Effectiveness and Cost-effectiveness of Artificial Intelligence-assisted Pathology for Prostate Cancer Diagnosis in Sweden: A Microsimulation Study.

BACKGROUND AND OBJECTIVE: Image-based artificial intelligence (AI) methods have shown high accuracy in prostate cancer (PCa) detection. Their impact on patient outcomes and cost effectiveness in comparison to human pathologists remains unknown. Our aim was to evaluate the effectiveness and cost-effectiveness of AI-assisted pathology for PCa diagnosis in Sweden. METHODS: We modeled quadrennial prostate-specific antigen (PSA) screening for men between the ages of 50 and 74 yr over a lifetime horizon using a health care perspective. Men with PSA ≥3 ng/ml were referred for standard biopsy (SBx), for which cores were either examined via AI followed by a pathologist for AI-labeled positive cores, or a pathologist alone. The AI performance characteristics were estimated using an internal STHLM3 validation data set. Outcome measures included the number of tests, PCa incidence and mortality, overdiagnosis, quality-adjusted life years (QALYs), and the potential reduction in pathologist-evaluated biopsy cores if AI were used. Cost-effectiveness was assessed using the incremental cost-effectiveness ratio. KEY FINDINGS AND LIMITATIONS: In comparison to a pathologist alone, the AI-assisted workflow increased the number of PSA tests, SBx procedures, and PCa deaths by ≤0.03%, and slightly reduced PCa incidence and overdiagnosis. AI would reduce the proportion of biopsy cores evaluated by a pathologist by 80%. At a cost of €10 per case, the AI-assisted workflow would cost less and result in <0.001% lower QALYs in comparison to a pathologist alone. The results were sensitive to the AI cost. CONCLUSIONS AND CLINICAL IMPLICATIONS: According to our model, AI-assisted pathology would significantly decrease the workload of pathologists, would not affect patient quality of life, and would yield cost savings in Sweden when compared to a human pathologist alone. PATIENT SUMMARY: We compared outcomes for prostate cancer patients and relevant costs for two methods of assessing prostate biopsies in Sweden: (1) artificial intelligence (AI) technology and review of positive biopsies by a human pathologist; and (2) a human pathologist alone for all biopsies. We found that addition of AI would reduce the pathology workload and save money, and would not affect patient outcomes when compared to a human pathologist alone. The results suggest that adding AI to prostate pathology in Sweden would save costs.

Association of multi-morbidity, social participation, functional and mental health with the self-rated health of middle-aged and older adults in India: a study based on LASI wave-1.

Self-rated health (SRH) is a well-established measure in public health to administer the general health of an individual. It can also be used to assess overall health status' relationship with the social, physical, and mental health of a person. In this study, we examine the association of SRH and various socio-economic & health-related factors such as multi-morbidity status, mental health, functional health, and social participation. Data used in this paper is collated from the first wave of Longitudinal Ageing Study in India (LASI) 2017-18. A total of 65,562 older adults aged 45 or above are considered in our study. Various indices (multimorbidity, social participation, functional and mental health) have been created to measure factors influencing the SRH of an individual. Overall, in the study population, around 18.4% of people reported poor SRH. Dominance Analysis results show that the contribution of multimorbidity in predicting poor SRH is highest, followed by functional health, mental health, and social participation. In a developing country like India, there is a dire need for policies having a holistic approach regarding the health and well-being of the older population.

Pool Testing for COVID-19: Suitable Splitting Procedure and Pool Size for India.

OBJECTIVE: Coronavirus disease (COVID-19) has emerged as a global pandemic for public health due to the large scale outbreak, therefore there is an urgent need to detect the infected cases quickly and isolate them in order to suppress the further spread of the disease. This study tries to identify a suitable pool testing method and algorithm for COVID-19. METHODS: This study tries to derive a general equation for the number of tests required for a pooled sample to detect every infected individual in the specific pool. The gain in pool testing over the normal procedure is quantified by the percentage of tests required compared to individual testing. RESULTS: The percentage of tests required by the pool testing strategy varies according to the different splitting procedures, the size of the pooled sample, and the probability of an individual being infected in the population. If the probability of infection is 0.05, then for a pool size of 32, only 14 tests are sufficient to detect every infected individual. CONCLUSION: The number of tests required to detect infected individuals by using the pooling method is much lower than individual testing. This may help us with increasing our testing capacity for COVID-19 by testing a large number of individuals in less time with limited resources.

