Individual Risk Of Infection Research Articles

Oral health and the presence of infectious microorganisms in hospitalized patients: a preliminary observational study

Objective Characterise oral health, and the presence in the oral cavity of pathogenic non-oral microorganisms potentially associated with nosocomial infections and antimicrobial resistance in non-intubated patients admitted to a Brazilian university hospital. Materials and methods An intraoral examination and oral swab were performed on hospitalized individuals at three different times, T1 (within 48 h of hospitalization), T2 (48 h after T1) and T3 (7 days after hospitalization). The oral health status was defined by the Oral Health Assessment Tool (OHAT) and Tongue Coating Status (TCS). The swabs were processed and microorganisms potentially related to nosocomial infections were phenotypically identified through colony morphology, staining and microscopy. Results The most prevalent microorganisms were Escherichia coli, Enterococcus spp., Enterobacter spp., Pseudomonas spp., Candida albicans and Staphylococcus aureus. The oral health status was considered median, and the tongue coating index was considered high throughout the study period. The prevalence of potentially pathogenic non-oral microorganisms was high and constant from the first 48 h to the seventh day of hospitalization. Conclusions The results point out that the mouth can act as a reservoir of epidemiologically important pathogens within hospital settings, even in patients without mechanical ventilation, thus increasing the risk of nosocomial infections in susceptible individuals. KEY MESSAGES The present study investigated the oral health status and the presence of pathogenic non-oral microorganisms in the oral cavity of patients hospitalized in the ward, non-intubated and mostly independent of self-care. The presence in the mouth of microorganisms related to the epidemiology of nosocomial infections and resistance to antimicrobials was high and constant from the first 48 h to the 7th day of hospitalization. The results of this study point out that the mouth can act as a reservoir of epidemiologically important pathogens within hospital settings even in patients without mechanical ventilation, increasing the risk of nosocomial infections in susceptible individuals.

Insomnia symptoms and risk of bloodstream infections: prospective data from the prospective population-based Nord-Trøndelag Health Study (HUNT), Norway.

Previous research suggests decreased immune function and increased risk of infections in individuals with insomnia. We examined the effect of insomnia symptoms on risk of bloodstream infections (BSIs) and BSI-related mortality in a population-based prospective study. A total of 53,536 participants in the second Norwegian Nord-Trøndelag Health Study (HUNT2) (1995-97) were linked to prospective data on clinically relevant BSIs until 2011. In Cox regression, we estimated hazard ratios (HRs) with 95% confidence intervals (CIs) for a first-time BSI and for BSI-related mortality (BSI registered ≤30 days prior to death) associated with insomnia symptoms. Compared with participants who reported "no symptoms", participants reporting having "difficulty initiating sleep" (DIS) often/almost every night had a HR for a first-time BSI of 1.14 (95% CI 0.96-1.34). Participants reporting "difficulties maintaining sleep" (DMS) often/almost every night had a HR of 1.19 (95% CI 1.01-1.40), whereas those having a feeling of "non-restorative sleep" once a week or more had a HR of 1.23 (95% CI 1.04-1.46). Participants frequently experiencing all three of the above symptoms had a HR of 1.39 (1.04-1.87), whilst those who had both DIS and DMS had a HR of 1.15 (0.93-1.41) and being troubled by insomnia symptoms to a degree that affected work performance was associated with a HR of 1.41 (95% CI 1.08-1.84). The HRs for BSI-related mortality suggest an increased risk with increasing insomnia symptoms, but the CIs are wide and inconclusive. We found that frequent insomnia symptoms and insomnia symptoms that affected work performance were associated with a weak positive increased risk of BSI.

Modeling the Probability of HIV Infection over Time in High-Risk Seronegative Participants Receiving Placebo in Five Randomized Double-Blind Placebo-Controlled HIV Pre-Exposure Prophylaxis Trials: A Patient-Level Pooled Analysis.

