scholarly journals Tuberculosis case notification data in Viet Nam, 2007 to 2012

2015 ◽  
Vol 6 (1) ◽  
pp. 7-14 ◽  
Author(s):  
Viet Nhung Nguyen ◽  
Binh Hoa Nguyen ◽  
Huyen Khanh Pham ◽  
Cornelia Hennig
2015 ◽  
Vol 6 (1) ◽  
pp. 15-24 ◽  
Author(s):  
Fukushi Morishita ◽  
Valérie Burrus Furphy ◽  
Miwako Kobayashi ◽  
Nobuyuki Nishikiori ◽  
Mao Tan Eang ◽  
...  

2020 ◽  
Author(s):  
Lei Cao ◽  
Ting-ting Huang ◽  
Jun-xia Zhang ◽  
Qi Qin ◽  
Si-yu Liu ◽  
...  

2016 ◽  
Vol 6 (3) ◽  
pp. 164-168 ◽  
Author(s):  
K. C. Takarinda ◽  
A. D. Harries ◽  
C. Sandy ◽  
T. Mutasa-Apollo ◽  
C. Zishiri

2016 ◽  
Vol 20 (9) ◽  
pp. 1192-1198 ◽  
Author(s):  
Z. G. Dememew ◽  
D. Habte ◽  
M. Melese ◽  
S. D. Hamusse ◽  
G. Nigussie ◽  
...  

2021 ◽  
Vol 10 (Supplement_1) ◽  
pp. S16-S17
Author(s):  
Gunasekera Kenneth ◽  
Warren Joshua ◽  
Cohen Ted

Abstract Background To meet the transmission reduction goals of the End TB strategy, there is a growing interest in identifying and targeting case-finding efforts to tuberculosis“hotspots,” geographic regions of active transmission. Collecting and interpreting spatial and pathogenic genetic information, the most reliable evidence of active transmission, is prohibitively resource-intensive under routine conditions in high-burden settings. Many countries maintain case-notification registers under routine conditions, representing an attractive source of data to investigate for transmission. However, notification data are imperfect. Areas of high incidence may reflect other underlying patterns, and individual-level covariate information and other information that may aid in its interpretation, such as baseline census data or other healthcare utilization data, is often unavailable. Despite imperfections, the accessibility of notification data demands further investigation. We examined notification data from 2005 to 2007 in a South American, high-burden setting where the household address of each case was geocoded. Subsequent investigation of notification data in the same setting from 2009 to 2012 additionally provided pathogen genetic evidence from all culture-positive cases suggesting regions of active transmission of tuberculosis. We investigated a disease mapping modeling approach leveraging only age-specified tuberculosis notification data to suggest hotspots of active tuberculosis transmission. Methods Given the absence of baseline population data at a comparable spatial resolution, we aggregated the point-referenced cases reported to the Peruvian National Tuberculosis Program from 2005 to 2007 within two of Lima’s four health districts into a grid with 400 m × 400 m cells. We used Bayesian hierarchical spatial modeling methodology to model the proportion of children cases of the total number of adult and child cases in each cell. Where the modeled proportion of child cases is higher than expected, we suggest that case notification is driven primarily by active transmission. Results This method identified several grid cells in which the proportion of child cases is higher than expected. The location of these grid cells was found to approximate the location of active transmission evidenced by a later genotyping study. Conclusions This evidence suggests that age-specified notification data, with all its limitations, may be sufficient to suggest hotspots of active transmission of tuberculosis. We additionally provide the first spatial evidence to support the long-cited belief that with respect to tuberculosis transmission, childhood cases may truly be “the canary in the coal mine.”


Author(s):  
Laura F White ◽  
Carlee B Moser ◽  
Robin N Thompson ◽  
Marcello Pagano

Abstract The reproductive number, or reproduction number, is a valuable metric in understanding infectious disease dynamics. There is a large body of literature related to its use and estimation. In the last 15 years, there has been tremendous progress in statistically estimating this number using case notification data. These approaches are appealing because they are relevant in an ongoing outbreak (e.g., for assessing the effectiveness of interventions) and do not require substantial modelling expertise to be implemented. In this review, we describe these methods and the extensions that have been developed. We provide insight into the distinct interpretations of the estimators proposed and provide real data examples to illustrate how they are implemented. Finally we conclude with a discussion of available software and opportunities for future development.


2012 ◽  
Vol 16 (12) ◽  
pp. 1625-1629 ◽  
Author(s):  
D. H. Vu ◽  
N. van Rein ◽  
F. G. J. Cobelens ◽  
T. T. H. Nguyen ◽  
V. H. Le ◽  
...  

2009 ◽  
Vol 138 (6) ◽  
pp. 802-812 ◽  
Author(s):  
N. HENS ◽  
M. AERTS ◽  
C. FAES ◽  
Z. SHKEDY ◽  
O. LEJEUNE ◽  
...  

SUMMARYThe force of infection, describing the rate at which a susceptible person acquires an infection, is a key parameter in models estimating the infectious disease burden, and the effectiveness and cost-effectiveness of infectious disease prevention. Since Muench formulated the first catalytic model to estimate the force of infection from current status data in 1934, exactly 75 years ago, several authors addressed the estimation of this parameter by more advanced statistical methods, while applying these to seroprevalence and reported incidence/case notification data. In this paper we present an historical overview, discussing the relevance of Muench's work, and we explain the wide array of newer methods with illustrations on pre-vaccination serological survey data of two airborne infections: rubella and parvovirus B19. We also provide guidance on deciding which method(s) to apply to estimate the force of infection, given a particular set of data.


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