Keywords
Tuberculosis, Active Case finding, Cash incentives, Community Health workers
This article is included in the TDR gateway.
Tuberculosis, Active Case finding, Cash incentives, Community Health workers
Tuberculosis (TB) remains the leading cause of death from a single infectious disease worldwide. Globally, an estimated 10 million people had tuberculosis in 20181. 24% of all TB cases are said to occur in the WHO African region, with 4% of these cases occurring in Nigeria1. TB mortality in Nigeria in 2016 was 39,933 deaths2. Although mortality from TB in Nigeria has dropped substantially over time2, the WHO Tuberculosis Report 2017 indicated that TB incidence has remained stagnant since the year 20003. This is largely due to delayed diagnosis and treatment of active cases. A significant proportion of people infected with TB do not report any symptoms at all, and are therefore less likely to seek care or to be diagnosed of TB4. This leads to under diagnosing of TB cases, which is a major barrier for TB control.
In 2018, 7 million new cases of TB were notified globally, achieving the UN high-level meeting target which was built on the WHO Flagship Initiative “Find. Treat. All. #EndTB”1. However, a large gap still remained between the number of cases reported and the estimated cases in 2018 (10 million cases). This gap was found to be due to underreporting and under diagnosis of TB globally, with India, Nigeria, Indonesia, and the Philippines being responsible for over 50% of the noticed gap1. Under-detection of TB cases, especially active cases, has dire consequences for the infected individuals, their communities, and the country at large, causing a setback in national TB programmes. Traditional case finding methods have consistently failed to bridge the gap between observed cases of TB and the estimated cases1, hence active case finding has been recommended by WHO, and implemented to increase case detection rates, notification and treatment of TB cases3,5.
Active case-finding involves planned screening of high-risk individuals, such as contacts of TB patients or people living with HIV (PLWHIV). It includes all methods for the identification and then treatment of those with TB who would otherwise not report to the healthcare system6. It is also effective in the identification of latent TB, thereby preventing future development of active TB cases6. Common screening tools for active case findings include history taking, sputum smear, and chest x-rays.
Nigeria is a high burden TB country with one of the lowest TB Case Detection rates (25%) and accounting for 8% of the total gap between incidence and reported cases globally in 20167. The South-South of Nigeria, where Akwa Ibom (AK) state is, contributed 12% to all forms of TB cases notified nationwide in 20168. The state also has the highest HIV prevalence rate of 5.5% compared to the national average of 1.4%8. This makes it imperative that the missing cases be traced as HIV is a major risk factor for TB disease. There are pockets of high TB burden communities in some LGAs in the state, making AKS one of the states with persistently low case detection of all forms of TB.
Many high-risk people such as co-infected, co-morbid, malnourished persons, and children with active TB do not experience typical TB symptoms in the early stages of the disease. They are therefore unlikely to seek care early and may not be properly diagnosed when they eventually seek care. Poor knowledge of symptoms and where to access care also affects the uptake of TB services in our communities. Active case detection has been initiated in high risk patient groups including PLWHIV and diabetes4. However, there are still gaps in the patient-initiated screening model. At the community level, passive case detection involves patient awareness and self-presentation to the health facility. It has been observed that awareness about TB is not incorporated into the usual health talks at facilities and outreaches in the state.
Community health workers (CHWs) are key to provision of health services at primary care level as they serve as first point of contact for care for many Nigerians especially those living in the rural areas. There are different categories of community health workers in Nigeria, including Community Health Extension Workers (CHEWs), Community Pharmacists (CPs) and Patent Medicine Vendors (PMVs). The CHEWs are health workers who have been formally trained in the Colleges of Health Technology. They are usually employed by the government to work in the primary health centers, though quite a number currently working in the PHCs in Akwa Ibom state are volunteers9.
Community Pharmacy is a branch of pharmacy practice which emphasizes providing medical services in a particular community. Community Pharmacists are responsible for dispensing medications, counselling patients according to their health needs, and providing basic access to health care10. Community Pharmacies are thought to be the first point of call for health services, in Nigeria, frequently more than the designated Primary Health Centers11. according to a study, reasons cited for preference of the CPs include ease of access, short waiting time, free consultation, and longer-term availability of the community pharmacists to the people12,13.
