of intervention strategies such as vaccination. Recent technological advances have enabled collection of time-resolved face-to-face human contact data using radio frequency proximity sensors. The acceptability and practicalities of using proximity devices within the developing country setting have not been investigated. We present and analyse data arising from a prospective study of 5 households in rural Kenya, followed through 3 consecutive days. Pre-study focus group discussions with key community groups were held. All residents of selected households carried wearable proximity sensors to collect data on their close (<1.5 metres) interactions. Data collection for residents of three of the 5 households was contemporaneous. Contact matrices and temporal networks for 75 individuals are defined and mixing patterns by age and time of day in household contacts determined. Our study demonstrates the stability of numbers and durations of contacts across days. The contact durations followed a broad distribution consistent with data from other settings. Contacts within households occur mainly among children and between children and adults, and are characterised by daily regular peaks in the morning, midday and evening. Inter-household contacts are between adults and more sporadic when measured over several days. Community feedback indicated privacy as a major concern especially regarding perceptions of non-participants, and that community acceptability required thorough explanation of study tools and procedures. Our results show for a low resource setting how wearable proximity sensors can be used to objectively collect high-resolution temporal data without direct supervision. The methodology appears acceptable in this population following adequate community engagement on study procedures. A target for future investigation is to determine the difference in contact networks within versus between households. We suggest that the results from this study may be used in the design of future studies using similar electronic devices targeting communities, including households and schools, in the developing world context. ELECTRONIC SUPPLEMENTARY MATERIAL The online version of this article (doi:10.1140/epjds/s13688-016-0084-2) contains supplementary material.
Collapse
Affiliation(s)
- Moses C Kiti
- />KEMRI - Wellcome Trust Research Programme, Kilifi, Kenya
| | - Michele Tizzoni
- />Data Science Laboratory, ISI Foundation, Via Alassio 11/c, Torino, 10126 Italy
| | - Timothy M Kinyanjui
- />KEMRI - Wellcome Trust Research Programme, Kilifi, Kenya
- />School of Mathematics, The University of Manchester, Manchester, UK
| | | | | | | | - Luca Cappa
- />Data Science Laboratory, ISI Foundation, Via Alassio 11/c, Torino, 10126 Italy
| | - André Panisson
- />Data Science Laboratory, ISI Foundation, Via Alassio 11/c, Torino, 10126 Italy
| | - Alain Barrat
- />Data Science Laboratory, ISI Foundation, Via Alassio 11/c, Torino, 10126 Italy
- />Aix-Marseille Université, Université de Toulon, CNRS, CPT, UMR 7332, Marseille, 13288 France
| | - Ciro Cattuto
- />Data Science Laboratory, ISI Foundation, Via Alassio 11/c, Torino, 10126 Italy
| | - D James Nokes
- />KEMRI - Wellcome Trust Research Programme, Kilifi, Kenya
- />School of Life Sciences and WIDER, University of Warwick, Coventry, UK
| |
Collapse
|
94
|
Blaser N, Zahnd C, Hermans S, Salazar-Vizcaya L, Estill J, Morrow C, Egger M, Keiser O, Wood R. Tuberculosis in Cape Town: An age-structured transmission model. Epidemics 2016; 14:54-61. [PMID: 26972514 PMCID: PMC4791535 DOI: 10.1016/j.epidem.2015.10.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2014] [Revised: 10/05/2015] [Accepted: 10/11/2015] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Tuberculosis (TB) is the leading cause of death in South Africa. The burden of disease varies by age, with peaks in TB notification rates in the HIV-negative population at ages 0-5, 20-24, and 45-49 years. There is little variation between age groups in the rates in the HIV-positive population. The drivers of this age pattern remain unknown. METHODS We developed an age-structured simulation model of Mycobacterium tuberculosis (Mtb) transmission in Cape Town, South Africa. We considered five states of TB progression: susceptible, infected (latent TB), active TB, treated TB, and treatment default. Latently infected individuals could be re-infected; a previous Mtb infection slowed progression to active disease. We further considered three states of HIV progression: HIV negative, HIV positive, on antiretroviral therapy. To parameterize the model, we analysed treatment outcomes from the Cape Town electronic TB register, social mixing patterns from a Cape Town community and used literature estimates for other parameters. To investigate the main drivers behind the age patterns, we conducted sensitivity analyses on all parameters related to the age structure. RESULTS The model replicated the age patterns in HIV-negative TB notification rates of Cape Town in 2009. Simulated TB notification rate in HIV-negative patients was 1000/100,000 person-years (pyrs) in children aged <5 years and decreased to 51/100,000 in children 5-15 years. The peak in early adulthood occurred at 25-29 years (463/100,000 pyrs). After a subsequent decline, simulated TB notification rates gradually increased from the age of 30 years. Sensitivity analyses showed that the dip after the early adult peak was due to the protective effect of latent TB and that retreatment TB was mainly responsible for the rise in TB notification rates from the age of 30 years. CONCLUSION The protective effect of a first latent infection on subsequent infections and the faster progression in previously treated patients are the key determinants of the age-structure of TB notification rates in Cape Town.
Collapse
Affiliation(s)
- Nello Blaser
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland
| | - Cindy Zahnd
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland
| | - Sabine Hermans
- Desmond Tutu HIV Centre, Institute for Infectious Disease & Molecular Medicine, University of Cape Town, South Africa; Department of Global Health, Academic Medical Center, University of Amsterdam, Amsterdam Institute for Global Health and Development,The Netherlands; Department of Internal Medicine, School of Medicine, Makerere University College of Health Sciences, Kampala, Uganda
| | - Luisa Salazar-Vizcaya
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland
| | - Janne Estill
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland
| | - Carl Morrow
- Desmond Tutu HIV Centre, Institute for Infectious Disease & Molecular Medicine, University of Cape Town, South Africa
| | - Matthias Egger
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland; School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa
| | - Olivia Keiser
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland.
| | - Robin Wood
- Desmond Tutu HIV Centre, Institute for Infectious Disease & Molecular Medicine, University of Cape Town, South Africa; Department of Medicine, University of Cape Town,, South Africa; Department of Clinical Research, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, UK
| |
Collapse
|
95
|
McCreesh N, Looker C, Dodd PJ, Plumb ID, Shanaube K, Muyoyeta M, Godfrey-Faussett P, Corbett EL, Ayles H, White RG. Comparison of indoor contact time data in Zambia and Western Cape, South Africa suggests targeting of interventions to reduce Mycobacterium tuberculosis transmission should be informed by local data. BMC Infect Dis 2016; 16:71. [PMID: 26861444 PMCID: PMC4746903 DOI: 10.1186/s12879-016-1406-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2015] [Accepted: 02/02/2016] [Indexed: 01/20/2023] Open
Abstract
Background In high incidence settings, the majority of Mycobacterium tuberculosis (M.tb) transmission occurs outside the household. Little is known about where people’s indoor contacts occur outside the household, and how this differs between different settings. We estimate the number of contact hours that occur between adults and adult/youths and children in different building types in urban areas in Western Cape, South Africa, and Zambia. Methods Data were collected from 3206 adults using a cross-sectional survey, on buildings visited in a 24-h period, including building function, visit duration, and number of adults/youths and children (5–12 years) present. The mean numbers of contact hours per day by building function were calculated. Results Adults in Western Cape were more likely to visit