The World Health Organization recommends pre-exposure prophylaxis (PrEP) for individuals at substantial risk of HIV infection. The aim of this analysis is to quantify the individual risk of HIV infection over time, using a large database of high-risk individuals (n = 5583). We used data from placebo recipients in five phase III PrEP trials: iPrEx, conducted in men who have sex with men and transgender women; VOICE, conducted in young women at high sexual risk; Partners PrEP, conducted in HIV serodiscordant heterosexual couples; TDF2, conducted in high-risk heterosexual men and women; and BTS, conducted in persons who inject drugs. The probability of HIV infection over time was estimated using NONMEM7.4. We identified predictors of HIV risk and found a substantial difference in the risk of infection among and within trial populations, with each study including a mix of low, moderate, and high-risk individuals (p < 0.05). Persons who were female at birth were at a higher risk of HIV infection than people who were male at birth. Final models were integrated in a tool that can assess person-specific risk and simulate cumulative HIV risk over time. These models can be used to optimize future PrEP clinical trials by identifying potential participants at highest risk.

Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study

BackgroundReal-time prediction is key to prevention and control of infections associated with health-care settings. Contacts enable spread of many infections, yet most risk prediction frameworks fail to account for their dynamics. We developed, tested, and internationally validated a real-time machine-learning framework, incorporating dynamic patient-contact networks to predict hospital-onset COVID-19 infections (HOCIs) at the individual level.MethodsWe report an international retrospective cohort study of our framework, which extracted patient-contact networks from routine hospital data and combined network-derived variables with clinical and contextual information to predict individual infection risk. We trained and tested the framework on HOCIs using the data from 51 157 hospital inpatients admitted to a UK National Health Service hospital group (Imperial College Healthcare NHS Trust) between April 1, 2020, and April 1, 2021, intersecting the first two COVID-19 surges. We validated the framework using data from a Swiss hospital group (Department of Rehabilitation, Geneva University Hospitals) during a COVID-19 surge (from March 1 to May 31, 2020; 40 057 inpatients) and from the same UK group after COVID-19 surges (from April 2 to Aug 13, 2021; 43 375 inpatients). All inpatients with a bed allocation during the study periods were included in the computation of network-derived and contextual variables. In predicting patient-level HOCI risk, only inpatients spending 3 or more days in hospital during the study period were examined for HOCI acquisition risk.FindingsThe framework was highly predictive across test data with all variable types (area under the curve [AUC]-receiver operating characteristic curve [ROC] 0·89 [95% CI 0·88–0·90]) and similarly predictive using only contact-network variables (0·88 [0·86–0·90]). Prediction was reduced when using only hospital contextual (AUC-ROC 0·82 [95% CI 0·80–0·84]) or patient clinical (0·64 [0·62–0·66]) variables. A model with only three variables (ie, network closeness, direct contacts with infectious patients [network derived], and hospital COVID-19 prevalence [hospital contextual]) achieved AUC-ROC 0·85 (95% CI 0·82–0·88). Incorporating contact-network variables improved performance across both validation datasets (AUC-ROC in the Geneva dataset increased from 0·84 [95% CI 0·82–0·86] to 0·88 [0·86–0·90]; AUC-ROC in the UK post-surge dataset increased from 0·49 [0·46–0·52] to 0·68 [0·64–0·70]).InterpretationDynamic contact networks are robust predictors of individual patient risk of HOCIs. Their integration in clinical care could enhance individualised infection prevention and early diagnosis of COVID-19 and other nosocomial infections.FundingMedical Research Foundation, WHO, Engineering and Physical Sciences Research Council, National Institute for Health Research (NIHR), Swiss National Science Foundation, and German Research Foundation.

Risk of Legionellosis in residential areas around farms irrigating with municipal wastewater.