On the other hand, the patent medicine vendors (PMVs) are individuals that currently work as lay health workers in the community, diagnosing, prescribing and treating minor and major ailments. There are currently no barriers to entry to this level of health workers in the sector, therefore, there is a proliferation of PMVs and CPs, with varied qualifications. The major characteristic here is that they are all self-trained with no regulatory body to guide their training. However, their activities are regulated by the Pharmaceutical Society of Nigeria14. This body sometimes carries out unannounced checks on their shops to assess that they adhere to stocking and dispensing of only oral medications.
Efficient development of human resource is necessary if TB control is to be achieved, hence, training of frontline health workers to improve their knowledge and attitude with respect to diagnosis, treatment and prevention of TB is an important strategy for TB control. Studies have shown that good TB knowledge scores among the health workers lead to better TB indices with respect to treatment and prevention. After a week-long training of community health workers in Mozambique, a 14.6% increase in TB case notification rate was noticed in the intervention group compared to a decrease of 16.5% in the control group15. Similarly, a facility-based study in Enugu reported a 100% increase in TB presumptive cases and TB cases diagnosed three months after health workers training was done16.
The use of cash incentives has been found to improve commitment of health workers, especially community health workers to their tasks. It has also been found to increase innovation and accountability among health care workers in other programs17,18. There is however inadequate evidence on the use of cash incentives as a motivation for improved case detection rate of TB among health workers, especially community health workers in low middle income countries.
This study aimed to assess the effect of training and cash incentives for referral of presumptive cases of Tuberculosis for appropriate diagnosis and treatment among CHWs in Nigeria. The main research questions were: Can cash incentives and training be used to motivate CHWs to increase TB case detection? The specific objectives were i) to determine effect of cash incentives on TB case detection ii) to determine effect of training CHWs on TB case detection. The hypotheses made in this study were that cash incentives and training will not improve case detection for TB.
Akwa Ibom State is located in the South-South region of Nigeria, one of the states in the oil-rich Niger Delta region with a population of about 5.5 million based on 2006 National population census report7. The state has 31 local governments distributed across the three senatorial districts. It has 368 Primary health centers, unevenly distributed across the local government areas (LGAs)19.
Akwa Ibom state has a high burden of TB and HIV. As at the time of this study, the USAID was carrying out TB control activities in 15 LGAs and TB Reach had projects in three of the remaining 16 LGAs. The study population was the 13 LGAs that were not covered by the ongoing TB programs. Following randomization, the LGAs selected for the study were Esit Eket and Uyo (Arm A), Ibiono Ibom and Ibesikpo (Arm B), and Nsit Ibom and Uruan (Arm C-Control).
The study was designed as a three-arm parallel cluster randomized control study. A panel survey sampling of all Community Health Workers identified as practicing in the communities was planned. There were no exclusions, only if the CHW declined to participate. All clusters were assumed to be equal at baseline,
The RCT was conducted in three high TB burden LGAs in the state. The LGAs were selected from the sampling frame of 13 LGAs, with the aid of the State Tuberculosis and Leprosy Control Officer (STBLCO). In each participating LGA, six PHC facilities offering DOTS treatment and services were selected by simple random sampling and included in the study. Each PHC with availability of DOTS treatment plus the catchment communities served as a cluster for this study. A total of 18 clusters were used in this study, six clusters per Arm. The allocation of PHCs is seen in Figure 1.
The study clusters were randomized to one of the three experimental arms with stratification according to LGA by the researchers. All CHWs in the study cluster were automatically assigned an intervention arm based on this randomization. Training and cash incentives were randomized to study cluster arms A and B. These were recruited with the aid of the facility focal persons, working with the Chairman of the PMVs. All CHWs in the catchment were given an invitation to the training, no exclusions were made, except the person declined to participate. The study PHC thus served as the focal point and referral centre for any cases picked up in the community by the CHWs.
Using the facility focal person as entry point, invitations were sent to all CHWs in the selected communities for a workshop to be conducted centrally in Uyo, the capital city. A panel survey of all PMVs, CPs and any other CHWs identified was conducted in each cluster. All CHWs in each cluster who participated in the workshop and gave consent were enrolled for the study. Blinding of participants to their allocated arms was not possible. To ensure that participants were blinded to the intervention, the clusters were allocated by LGA. LGAs chosen were not contiguous. The training was also conducted on three separate days i.e., Arm A on Day 1, Arm B on Day 2, and Control Arm on Day 3. Blinding of assessors to the different arms was also not possible.