workplaces, and less likely to visit shops and churches than adults in Zambia. Adults in Western Cape spent longer per visit in other homes and workplaces than adults in Zambia. More adults/youths were present at visits to shops and churches in Western Cape than in Zambia, and fewer at homes and hairdressers. More children were present at visits to shops in Western Cape than in Zambia, and fewer at schools and hairdressers. Overall numbers of adult/youth indoor contact hours were the same at both sites (35.4 and 37.6 h in Western Cape and Zambia respectively, p = 0.4). Child contact hours were higher in Zambia (16.0 vs 13.7 h, p = 0.03). Adult/youth and child contact hours were highest in workplaces in Western Cape and churches in Zambia. Compared to Zambia, adult contact hours in Western Cape were higher in workplaces (15.2 vs 8.0 h, p = 0.004), and lower in churches (3.7 vs 8.6 h, p = 0.002). Child contact hours were higher in other peoples’ homes (2.8 vs 1.6 h, p = 0.03) and workplaces (4.9 vs 2.1 h, p = 0.003), and lower in churches (2.5 vs 6.2, p = 0.004) and schools (0.4 vs 1.5, p = 0.01). Conclusions Patterns of indoor contact between adults and adults/youths and children differ between different sites in high M.tb incidence areas. Targeting public buildings with interventions to reduce M.tb transmission (e.g. increasing ventilation or UV irradiation) should be informed by local data. Electronic supplementary material The online version of this article (doi:10.1186/s12879-016-1406-5) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Nicky McCreesh
- TB Modelling Group, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
| | - Clare Looker
- TB Modelling Group, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
| | - Peter J Dodd
- TB Modelling Group, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK. .,Health Economics and Decision Science, School of Health and Related Research, University of Sheffield, Sheffield, UK.
| | - Ian D Plumb
- TB Modelling Group, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
| | - Kwame Shanaube
- ZAMBART Project, School of Medicine, University of Zambia, Lusaka, Zambia.
| | - Monde Muyoyeta
- ZAMBART Project, School of Medicine, University of Zambia, Lusaka, Zambia. .,TB Department, Centre for Infectious Disease Research in Zambia, Lusaka, Zambia.
| | - Peter Godfrey-Faussett
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK.
| | - Elizabeth L Corbett
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK. .,HIV and TB Theme, Malawi Liverpool Wellcome Trust Clinical Research Programme, Blantyre, Malawi.
| | - Helen Ayles
- ZAMBART Project, School of Medicine, University of Zambia, Lusaka, Zambia. .,Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK.
| | - Richard G White
- TB Modelling Group, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
| |
Collapse
|
96
|
Yates TA, Khan PY, Knight GM, Taylor JG, McHugh TD, Lipman M, White RG, Cohen T, Cobelens FG, Wood R, Moore DAJ, Abubakar I. The transmission of Mycobacterium tuberculosis in high burden settings. THE LANCET. INFECTIOUS DISEASES 2016; 16:227-38. [PMID: 26867464 DOI: 10.1016/s1473-3099(15)00499-5] [Citation(s) in RCA: 118] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2015] [Revised: 11/03/2015] [Accepted: 11/26/2015] [Indexed: 01/06/2023]
Abstract
Unacceptable levels of Mycobacterium tuberculosis transmission are noted in high burden settings and a renewed focus on reducing person-to-person transmission in these communities is needed. We review recent developments in the understanding of airborne transmission. We outline approaches to measure transmission in populations and trials and describe the Wells-Riley equation, which is used to estimate transmission risk in indoor spaces. Present research priorities include the identification of effective strategies for tuberculosis infection control, improved understanding of where transmission occurs and the transmissibility of drug-resistant strains, and estimates of the effect of HIV and antiretroviral therapy on transmission dynamics. When research is planned and interventions are designed to interrupt transmission, resource constraints that are common in high burden settings-including shortages of health-care workers-must be considered.
