Union Territory
CBNAAT = cartridge-based nucleic acid amplification test, CI = confidence interval, NA = not available and NNS = number needed to screen to diagnose on person with TB. a Uncertain if the denominator is the true number of presumptive TB cases identified after screening.
Table 3 provides details of the activities and outcomes provided by the agencies that implemented ACF in partnership with the NTEP.
Activities and outcomes of the active case finding (ACF) conducted by implementing partner agencies.
Years | Target Population Mapped | Numbers Screened from Population Mapped (%) | TB Tested in Those with Presumptive TB (%) and among Those Screened [%] | TB Diagnostic Tests | TB Diagnosed (%; 95% CI) | NNS | ||||
---|---|---|---|---|---|---|---|---|---|---|
Partner Agency | Sputum Positive (%) | X-ray Abnormal (%) | CBNAAT Positive (%) | Anti-TB Treatment Initiated (%) | ||||||
The Union Axshya Project | 2013-2015 | NA | 8,120,015 households (NA) | 225,443/541,406 (41.6) [NA] | 21,268/225,443 (9.4) | Nil | Nil | 21,268 (9.4; 9.3 to 9.6) | NA | 20,589 (96.8) |
(The Global Fund) | 2015-2017 | NA | 9,003,299 households (NA) | 272,836/535,613 (50.9) [NA] | 25,493/272,836 (9.3) | Nil | Nil | 25,493 (9.3; 9.2 to 9.5) | NA | 24,524 (96.2) |
2018-2020 | NA | 25,575,009 (NA) | 216,075/292,557 (73.9) [0.9] | 15,550/216,075 (7.2) | 4190/10,136 (41.3) | 784/2166 (36.0) | 21,012 (9.7; 9.6 to 9.9) | 1217 | 18,373 (87.4) | |
ICMR TIE-TB (The Global Fund) | 2015-2017 | 6,117,597 | 55,707 (0.91) | 49,998/49,998 (100) [89.7] | 2091/49,998 (4.2) | 5,272/45,840 (11.5) | NA | 4286 (8.5; 8.3–8.8) | 13 | 4286 (100) |
KHPT Project THALI (USAID) | 2017-2019 | NA | NA | 21,171/28,473 (74.3) [NA] | 1578/NA | NA | 30/NA | 2247 (10.6; 10.2 to 11.0) | NA | 2174 (96.8) |
World Health Partners (USAID) | 2017–2019 | 1,707,990 | 381,761 (22.3%) | 6254/6254 (100) [1.6] | 451/6254 (7.2) | Nil | Nil | 451 (7.2; 6.6 to 7.9) | 847 | 451 (100) |
2018-2020 | NA | 18,705 (NA) | 1155/1398 (82.6) [6.1] | 46/279 (16.5) | 156/1155 (13.5) | 13/192 (6.8) | 215 (18.6; 16.5 to 21.0) | 87 | 215 (100) | |
2019 | NA | 20,863 (NA) | 501/501 (100) [2.4] | 34/501 (6.8) | Nil | Nil | 34 (6.8; 4.9 to 9.3) | 614 | 34 (100) | |
2018-2019 | NA | 1389 (NA) | 19/42 (45.2) [1.3] | 1/19 (5.3) | Nil | Nil | 1 (5.3; 0.9 to 24.6) | 1389 | 1 (100) | |
World Vision (The Global Fund) | 2015-2017 | 3,535,072 | 1.8 million households (NA) | 71,980/NA (NA) [NA] | NA | NA/71,980 | NA | 34,761 (48.4; 48.0 to 48.7) | NA | 34,761 (100) |
CBNAAT = cartridge-based nucleic acid amplification test, CI = confidence interval, ICMR = Indian Council for Medical Research, KHPT = Karnataka Health Promotion Trust, NA = not available and USAID = United States Agency for International Development.
Table 4 reproduces summary data on ACF activities provided to the NTEP by all the states and union territories in India for the year 2020 that were published in the annual report of the NTEP [ 22 ].
Summary data from the National Tuberculosis Elimination Programme for the active case finding (ACF) activities in 2020 from the states and union territories (ranked by population size) *.
