必须估计猪甲型流感病毒(swIAV)的过去和现在的负担,因为猪充当混合容器,被认为是新出现的IAV变体的潜在来源。本系统评价和荟萃分析的目的是整合有关韩国家猪中swIAV的患病率和血清阳性率的数据,并评估影响这些结果的重要风险因素。使用指定的搜索字符串搜索了八个数据库,以评估韩国猪中swIAV的患病率和血清阳性率;三位作者应用了一组预先确定的纳入标准后,确定了27项合格研究。使用随机效应荟萃分析,将报告的患病率和血清阳性率按0至1的比例分别汇总。为了识别和量化异质性的潜在来源,子组,和荟萃回归分析使用协变量(出版物类型,swIAV亚型,猪的生长阶段,采样区域,出版年份,采样季节,设施,检测方法,样品类型,和样本量)。家猪的总体患病率和血清阳性率分别为0.05[95%置信区间(CI):0.05-0.12]和0.35(95%CIs:0.14-0.63),分别。为了确定协变量对效应大小的影响,使用具有校正后的Akaike信息标准值的预测因子重要性估计确定合适的元回归模型.因此,最佳拟合模型包括两个协变量,出版年份和样本量,在亚组分析中与高度异质性显著相关。此外,数据可视化显示swIAV患病率与血清阳性率和猪的特定生长阶段之间存在显著的非线性关联.这些发现表明,在大型农场中对不同生长阶段的猪进行定期监测可能有助于确定该地区跨物种的SwIAV传播状况,从而最大限度地降低大流行风险。
The past and current burden of swine influenza A viruses (swIAV) must be estimated since pigs act as mixing vessels and are considered a potential source of newly emerging IAV variants. The objective of this systematic
review and meta-analysis was to integrate data on the prevalence and seroprevalence of swIAV in South Korean domestic pigs and evaluate important risk factors that influence these outcomes. Eight databases were searched for studies that evaluated the prevalence and seroprevalence of swIAV in South Korean pigs using a specified search string; twenty-seven eligible studies were identified after application of a set of pre-determined inclusion criteria by three authors. The reported prevalence and seroprevalence were pooled separately in proportions between 0 and 1, using a random-effect meta-analysis. To identify and quantify potential sources of heterogeneity, subgroup, and meta-regression analyses were conducted using covariates (publication type, swIAV subtype, growth stage of pigs, sampling region, publication year, sampling season, facility, detection method, sample type, and sample size). The overall prevalence and seroprevalence in domestic pigs were 0.05 [95% confidence intervals (CIs): 0.05-0.12] and 0.35 (95% CIs: 0.14-0.63), respectively. To identify the impact of covariates on effect size, a suitable meta-regression model was determined using predictor importance estimates with corrected Akaike information criterion values. Consequently, the best-fit model included two covariates, publication year and sample size, which were significantly associated with high heterogeneity in the subgroup analysis. Furthermore, data visualization depicted a significant non-linear association between swIAV prevalence and seroprevalence and specific growth stages of pigs. These findings suggest that the periodic monitoring of pigs at different growth stages in large farms may help to establish the status of swIAV-spread across species in the region, and thereby minimize pandemic risk.