Collapse
Affiliation(s)
- Tom A Yates
- Centre for Infectious Disease Epidemiology, Research Department of Infection and Population Health, University College London, London, UK; Wellcome Trust Africa Centre for Population Health, Mtubatuba, South Africa, London School of Hygiene & Tropical Medicine, London, UK.
| | - Palwasha Y Khan
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK; Tuberculosis Centre, London School of Hygiene & Tropical Medicine, London, UK; Karonga Prevention Study, Chilumba, Malawi
| | - Gwenan M Knight
- Tuberculosis Centre, London School of Hygiene & Tropical Medicine, London, UK; Tuberculosis Modelling Group, London School of Hygiene & Tropical Medicine, London, UK; National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance, Imperial College London, London, UK
| | - Jonathon G Taylor
- UCL Institute for Environmental Design and Engineering, Bartlett School of Environment, Energy and Resources, University College London, London, UK
| | - Timothy D McHugh
- Centre for Clinical Microbiology, University College London, London, UK
| | - Marc Lipman
- Division of Medicine, University College London, London, UK
| | - Richard G White
- Tuberculosis Centre, London School of Hygiene & Tropical Medicine, London, UK; Tuberculosis Modelling Group, London School of Hygiene & Tropical Medicine, London, UK
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | - Frank G Cobelens
- Department of Global Health, Academic Medical Center, Amsterdam, Netherlands; KNCV Tuberculosis Foundation, The Hague, Netherlands
| | - Robin Wood
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK; Tuberculosis Centre, London School of Hygiene & Tropical Medicine, London, UK; The Desmond Tutu HIV Centre, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - David A J Moore
- Tuberculosis Centre, London School of Hygiene & Tropical Medicine, London, UK; Department of Clinical Research, London School of Hygiene & Tropical Medicine, London, UK
| | - Ibrahim Abubakar
- Centre for Infectious Disease Epidemiology, Research Department of Infection and Population Health, University College London, London, UK; MRC Clinical Trials Unit at University College London, University College London, London, UK
| |
Collapse
|
97
|
Dodd PJ, Looker C, Plumb ID, Bond V, Schaap A, Shanaube K, Muyoyeta M, Vynnycky E, Godfrey-Faussett P, Corbett EL, Beyers N, Ayles H, White RG. Age- and Sex-Specific Social Contact Patterns and Incidence of Mycobacterium tuberculosis Infection. Am J Epidemiol 2016; 183:156-66. [PMID: 26646292 PMCID: PMC4706676 DOI: 10.1093/aje/kwv160] [Citation(s) in RCA: 91] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2014] [Accepted: 06/15/2015] [Indexed: 11/13/2022] Open
Abstract
We aimed to model the incidence of infection with Mycobacterium tuberculosis among adults using data on infection incidence in children, disease prevalence in adults, and social contact patterns. We conducted a cross-sectional face-to-face survey of adults in 2011, enumerating "close" (shared conversation) and "casual" (shared indoor space) social contacts in 16 Zambian communities and 8 South African communities. We modeled the incidence of M. tuberculosis infection in all age groups using these contact patterns, as well as the observed incidence of M. tuberculosis infection in children and the prevalence of tuberculosis disease in adults. A total of 3,528 adults participated in the study. The reported rates of close and casual contact were 4.9 per adult per day (95% confidence interval: 4.6, 5.2) and 10.4 per adult per day (95% confidence interval: 9.3, 11.6), respectively. Rates of close contact were higher for adults in larger households and rural areas. There was preferential mixing of close contacts within age groups and within sexes. The estimated incidence of M. tuberculosis infection in adults was 1.5-6 times higher (2.5%-10% per year) than that in children. More than 50% of infections in men, women, and children were estimated to be due to contact with adult men. We conclude that estimates of infection incidence based on surveys in children might underestimate incidence in adults. Most infections may be due to contact with adult men. Treatment and control of tuberculosis in men is critical to protecting men, women, and children from tuberculosis.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | - Richard G. White