State/Union Territory (Estimated Population in Millions) | Vulnerable Target Population Mapped from State Population (%) | Numbers Screened from Mapped Target Population (%) | Numbers with Presumptive TB Tested from Those Screened (%) | TB Diagnosed in Those Tested (%; 95% CI) | Number Needed to Screen (NNS) | |
---|---|---|---|---|---|---|
1 | Uttar Pradesh (223.43) | 44,019,832 (18.9) | 43,255,104 (98.3) | 156,980 (0.4) | 10,121 (6.5; 6.3 to 6.6) | 4274 |
2 | Maharashtra (125.74) | 85,791,971 (68.2) | 333,161 (0.4) | 311,650 (93.5) | 12,823 (4.1; 4.0 to 4.2) | 26 |
3 | Bihar (124.76) | 884,094 (0.7) | 13,776 (1.6) | 49 (0.4) | 7 (1.3; 7.1 to 26.7) | 1968 |
4 | West Bengal (99.91) | 13,608,540 (13.6) | 11,997,372 (88.2) | 232,599 (1.9) | 1810 (0.8; 0.7 to 0.8) | 6628 |
5 | Madhya Pradesh (84.36) | 14,668,164 (17.4) | 1,070,951 (7.3) | 44,341 (4.1) | 4912 (11.1; 10.8 to 11.4) | 218 |
6 | Tamil Nadu (81.4) | 1,148,451 (1.4) | 281,122 (24.5) | 14,744 (5.2) | 395 (2.7; 2.4 to 3.0) | 711 |
7 | Rajasthan (79.92) | 8,090,518 (10.1) | 6,906,255 (85.4) | 43,083 (0.6) | 1067 (2.5; 2.3 to 2.6) | 6473 |
8 | Gujarat (69.76) | 65,882,010 (94.4) | 50,847,334 (77.2) | 121,466 (0.2) | 4565 (3.8; 3.7 to 3.9) | 11,138 |
9 | Karnataka (68.51) | 15,507,273 (22.6) | 92,436 (0.6%) | 87,505 (94.7) | 2939 (3.4; 3.3 to 3.5) | 31 |
10 | Andhra Pradesh (52.54) | 1,335,818 (2.5) | 1,151,885 (86.2) | 51,982 (4.5) | 1685 (3.2; 3.1 to 3.4) | 683 |
11 | Odisha (46.32) | 45,292,673 (97.8) | 41,965,511 (92.7) | 222,198 (0.5) | 5116 (2.3; 2.2 to 2.4) | 8202 |
12 | Jharkhand (39.48) | 14,854,650 (37.6) | 15,230 (0.1) | 10,731 (70.5) | 1891 (17.6; 16.9 to 18.4) | 8 |
13 | Telangana (37.92) | 754,912 (2.0) | 60,632 (8.0) | 4822 (8.0) | 1207 (25.0; 23.8 to 26.3) | 50 |
14 | Assam (35.05) | 79,329 (0.2) | 15,243 (19.2) | 2029 (13.3) | 91 (4.5; 3.7 to 5.5) | 167 |
15 | Kerala (35.44) | 1,171,034 (3.4) | 37,685 (3.2) | 29,166 (77.4) | 802 (2.8; 2.6 to 2.9) | 47 |
16 | Punjab (30.67) | 4,856,533 (15.8) | 4,317,208 (88.9) | 5371 (0.1) | 529 (9.9; 9.1 to 10.7) | 8161 |
17 | Chhattisgarh (30.03) | 571,344 (1.9) | 7462 (1.3) | 6436 (86.3) | 170 (2.6; 2.3 to 3.1) | 44 |
18 | Haryana (29.44) | 9,889,536 (33.6) | 8,282,557 (83.8) | 30,539 (0.4) | 866 (2.8; 2.7 to 3.0) | 9564 |
UT1 | Delhi (19.05) | 1716 (0.0) | 985 (57.4) | 256 (26.0) | 30 (11.7; 8.3 to 16.2) | 33 |
UT2 | Jammu & Kashmir (14.50) | 422,954 (2.9) | 141,814 (33.5) | 15,254 (10.8) | 190 (1.3; 1.1 to 2.4) | 746 |
19 | Uttarakhand (11.63) | 1,291,237 (11.1) | 1,785,11 (13.8) | 2953 (1.7) | 100 (3.4; 2.8 to 4.1) | 1785 |
20 | Himachal Pradesh (7.5) | 7,485,901 (99.8) | 22,709 (0.3) | 15,852 (69.8) | 595 (3.8: 3.5 to 4.1) | 38 |
