Surveillance Dataset Research Articles

A Lightweight Person Detector for Surveillance Footage Based on YOLOv8n

To enable person detection tasks in surveillance footage to be deployed on edge devices and their efficient performance in resource-constrained environments in real-time, a lightweight person detection model based on YOLOv8n was proposed. This model balances high accuracy with low computational cost and parameter size. First, the MSBlock module was introduced into YOLOv8n. Then, a series of modifications were made to the MSBlock structure. Next, a heterogeneous PAFPN with improved MSBlock was formed using heterogeneous convolution kernels. Finally, AKConv, a variable kernel convolution, was applied to further reduce the number of parameters and the computational cost while improving accuracy. A series of experiments demonstrated that, due to these improvements, the proposed lightweight model achieved a reduction of nearly 10% in parameter size and 5% in the floating-point computational cost compared to the original YOLOv8n. Additionally, on a custom surveillance dataset, the model shows a 1.4% improvement in mAP@0.5:0.95, and on a more complex subset of the PASVOC public dataset, the model achieved a 2.8% improvement in mAP@0.5 and a 1.2% improvement in mAP@0.5:0.95, proving the high accuracy and generalization ability of the improved lightweight model.

Spatial and temporal analysis of West Nile virus infection in mosquito and human populations based on arboviral detection testing data

West Nile virus (WNV) is a mosquito-borne zoonotic flavivirus which often causes asymptomatic infection in humans but may develop into a deadly neuroinvasive disease. In this study, we aimed to investigate variables potentially associated with human WNV infection using human and mosquito WNV surveillance and monitoring datasets, established over 20 years, from 2003 to 2022, across the province of Ontario, Canada. We combined climatic and geographic data, mosquito surveillance data (n = 3010 sites), blood donation arboviral detection testing data in the human population, and demographic and socio-economic data from Canadian population censuses. We hypothesized that spatio-temporal indices related to mosquito vector habitat and phenology, in addition to human demographic and socio-economic factors, were associated with WNV infection in the human population. Our results show that high habitat suitability of the main WNV vector in southerly locations of this region, Cx. pipiens/restuans (IRR = 2.0), and variables related to lower income (IRR = 2.8), and shelter infrastructure spending (IRR = 0.7), were key risk factors associated with WNV infection among blood donors from 2003 to 2022 across Ontario (R2 = 0.67). These results may inform points of entry for practical intervention aimed at reducing risk of mosquito-borne pathogens in Canada.

The current state of data to estimate prevalence and severity of food allergies in the United States

Currently, in the United States, there is no comprehensive surveillance system to collect data on food allergies; however, prevalence and severity data are critical to quantify the burden of food allergies, develop evidence-based control measures, detect emerging issues, and monitor trends. To address this gap, we conducted a literature search, and expert interviews to identify surveillance systems and datasets that could be used to estimate the prevalence and severity of food allergies in the United States overall and for specific foods. Inclusion and exclusion criteria were developed and used to screen each data source. Selected articles were evaluated according to quality parameters to identify the most appropriate studies. Most studies estimated prevalence of food allergies in children, investigated multiple foods, and used surveys to collect self-reported data. Despite the high quality, robust study designs, and comprehensive survey instruments that currently exist, the studies identified do not provide sufficiently recent data to estimate the current burden of food allergies in the country. Studies lack consistencies across the years making the analysis of trends over time a challenge. National surveys conducted by Northwestern University in 2009/2010 and 2015/2016 represented the best available data; however, these data are likely outdated and are limited in assessing temporal food allergy trends. Data to accurately estimate the current prevalence and severity of food allergies and related trends is lacking. U.S. public health agencies should explore the development of a comprehensive surveillance program to address this gap and help inform evidence-based policies in food allergy management and prevention.

Abnormal events detection using spatio-temporal saliency descriptor and fuzzy representation analysis

In recent years, the research on abnormal events detection is a significant work in surveillance video. Many researchers have been attracted by this work for the past two decades. As a result, several abnormal event detection approaches have been developed. Though several approaches have been used in the field still many problems remain to get the abnormal events detection accuracy. Moreover, many feature representations have limited capability to describe the content since several research works applied hand craft features, this type of feature can work in limited problems. To overcome this problem, this paper introduced the novel feature descriptor namely STS-D (Spatial and Temporal Saliency - Descriptor), which includes spatial and temporal information of the objects. This feature descriptor efficiently describes the shape and speed of the object. To find the anomaly score, fuzzy representation is modeled to efficiently differentiate the normal and abnormal events using fuzzy membership degree. The benchmark datasets UMN, UCSD Ped1 and Ped2 and real time roadway surveillance dataset are used to evaluate the performance of the proposed approach. Also, several existing abnormal events detection approaches are used to compare with the proposed method to evaluate the effectiveness of the proposed work.