The conservation of freshwater is of both global and national importance, and in the United States, agriculture is one of the largest consumers of this resource. Reduction of the strain farming puts on local surface or groundwater is vital for ensuring resilience in the face of climate change, and one possible option is to irrigate with a combination of freshwater and reclaimed water from municipal wastewater treatment facilities. However, this wastewater can contain pathogens that are harmful to human health, such as Legionella pneumophila, which is a bacterium that can survive aerosolization and airborne transportation and cause severe pneumonia when inhaled. To assess an individual adult's risk of infection with L. pneumophila from a single exposure to agricultural spray irrigation, a quantitative microbial risk assessment was conducted for a scenario of spray irrigation in central Illinois, for the growing seasons in 2017, 2018, and 2019. The assessment found that the mean risk of infection for a single exposure exceeded the safety threshold of 10-6 infections/exposure up to 1km from a low-pressure irrigator and up to 2km from a high-pressure irrigator, although no median risk exceeded the threshold for any distance or irrigator pressure. These findings suggest that spray irrigation with treated municipal wastewater could be a viable option for reducing freshwater consumption in Midwest farming, as long as irrigation on windy days is avoided and close proximity to the active irrigator is limited.

Risk of Severe Coronavirus Disease 2019 Disease in Individuals With Down Syndrome: A Matched Cohort Study From a Large, Integrated Health Care System.

BackgroundDown syndrome (DS) is associated with an increased risk of infections attributed to immune defects. Whether individuals with DS are at an increased risk of severe coronavirus disease 2019 (COVID-19) remains unclear.MethodsIn a matched cohort study, we evaluated the risk of COVID-19 infection and severe COVID-19 disease in individuals with DS and their matched counterparts in a pre-COVID-19 vaccination period at Kaiser Permanente Southern California. Multivariable Cox proportion hazard regression was used to investigate associations between DS and risk of COVID-19 infection and severe COVID-19 disease.ResultsOur cohort included 2541 individuals with DS and 10 164 without DS matched on age, sex, and race/ethnicity (51.6% female, 53.3% Hispanic, median age 25 years [interquartile range, 14–38]). Although the rate of COVID-19 infection in individuals with DS was 32% lower than their matched counterparts (adjusted hazard ratio [aHR], 0.68; 95% confidence interval [CI], .56–.83), the rate of severe COVID-19 disease was 6-fold higher (aHR, 6.14; 95% CI, 1.87–20.16).ConclusionsAlthough the risk of COVID-19 infection is lower, the risk of severe disease is higher in individuals with DS compared with their matched counterparts. Better infection monitoring, early treatment, and promotion of vaccine for COVID-19 are warranted for DS populations.

A compelling demonstration of why traditional statistical regression models cannot be used to identify risk factors from case data on infectious diseases: a simulation study

BackgroundRegression models are often used to explain the relative risk of infectious diseases among groups. For example, overrepresentation of immigrants among COVID-19 cases has been found in multiple countries. Several studies apply regression models to investigate whether different risk factors can explain this overrepresentation among immigrants without considering dependence between the cases.MethodsWe study the appropriateness of traditional statistical regression methods for identifying risk factors for infectious diseases, by a simulation study. We model infectious disease spread by a simple, population-structured version of an SIR (susceptible-infected-recovered)-model, which is one of the most famous and well-established models for infectious disease spread. The population is thus divided into different sub-groups. We vary the contact structure between the sub-groups of the population. We analyse the relation between individual-level risk of infection and group-level relative risk. We analyse whether Poisson regression estimators can capture the true, underlying parameters of transmission. We assess both the quantitative and qualitative accuracy of the estimated regression coefficients.ResultsWe illustrate that there is no clear relationship between differences in individual characteristics and group-level overrepresentation —small differences on the individual level can result in arbitrarily high overrepresentation. We demonstrate that individual risk of infection cannot be properly defined without simultaneous specification of the infection level of the population. We argue that the estimated regression coefficients are not interpretable and show that it is not possible to adjust for other variables by standard regression methods. Finally, we illustrate that regression models can result in the significance of variables unrelated to infection risk in the constructed simulation example (e.g. ethnicity), particularly when a large proportion of contacts is within the same group.ConclusionsTraditional regression models which are valid for modelling risk between groups for non-communicable diseases are not valid for infectious diseases. By applying such methods to identify risk factors of infectious diseases, one risks ending up with wrong conclusions. Output from such analyses should therefore be treated with great caution.

