The impact of COVID-19 pandemic on reported notifiable infectious diseases in China: An interrupted time series analysis.

OBJECTIVE: During major public health crises, the epidemiological characteristics of commonly reported infectious diseases may change. Based on routinely reported data on notifiable infectious diseases, we investigated whether the incidence and mortality of infectious diseases in China were affected by the coronavirus disease (COVID-19). METHODS: We analyzed monthly reported data on nationally notifiable infectious diseases from January 2013 to March 2024. Using an interrupted time series (ITS) design, we performed Poisson regression to assess changes in the incidence and mortality rates of infectious diseases before, during, and after the COVID-19 pandemic. RESULTS: We found that during the COVID-19 outbreak, the reported incidence of nationally notifiable infectious diseases significantly and immediately decreased (Relative Risk (RR)=0.6206; 95% Confidence Interval (CI)= 0.6201-0.6211), and so did the mortality rate (RR=0.7814; 95% CI=0.7696-0.7933). After the end of pandemic control measures, the reported incidence surged abruptly starting from January 2023, showing a sharp rise compared to the pre-pandemic period (RR=1.8608; 95% CI=1.8595-1.8621). The reported mortality increased, too (RR=1.081; 95% CI=1.0638-1.0984). CONCLUSION: The overall incidence and mortality of infectious diseases decreased immediately after the outbreak of COVID-19. After the end of pandemic control measures, their incidence rate sharply increased, and their mortality rate also rose.

Cost-effectiveness analysis of artificial intelligence-based diabetic retinopathy screening in rural China based on the Markov model.

This study assessed the cost-effectiveness of different diabetic retinopathy (DR) screening strategies in rural regions in China by using a Markov model to make health economic evaluations. In this study, we determined the structure of a Markov model according to the research objectives, which required parameters collected through field investigation and literature retrieval. After perfecting the model with parameters and assumptions, we developed a Markov decision analytic model according to the natural history of DR in TreeAge Pro 2011. For this model, we performed Markov cohort and cost-effectiveness analyses to simulate the probabilistic distributions of different developments in DR and the cumulative cost-effectiveness of artificial intelligence (AI)-based screening and ophthalmologist screening for DR in the rural population with diabetes mellitus (DM) in China. Additionally, a model-based health economic evaluation was performed by using quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios. Last, one-way and probabilistic sensitivity analyses were performed to assess the stability of the results. From the perspective of the health system, compared with no screening, AI-based screening cost more (the incremental cost was 37,257.76 RMB (approximately 5,211.31 US dollars)), but the effect was better (the incremental utility was 0.33). Compared with AI-based screening, the cost of ophthalmologist screening was higher (the incremental cost was 14,886.76 RMB (approximately 2,070.19 US dollars)), and the effect was worse (the incremental utility was -0.31). Compared with no screening, the incremental cost-effectiveness ratio (ICER) of AI-based DR screening was 112,146.99 RMB (15,595.47 US dollars)/QALY, which was less than the threshold for the ICER (< 3 times the per capita gross domestic product (GDP), 217,341.00 RMB (30,224.03 US dollars)). Therefore, AI-based screening was cost-effective, which meant that the increased cost for each additional quality-adjusted life year was merited. Compared with no screening and ophthalmologist screening for DR, AI-based screening was the most cost-effective, which not only saved costs but also improved the quality of life of diabetes patients. Popularizing AI-based DR screening strategies in rural areas would be economically effective and feasible and can provide a scientific basis for the further formulation of early screening programs for diabetic retinopathy.

Effect of language therapy alone for developmental language disorder in children: A meta-analysis.

