Geographic Pattern of Typhoid Fever in India: A Model-Based Estimate of Cohort and Surveillance Data

Abstract

Background: Typhoid fever remains a major public health problem in India. Recently, the Surveillance for Enteric Fever in India program completed a multisite surveillance study. However, data on subnational variation in typhoid fever are needed to guide the introduction of the new typhoid conjugate vaccine in India.

Methods: We applied a geospatial statistical model to estimate typhoid fever incidence across India, using data from 4 cohort studies and 6 hybrid surveillance sites from October 2017 to March 2020. We collected geocoded data from the Demographic and Health Survey in India as predictors of typhoid fever incidence. We used a log linear regression model to predict a primary outcome of typhoid incidence.

Results: We estimated a national incidence of typhoid fever in India of 360 cases (95% confidence interval [CI], 297-494) per 100 000 person-years, with an annual estimate of 4.5 million cases (95% CI, 3.7-6.1 million) and 8930 deaths (95% CI, 7360-12 260), assuming a 0.2% case-fatality rate. We found substantial geographic variation of typhoid incidence across the country, with higher incidence in southwestern states and urban centers in the north.

Conclusions: There is a large burden of typhoid fever in India with substantial heterogeneity across the country, with higher burden in urban centers.

Keywords: enteric fever; geospatial model India; public health; salmonella; typhoid fever; vaccination.

Figure 1.

Summary of the study design for prediction of typhoid incidence in India. The study design followed the outlined process in the figure. We used Demographic and Health Survey (DHS) data on model variables to serve as predictors of typhoid incidence. The DHS variable data were averaged at a cluster level and then interpolated on a 5 × 5-km grid. We geographically intersected the DHS model variable data with the Surveillance for Enteric Fever in India (SEFI) data on observed typhoid incidence. We calibrated a model to estimate the relationship between each DHS model variable and typhoid incidence, and then we utilized a backward selection algorithm for variable selection. When the Akaike Information Criterion was minimized, we used the selected variable(s) as the predictor of typhoid incidence for the model. The rectangles refer to input/output data. The rhomboid shape refers to data processing. The gray shaded color indicates that additional data/processing steps.

Figure 2.

Spatial distribution of the 10 Surveillance for Enteric Fever in India (SEFI) study sites in India. The circles indicate the location of the 10 SEFI sites. The pink circles refer to 4 cohort study sites in Tier 1, and orange circles refer to 6 hybrid surveillance surveillance study sites in Tier 2. The size of the circles were categorized in 3 levels of incidence: fewer than 200 cases per 100 000 person-years (small circle), 201–1000 cases per 100 000 person-years (medium circle), and over 1000 cases per 100 000 person-years (large circle).

Figure 3.

Predicted incidence of typhoid fever in India. We calibrated a statistical model to predict typhoid fever incidence in India using data from 10 Surveillance for Enteric Fever in India study sites. We used a log linear regression model to predict typhoid incidence across the country using secondary data obtained from Demographic and Health Survey in India. The estimated incidence was at 5 × 5-km grid level and was aggregated at state level and mapped in (a). The histogram of incidence at original grid level was visualized in (b).