A novel textual track-data-based approach for estimating individual infection risk of COVID-19.

With the recurrence of infectious diseases caused by coronaviruses, which pose a significant threat to human health, there is an unprecedented urgency to devise an effective method to identify and assess who is most at risk of contracting these diseases. China has successfully controlled the spread of COVID‐19 through the disclosure of track data belonging to diagnosed patients. This paper proposes a novel textual track‐data‐based approach for individual infection risk measurement. The proposed approach is divided into three steps. First, track features are extracted from track data to build a general portrait of COVID‐19 patients. Then, based on the extracted track features, we construct an infection risk indicator system to calculate the infection risk index (IRI). Finally, individuals are divided into different infection risk categories based on the IRI values. By doing so, the proposed approach can determine the risk of an individual contracting COVID‐19, which facilitates the identification of high‐risk populations. Thus, the proposed approach can be used for risk prevention and control of COVID‐19. In the empirical analysis, we comprehensively collected 9455 pieces of track data from 20 January 2020 to 30 July 2020, covering 32 provinces/provincial municipalities in China. The empirical results show that the Chinese COVID‐19 patients have six key features that indicate infection risk: place, region, close‐contact person, contact manner, travel mode, and symptom. The IRI values for all 9455 patients vary from 0 to 43.19. Individuals are classified into the following five infection risk categories: low, moderate‐low, moderate, moderate‐high, and high risk.

Individual, Household, and Community Drivers of Dengue Virus Infection Risk in Kamphaeng Phet Province, Thailand.

Dengue virus (DENV) often circulates endemically. In such settings with high levels of transmission, it remains unclear whether there are risk factors that alter individual infection risk. We tested blood taken from individuals living in multigenerational households in Kamphaeng Phet province, Thailand for DENV antibodies (N = 2364, mean age 31 years). Seropositivity ranged from 45.4% among those 1-5 years old to 99.5% for those >30 years. Using spatially explicit catalytic models, we estimated that 11.8% of the susceptible population gets infected annually. We found that 37.5% of the variance in seropositivity was explained by unmeasured household-level effects with only 4.2% explained by spatial differences between households. The serostatus of individuals from the same household remained significantly correlated even when separated by up to 15 years in age. These findings show that despite highly endemic transmission, persistent differences in infection risk exist across households, the reasons for which remain unclear.

Assessment of SARS-CoV-2 airborne infection transmission risk in public buses

Public transport environments are thought to play a key role in the spread of SARS-CoV-2 worldwide. Indeed, high crowding indexes (i.e. high numbers of people relative to the vehicle size), inadequate clean air supply, and frequent extended exposure durations make transport environments potential hotspots for transmission of respiratory infections. During the COVID-19 pandemic, generic mitigation measures (e.g. physical distancing) have been applied without also considering the airborne transmission route. This is due to the lack of quantified data about airborne contagion risk in transport environments.In this study, we apply a novel combination of close proximity and room-scale risk assessment approaches for people sharing public transport environments to predict their contagion risk due to SARS-CoV-2 respiratory infection. In particular, the individual infection risk of susceptible subjects and the transmissibility of SARS-CoV-2 (expressed through the reproduction number) are evaluated for two types of buses, differing in terms of exposure time and crowding index: urban and long-distance buses. Infection risk and reproduction number are calculated for different scenarios as a function of the ventilation rates (both measured and estimated according to standards), crowding indexes, and travel times. The results show that for urban buses, the close proximity contribution significantly affects the maximum occupancy to maintain a reproductive number of <1. In particular, full occupancy of the bus would be permitted only for an infected subject breathing, whereas for an infected subject speaking, masking would be required. For long-distance buses, full occupancy of the bus can be maintained only if specific mitigation solutions are simultaneously applied. For example, for an infected person speaking for 1 h, appropriate filtration of the recirculated air and simultaneous use of FFP2 masks would permit full occupancy of the bus for a period of almost 8 h. Otherwise, a high percentage of immunized persons (>80%) would be needed.