21 | Tripura (3.96) | 198,624 (5.0) | 98,845 (49.8) | 9084 (9.2) | 109 (1.2; 1.0 to 1.5) | 906 |
22 | Meghalaya (3.66) | 1,435,077 (39.2) | 532,359 (37.1) | 1064 (0.2) | 28 (2.6; 1.8 to 3.8) | 19,012 |
23 | Manipur (3.12) | 53,336 (1.7) | 32,289 (60.5) | 3802 (11.8) | 52 (1.4; 1.0 to 1.8) | 621 |
24 | Nagaland (2.07) | 91,005 (4.4) | 23,272 (25.6) | 1291 (5.5) | 23 (1.8; 1.2 to 2.7) | 1011 |
25 | Arunachal Pradesh (1.64) | 56,236 (3.4) | 48,925 (87.0) | 2350 (4.8) | 73 (3.1; 2.5 to 3.9) | 670 |
26 | Goa (1.54) | NA | NA | NA | NA | NA |
UT3 | Puducherry (1.50) | 16,152 (1.1) | 10,886 (67.4) | 109 (1.0) | 5 (4.6; 2.0 to 10.1) | 2177 |
27 | Mizoram (1.26) | 1,35,399 (10.7) | 59,883 (44.2) | 293 (0.5) | 8 (2.7; 1.4 to 5.3) | 7485 |
UT4 | Chandigarh (1.17) | 145,297 (12.4) | 6962 (4.8) | 703 (10.1) | 36 (5.1; 3,7 to 7.0) | 193 |
UT5 | Dadra & Nagar Haveli; Daman & Diu (0.80) | NA | NA | NA | NA | NA |
28 | Sikkim (0.66) | 62,853 (9.6) | 11,034 (17.6) | 149 (1.4) | 4 (2.7; 1.1 to 6.7) | 2759 |
UT6 | Andaman & Nicobar (0.39) | 389,615 (99.0) | 44,762 (11.5) | 432 (1.0) | 21 (4.9; 3.2 to 7.3) | 2130 |
UT7 | Ladakh (0.34) | 5952 (1.7) | 5952 (100) | 14 (0.2) | 0 (0) | NA |
UT8 | Lakshadweep (0.07) | 70,070 (100) | 70,070 (100) | 509 (0.7) | 3 (0.6; 0.2 to 1.7) | 23,356 |
India (1,377.54) | 340,268,106 (24.7) | 171,940,182 (50.5) | 1,429,806 (0.8) | 52,273 (3.66; 3.63 to 3.69) | Median: 906 (IQR 108 to 6550) |
* Modified from Annexure 6 in the India TB report 2021 [ 22 ]. CI = confidence Interval, IQR = interquartile range and UT = Union territory.
The state-wise breakup of the proportions diagnosed with TB by different diagnostic tests was not reported.
We used the numerical data provided in these tables ( Table 2 , Table 3 and Table 4 ) and national ACF data for 2018 and 2019 ( Supplementary Tables S2 and S3 ) to evaluate the screening, diagnostic and treatment activities undertaken by the states and union territories and their implementing partners against the expected indicators envisioned by the NTEP for ACF ( Table 1 ).
The NTEP expects that 110,000 per million vulnerable population (11%) should be mapped for community-based screening.
The national ACF data for 2020 ( Table 4 ) revealed that, despite the disruption caused by the ongoing SARS-CoV-2/COVID-19 pandemic, nearly 25% of India’s 13,378 million population was mapped for screening. This proportion ranged across the states from <1% to 100% (median 10.7%). In 2020, 53% of the states and union territories ( Table 4 ) and 61% in 2019 ( Supplementary Table S3 ) met or exceeded the NTEPs expected indicators for mapping vulnerable populations.
The NTEP expects that >90% of the mapped target vulnerable population should be screened for symptoms of TB.