All HCWs were targeted in each cluster, as the study was designed as a panel survey. However, a panel survey was not possible as there was large in-and out migration of the PMVs, especially those who originally sent their apprentices to the training. All the HCWs trained in the clusters were followed up for the duration of the study. At endline, we deployed the same method used in recruitment at baseline, targeting all HCWs in the study clusters. This design was used to minimize sampling error and take into account the design effect, and prevent contamination across the three study arms. Therefore, participants were selected as two independent cross-sectional samples.
The interest in evaluating training and cash incentives concurrently informed the choice of a multi-arm cluster randomized trial. The advantages of this design include increasing the chances of finding an effective intervention and lower costs as the two arms run at the same time. It is also documented that sharing a control arm reduces the sample size relative to performing separate 2-arm trials20.
1. Training of CHWs and subsequent outreaches and community health education campaigns to the study population to improve knowledge on TB control services available. A total of 158 HCWs were trained, 85 in Arm A and 73 in Arm B. They were instructed to carry out community education campaigns, identify presumptive TB cases in communities, collect sputum, and make referrals to the PHC for treatment. The control Arm C had 82 HCWs.
A one-day training session was carried out for each of the arms separately. This was a face-to-face workshop with participants grouped into three clusters. The allocation ratio for the three clusters was 1:1:1 as shown in Figure 2. The training was conducted by the researchers in collaboration with the State Tuberculosis and Leprosy Control Program office. Prior to the workshop, a Training of Trainers was conducted by the Lead Researcher, AKSTBLP State Coordinator and researchers. The Workshop facilitators were trained on the course contents. This was to develop a shared understanding of the aims of the workshop and familiarize them with the contents of the workshop materials.
Training manuals were developed specifically for the research and distributed to participants. Facilitators used both the training manuals and power point presentations developed from the manuals. Facilitators used participatory learning methods to deliver the course contents. The contents of the training was based on the module developed for active TB case finding for community health workers through house-to-house search for community based organizations (CBOs) and CHWs by the National Tuberculosis and Leprosy Control Program (NTBLCP)21. The sessions included training on basic symptoms, misconceptions diagnosis and treatment of TB, identification of presumptive TB cases, sputum collection and transportation and linking TB patients to care and treatment.
Participants were also taught how to collect sputum samples by the State Laboratory Focal Person for TB. Sputum cups were donated by the STBLCP program, the researchers procured transportation media (plastic bowls that could contain 4 sputa cups). At the end of the training, each participant was given a container for transporting sputum, 4 sputa cups, a presumptive case referral booklet and information, education and communication (IEC) materials to be used in educating clients.
Participants were instructed to carry out community education campaigns, identify presumptive TB cases in communities, collect sputum and make referrals to the PHC for treatment. The control Arm C had 41 CHWs. They were also requested to organize outreach health education campaigns in the communities.
2. Cash incentives to PMVs and CPs for referral of presumptive cases for screening. The intervention Arm A (85) received cash incentives of 200 naira (USD0.78) for every presumptive case referred for screening.
Besides these, there were also supportive supervisory visits (SSVs) to the CHWs, where they were encouraged to carry out outreaches. The CHWs were to identify Presumptive cases, collect sputum samples, transport the smears to the PHCs from where they were transported to designated laboratories. The results were sent back through the PHC TB focal persons and all individual positive results were offered treatment at home or the local health post.
The interventions took place in the PHCs and communities where the CHWs, PMVs and CPs practiced. While most of the CHEWs worked in the public sector PHCs, the CPs and PMVs work in the private sector, setting up their shops with the sole aim of making profit. The baseline interventions were carried out in April 2019 and follow-up was carried out quarterly until March 2020. The trial ended as scheduled after one year, though the COVID-19 pandemic lockdown interrupted endline data collection and close-out of project.
Data collection was carried out for a period of 12 months starting in April 2019 and ended in March 2020. Data collection was done via quarterly supportive supervisory visits (SSV) made to the trained Community Health Extension Workers (CHEWs), PMVs and CPs. During the quarterly SSVs, data was collected from the CHWs and compared with the facility TB register. The number of presumptive cases referred, and number of outreaches conducted during the quarter were documented.