Reconstructing antibody dynamics to estimate the risk of influenza virus infection

For >70 years, a 4-fold or greater rise in antibody titer has been used to confirm influenza virus infections in paired sera, despite recognition that this heuristic can lack sensitivity. Here we analyze with a novel Bayesian model a large cohort of 2353 individuals followed for up to 5 years in Hong Kong to characterize influenza antibody dynamics and develop an algorithm to improve the identification of influenza virus infections. After infection, we estimate that hemagglutination-inhibiting (HAI) titers were boosted by 16-fold on average and subsequently decrease by 14% per year. In six epidemics, the infection risks for adults were 3%–19% while the infection risks for children were 1.6–4.4 times higher than that of younger adults. Every two-fold increase in pre-epidemic HAI titer was associated with 19%–58% protection against infection. Our inferential framework clarifies the contributions of age and pre-epidemic HAI titers to characterize individual infection risk.

Can naive Bayes classifier predict infection in a close contact of COVID-19? A comparative test for predictability of the predictive model and healthcare workers in Japan

BackgroundThose who are found in close contact with COVID-19 patients and are also negative by polymerase chain reaction (PCR) test may act without waiting for the incubation period to elapse, can become infectious and spread the infection. MethodsA machine learning model that can evaluate the risk of infection in close contact with COVID-19 patients within the incubation period from the contact status reported from the index case was created using posterior probabilities. To confirm actual predictability, a verification test was conducted on 169 new close contacts, and the machine learning model was compared with four experienced healthcare workers for the predictability. ResultsIn a verification test, 33 of the 169 contacts were infected with COVID-19 during the incubation period, and 13 of 33 were negative on initial PCR test, after that the disease developed and their PCR test became positive. The machine learning model predicted the eventual infection in 12 of 13 patients who had negative results on the initial PCR test. In the verification test, the sensitivity of the machine learning model was 0.879 and the specificity was 0.588. The mean−standard deviation of the sensitivity and the specificity of the four health care workers was 0.568 (0.230) for sensitivity and 0.689 (0.103) for specificity. ConclusionIf it is possible to convey that individual risk of infection, the close contact may take suppressive action during the incubation period regardless of the result of the initial PCR test, thereby preventing secondary spread of infection.

Risk of severe COVID-19 infection in individuals with severe mental disorders, substance use disorders, and common mental disorders

ObjectiveTo evaluate the risk of severe COVID-19 in individuals with severe mental disorders, substance use disorders, and common mental disorders in the total adult population of Region Stockholm (N = 1,516,270), and to explore possible underlying mechanisms to the increased risk. MethodsIn this prospective cohort study, we examined the risk of hospitalization and treatment in an intensive care unit (ICU) with COVID-19, and death from COVID-19 for individuals with mental disorders. Associations were step by step adjusted for (1) sociodemographic/economic factors, (2) indicators of virus exposure, (3) somatic conditions, and (4) psychopharmacological treatment. ResultsIn model 1 (adjusted for age, sex and living in a care home for elderly people), people with a mental disorder had increased risks for inpatient care (HR = 1.5), ICU care (HR = 1.5), and mortality (HR = 1.4) from COVID-19. There was an increased risk of dying from COVID-19 in all subgroups of mental disorders, particularly in people with a severe mental disorder (HR = 1.9). Different covariates had different effects on the association depending on the outcome and on sex, age, or psychiatric diagnosis of the participants. ConclusionPeople with mental disorders have an increased risk of severe COVID-19, including mortality. The increased risk was partly explained by the examined covariates.