In 2020, around 51% (range <1–100%; median 37%) of those mapped across the states were screened ( Table 4 ). Eleven states and union territories screened more than 75% of the target population in 2020 but only in 4/34 (12%) with the available data did this exceed the 90% expectation of the NTEP. For 2018 ( Table S2 ) and 2019 ( Table S3 ), the proportion of states and union territories meeting or exceeding this NTEP indicator was 19% and 16%, respectively.
The NTEP expects that around 5% of people in the community with TB symptoms will be identified through house-to-house screening.
However, the proportions identified with presumptive TB through symptom screening in the states and union territories is not reported in the annual TB reports ( Table 4 , Tables S2 and S3 ).
From the available data for 2017–2019 from the state programme managers, the proportion identified as having presumptive TB from among those screened ranged from <1% in three states, 5% to 10% in three states and 20% to 67% in two others ( Table 2 ). These proportions varied across the three years of reporting. The proportion of responding states that met or exceeded the NTEP expectation for presumptive TB cases identified through ACF ranged from 14% in 2017, 83% in 2018 to 0% in 2019 ( Table 2 ).
The NTEP has not set minimum indicators for the proportion to be tested for TB among those screened ( Table 1 ).
However, the data are reported annually for the proportions tested from among those screened. From 2018 to 2020, <1% of those screened for symptoms of TB in ACF activities across the states and union territories in India underwent TB diagnostic tests ( Tables S2 and S3 and Table 4 ).
The NTEP expects that >95% of those identified with presumptive TB will be tested for TB.
This information is not reported in the annual TB reports ( Tables S2 and S3 and Table 4 ). The available data from the responding states and union territories revealed that 40% (two out of five) in 2017, 29% (two out of seven) in 201 and 17% (one out of six) in 2019 met or exceeded the NTEP indicators for the proportions with presumptive TB who were tested for TB ( Table 2 ).
The NTEP expects that 5% (minimum >2% to 3%) of those tested would have sputum smear-positive test results.
Sputum smear positivity rates were not available from the national summary tables for the ACF programme ( Tables S2 and S3 and Table 4 ).
Sputum smear positivity rates exceeded the 2% minimum expected in 8/10 responding states and union territories for all or most of the years reported ( Table 2 ). The higher smear positivity seen in the partner agencies data (5.3–16.5%) reflect their focus on screening populations at a higher risk of undiagnosed TB through ACF activities that occurred throughout the year ( Table 3 ).
The NTEP expects that >90% of sputum smear-negative TB patients will be examined by chest X-ray or CBNAAT or both.
This data is not provided in the annual TB reports ( Tables S2 and S3 and Table 4 ).
Most responding states did not report the number of people with sputum smear-negative results who underwent further testing with chest X-rays or CBNAAT or both ( Table 2 and Table 3 ). Where this could be estimated, the data revealed that only 29% (two out of seven) of the responding states in 2017, 50% (three out of six) in 2018 and 20% (one out of five) in 2019 performed chest X-ray or CBNAAT on >90% those with sputum smear-negative results ( Table 3 ).
The NTEP expects that at least 5% of people undergoing diagnostic tests in ACF programmes would be diagnosed with TB (all forms).
Based on the available data from the states and union territories ( Table 2 ), this expectation was met or exceeded by 57% (four out of seven) in 2017, 33% (two out of six) in 2018 and 60% (three out of five) in 2019. The implementing partners exceeded this target for all years of their activities (5.3–48.4%; Table 3 ). Data from more complete nationwide datasets revealed that this expectation was achieved or exceeded in 30% (10/33) of the states and union territories in 2018 ( Table S2 ), 23% (7/31) in 2019 ( Table S3 ) and 21% (7/34) in 2020 ( Table 4 ).
The NTEP expects that >95% of TB patients diagnosed through ACF activities should be initiated on anti-TB treatment.
The proportions started on anti-TB through ACF activities in India from 2017 to 2020 were not available from the ACF annual reports published by the NTEP [ 19 , 20 , 21 , 22 ].
The NTEP programme managers stated that, although all diagnosed patients were notified about the NTEP via Nikshay ( https://nikshay.in , accessed on 21 January 2021), the national web-based TB information management system, they could not retrieve treatment information specific to their ACF patients, as Nikshay does not have a dedicated ACF module. Four states and one union territory provided data from their own records of the numbers initiated on treatment ( Table 2 ), and treatment completion ranged from 89% to 100%. The partner agencies reported that 96–100% were started on anti-TB treatment, but none provided data for the treatment outcomes ( Table 3 ).