A pre-intervention assessment of CHWs knowledge on TB was done using a pre-tested self-administered structured questionnaire. This was a standardized and validated questionnaire used by the national TB program for the 2017 TB Prevalence survey in Nigeria. The questionnaires were administered just before the training workshop for study arms A and B. However, they were administered at the six PHC facilities for the control arm as there was no training conducted for them. A post-intervention assessment was also done using the same tool after 12 months, in the PHC facilities in each of the clusters.
All materials used for training and data collection have been made available in the Dryad and Zenodo repositories22.
The Primary outcomes for the intervention were number of presumptive cases referred to the facility. However, TB case notification was used in analysis instead of presumptive cases. Two reasons accounted for this change i) presumptive cases are generally not used as a measure of TB control and ii) some of the PHC clusters were new DOTS facilities and had zero presumptive cases pre-intervention. Case notification data was accessed from the State TB program database. Although the original intention was to assess outcome at cluster level, final analysis was carried out at LGA level. Secondary outcomes included number of outreaches conducted per cluster and proportion of CHWs with correct knowledge post intervention analyzed at cluster level. The proportion of CHWs with correct knowledge post-intervention has been fully discussed in another paper. The number of outreaches was also analyzed by LGAs.
Data was collected and entered into Microsoft excel spreadsheet, version 2013, then collated and analyzed using STATA version 13 (Stata, RRID:SCR_012763) and GraphPad Prism version 8 (GraphPad Prism, RRID:SCR_002798) . The statistician was blinded to the study allocation until the data set was ready for final analysis. Descriptive data were analysed using summary statistics. Chi-square(χ2) tests were used to test differences in proportions over the study period. Continuous variables with normal distributions were compared using means and SD and tested using parametric statistics. An open access alternative software than can perform these same functions would be R (R Project for Statistical Computing, RRID:SCR_001905).
Ethical Approval was sought and obtained from the University of Uyo Institutional Health Ethics Research Board (UUTH/AD/S/93/VOLXXI/253). Approval was also obtained from the State Ministry of Health Ethics Review Board(MH/PRS/99/VOL.5/511). Individual verbal and written consent were sought and obtained from all participants. Verbal permission was also sought and granted by the Local Government PMV chairmen.
Only CHWs who consented to participate were recruited into the study. All presumptive cases identified were referred for screening and positive cases were linked up to the State TB program for treatment with DOTS.
Participants were recruited in April 2019 and followed up for one year. The trial ended in a year as intended. In May 2020, the endline assessment was done. All 18 clusters were analyzed as intended, six clusters per Arm. A total of 240 HCWs were recruited as follows: Arm A (85), Arm B (73) and Arm C(82). See Figure 2.
A total of 158 CHWs were trained at the beginning of the intervention, clustered as Arm A and Arm B. Table 1 shows the socio demographic characteristics of participants. Of these, 62.5% were females, 87.9% were less than 40 years old and 33.3% had a tertiary level of education. Majority of the participants were PMVs (78.8%), while 17.9% were CHEWs and 3.3% identified themselves as students, auxiliary nurses and were categorized as ‘others’.