Population preferences for non-pharmaceutical interventions to control the SARS-CoV-2 pandemic: trade-offs among public health, individual rights, and economics

ProblemPolicymakers must decide on interventions to control the pandemic. These decisions are driven by weighing the risks and benefits of various non-pharmaceutical intervention alternatives. Due to the nature of the pandemic, these decisions are not based on sufficient evidence regarding the effects, nor are decision-makers informed about the willingness of populations to accept the economic and health risks associated with different policy options. This empirical study seeks to reduce uncertainty by measuring population preferences for non-pharmaceutical interventions.MethodsAn online-based discrete choice experiment (DCE) was conducted to elicit population preferences. Respondents were asked to choose between three pandemic scenarios with different interventions and impacts of the Corona pandemic. In addition, Best–worst scaling (BWS) was used to analyze the impact of the duration of individual interventions on people’s acceptance. The marginal rate of substitution was applied to estimate willingness-to-accept (WTA) for each intervention and effect by risk of infection.ResultsData from 3006 respondents were included in the analysis. The DCE showed, economic effect of non-pharmaceutical measures had a large impact on choice decisions for or against specific lockdown scenarios. Individual income decreases had the most impact. Excess mortality and individual risk of infection were also important factors influencing choice decisions. Curfews, contact restrictions, facility closures, personal data transmissions, and mandatory masking in public had a lesser impact. However, significant standard deviations in the random parameter logit model (RPL) indicated heterogeneities in the study population. The BWS results showed that short-term restrictions were more likely to be accepted than long-term restrictions. According to WTA estimates, people would be willing to accept a greater risk of infection to avoid loss of income.DiscussionThe results can be used to determine which consequences of pandemic measures would be more severe for the population. For example, the results show that citizens want to limit the decline in individual income during pandemic measures. Participation in preference studies can also inform citizens about potential tradeoffs that decision-makers face in current and future decisions during a pandemic. Knowledge of the population’s preferences will help inform decisions that consider people’s perspectives and expectations for the future.Survey results can inform decision-makers about the extent to which the population is willing to accept certain lockdown measures, such as curfews, contact restrictions, lockdowns, or mandatory masks.

A novel nomogram based on nutritional and immune status predicting postoperative intra-abdominal infection in colorectal cancer.

This study aimed to investigate independent risk factors for intra-abdominal infection and to construct a nomogram to identify colorectal patients at a high risk of intra-abdominal infection. Clinical data of patients undergoing radical resection of colorectal cancer from January 2019 to December 2021 were retrospectively included in this study. Patients were divided into two groups according to postoperative intra-abdominal infection. Clinicopathological indicators, intraoperative conditions, and postoperative complications were compared between the two groups, logistic regression was used to look for independent risk factors for intra-abdominal infection, and a nomogram was constructed based on independent risk factors. 402 colorectal cancer patients were enrolled in this study, and 46 patients (11.4%) developed intra- abdominal infections after surgery. The independent risk factors for intra-abdominal infection were preoperative albumin, lymphocyte-white cell ratio (LWR) <0.17, low subcutaneous fat mass, and low skeletal muscle mass. The nomogram model for intra-abdominal infection was able to reliably quantify the risk of intraabdominal infection with strong optimism-adjusted discrimination (concordance index=0.931). Furthermore, decision curve analysis showed that the nomogram was clinically useful and had a better discriminative ability to recognize patients at high risk than the risk factors alone. In conclusion, we found that preoperative albumin, LWR <0.17, low subcutaneous fat mass, and low skeletal muscle mass were significantly correlated with intra-abdominal infection. Our nomogram was a simple and practical instrument to quantify the individual risk of intra-abdominal infection.