Implantable cardioverter defibrillators in people dying with cancer: A SEER-Medicare analysis of ICD prevalence and association with aggressive end-of-life care.

The shared risk profiles for cancer and heart disease suggest many individuals with cancer may have an implantable cardioverter defibrillator (ICD). ICDs can have dramatic cancer end-of-life care implications including painful and distressing shocks. ICD prevalence and association with aggressive end-of-life care among individuals with breast, colorectal, and pancreatic cancer was evaluated using the Surveillance, Epidemiology, and End Results-Medicare dataset. A total of 37,306 Medicare beneficiaries aged ≥66 years with stage 3 or 4 cancer who died between 2005 and 2016 were identified. ICD prevalence, ICD-related care utilization that might present opportunities to discuss end-of-life implications and association with aggressive end-of-life care (>1 emergency department visit, intensive care unit admission, >1 hospitalization, terminal hospitalization, chemotherapy, and invasive or life-extending procedures) in the last month of life was assessed using multivariable logistic regression. Among cancer decedents, 6% had an ICD. More individuals with an ICD (31%) died in the hospital than individuals without an ICD (25%; p<.001). Half (46%) of individuals with an ICD had device programming or interrogation visits that could be an opportunity for device discussion. In adjusted models, ICD presence was associated with higher odds of every indicator of aggressive end-of-life care other than chemotherapy. Many older cancer decedents in the United States had an ICD, and those with ICDs received more aggressive care at the end of life. Results suggest there are opportunities to discuss ICD and goals of care, raise awareness and encourage shared decision-making for this population to ensure goal-concordant care, and improve end-of-life care quality.

Integrating information from historical data into mechanistic models for influenza forecasting.

Seasonal influenza causes significant annual morbidity and mortality worldwide. In France, it is estimated that, on average, 2 million individuals consult their GP for influenza-like-illness (ILI) every year. Traditionally, mathematical models used for epidemic forecasting can either include parameters capturing the infection process (mechanistic or compartmental models) or rely on time series analysis approaches that do not make mechanistic assumptions (statistical or phenomenological models). While the latter make extensive use of past epidemic data, mechanistic models are usually independently initialized in each season. As a result, forecasts from such models can contain trajectories that are vastly different from past epidemics. We developed a mechanistic model that takes into account epidemic data from training seasons when producing forecasts. The parameters of the model are estimated via a first particle filter running on the observed data. A second particle filter is then used to produce forecasts compatible with epidemic trajectories from the training set. The model was calibrated and tested on 35 years' worth of surveillance data from the French Sentinelles Network, representing the weekly number of patients consulting for ILI over the period 1985-2019. Our results show that the new method improves upon standard mechanistic approaches. In particular, when retrospectively tested on the available data, our model provides increased accuracy for short-term forecasts (from one to four weeks into the future) and peak timing and intensity. Our new approach for epidemic forecasting allows the integration of key strengths of the statistical approach into the mechanistic modelling framework and represents an attempt to provide accurate forecasts by making full use of the rich surveillance dataset collected in France since 1985.

A Statistical Model for Inference of Recent and Incident HIV Infection Using Surveillance Data on Individuals Newly Diagnosed With HIV Infection in Scotland