Variables | Arm A Training and Cash Incentives (n=85) | Arm B Training (n=73) | Control (n=82) | Total (n=240) | Statistical indices |
---|---|---|---|---|---|
Sex Male Female | 38 (44.7) 47 (55.3) | 30 (41.1) 43 (58.9) | 22 (26.8) 60 (73.2) | 90 (37.5) 150 (62.5) | Df=2 X2 =3.7864 P value=0.151 |
Age (years) ≤30 31–40 ≥41 | 32 (37.7) 38 (44.7) 15 (17.6) | 39 (53.4) 24 (32.9) 10 (13.7) | 44 (53.7) 34 (41.5) 4 (4.9) | 115 (47.9) 96 (40.0) 29 (12.1) | Df=2 P value=0.108 |
Level of education Primary Secondary Tertiary | 1 (1.2) 54 (63.5) 30 (35.3) | 4 (5.5) 43 (58.9) 26 (35.6) | 0 (0.0) 58 (70.7) 24 (29.3) | 5 (2.1) 155 (64.6) 80 (33.3) | Df=2 P value=0.389 |
Job title PMVs PHC workers Others | 68 (80.0) 15 (17.7) 2 (2.3) | 57 (78.1) 14 (19.2) 2 (2.7) | 64 (78.0) 14 (17.1) 4 (4.9) | 189 (78.8) 43 (17.9) 8 (3.3) | Df=4 P value=0.936 |
Duration at the current position Less than 1year 1–4 years 5–9 years 10–14 years ≥15 years | 12 (14.1) 26 (30.6) 16 (18.8) 17 (20.0) 14 (16.5) | 15 (20.5) 29 (39.7) 16 (21.9) 4 (5.5) 9 (12.3) | 8 (9.8) 22 (26.8) 34 (41.5) 10 (12.2) 8 (9.8) | 35(14.6) 77(32.1) 66(27.5) 31 (12.9) 31(12.9) | Df=8 P value=0.035* |
Type of facility PHC Chemist shop Others | 17 (20.0) 44 (51.8) 24 (28.2) | 16 (21.9) 56 (76.7) 1 (1.4) | 16 (19.5) 66 (80.5) 0 (0.0) | 49(20.4) 166(69.2) 25(10.4) | Df=4 P value<0.0001* |
Trained on TB Yes No | 15 (17.7) 70 (82.3) | 21 (28.8) 52 (71.2) | 36 (43.9) 46 (56.1) | 72(30.0) 168(70.0) | Df=2 X2 =9.7982 Pvalue=0.007* |
Have access to TB guideline Yes No | 11 (12.9) 74 (87.1) | 21 (28.8) 52 (71.2) | 26 (31.7) 56 (68.3) | 58(24.2) 182(75.8) | Df=2 X2 =8.0613 P value=0.018* |
The total of CHWs who had received a training on TB within the last two years was 72, representing 30% of the population. A significantly higher proportion of these were in the Control Arm (43.9%). Also 24.2% of the CHWs had access to TB guidelines, with a higher proportion of those in the control group stating that they had access to TB guidelines (31.7%).
Table 2 shows the socio-demographic characteristics of participants at endline across the three arms. At endline, there was female preponderance (53.4%), 81.9% were below 40 years old, majority of them had attained secondary education (64.3%) and 77.4% of them being PMVs. There was a statistically significant difference in the duration of current job of the HCWs across the three arms (p=0.006), and in the type of health facility across the three arms (p<0.001).
Data from the intervention shows a total of 394 presumptive cases notified from the intervention. This represents 30.3% of all presumptive case notification from the LGAs covered by this intervention and 41.6% of all presumptive case notifications from the PHCs serving as cluster focal facilities. Figure 3 shows the trend in presumptive TB case notification in the intervention LGAs.
Data from the intervention suggests an increase of 14.4% in case notification rates between 2018 and 2019 for Arm A (Cash incentives and Training) and an increase of 7.4% in Arm B. However, as seen in Table 3–Table 5, the increases were not statistically significant.
Arm A (Cash Incentives + Training) | Arm C (Control) | ||
---|---|---|---|
Pre Intervention (2018) | 362(46.7) | 131(48.2) | Df=1 X2= 1.999 P value=0.1575 |
Post Intervention(2019) | 414(53.3) | 183(51.8) |
Arm B (Training only) | Arm C (Control) | ||
---|---|---|---|
Pre Intervention(2018) | 362(50.57) | 414(44.04) | Df=1 X2=2.250 P value=0.1336 |
Post Intervention(2019) | 131(49.43) | 183(55.96) |
A total of 120 outreaches were conducted during the intervention period. For Arm A, there was a 144.8% increase in number of outreaches over the previous year (pre-intervention). Arm B recorded a 46.7% increase while the control Arm C showed a 22.7% increase in number of outreaches (Table 6). These differences were however not statistically significant.
This study aimed to assess the effect of cash incentives and training on active case finding for TB in a high burden setting. A total of 158 CHWs were trained at the beginning of the intervention. The majority were females, less than 40 years old, and had a tertiary level of education. Most were also PMVs (78.8%), while only 17.9% were CHEWs. Both baseline and endline characteristics of the CHWs showed striking similarities. This shows that the CHWs sampled were essentially the same. Studies in Kenya had reported similar characteristics for CHWs23,24. The study showed that 30% of the respondents had previously been trained on TB. Our findings gives evidence that PMVs are the first point of contact for care especially in rural communities, where our study was based.