The NTEP performance indicators do not include the NNS.
The NNS varied within the states for each year of ACF activity and between the states in the data from the state programme managers ( Table 2 ), implementation partners ( Table 3 ) and national ACF data for 2018–2020 ( Tables S2 and S3 and Table 4 ).
The data uniformly demonstrated that the higher the proportions that are tested for TB among those screened, and the more accurate the tests used to diagnose TB are, the lower the NNS. For example, in 2020 ( Table 4 ), the state with the lowest NNS (Jharkhand) tested 71% of those screened and diagnosed TB in 17.6% of them, resulting in an NNS of 8. The highest NNS of 23,356 was seen in Lakshadweep in the same year, when 0.7% of those screened were tested, and TB was diagnosed in only 0.3% of those tested. Additionally key in this relationship is the risk of TB in the proportions screened and tested.
The NTEP performance indicators do not include the assessment of TB notifications due to ACF programmes.
Data for the impact of ACF on TB notifications in the states and union territories were not provided in the annual TB reports [ 19 , 20 , 21 , 22 ].
Among the partner agencies, only the ICMR TIE-TB project provided data that assessed the impact of the mobile diagnostic units. Of the 24,043 total TB notifications from all sources from October 2017 to June 2018 from the five states that were covered by the project, 3816 (16%) were notified by the mobile diagnostic vans. This proportion varied across the five states (4–25%; median 18%).
The project also estimated that the mean out-of-pocket expenditure for treatment (travel, consultation, investigations, medicines and ancillary costs such as food) was reduced by 78% for patients serviced by mobile vans (average cost INR 255) compared to if they had availed themselves of services through standard government facilities (average cost INR 1163). The reduction in personal expenditure was even greater when compared to treatment at private facilities (average cost INR 6897; 47% spend more than INR 10,000). The project also demonstrated modest reductions in the time to seek consultations, being diagnosed and starting treatment compared to using standard government facilities or private services.
In Table 5 , we list the challenges expressed by programme managers in implementing ACF, obtained from the responses in the data proformas returned by the programme managers of the state NTEP programmes and the partner agencies and through discussions with some of them.
Challenges in implementing ACF activities as perceived by implementers.
Category | Challenges | Description |
---|---|---|
Health system challenges leading to pre-diagnostic drop-outs and poor documentation of ACF referrals, TB notifications, treatment outcomes and impact of ACF | Poor access to health facilities | Failure to get tested at health facilities due to the distance and time taken to travel, difficulties in finding transport at convenient times, loss of wages incurred due to travel times. |
Non-availability of all diagnostic tests at peripheral health institutions | Chest radiography and GeneXpert are often not available at one place, but at different levels of health care provision (secondary and tertiary hospitals). This makes it difficult for people to complete the required tests in a day. | |
Difficulties in accessing radiography services at secondary hospitals | ACF patients are not considered a priority compared to emergency referrals; shortages in materials, resources and equipment malfunction also contribute. | |
Poor documentation of ACF referrals for diagnostic tests Lack of a separate ACF module in the data management system | Referral slips given by field staff for diagnostic tests are often misplaced by patients or are not entered in diagnostic facilities as an ACF referral. Nikshay, the online data management tool, does not specifically link TB notifications identified by the ACF programme with treatment outcomes. | |
Healthcare provision challenges leading to poor ACF screening and diagnostic outcomes | Poor TB awareness among general population | Despite time and effort spent on advocacy, communication and social mobilisation, large segments of the vulnerable population are unaware of the importance of the ACF programme and were unwilling to fully comply with ACF requirements. |
Obtaining an exact denominator of the population, and the geographical boundaries of areas to be mapped | Difficulty in accurately estimating the number of people residing in geographical areas that are mapped. Figures from the previous census are not dynamic and do not accurately reflect the actual population numbers, or its composition, at the time of ACF activities. In many areas, the geographical boundaries of the areas mapped are not clearly demarcated and often overlapped with adjacent areas. | |
Difficulties due to mountainous terrains and hard-to access areas | Areas in the country with mountainous terrains (as in Leh and Kargil in Ladakh), or other hard-to-reach areas, make it difficult for ACF teams to screen all of the mapped populations. | |