Background: To inform global ambitions to end AIDS, evaluation of progress toward HIV incidence reduction requires robust methods to measure incidence. Although HIV diagnosis date in routine HIV/AIDS surveillance systems are often used as a surrogate marker for incidence, it can be misleading if acquisition of transmission occurred years before testing. Other information present in data such as antibody testing dates, avidity testing result, and CD4 counts can assist, but the degree of missing data is often prohibitive. Methods: We constructed a Bayesian statistical model to estimate the annual proportion of first ever HIV diagnoses in Scotland (period 2015–2019) that represent recent HIV infection (ie, occurring within the previous 3–4 months) and incident HIV infection (ie, infection within the previous 12 months), by synthesizing avidity testing results and surveillance data on the interval since last negative HIV test. Results: Over the 5-year analysis period, the model-estimated proportion of incident infection was 43.9% (95% CI: 40.9 to 47.0), and the proportion of recent HIV infection was 21.6% (95% CI: 19.1 to 24.1). Among the mode of HIV acquisition categories, the highest proportion of recent infection was estimated for people who inject drugs: 27.4% (95% CI: 20.4 to 34.4). Conclusions: The Bayesian approach is appropriate for the high prevalence of missing data that can occur in routine surveillance data sets. The proposed model will aid countries in improving their understanding of the number of people who have recently acquired their infection, which is needed to progress toward the goal of HIV transmission elimination.

Night time Surveillance in Communication Using YOLOv3

Nighttime surveillance presents significant challenges due to low visibility, varying lighting conditions, and interference from artificial light sources. Hence, this study proposed an effective object detection framework using the YOLOv3 model to enhance real-time monitoring in night surveillance applications, specifically within communication and security systems. YOLOv3's architecture, with its multi-scale detection and use of predefined anchor boxes, enables robust detection of objects under low-light environments and amidst light interference from vehicles and streetlights. The proposed system is tested on a night surveillance dataset, where it demonstrates high precision and speed in identifying objects, making it suitable for real-time applications. With Mean Average Precision (mAP) 87.9%, YOLOv3 effectively balances detection accuracy with inference time, ensuring minimal latency in live surveillance feeds. The results indicate that YOLOv3 outperforms traditional models such as Faster RCNN, particularly in detecting small objects under poor illumination. This approach offers a reliable solution for enhancing communication and security systems in nighttime surveillance scenarios.

The Role of Residential Segregation in Treatment and Outcomes of Ductal Carcinoma In Situ of the Breast.

It remains unclear whether residential segregation impacts on clinical treatment and outcomes for ductal carcinoma in situ (DCIS), a nonobligate precursor to invasive breast cancer (IBC). This population-based retrospective cohort study included adult non-Hispanic White and Black women diagnosed with unilateral DCIS between January 1990 and December 2015, followed through December 2016, and identified from the Surveillance, Epidemiology, and End Results dataset. County-level racialized economic segregation was measured using the Index of Concentration at the Extremes. Multilevel logistic regression and Cox proportional hazards regression accounting for county-level clustering were used to estimate the ORs of local treatment and HRs of subsequent IBC and mortality. Of 103,898 cases, mean age was 59.5 years, 12.5% were non-Hispanic Black, 87.5% were non-Hispanic White, 97.5% underwent surgery, 64.5% received radiotherapy following breast-conserving surgery (BCS), 7.1% developed IBC, and 18.6% died from all causes. Among women living in the least versus most privileged counties, we observed higher odds of receiving mastectomy [vs. BCS; OR = 1.51; 95% confidence interval (CI), 1.35-1.69; Ptrend < 0.001] and radiotherapy following BCS(OR = 1.27; 95% CI, 1.07-1.51; Ptrend < 0.01); the risk was higher in subsequent ipsilateral IBC (HR = 1.16; 95% CI, 1.02-1.32; Ptrend = 0.04), not in breast cancer-specific mortality (HR = 1.04; 95% CI, 0.88-1.23; Ptrend = 0.56). The results provide evidence for disparities in clinical treatment for DCIS and prognostic outcomes among women in racially and economically segregated counties. Our findings may inform geographically targeted multilevel interventions to reduce breast cancer burden and improve breast cancer care and equity.

Personnel Monitoring in Shipboard Surveillance Using Improved Multi-Object Detection and Tracking Algorithm.