Our study demonstrated an increase in presumptive case notifications in the intervention Arms. With data from the intervention facilities accounting for a total of 394 presumptive cases representing 41.6% of all presumptive case notifications from the PHCs serving as cluster focal facilities. This also accounted for 30.3% of all presumptive case notification from the LGAs covered by the intervention. Other studies have documented evidence that community based interventions for active case finding leads to improved outcomes for TB control programs25–27. Such interventions include training of health care workers on presumptive case detection, sputum collection, and referral for treatment. However, evidence seemed to find that these led to short term improvements26.
The index study demonstrated a 7.4% increase in the training Arm, suggesting an effect of training increased presumptive cases detected. This compares with previous studies on training interventions to improve health outcomes. In a systematic review of interventions on cardiovascular disease prevention and management, eight studies showed improved knowledge of health care workers after training, and retention of acquired knowledge up to six months after training28. In a study in China, it was found that training improved the knowledge of definitions, case detection and laboratory diagnosis of TB up to a year after training29. Similarly, studies done in Fiji show TB case notification rates during the period of training activities increased significantly compared with the years when no training activities took place27.
We also found a 14.4% increase in case notification in the cash incentives Arm and 7.4% in the training Arm. This study utilized case notification instead of case detection rates (CDR). This is because CDR uses incidence as a denominator, which is a very uncertain estimate as it is not measured but estimated. Similar observations have been made in previous studies17,30. In an evaluation of TB REACH Wave 1 interventions in Pakistan, sputum smear positive (SS+) TB notifications increased by 24.9%. It was also found that among 19 projects with control populations, sputum smear positive TB case notifications increased by 36.9%, while in the control populations a 3.6% decrease was observed31. Again, this contrasts with findings of the index study which recorded increases in the intervention arms (14.4% and 7.4%), and an even larger increase (39%) in the control Arm. Our findings are also similar to that seen in Mozambique where there was a 14.6% increase in notification of all forms of people with TB over the baseline, However, contrary to what we found in our study, the control districts had a decrease in notification(-16.5%)15.
Cost effectiveness analysis also demonstrates that incentive-driven active case finding of TB was more effective and less expensive per disability-adjusted life years (DALY) averted when compared to the baseline passive case finding. This was seen in a study in Pakistan, when both alternatives are compared to a common baseline situation of no screening32.
The paradoxical increase in case notification found in our study was thought to be due to the fact there was another ongoing intervention in the LGAs. This was evident in the findings that a higher proportion of the participants in the control group had received training on TB prior to the commencement of this study, and also had access to TB guidelines.
With regard to TB outreach activities carried out during the one-year period, A marked increase was observed across all three study arms, with the training and cash incentive arm having an increase of about 145%, far higher than was noticed in the training only and control arms. Community outreach programmes have been found in some studies to improve detection of symptomatic and infectious TB cases in the community21. However, others have found that cases detected through TB outreaches were the less infectious cases with few symptoms who are reluctant to commence or complete treatment22. Nevertheless, it is important to track and notify all forms of TB in the community if the End TB Strategy goal of reduction of incidence by 80% is to be achieved33.
Other components of this study included cash support for outreaches and supportive supervision. Although a recently concluded systematic review conducted to inform the recent World Health Organization (WHO) guidelines on CHW programmes reported “very low certainty” regarding the evidence on supportive supervision34, other studies have demonstrated improved performance of CHWs with supportive supervision35. In a recent review of literature on CHW productivity, the authors suggested that productivity was based on a combination of three elements: (1) knowledge and skills, (2) motivation, and (3) the work environment. The work environment encompassed workload, supervision, supplies and equipment, and level of respect that other health workers had for the CHWs. In their review, the authors maintained that supportive supervision was a critical factor in creating and maintaining an enabling work environment. In another recent study, the majority of participants stated that supervision was one of the most important factors for maintaining a functional cadre of motivated CHWs because supervisors serve as a link between CHWs and the health system. The support that supervisors can provide CHWs helps them to feel valued and feel like an important part of a larger organization36,37. The findings of these components of our study will be fully discussed in another paper.