Weight-based cefuroxime dosing provides comparable orthopedic target tissue concentrations between weight groups - a microdialysis porcine study.

Antibiotic prophylaxis is a key element in prevention of surgical site infections. For the majority of orthopedic procedures, antibiotic administration follows fixed dosing regimens irrespective of weight. However, this may result in insufficient antibiotic target tissue concentrations and higher risk of surgical site infections in obese individuals. The aim of this study was to investigate the effect of weight-based cefuroxime dosing on plasma and target tissue concentrations. Eighteen female pigs were allocated into three groups differentiated by weight: 53-57 kg, 73-77 kg, and 93-97 kg. Microdialysis catheters were placed for continuous sampling in bone, muscle, and subcutaneous tissue during an 8h sampling interval. Blood samples were collected as reference. Cefuroxime was administered intravenously as a bolus according to weight (20 mg/kg). The primary endpoint was the time above the cefuroxime minimal inhibitory concentration for Staphylococcus aureus (T > MIC (4 μg/mL)). Comparable target tissue T > MICs (4 μg/mL) were found between weight groups. Mean T > MIC ranged between 116-137 min for plasma, 118-154 min for bone, 109-146 min for the skeletal muscle, and 117-165 min for subcutaneous tissue across the groups. Weight-based cefuroxime (20 mg/kg) dosing approach provides comparable perioperative plasma and target tissue T > MIC (4 μg/mL) in animals between 50-100 kg body weight, and thus a comparable prophylaxis of surgical site infections.

Differences in COVID-19 Risk by Race and County-Level Social Determinants of Health among Veterans.

COVID-19 disparities by area-level social determinants of health (SDH) have been a significant public health concern and may also be impacting U.S. Veterans. This retrospective analysis was designed to inform optimal care and prevention strategies at the U.S. Department of Veterans Affairs (VA) and utilized COVID-19 data from the VAs EHR and geographically linked county-level data from 18 area-based socioeconomic measures. The risk of testing positive with Veterans’ county-level SDHs, adjusting for demographics, comorbidities, and facility characteristics, was calculated using generalized linear models. We found an exposure–response relationship whereby individual COVID-19 infection risk increased with each increasing quartile of adverse county-level SDH, such as the percentage of residents in a county without a college degree, eligible for Medicaid, and living in crowded housing.

People With SUDs at Higher Risk of Breakthrough COVID-19 Infection

People With SUDs at Higher Risk of Breakthrough COVID-19 Infection

Neighbourhood-level income and Zika virus infection during pregnancy in Recife, Pernambuco, Brazil: an ecological perspective, 2015–2017

Zika virus (ZIKV) infections during pregnancy can lead to adverse neurodevelopmental and clinical outcomes in congenitally infected offspring. As the city of Recife in Pernambuco State, Brazil—the epicentre of the...

Development of Risk Assessment Index System on HIV infection among young students based on Delphi method

Objective: To develop a Risk Assessment Index System (RAIS) on HIV infection among young students based on Delphi method and to provide individual HIV infection risk assessment, targeted prevention and control measures. Methods: Delphi method was applied to determine the index system and weight of the assessment tool through three rounds of expert consultation and overall consideration of opinions and suggestions from 19 experts. Results: The positivity coefficients of three rounds of expert consultation were 100%. The authority coefficient of experts was between 0.887 and 0.945. The Kendall's W coefficients through first, second and third round specialist consultation was 0.379, 0.329 and 0.248, respectively (all P<0.001). The coefficients of variation in the third round were all less than 0.25, indicating that experts' opinion tend to be consistent and the results are highly reliable. The HIV infection risk assessment index system among young students consisted of 7 first grade indices and 54 second grade indices, of which weight was calculated. Conclusions: The RAIS on HIV infection for young students was initially established based on Delphi method, and could be used in the development of HIV infection risk assessment tools for personalized prevention and intervention among young students. However, the reliability, validity and effect of this assessment index system need to be further evaluated.