Challenges faced by patients and families leading to poor compliance with ACF requirements | Pressure to undergo screening and testing | People identified with presumptive TB often do not feel unwell. Requests to visit designated diagnostic centres are perceived as undue pressure from the health workers, particularly if they are busy and if the travel involves long distances and time away from productive work |
Non-availability of all family members during screening visits | Not all family members can be present when health workers made home-visits. Available family members may find it difficult to accurately report symptoms in other family members. | |
Non-availability of investigations | Patients are dissatisfied when tests are unavailable when they visit diagnostic facilities, and they have to make multiple visits to complete their tests. | |
Out-of-pocket expenditure for diagnostic tests | Diagnostic tests are provided free of cost at government-designated facilities. Testing at private diagnostic facilities is often more convenient, but the expenditure involved is considerably greater. |
India has the highest TB burden in the world. In 2019, India accounted for the largest proportion of people worldwide diagnosed with TB and drug-resistant TB and the largest proportion of under-reported or undiagnosed TB cases [ 26 ]. Implementing a successful community-based ACF programme in a country with a population of over 1.3 billion people is a herculean task. This is the first paper to evaluate the available data from India’s ACF programme against the performance indicators set by the NTEP for ACF and to use the NNS to assess the efficiency of ACF in the states and union territories of the country.
This review identified six gaps in India’s ACF programme where the data for the outcomes fell short of the expected performance indicators.
Strategic gap 1: The deficiencies in the proportions mapped from the vulnerable target populations and those screened among the mapped populations.
Strategic gap 2: The discrepancy in reporting the proportions tested among those screened for TB (which is not among the NTEP’s performance indicators), instead of the proportions tested among those identified with presumptive TB (which is a performance indicator for which national data were unavailable).
Strategic gap 3: The deficiencies in ensuring that over 95% of the people that identified with presumptive TB underwent diagnostic testing.
Strategic gap 4: The lack of data to evaluate whether >90% with negative sputum test results underwent additional diagnostic tests.
Strategic gap 5: The deficits in achieving the NTEP’s minimum expected diagnostic yield of 5% TB cases diagnosed among those tested in the ACF programme.
Strategic gap 6: The lack of data from national reports for the proportions initiating and completing treatment in the ACF programmes (and the resultant lack of data to assess the impact of ACF).
In addition to these gaps, the data in this review demonstrate that if a larger proportion of those screened for TB are tested with accurate diagnostic tests, then the NNS would be lower than it currently is in many state ACF campaigns.
These gaps identify strategic points where various interventions could improve the effectiveness of ACF campaigns.
The gaps identified in mapping vulnerable target populations at a high risk of TB (Strategic gap 1) indicate the need for more accurate and updated TB prevalence data than what is currently available from national and subnational surveys and prevalence studies [ 27 , 28 , 29 , 30 , 31 ]. The challenges experienced by programme managers in mapping vulnerable populations ( Table 5 ) also indicate the need for updated census data and better delineation of the geographical boundaries to be mapped.
One reason identified by programme managers, and echoed in other enquiries [ 32 ], contributing to the low uptake of screening in some areas is the perception in segments of the public about TB. ACF programmes that occur only periodically will have less opportunities to influence public opinion. They also will identify fewer people with undetected TB.
If ACF activities in India are to scale up from campaign mode, more sustained ACF activities must be considered. One option is to integrate ACF with other surveillance activities [ 26 ]. This was successful in 2000 with the active case search and TB-COVID bidirectional screening that enabled TB notifications in India to increase after the lockdowns were lifted [ 22 ]. Scaling up ACF in India also provides an opportunity to align these activities within the broader perspective of the WHO’s multisectoral accountability framework (MAF-TB) [ 33 ]. This would also address the risk factors and determinants of TB and enable collaboration with agencies and stakeholders working on the other sustainable development goals [ 34 ].
Utilising innovative ways to mobilise and use community networks could also be considered. One such approach is the ‘seed-and-recruit’ approach that has been well-received, was deemed feasible and identified more bacteriologically confirmed cases than one-off ACF activities in the countries that have used this approach [ 35 , 36 , 37 ].