Detecting and tracking personnel onboard is an important measure to prevent ships from being invaded by outsiders and ensure ship security. Ships are characterized by more cabins, numerous equipment, and dense personnel, so there are problems such as unpredictable personnel trajectories, frequent occlusions, and many small targets, which lead to the poor performance of existing multi-target-tracking algorithms on shipboard surveillance videos. This study conducts research in the context of onboard surveillance and proposes a multi-object detection and tracking algorithm for anti-intrusion on ships. First, this study designs the BR-YOLO network to provide high-quality object-detection results for the tracking algorithm. The shallow layers of its backbone network use the BiFormer module to capture dependencies between distant objects and reduce information loss. Second, the improved C2f module is used in the deep layer of BR-YOLO to introduce the RepGhost structure to achieve model lightweighting through reparameterization. Then, the Part OSNet network is proposed, which uses different pooling branches to focus on multi-scale features, including part-level features, thereby obtaining strong Re-ID feature representations and providing richer appearance information for personnel tracking. Finally, by integrating the appearance information for association matching, the tracking trajectory is generated in Tracking-By-Detection mode and validated on the self-constructed shipboard surveillance dataset. The experimental results show that the algorithm in this paper is effective in shipboard surveillance. Compared with the present mainstream algorithms, the MOTA, HOTP, and IDF1 are enhanced by about 10 percentage points, the MOTP is enhanced by about 7 percentage points, and IDs are also significantly reduced, which is of great practical significance for the prevention of intrusion by ship personnel.

P068 IDENTIFYING THRESHOLD BIAS IN RECORDED BLOOD PRESSURE

Background and Objective: Even when blood pressure (BP) is measured accurately, BP may be misrecorded. “Threshold bias” occurs when BP measurements are intentionally or unintentionally recorded at values just below BP control goals and may occur due to incentives to improve BP control or to avoid medication titration. Our objectives were to 1) examine the potential for threshold bias in datasets with and without a BP control goal from two countries and 2) propose a method for detecting threshold bias. Methods: We examined the distribution of recorded BP measurements among individuals taking antihypertensive medication in clinical practice or trials with a BP goal &lt;140/90 mmHg and population-based surveillance studies without a BP goal from Bangladesh and the US. Then, we explored whether surveillance data could be used as a reference to evaluate the likelihood of threshold bias in clinical data. We calculated the ratio of BP measurements recorded 10 mmHg below versus above thresholds of interest (i.e., the “threshold ratio,” or number of systolic BP [SBP] measurements 130-139 mmHg over 140-149 mmHg) and used bootstrapping to determine the probability of observing ratios that may indicate threshold bias. We compared the “threshold ratio” from clinical data to the probabilities determined from surveillance data. Results: Visually, we observed a cluster of measurements below SBP 140 mmHg in the clinical but not the surveillance datasets from Bangladesh and the US (Figure). Based on 10,000 random draws of 100 observations in the Bangladesh surveillance dataset, the probability of observing a “threshold ratio” of 3.56 in the clinical dataset was &lt;2%, indicating a high probability of threshold bias. Using the same procedure in the US surveillance dataset, the probability of observing a “threshold ratio” of 2.26 in the clinical dataset was &lt;10%, indicating potential concern. Conclusions: Threshold bias may be an underrecognized but common problem in clinical settings with a BP goal. Our proposed approach to detecting threshold bias should be tested in other settings.

Early-onset cancer incidence in the United States by race/ethnicity between 2011 and 2020

We characterized trends in early onset (aged 20–49) cancer incidence by race/ethnicity and sex using the 2011–2020 Surveillance, Epidemiology, and End Results (SEER) Program dataset. We estimated age-standardized cancer incidence rates, incidence rate ratios (IRR), and annual percentage changes (APC) with 95 % confidence intervals (CI). During the time period examined, cancer incidence increased for female breast (APC: 0.64; 95 % CI: 0.10, 1.20), female colorectal (APC: 2.16; 95 % CI: 1.22, 3.10), and male colorectal (APC: 2.49; 95 % CI: 1.81, 3.19) cancer. Among racial/ethnic groups examined, Hispanic individuals had the largest increases in female all sites (APC: 1.31; 95 % CI: 0.38, 2.25), female breast (APC: 1.04; 95 % CI: 0.29, 1.81), and female (APC: 4.67; 95 % Cl: 3.07, 6.30) and male (APC: 3.53; 95 % CI: 2.58, 4.49) colorectal cancer incidence. Further research is needed to clarify the causal mechanisms driving these patterns.