- Correspondence to Dr. Richard G. White, TB Modelling Group, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT (e-mail: )
| |
Collapse
|
98
|
Andrews JR, Basu S, Dowdy DW, Murray MB. The epidemiological advantage of preferential targeting of tuberculosis control at the poor. Int J Tuberc Lung Dis 2016; 19:375-80. [PMID: 25859990 DOI: 10.5588/ijtld.14.0423] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Tuberculosis (TB) remains disproportionately concentrated among the poor, yet known determinants of TB reactivation may fail to explain observed disparities in disease rates according to wealth. Reviewing data on TB disparities in India and the wealth distribution of known TB risk factors, we describe how social mixing patterns could be contributing to TB disparities. Wealth-assortative mixing, whereby individuals are more likely to be in contact with others from similar socio-economic backgrounds, amplifies smaller differences in risk of TB, resulting in large population-level disparities. As disparities and assortativeness increase, TB becomes more difficult to control, an effect that is obscured by looking at population averages of epidemiological parameters, such as case detection rates. We illustrate how TB control efforts may benefit from preferential targeting toward the poor. In India, an equivalent-scale intervention could have a substantially greater impact if targeted at those living below the poverty line than with a population-wide strategy. In addition to potential efficiencies in targeting higher-risk populations, TB control efforts would lead to a greater reduction in secondary TB cases per primary case diagnosed if they were preferentially targeted at the poor. We highlight the need to collect programmatic data on TB disparities and explicitly incorporate equity considerations into TB control plans.
Collapse
Affiliation(s)
- J R Andrews
- Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - S Basu
- Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, California, USA
| | - D W Dowdy
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - M B Murray
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|
99
|
Iyengar P, von Mollendorf C, Tempia S, Moerdyk A, Valley-Omar Z, Hellferscee O, Martinson N, Chhagan M, McMorrow M, Gambhir M, Cauchemez S, Variava E, Masonoke K, Cohen AL, Cohen C. Case-ascertained study of household transmission of seasonal influenza - South Africa, 2013. J Infect 2015; 71:578-86. [PMID: 26366941 PMCID: PMC4667753 DOI: 10.1016/j.jinf.2015.09.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2015] [Revised: 08/28/2015] [Accepted: 09/01/2015] [Indexed: 11/21/2022]
Abstract
OBJECTIVES The household is important in influenza transmission due to intensity of contact. Previous studies reported secondary attack rates (SAR) of 4-10% for laboratory-confirmed influenza in the household. Few have been conducted in middle-income countries. METHODS We performed a case-ascertained household transmission study during May-October 2013. Index cases were patients with influenza-like-illness (cough and self-reported or measured fever (≥38 °C)) with onset in the last 3 days and no sick household contacts, at clinics in South Africa. Household contacts of index cases with laboratory-confirmed influenza were followed for 12 days. RESULTS Thirty index cases in 30 households and 107/110 (97%) eligible household contacts were enrolled. Assuming those not enrolled were influenza negative, 21/110 household contacts had laboratory-confirmed influenza (SAR 19%); the mean serial interval was 2.1 days (SD = 0.35, range 2-3 days). Most (62/82; 76%) household contacts who completed the risk factor questionnaire never avoided contact and 43/82 (52%) continued to share a bed with the index case after illness onset. CONCLUSION SAR for laboratory-confirmed influenza in South Africa was higher than previously reported SARs. Household contacts did not report changing behaviors to prevent transmission. These results can be used to understand and predict influenza transmission in similar middle-income settings.
Collapse
Affiliation(s)
- Preetha Iyengar
- Epidemic Intelligence Service, Centers for Disease Control and Prevention, 1600 Clifton Rd, Atlanta, GA, USA; Global Disease Detection Branch, Division of Global Health Protection, Centers for Disease Control and Prevention, 1600 Clifton Rd, Atlanta, GA, USA; US Public Health Service, 5600 Fishers Ln, Rockville, MD, USA.
