Need for HTA supported risk factor screening for hypertension and diabetes in Nepal: A systematic scoping review.

Objective: Health Technology Assessment (HTA) is a comprehensive and important tool for assessment and decision-making in public health and healthcare practice. It is recommended by the WHO and has been applied in practice in many countries, mostly the developed ones. HTA might be an important tool to achieve universal health coverage (UHC), especially beneficial to low-and-middle-income countries (LMIC). Even though the Package for Essential Non-communicable Diseases (PEN) has already been initiated, there is a clear policy gap in the HTA of any health device, service, or procedure, including the assessment of cardiovascular risk factors (CVRFs) in Nepal. Hence, we carried out the review to document the HTA supported evidence of hypertension and diabetes screening, as CVRFs in Nepal. Materials and methods: We searched in PubMed, Cochrane, and Google Scholar, along with some gray literature published in the last 6 years (2016-2021) in a systematic way with a controlled vocabulary using a well-designed and pilot tested search strategy, screened them, and a total of 53 articles and reports that matched the screening criteria were included for the review. We then, extracted the data in a pre-designed MS-Excel format, first in one, and then, from it, in two, with more specific data. Results: Of 53 included studies, we reported the prevalence and/or proportion of hypertension and diabetes with various denominators. Furthermore, HTA-related findings such as cost, validity, alternative tool or technology, awareness, and intervention effectiveness have been documented and discussed further, however, not summarized due to their sparingness. Conclusion: Overall, the prevalence of DM (4.4-18.8%) and HTN (17.2-70.0%) was reported in most studies, with a few, covering other aspects of HTA of DM/HTN. A national policy for establishing an HTA agency and some immediately implementable actions are highly recommended.

Adopting social health insurance in Nepal: A mixed study.

Objective: The Social Health Insurance Program (SHIP) shares a major portion of social security, and is also key to Universal Health Coverage (UHC) and health equity. The Government of Nepal launched SHIP in the Fiscal Year 2015/16 for the first phase in three districts, on the principle of financial risk protection through prepayment and risk pooling in health care. Furthermore, the adoption of the program depends on the stakeholders' behaviors, mainly, the beneficiaries and the providers. Therefore, we aimed to explore and assess their perception and experiences regarding various factors acting on SHIP enrollment and adherence. Methods: A cross-sectional, facility-based, concurrent mixed-methods study was carried out in seven health facilities in the Kailali, Baglung, and Ilam districts of Nepal. A total of 822 beneficiaries, sampled using probability proportional to size (PPS), attending health care institutions, were interviewed using a structured questionnaire for quantitative data. A total of seven focus group discussions (FGDs) and 12 in-depth interviews (IDIs), taken purposefully, were conducted with beneficiaries and service providers, using guidelines, respectively. Quantitative data were entered into Epi-data and analyzed with SPSS, MS-Excel, and Epitools, an online statistical calculator. Manual thematic analysis with predefined themes was carried out for qualitative data. Percentage, frequency, mean, and median were used to describe the variables, and the Chi-square test and binary logistic regression were used to infer the findings. We then combined the qualitative data from beneficiaries' and providers' perceptions, and experiences to explore different aspects of health insurance programs as well as to justify the quantitative findings. Results and prospects: Of a total of 822 respondents (insured-404, uninsured-418), 370 (45%) were men. Families' median income was USD $65.96 (8.30-290.43). The perception of insurance premiums did not differ between the insured and uninsured groups (p = 0.53). Similarly, service utilization (OR = 220.4; 95% CI, 123.3-393.9) and accessibility (OR = 74.4; 95% CI, 42.5-130.6) were found to have high odds among the insured as compared to the uninsured respondents. Qualitative findings showed that the coverage and service quality were poor. Enrollment was gaining momentum despite nearly a one-tenth (9.1%) dropout rate. Moreover, different aspects, including provider-beneficiary communication, benefit packages, barriers, and ways to go, are discussed. Additionally, we also argue for some alternative health insurance schemes and strategies that may have possible implications in our contexts. Conclusion: Although enrollment is encouraging, adherence is weak, with a considerable dropout rate and poor renewal. Patient management strategies and insurance education are recommended urgently. Furthermore, some alternate schemes and strategies may be considered.

The performance and yield of tuberculosis testing algorithms using microscopy, chest x-ray, and Xpert MTB/RIF.

SETTING: The introduction of Xpert MTB/RIF (Xpert) and renewed interest in chest x-ray (CXR) for tuberculosis testing has provided additional choices to the smear-based diagnostic algorithms used by TB programs previously. More programmatic data is needed to better understand the implications of possible approaches. OBJECTIVE: We sought to evaluate how different testing algorithms using microscopy, Xpert and CXR impacted the number of people detected with TB in a district hospital in Nepal. DESIGN: Consecutively recruited patients with TB-related symptoms were offered smear microscopy, CXR and Xpert. We tested six hypothetical algorithms and compared yield, bacteriologically positive (Bac+) cases missed, and tests conducted. RESULTS: Among 929 patients, Bac+ prevalence was 17.3% (n = 161). Smear microscopy detected 121 (75.2% of Bac+). Depending on the radiologists' interpretation of CXR, Xpert testing could be reduced by (31%-60%). Smear microscopy reduced Xpert cartridge need slightly, but increased the overall diagnostic tests performed. CONCLUSION: Xpert detected a large proportion of Bac+ TB cases missed by microscopy. CXR was useful in greatly reducing the number of diagnostic tests needed even among presumptive TB patients. Loose CXR readings should be used to identify more people for TB testing. More analysis of costs and standardized CXR reading should be considered.

Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems.

Deep learning (DL) neural networks have only recently been employed to interpret chest radiography (CXR) to screen and triage people for pulmonary tuberculosis (TB). No published studies have compared multiple DL systems and populations. We conducted a retrospective evaluation of three DL systems (CAD4TB, Lunit INSIGHT, and qXR) for detecting TB-associated abnormalities in chest radiographs from outpatients in Nepal and Cameroon. All 1196 individuals received a Xpert MTB/RIF assay and a CXR read by two groups of radiologists and the DL systems. Xpert was used as the reference standard. The area under the curve of the three systems was similar: Lunit (0.94, 95% CI: 0.93-0.96), qXR (0.94, 95% CI: 0.92-0.97) and CAD4TB (0.92, 95% CI: 0.90-0.95). When matching the sensitivity of the radiologists, the specificities of the DL systems were significantly higher except for one. Using DL systems to read CXRs could reduce the number of Xpert MTB/RIF tests needed by 66% while maintaining sensitivity at 95% or better. Using a universal cutoff score resulted different performance in each site, highlighting the need to select scores based on the population screened. These DL systems should be considered by TB programs where human resources are constrained, and automated technology is available.