Antibiotic Use and Survival in Patients With Late-Stage NSCLC Treated With Chemoimmunotherapy

IntroductionImmunotherapy has improved survival in patients with advanced NSCLC. Efficacy may decrease when patients are treated with antibiotics, possibly due to gut microbiome disruption, but few studies have investigated this using real-world, patient-level populations in the United States. MethodsWe have analyzed antibiotic use in patients with stage IV first, primary NSCLC diagnosed in 2015 and treated with chemotherapy or chemoimmunotherapy, drawn from the Surveillance, Epidemiology, and End Results-Medicare data set. Patients had to have continuous part A, part B, and part D Medicare coverage. Survival was determined through Kaplan-Meier and Cox proportional hazards models. All data analyses were performed using SAS. ResultsThe study included 788 patients, 440 (56%) of whom received antibiotics within 2 months before or after starting systemic treatment. The median follow-up time was 11.64 months. There was a statistically significant difference in survival for patients who received antibiotics (p = 0.007) and who had more than 1 round of antibiotics versus zero or 1 round (p ≤ 0.0001). After adjustment, receipt of antibiotics (hazard ratio [HR]adj: 1.17, 95% confidence interval [CI]: 0.99–1.37) and receipt of multiple rounds of antibiotics (HRadj: 1.35, 95% CI: 1.14–1.60) were statistically significantly associated with worse survival. Among just those receiving chemoimmunotherapy (n = 203; 26%), there was still an increased risk of death for those receiving multiple antibiotic rounds (HRadj: 1.52, 95% CI: 1.09–2.13). ConclusionsAntibiotic use concurrent with chemoimmunotherapy seems to be associated with worse survival. This is more pronounced when more cycles of antibiotics are given. IRB approval numberSTUDY-19-00500.

Canada's provincial COVID-19 pandemic modelling efforts: A review of mathematical models and their impacts on the responses.

Mathematical modelling played an important role in the public health response to COVID-19 in Canada. Variability in epidemic trajectories, modelling approaches, and data infrastructure across provinces provides a unique opportunity to understand the factors that shaped modelling strategies. Provinces implemented stringent pandemic interventions to mitigate SARS-CoV-2 transmission, considering evidence from epidemic models. This study aimed to summarize provincial COVID-19 modelling efforts. We identified modelling teams working with provincial decision-makers, through referrals and membership in Canadian modelling networks. Information on models, data sources, and knowledge translation were abstracted using standardized instruments. We obtained information from six provinces. For provinces with sustained community transmission, initial modelling efforts focused on projecting epidemic trajectoriesand healthcare demands, and evaluating impacts of proposed interventions. In provinces with low community transmission, models emphasized quantifying importation risks. Most of the models were compartmental and deterministic, with projection horizons of a few weeks. Models were updated regularly or replaced by new ones, adapting to changing local epidemic dynamics, pathogen characteristics, vaccines, and requests from public health. Surveillance datasets for cases, hospitalizations and deaths, and serological studies were the main data sources for model calibration. Access to data for modelling and the structure for knowledge translation differed markedly between provinces. Provincial modelling efforts during the COVID-19 pandemic were tailored to local contexts and modulated by available resources. Strengthening Canadian modelling capacity, developing and sustaining collaborations between modellers and governments, and ensuringearlier access to linked and timely surveillance data could help improve pandemic preparedness.

What We (Don't) Know about the Infectious Disease Burden Among Youth Experiencing Homelessness in the United States and Canada.

Youth experiencing homelessness (YEH) and sexual and gender minority (SGM) YEH may be at increased risk for infectious diseases due to living arrangements, risk behaviors, and barriers to healthcare access that are dissimilar to those of housed youth and older adults experiencing homelessness. To better understand infectious diseases among YEH populations, we synthesized findings from 12 peer-reviewed articles published between 2012 to 2020 which enumerated YEH or SGM YEH infectious disease burden in locations across the U.S. or Canada. Pathogens presented in the studies were limited to sexually transmitted infections (STIs) and bloodborne infections (BBI). Only three studies enumerated infectious diseases among SGM YEH. There was a dearth of comparison data by housing status (ex., sheltered versus unsheltered youth), SGM identity, or other relevant counterfactual groups in the identified studies. We also introduce three publicly available, national-level surveillance datasets from the U.S. or Canada that quantify certain STIs, BBIs, and tuberculosis among YEH, which may be used for future disease burden assessments. Our review calls for more comprehensive YEH-centered research that includes multimodal data collection and timely disease surveillance to improve estimates of infectious diseases among this vulnerable population.

DODGE: automated point source bacterial outbreak detection using cumulative long term genomic surveillance.