| | - Claire von Mollendorf
- Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases, 1 Modderfontein Rd, Sandringham, Johannesburg, South Africa; School of Public Health, Faculty of Health Science, University of the Witwatersrand, 1 Jan Smuts Avenue, Braamfontein, Johannesburg, South Africa
| | - Stefano Tempia
- Influenza Program, Centers for Disease Control and Prevention-South Africa, PO Box 9536, Pretoria, South Africa; Influenza Division, Centers for Disease Control and Prevention, 1600 Clifton Rd, Atlanta, GA, USA
| | - Alexandra Moerdyk
- Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases, 1 Modderfontein Rd, Sandringham, Johannesburg, South Africa
| | - Ziyaad Valley-Omar
- Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases, 1 Modderfontein Rd, Sandringham, Johannesburg, South Africa
| | - Orienka Hellferscee
- Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases, 1 Modderfontein Rd, Sandringham, Johannesburg, South Africa
| | - Neil Martinson
- Perinatal HIV Research Unit, University of the Witwatersrand, Johns Hopkins University Center for TB Research, 1550 Orleans Street, Baltimore, MD, USA
| | - Meera Chhagan
- Department of Pediatrics, University of KwaZulu-Natal, King George V Ave, Glenwood, Durban, South Africa
| | - Meredith McMorrow
- US Public Health Service, 5600 Fishers Ln, Rockville, MD, USA; Influenza Division, Centers for Disease Control and Prevention, 1600 Clifton Rd, Atlanta, GA, USA
| | - Manoj Gambhir
- Modeling Unit, National Center for Immunization and Respiratory Disease, Centers for Disease Control and Prevention, 1600 Clifton Rd, Atlanta, GA, USA; Epidemiological Modelling Unit, Department of Epidemiology and Preventive Medicine, Monash University, 99 Commercial Road, Melbourne, Australia
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, 28 rue du Docteur Roux, Paris, France
| | - Ebrahim Variava
- Department of Medicine, Klerksdorp Tshepong Hospital Complex and University of the Witwatersrand, Corner of OR Tambo and John Orr Street, Klerksdorp, South Africa
| | - Katlego Masonoke
- Perinatal HIV Research Unit, University of the Witwatersrand, Johns Hopkins University Center for TB Research, 1550 Orleans Street, Baltimore, MD, USA
| | - Adam L Cohen
- US Public Health Service, 5600 Fishers Ln, Rockville, MD, USA; Influenza Program, Centers for Disease Control and Prevention-South Africa, PO Box 9536, Pretoria, South Africa; Influenza Division, Centers for Disease Control and Prevention, 1600 Clifton Rd, Atlanta, GA, USA
| | - Cheryl Cohen
- Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases, 1 Modderfontein Rd, Sandringham, Johannesburg, South Africa; School of Public Health, Faculty of Health Science, University of the Witwatersrand, 1 Jan Smuts Avenue, Braamfontein, Johannesburg, South Africa.
| |
Collapse
|
100
|
Nardell EA. Transmission and Institutional Infection Control of Tuberculosis. Cold Spring Harb Perspect Med 2015; 6:a018192. [PMID: 26292985 DOI: 10.1101/cshperspect.a018192] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Tuberculosis (TB) transmission control in institutions is evolving with increased awareness of the rapid impact of treatment on transmission, the importance of the unsuspected, untreated case of transmission, and the advent of rapid molecular diagnostics. With active case finding based on cough surveillance and rapid drug susceptibility testing, in theory, it is possible to be reasonably sure that no patient enters a facility with undiagnosed TB or drug resistance. Droplet nuclei transmission of TB is reviewed with an emphasis on risk factors relevant to control. Among environmental controls, natural ventilation and upper-room ultraviolet germicidal ultraviolet air disinfection are the most cost-effective choices, although high-volume mechanical ventilation can also be used. Room air cleaners are generally not recommended. Maintenance is required for all engineering solutions. Finally, personal protection with fit-tested respirators is used in many situations where administrative and engineering methods cannot assure protection.
Collapse
Affiliation(s)
- Edward A Nardell
- Division of Global Health Equity, Brigham & Women's Hospital, Boston, Massachusetts 02115
| |
Collapse
|