Despite numerous studies on the treatment of developmental language disorder (DLD), the intervention effect has long been debated. Systematic reviews of the effect of language therapy alone are rare. This evidence-based study investigated the effect of language therapy alone for different expressive and receptive language levels in children with DLD. Publications in databases including PubMed, the Cochrane Library, the Wanfang Database and the China National Knowledge Infrastructure were searched. Randomized controlled trials were selected. The methodological quality of the included trials was assessed using the modified Jadad method. RevMan 5.3 software was used for the data analysis. Fifteen trials were included in this study. Compared with the control (no or delayed intervention) group, the intervention group showed significant differences in overall expressive language development [standard mean differences (SMD), 0.46; 95% confidence interval (CI), 0.12-0.80], mean length of utterances in a language sample (SMD, 2.16; 95% CI, 0.39-3.93), number of utterances in a language sample (SMD, 0.52; 95% CI, 0.21-0.84), parent reports of expressive phrase complexity (SMD, 1.24; 95% CI, 0.78-1.70), overall expressive vocabulary development (SMD, 0.43; 95% CI, 0.17-0.69) and different words used in a language sample (SMD, 0.62; 95% CI, 0.35-0.88). However, language therapy did not show satisfactory long-term effects on DLD. Although language therapy is helpful in improving the performance of children with DLD, its long-term effect is unsatisfactory.

Clinical evaluation of AI-assisted screening for diabetic retinopathy in rural areas of midwest China.

BACKGROUND: Although numerous studies have described the application of artificial intelligence (AI) in diabetic retinopathy (DR) screening among diabetic populations, studies among populations in rural areas are rare. The purpose of this study was to evaluate the application value of an AI-based diagnostic system for DR screening in rural areas of midwest China. METHODS: In this diagnostic accuracy study, diabetes mellitus (DM) patients in the National Basic Public Health Information Systems of Licheng County and Lucheng County of Changzhi city from July to December 2020 were selected as the target population. A total of 7824 eyes of 3933 DM patients were enrolled in this screening; the patients included 1395 males and 2401 females, with an average age of 19-87 years (63±8.735 years). All fundus photographs were collected by a professional ophthalmologist under natural pupil conditions in a darkroom using the Zhiyuan Huitu fundus image AI analysis software EyeWisdom. The AI-based diagnostic system and ophthalmologists were tasked with diagnosing the photos independently, and the consistency rate, sensitivity and specificity of the two methods in diagnosing DR were calculated and compared. RESULTS: The prevalence rates of DR according to the ophthalmologist and AI diagnoses were 22.7% and 22.5%, respectively; the consistency rate was 81.6%. The sensitivity and specificity of the AI system relative to the ophthalmologists' grades were 81.2% (95% confidence interval [CI]: 80.3% 82.1%) and 94.3% (95% CI: 93.7% 94.8%), respectively. There was no significant difference in diagnostic outcomes between the methods (χ2 = 0.329, P = 0.566, P>0.05), and the AI-based diagnostic system had high consistency with the ophthalmologists' diagnostic results (κ = 0.752). CONCLUSION: Our research demonstrated that DR patients in rural area hospitals can be screened feasibly. Compared with that of the ophthalmologists, however, the accuracy of the AI system must be improved. The results of this study might lend support to the large-scale application of AI in DR screening among different populations.

Family environmental risk factors for developmental speech delay in children in Northern China.

Most reported risk factors for developmental speech delay (DSD) remain controversial, and studies on paternal influencing factors are rare. This study investigated family environmental risk factors for DSD in northern China. The medical records of 276 patients diagnosed with DSD at four centres between October 2018 and October 2019 were retrospectively analysed. A questionnaire was designed that contained items such as maternal age at the child's birth, child sex, child age, birth order, family type and parental personality. Patients whose medical records lacked complete information for this investigation were contacted by e-mail or phone. Additionally, 339 families whose children received routine physical examinations at the four involved centres completed the survey. Data were collected, and potential risk factors were analysed using the t test or chi-square test; the obtained outcomes were subjected to multivariable logistic regression for further analysis. The multivariable regression showed that older maternal age at the child's birth (OR = 1.312 (1.192-1.444), P < 0.001), introverted paternal personality (OR = 0.023 (0.011-0.048), P < 0.001), low average parental education level (OR = 2.771 (1.226-6.263), P = 0.014), low monthly family income (OR = 4.447 (1.934-10.222), P < 0.001), and rare parent-child communication (OR = 6.445 (3.441-12.072), P < 0.001) were independent risk factors for DSD in children in North China. The study results may provide useful data for broadening and deepening the understanding of family risk factors for DSD.