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ISDS 2012 Conference Abstracts

Disease Mapping with Spatially Uncertain Data
Justin Manjourides*1, Ted Cohen2, 3, Caroline Jeffery4 and Marcello Pagano5

1Dept of Health Sciences, Northeastern University, Boston, MA, USA; 2Div of Global Health Equity, Brigham & Women’s Hospital,
Boston, MA, USA; 3Dept of Epidemiology, Harvard School of Public Health, Boston, MA, USA; 4Intl Health Group, Liverpool School of
Tropical Medicine, Liverpool, United Kingdom; 5Dept of Biostatistics, Harvard School of Public Health, Boston, MA, USA

Objective
Uncertainty regarding the location of disease acquisition, as well

as selective identification of cases, may bias maps of risk. We propose
an extension to a distance-based mapping method (DBM) that incor-
porates weighted locations to adjust for these biases. We demonstrate
this method by mapping potential drug-resistant tuberculosis (DRTB)
transmission hotspots using programmatic data collected in Lima,
Peru.

Introduction
Uncertainty introduced by the selective identification of cases must

be recognized and corrected for in order to accurately map the dis-
tribution of risk. Consider the problem of identifying geographic
areas with increased risk of DRTB. Most countries with a high TB
burden only offer drug sensitivity testing (DST) to those cases at
highest risk for drug-resistance. As a result, the spatial distribution
of confirmed DRTB cases under-represents the actual number of
drug-resistant cases[1]. Also, using the locations of confirmed DRTB
cases to identify regions of increased risk of drug-resistance may bias
results towards areas of increased testing. Since testing is neither done
on all incident cases nor on a representative sample of cases, current
mapping methods do not allow standard inference from program-
matic data about potential locations of DRTB transmission.

Methods
We extend a DBM method [2] to adjust for this uncertainty. To

map the spatial variation of the risk of a disease, such as DRTB, in a
setting where the available data consist of a non-random sample of
cases and controls, we weight each address in our study by the prob-
ability that the individual at that address is a case (or would test pos-
itive for DRTB in this setting). Once all locations are assigned
weights, a prespecified number of these locations (from previously
published country-wide surveillance estimates) will be sampled,
based on these weights, defining our cases. We assign these sampled
cases to DRTB status, calculate our DBM, repeat this random selec-
tion and create a consensus map[3].

Results
Following [2], we select reassignment weights by the inverse prob-

ability of each untested case receiving DST at their given location.
These weights preferentially reassign untested cases located in re-
gions of reduced testing, reflecting an assumption that in areas where
testing is common, individuals most at risk are tested. Fig. 1 shows
two risk maps created by this weighted DBM, one on the unadjusted
data (Fig.1, L) and one using the informative weights (Fig. 1, R). This

figure shows the difference, and potentially the improvement, made
when information related to the missingness mechanism, which in-
troduces spatial uncertainty, is incorporated into the analysis.

Conclusions
The weighted DBM has the potential to analyze spatial data more

accurately, when there is uncertainty regarding the locations of cases.
Using a weighted DBM in combination with programmatic data from
a high TB incidence community, we are able to make use of routine
data in which a non-random sample of drug resistant cases are de-
tected to estimate the true underlying burden of disease.

(L) Unweighted DBM of risk of a new TB case that received DST being
positive for DRTB, compared to all new TB cases that received DST. (R)
Weighted DBM of the risk of a new TB case that received DST being posi-
tive for DRTB, based on lab-confirmed DRTB cases and IPW selected non-
DST TB cases, compared to all new TB cases.

Keywords
surveillance; multiple addresses; distance based

References

[1] H Lin, et al. Assessing spatiotemporal patterns of multidrug-resistant
and drug-sensitive tuberculosis in a south american setting. Epi In-
fect, 2010.

[2] C Jeffery. Disease mapping and statistical issues in public health sur-
veillance. PhD thesis, Harvard University, 2010.

[3] J Manjourides, et al. Identifying multidrug resistant tuberculosis trans-
mission hotspots using routinely collected data. Tuberculosis, 92(3),
2012.

*Justin Manjourides
E-mail: Justin.manjourides@gmail.com

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(1):e18, 2013