Integrating the available information in Nikshay, the national TB patient management system that also serves as the national TB surveillance system, with geographic information system (GIS) mapping, could provide better estimates of TB prevalence (Strategic gap 1). This data would better inform the state NTEP managers while planning ACF mapping and screening activities, especially in urban areas [ 26 , 38 ].
A dedicated ACF module is currently unavailable in Nikshay, and this was perceived as an implementation challenge ( Table 5 ). A module in Nikshay to document all ACF activities, including the actual number of people in households that were interviewed for symptoms and how many individuals were not, would further address strategic gap 1. The Nikshay mobile app could be used to enter this data by authorised NTEP field staff, as is done by community health workers in other high-TB burdened countries with successful ACF programmes [ 38 ]. Using the mobile app could also streamline the data captured about ACF activities that currently relies on paper forms in many places.
If ACF activities are linked in Nikshay to the diagnostic investigations performed for each person identified through ACF, it would then be possible to generate data on the sputum samples tested. Sputum smear-positive and smear-negative results could be sent to ACF personnel (even through automated messages) to decide on further tests or to facilitate prompt TB notification and treatment initiation and to assess the risk of false-positive diagnoses resulting from screening (Strategic gaps 3, 4 and 6).
This would also permit ACF personnel and managers to review the proportions that did not undergo further tests, assess the reasons for this and encourage a return for tests, with mobile diagnostic units stationed in convenient locations to facilitate this. This would help to reduce pre-diagnosis dropouts (Strategic gaps 3–5).
Linking details of patients diagnosed with TB by ACF in Nikshay and providing real-time access of this data to ACF personnel would also help reduce post-diagnosis drop-outs and provide data about treatment initiation and completion rates (Strategic gap 6).
This information in the ACF module in Nikshay would also provide granular information at a subdistrict level that could be used to assess the impact of ACF activities on TB notifications and treatment outcomes for patients diagnosed by community-based ACF versus more targeted approaches (Strategic gap 6). This information could also be used to track temporal trends in TB identification from different areas in a district and state that can used in refining mapping and screening activities for future rounds of ACF activities (Strategic gap 1).
The India TB report of ACF activities in 2020 contains a bubble plot of the NNS for each state and each high-risk group that was generated through Nikshay using the data provided by the states [ 22 ]. The available NNS data, if linked specifically to ACF activities, could be used to guide strategic planning and implementation decisions to improve the efficiency of the ACF activities.
Making better use of Nikshay for ACF would a cost-effective intervention that will contribute immensely to reducing the six strategic gaps in the ACF cascade identified in this review.
If the aim of ACF is to diagnose people with undetected TB in the community, house-to-house screening for TB symptoms will be insufficient. Many national prevalence surveys across Asia have shown that 40–70% of people detected to have bacteriologically confirmed TB do not report TB symptoms that meet the screening criteria for presumptive TB. Many were detected only because the entire eligible survey population was screened using chest X-rays [ 39 , 40 , 41 , 42 ]. In addition, relying on symptom screening would also miss a large proportion of people with subclinical TB who are usually diagnosed by chest X-ray abnormalities or with molecular techniques [ 43 , 44 ].
This implies that careful consideration should be given to expanding the number of people screened who are offered diagnostic tests, irrespective of whether they have symptoms that meet the criteria for presumptive TB. Selecting the asymptomatic people who are offered additional tests needs to be guided by operational research [ 45 ]. This will help in addressing strategic gaps 1–3.
The data in this review does not provide clarity on the diagnostic algorithm that would provide the best yield. The use of mobile diagnostic units with digital X-rays and sputum smear microscopy facilities is a pragmatic alternative with the benefits of getting rapid results, as demonstrated by the TIE-TB project. The WHO recommends the use of computer-aided diagnosis (CAD) for interpreting digital X-rays in screening and triage for TB disease in adults over 15 years of age [ 26 ]. The results of operational research should guide the introduction of CAD technologies into scaled-up ACF activities in India.
Expanding the use of Xpert MTB/RIF in ACF programmes is clearly likely to increase the TB notification rates and the numbers initiating treatment [ 39 , 46 , 47 ]. This expansion is likely to be cost-effective compared to using cheaper tests with lower accuracy [ 48 , 49 ]. This will address strategic gap 5 and also contribute to reducing the NNS.
