Electronic Case based Surveillance: Using predictive modelling and real-time data to plan Active TB Case Finding in Pakistan


Pakistan has one of the highest burdens of Tuberculosis (TB) in the world, a disease that infected as many as 10 million people and caused 1.4 million deaths globally in 2019.

Around a quarter of the world’s population are thought to be infected with latent TB. The disease can remain dormant for years, until, for example, the immune system is suppressed by diseases such as HIV or diabetes or with advancing age. One in ten of those infected will develop active TB disease during their lifetime.

This lengthy and unpredictable incubation period makes detection of TB difficult using conventional measures and is one of the reasons why the disease has been so successful in remaining hidden and widely distributed throughout communities in low- and middle-income countries, posing unique challenges for halting the transmission of the disease.

There is considerable evidence for the benefits of using active case finding (ACF) to locate people with TB in communities instead of waiting for people to seek healthcare in facilities. Using mobile diagnostics to find people with TB in the community leads to faster referral to health facilities and treatment, which leads to better health outcomes, reduced transmission, and fewer new cases of TB. ACF can fill the TB case detection gap which is often a challenge for hard to reach communities. However, resource and logistical constraints mean that not all communities can be screened, therefore, the challenge is to determine where and among which populations to look. This is compounded in Pakistan due to political instability, security risks, and a population of over 212 million spread out over a vast geographical area. To overcome these issues a specific and targeted approach to community case finding is required.