Social Enterprise Model (SEM) for private sector tuberculosis screening and care in Bangladesh

Abstract

Background In Bangladesh, about 80% of healthcare is provided by the private sector. Although free diagnosis and care is offered in the public sector, only half of the estimated number of people with tuberculosis are diagnosed, treated, and notified to the national program. Private sector engagement strategies often have been small scale and time limited. We evaluated a Social Enterprise Model combining external funding and income generation at three tuberculosis screening centres across the Dhaka Metropolitan Area for diagnosing and treating tuberculosis. Methods and findings The model established three tuberculosis screening centres across Dhaka Metropolitan Area that carried the icddr,b brand and offered free Xpert MTB/RIF tests to patients visiting the screening centres for subsidized, digital chest radiographs from April 2014 to December 2017. A network of private and public health care providers, and community recommendation was formed for patient referral. No financial incentives were offered to physicians for referrals. Revenues from radiography were used to support screening centres’ operation. Tuberculosis patients could choose to receive treatment from the private or public sector. Between 2014 and 2017, 1,032 private facilities networked with 8,466 private providers were mapped within the Dhaka Metropolitan Area. 64, 031 patients with TB symptoms were referred by the private providers, public sector and community residents to the three screening centres with 80% coming from private providers. 4,270 private providers made at least one referral. Overall, 10,288 pulmonary and extra-pulmonary tuberculosis cases were detected and 7,695 were bacteriologically positive by Xpert, corresponding to 28% of the total notifications in Dhaka Metropolitan Area. Conclusion The model established a network of private providers who referred individuals with presumptive tuberculosis without financial incentives to icddr,b’s screening centres, facilitating a quarter of total tuberculosis notifications in Dhaka Metropolitan Area. Scaling up this approach may enhance national and international tuberculosis response.

Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms

“…19 In this retrospective study, we included all individuals aged 15 years or older who presented or were referred to three tuberculosis screening centres in Dhaka, Bangladesh, between May 15, 2014, and Oct 4, 2016 (appendix 3 p 3). 20 Younger individuals were not included in the analysis as some of the AI algorithms assessed are only approved for use in individuals aged 15 years or older (appendix 3 pp 4-6).…”

“…Previously treated cases Female Male values (figure 2). Example report outputs from AI algorithms are provided in appendix 3 (pp [16][17][18][19][20].…”

Xpert MTB/RIF Ultra assay for the detection of Mycobacterium tuberculosis in people with negative conventional Xpert MTB/RIF but chest imaging suggestive of tuberculosis in Dhaka, Bangladesh

“…We conducted this prospective cohort study in TB Screening and Treatment Centres (TBSTCs) established by icddr,b under the Social Enterprise Model, located at Mohakhali, Dhanmondi, Golapbagh, Mirpur and Old Dhaka neighborhoods of urban Dhaka (Banu et al, 2020). Patients visiting these TBSTCs were mostly referred by private and public health providers or by one of the 104 NTPaffiliated facilities across Dhaka for TB diagnosis.…”

Sub-district level correlation between tuberculosis notifications and socio-demographic factors in Dhaka City corporation, Bangladesh

“…All people who started treatment in each ward were reported (via a combination of paper-based and electronic records) to the TB reporting centre, which then transmitted an aggregate notification to the NTP on a quarterly basis [10]. In Bangladesh, these notifications include private sector clinics; nevertheless, it is estimated that 30-40% of incident TB cases are not notifiedeither because the diagnosis is missed entirely, or because it is made in a private clinic that does not interface with the NTP [11]. For this study, we generated estimates of ward-level TB notification rates by aggregating the number of notified TB cases in 2017 (the most recent, available data) within each ward and dividing it by the population size of the ward (assuming 5% annual growth rate in Dhaka city from the 2011 national census).…”