Screening fewer people but testing more of them with accurate diagnostic tools would increase the diagnostic yield and also reduce the NNS (Strategic gaps 1–5). This strategy should be weighed against the current strategy of setting targets to screen large numbers but testing only a small proportion who meet the symptom criteria [ 26 ].
Some of the limitations of this review relate to the data available from the states and union territories and the partner agencies. Not all states and partner agencies provided the data requested. Additionally, there were lacunae in the data proformas returned by the states and partner agencies.
We also made some changes to the review process after the protocol of this review was registered that was necessitated by the data available for evaluation ( Supplementary Document S1 ).
The challenges faced in implementing ACF were collated from discussions with the NTEP and partner agency programme managers and the responses provided in the data proformas returned by them. These discussions were limited to the programme managers that we were able to reach and, also, did not necessarily capture the difficulties faced by other ACF personnel. They were also not systematic evaluations using formal qualitative methods. However, they provided valuable insights into some of the strategic gaps identified.
This review and synthesis of programme activities and outcomes of the ACF campaign launched by the NTEP identified six broad areas where there are gaps between the expectations of the NTEP and the available outcome data from the states and partners implementing ACF. These gaps provided opportunities to intervene strategically, and this review suggests possible interventions that could be considered to improve the efficacy and effectiveness of ACF.
We thank the Central Tuberculosis Division, Ministry of Health and Family Welfare, New Delhi and the State Tuberculosis Offices for their timely support in sharing data and information on active case finding conducted by the states. Our special thanks to the implementing partner agencies (The Union, ICMR-TIE TB Project, Karnataka Health Promotional Trust, World Health Partners and World Vision) for sharing the details of the active case finding activities executed in their projects. We acknowledge Paul Garner, READ-IT Director, Liverpool School of Tropical Medicine for his support during the initial phase of prioritising the research question for systematic review.
The following are available online at https://www.mdpi.com/article/10.3390/tropicalmed6040206/s1 , Box S1: Data proforma, Document S1: Data management and analysis, Table S1: Summary of the completeness of reporting of the ACF activities, Table S2: Summary data from the NTEP for active case finding (ACF) activities in India (2018) and Table S3: Summary data from the NTEP for active case finding (ACF) activities in India (2019).
Conceptualisation, S.B.N., P.T. (Pruthu Thekkur), S.S., J.T., R.R., K.S.S. and P.T. (Prathap Tharyan); methodology, S.B.N., P.T. (Pruthu Thekkur), S.S., K.D.S. and P.T. (Prathap Tharyan); data collection and validation, S.B.N., P.T. (Pruthu Thekkur), S.S., K.D.S., K.S.S. and P.T. (Prathap Tharyan); formal analysis, S.B.N., P.T. (Pruthu Thekkur), S.S. and P.T. (Prathap Tharyan); data curation, S.B.N., P.T. (Pruthu Thekkur), S.S., K.D.S. and P.T. (Prathap Tharyan); writing—original draft preparation, S.B.N.; writing—review and editing, P.T. (Prathap Tharyan), P.T. (Pruthu Thekkur), S.S., K.D.S., K.S.S. and S.B.N.; visualisation, P.T. (Prathap Tharyan), P.T. (Pruthu Thekkur), S.S. and S.B.N.; supervision, P.T. (Prathap Tharyan) and S.S.; and project administration, K.D.S. All authors have read and agreed to the published version of the manuscript.
This publication is associated with the Research, Evidence and Development Initiative (READ-It). READ-It (project number 300342-104) is funded by UK aid from the UK government; however, the views expressed do not necessarily reflect the UK government’s official policies.
Not Applicable.
Data availability statement, conflicts of interest.
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of the data; in the writing of the manuscript or in the decision to publish the results.
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Introduction. Tuberculosis (TB) is caused by strains of Mycobacterium tuberculosis (M. Tuberculosis). TB is a primarily pulmonary infection spread by airborne droplet transmission. The development and spread of drug-resistant strains of M. tuberculosis greatly jeopardize TB control efforts. In the US, 91 cases of MDR-TB were reported in 2014.
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Active Case Finding for Tuberculosis in India