A limitation of this study was the inability to completely retain all HCWs recruited at baseline, this led to the analysis at LGA level instead of the cluster level originally planned. However, the similarity in baseline and endline characteristic shows the participants were essentially similar and were thus comparable
Another limitation of this study was the ongoing parallel active case finding intervention in the control communities, during the period of the study. This explains the somewhat paradoxical findings. In retrospect, a baseline assessment of all participants before randomization would have helped to mitigate this effect. However, the study findings are generalizable and applicable to other high burden TB settings.
The study demonstrated an increase in TB case notification and outreaches among health workers that received cash incentives and training. There was a double increase in case notifications in the Arms that received cash incentives compared to the Arm that received training only. TB outreach programmes however saw considerable increase in the LGAs in the training and cash incentive arm compared to the training only and control arms.
The results from this study suggest that the cash incentives given the CHWs motivated them to improve presumptive case detection above the previous years. These findings support the use of incentives for CHWs in high-burden TB settings to improve TB case detection rates. Therefore, cash incentives and training can be used to motivate lay health workers and integrate them into the TB control program to improve TB case detection.
These results add to the growing evidence base showing that different multicomponent approaches like training, supporting health workers and cash incentives for TB case finding can have a significant improvement on TB notifications.
We recommend implementation of the intervention in other low case detection, but high TB burden settings. Future researchers into active case finding for TB in high burden areas need to consider carefully the recruitment and randomization process. Further studies are needed, however, to demonstrate the cost-effectiveness of using cash incentives in TB case finding in high burden settings.
Dryad: Improving Tuberculosis case finding in Nigeria. https://doi.org/10.5061/dryad.tht76hf0737.
This project contains the following underlying data.
Readme_Improving Tuberculosis case finding in Nigeria.xlsx (files of all variables in csv file)
TB Community data Post intervention.xlsx (data from post intervention community survey in csv file)
TB Community data pre-intervention.xlsx (data from post intervention community survey in csv file)
TB Health care workers Baseline.xlsx (data from baseline survey of health care workers in csv file)
TB Health care workers endline.xlsx (data from endline survey of health care workers in csv file)
TB study Outcome (summary data from intervention and state TBL program).xlsx
Data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).
Zenodo: Improving Tuberculosis case finding in Nigeria22. https://doi.org/10.5281/zenodo.5062448.
This project contains the following extended data:
Ethical_Approval_2_SMOH.jpg (ethical approval from the Akwa Ibom State Ministry of Health)
Ethical_Approval_TB_1.pdf (ethical approval from the University of Uyo Teaching Hospital)
FINAL_RESEARCH_PROTOCOL._FOR_TB_STUDYdocx.docx (Research protocol used for the study)
Questionnaires_for_the_TB_Study.zip (Health care worker and community questionnaires used for the survey)
Training_Kit.zip (copy of modules used for training intervention)
TrialApprovalLetter3.pdf (Trial approval letter from PanAfrican Clinical Trial Registry)
CONSORT checklist for ‘A Randomized Control Trial to Test Effect of Cash Incentives and Training on Active Casefinding for Tuberculosis among Community Health Workers in Nigeria’ https://doi.org/10.5281/zenodo.5062448.
The authors acknowledge Mr. Edidiong Umoh and Mrs Ekom Ekwo for the coordination of the field work and database for the entire project roles. We also acknowledge the Health Systems Research Hub, University of Uyo for providing the platform to conduct this study.
This study was registered in the Pan African Clinical Trial Registry (www.pactr.org) database, with the unique identification number PACTR202010691865364.
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Is the work clearly and accurately presented and does it cite the current literature?
Partly
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
No
If applicable, is the statistical analysis and its interpretation appropriate?
Partly
Are all the source data underlying the results available to ensure full reproducibility?
Yes
Are the conclusions drawn adequately supported by the results?
No
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Maternal and Child Health
Is the work clearly and accurately presented and does it cite the current literature?
Yes
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Yes
If applicable, is the statistical analysis and its interpretation appropriate?
No
Are all the source data underlying the results available to ensure full reproducibility?
Yes
Are the conclusions drawn adequately supported by the results?
No
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Tuberculosis, epidemiology, public health
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