Incubation period for COVID-19: a systematic review and meta-analysis.

Aim: This study aims to conduct a review of the existing literature about incubation period for COVID-19, which can provide insights to the transmission dynamics of the disease. Methods: A systematic review followed by meta-analysis was performed for the studies providing estimates for the incubation period of COVID-19. The heterogeneity and bias in the included studies were tested by various statistical measures, including I2 statistic, Cochran's Q test, Begg's test and Egger's test. Results: Fifteen studies with 16 estimates of the incubation period were selected after implementing the inclusion and exclusion criteria. The pooled estimate of the incubation period is 5.74 (5.18, 6.30) from the random effects model. The heterogeneity in the selected studies was found to be 95.2% from the I2 statistic. There is no potential bias in the included studies for meta-analysis. Conclusion: This review provides sufficient evidence for the incubation period of COVID-19 through various studies, which can be helpful in planning preventive and control measures for the disease. The pooled estimate from the meta-analysis is a valid and reliable estimate of the incubation period for COVID-19.

An assessment of anthropometric indices and its association with NCDs among the older adults of India: evidence from LASI Wave-1.

BACKGROUND: The purpose of this study is to assess the status of physical body indices such as body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR) among the older adults aged 45 and above in India. Further, to explore the association of anthropometric indices with various non-communicable morbidities. METHODS: The study uses secondary data of the Longitudinal Ageing Survey's first wave in India (2017-18). The national representative sample for older adults 45 and above (65,662) considered for the analysis. The prevalence of the non-communicable diseases (NCDs) included in the study is based on the self-reporting of the participants. Diseases included are among the top ten causes of death, such as cancer, hypertension, stroke, chronic heart diseases, diabetes, chronic respiratory diseases, and multi-morbidity. Multi-morbidity is a case of having more than one of the morbidities mentioned above. BMI-obese indicates an individual having a BMI ≥30, and the critical threshold value for high-risk WC for men is ≥102 cm while for women is ≥88 cm. The critical limit for the high-risk WHR for men and women is ≥0.90 and ≥ 0.85, respectively. Descriptive statistics and multiple logistic regressions are used to assess the association BMI, WC, and WHR with non-communicable morbidities. RESULTS: Based on the multivariate-adjusted model, odds shows that an Indian older adult aged 45 and above is 2.3 times more likely (AOR: 2.33; 95% CI (2.2, 2.5)) by obesity, 61% more likely (AOR: 1.61; 95% CI (1.629, 1.631)) by high-risk WHR and 98% more likely (AOR: 1.98; 95% CI (1.9, 2.1)) by high-risk WC to develop CVDs than their normal counterparts. Similarly, significant positive associations of obesity, high-risk WC, and high-risk WHR were observed with other NCDs and multi-morbidity. CONCLUSION: Our study shows that obesity, high-risk WC, and high-risk WHR are significant risks for developing NCDs and multi-morbidity among the older adults in India. There is a need for a multi-sectoral approach to reduce the share of the elderly population in high-risk groups of BMIs, WHR, and WC.

Estimates of serial interval for COVID-19: A systematic review and meta-analysis.

BACKGROUND: On 11th March 2020, the World Health Organization declared COVID-19 as Pandemic. The estimation of transmission dynamics in the initial days of the outbreak of any infectious disease is crucial to control its spread in a new area. The serial interval is one of the significant epidemiological measures that determine the spread of infectious disease. It is the time interval between the onset of symptoms in the primary and secondary case. OBJECTIVE: The present study aimed at the qualitative and quantitative synthesis of the currently available evidence for the serial interval of COVID-19. METHODOLOGY: Data on serial intervals were extracted from 11 studies following a systematic review. A meta-analysis was performed to estimate the pooled estimate of the serial interval. The heterogeneity and bias in the included studies were tested by various statistical measures and tests, including I2 statistic, Cochran's Q test, Egger's test, and Beggs's test. RESULT: The pooled estimate for the serial interval was 5.40 (5.19, 5.61) and 5.19 (4.37, 6.02) days by the fixed and random effects model, respectively. The heterogeneity between the studies was found to be 89.9% by I2 statistic. There is no potential bias introduced in the meta-analysis due to small study effects. CONCLUSION: The present review provides sufficient evidence for the estimate of serial interval of COVID-19, which can help in understanding the epidemiology and transmission of the disease. The information on serial interval can be useful in developing various policies regarding contact tracing and monitoring community transmission of COVID-19.