ImportancePost−COVID-19 condition (PCC) is a complex heterogeneous disorder that has affected the lives of millions of people globally. Identification of potential risk factors to better understand who is at risk of developing PCC is important because it would allow for early and appropriate clinical support.ObjectiveTo evaluate the demographic characteristics and comorbidities that have been found to be associated with an increased risk of developing PCC.Data sourcesMedline and Embase databases were systematically searched from inception to December 5, 2022.Study SelectionThe meta-analysis included all published studies that investigated the risk factors and/or predictors of PCC in adult (≥18 years) patients.Data Extraction and SynthesisOdds ratios (ORs) for each risk factor were pooled from the selected studies. For each potential risk factor, the random-effects model was used to compare the risk of developing PCC between individuals with and without the risk factor. Data analyses were performed from December 5, 2022, to February 10, 2023.Main Outcomes and MeasuresThe risk factors for PCC included patient age; sex; body mass index, calculated as weight in kilograms divided by height in meters squared; smoking status; comorbidities, including anxiety and/or depression, asthma, chronic kidney disease, chronic obstructive pulmonary disease, diabetes, immunosuppression, and ischemic heart disease; previous hospitalization or ICU (intensive care unit) admission with COVID-19; and previous vaccination against COVID-19.ResultsThe initial search yielded 5334 records of which 255 articles underwent full-text evaluation, which identified 41 articles and a total of 860 783 patients that were included. The findings of the meta-analysis showed that female sex (OR, 1.56; 95% CI, 1.41-1.73), age (OR, 1.21; 95% CI, 1.11-1.33), high BMI (OR, 1.15; 95% CI, 1.08-1.23), and smoking (OR, 1.10; 95% CI, 1.07-1.13) were associated with an increased risk of developing PCC. In addition, the presence of comorbidities and previous hospitalization or ICU admission were found to be associated with high risk of PCC (OR, 2.48; 95% CI, 1.97-3.13 and OR, 2.37; 95% CI, 2.18-2.56, respectively). Patients who had been vaccinated against COVID-19 with 2 doses had a significantly lower risk of developing PCC compared with patients who were not vaccinated (OR, 0.57; 95% CI, 0.43-0.76).Conclusions and RelevanceThis systematic review and meta-analysis demonstrated that certain demographic characteristics (eg, age and sex), comorbidities, and severe COVID-19 were associated with an increased risk of PCC, whereas vaccination had a protective role against developing PCC sequelae. These findings may enable a better understanding of who may develop PCC and provide additional evidence for the benefits of vaccination.Trial RegistrationPROSPERO Identifier: CRD42022381002

In our sample of the local population the combined olfactory and odour identification scores for healthy volunteers and patients with olfactory disorders are comparable with the normative data published on large samples of European populations. However, modification of a few of the distracters is recommended for British patients based on our findings.

Schistosomiasis control in China is facing a new challenge due to the rebound of epidemics in many areas and the unsustainable effects of the chemotherapy-based control strategy. Identifying high-risk regions for schistosomiasis is an important first step for an effective and sustainable strategy. Direct surveillance of snail habitats to detect high-risk regions is costly and no longer a desirable approach, while indirect monitoring of acute schistosomiasis may be a satisfactory alternative. To identify high-risk regions for schistosomiasis, we jointly used multiplicative and additive models with the kernel smoothing technique as the main approach to estimate the relative risk (RR) and excess risk (ER) surfaces by analyzing surveillance data for acute schistosomiasis. The feasibility of detecting high-risk regions for schistosomiasis through nonparametric spatial analysis was explored and confirmed in this study, and two significant high-risk regions were identified. The results provide useful hints for improving the national surveillance network for acute schistosomiasis and possible approaches to utilizing surveillance data more efficiently. In addition, the commonly used epidemiological indices, RR and ER, are examined and emphasized from the spatial point of view, which will be helpful for exploring many other epidemiological indices.

Schistosomiasis control in China has, in general, been very successful during the past several decades. However, the rebounding of the epidemic situation in some areas in recent years raises concerns about a sustainable control strategy of which locating active transmission sites (ATS) is a necessary first step. This study presents a systematic approach for locating schistosomiasis ATS by combining the approaches of identifying high risk regions for schisotosmiasis and extracting snail habitats. Environmental, topographical, and human behavioural factors were included in the model. Four significant high-risk regions were detected and 6 ATS were located. We used the normalized difference water index (NDWI) combined with the normalized difference vegetation index (NDVI) to extract snail habitats, and the pointwise 'P-value surface' approach to test statistical significance of predicted disease risk. We found complicated non-linear relationships between predictors and schistosomiasis risk, which might result in serious biases if data were not properly treated. We also found that the associations were related to spatial scales, indicating that a well-designed series of studies were needed to relate the disease risk with predictors across various study scales. Our approach provides a useful tool, especially in the field of vector-borne or environment-related diseases.

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