The reliable and timely recognition of outbreaks is a key component of public health surveillance for foodborne diseases. Whole genome sequencing (WGS) offers high resolution typing of foodborne bacterial pathogens and facilitates the accurate detection of outbreaks. This detection relies on grouping WGS data into clusters at an appropriate genetic threshold. However, methods and tools for selecting and adjusting such thresholds according to the required resolution of surveillance and epidemiological context are lacking. Here we present DODGE (Dynamic Outbreak Detection for Genomic Epidemiology), an algorithm to dynamically select and compare these genetic thresholds. DODGE can analyse expanding datasets over time and clusters that are predicted to correspond to outbreaks (or "investigation clusters") can be named with established genomic nomenclature systems to facilitate integrated analysis across jurisdictions. DODGE was tested in two real-world Salmonella genomic surveillance datasets of different duration, 2 months from Australia and 9 years from the United Kingdom. In both cases only a minority of isolates were identified as investigation clusters. Two known outbreaks in the United Kingdom dataset were detected by DODGE and were recognized at an earlier timepoint than the outbreaks were reported. These findings demonstrated the potential of the DODGE approach to improve the effectiveness and timeliness of genomic surveillance for foodborne diseases and the effectiveness of the algorithm developed. DODGE is freely available at https://github.com/LanLab/dodge and can easily be installed using Conda.

Implementation and evaluation of a SARI surveillance system in a tertiary hospital in Scotland in 2021/2022

ObjectiveTo set up and evaluate a new surveillance system for severe acute respiratory infection (SARI) in Scotland. Study designCross-sectional study and evaluation of surveillance system. MethodsThe SARI case definition comprised patients aged 16 years or over with an acute respiratory illness presentation requiring testing for influenza and SARS-CoV-2 and hospital admission. Data were collected from SARI cases by research nurses in one tertiary teaching hospital using a bespoke data collection tool from November 2021 to May 2022. Descriptive analyses of SARI cases were carried out. The following attributes of the surveillance system were evaluated according to Centers for Disease Control and Prevention (CDC) guidelines: stability, data quality, timeliness, positive predictive value, representativeness, simplicity, acceptability and flexibility. ResultsThe final surveillance dataset comprised 1163 records, with cases peaking in ISO week 50 (week ending 19/12/2021). The system produced a stable stream of surveillance data, with the proportion of SARI records with sufficient information for effective surveillance increasing from 65.4% during the first month to 87.0% over time. Similarly, the proportion where data collection was completed promptly was low initially, but increased to 50%–65% during later periods. ConclusionSARI surveillance was successfully established in one hospital, but for a national system, additional sentinel hospital sites across Scotland, with flexibility to ensure consistently high data completeness and timeliness are needed. Data collection should be automated where possible, and demands on clinicians minimised. SARI surveillance should be embedded and resourced as part of a national respiratory surveillance strategy.

A Military Audio Dataset for Situational Awareness and Surveillance

Audio classification related to military activities is a challenging task due to the high levels of background noise and the lack of suitable and publicly available datasets. To bridge this gap, this paper constructs and introduces a new military audio dataset, named MAD, which is suitable for training and evaluating audio classification systems. The proposed MAD dataset is extracted from various military videos and contains 8,075 sound samples from 7 classes corresponding to approximately 12 hours, exhibiting distinctive characteristics not presented in academic datasets typically used for machine learning research. We present a comprehensive description of the dataset, including its acoustic statistics and examples. We further conduct a comprehensive sound classification study of various deep learning algorithms on the MAD dataset. We are also releasing the source code to make it easy to build these systems. The presented dataset will be a valuable resource for evaluating the performance of existing algorithms and for advancing research in the field of acoustic-based hazardous situation surveillance systems.

Recent HIV infection and associated factors among newly diagnosed HIV cases in the Southwest Ethiopia Regional State: HIV case-based surveillance analysis (2019–2022)

BackgroundGlobally, there were an estimated 39 million people living with HIV with 1.3 million new HIV infections by the end of 2023. The Sub-Saharan Africa accounted 51% of new HIV infections. HIV case-based Surveillance collects data on newly diagnosed HIV cases, recent HIV infections, and other sentinel events, aiding evidence-based decision making. There is limited evidence on these in Ethiopia. The objective of this study is to determine the incidence proportion of recent infections and associated factors among newly diagnosed HIV cases and their distribution by person, place, and time in the Southwest Ethiopia Regional State.MethodsA retrospective analysis was conducted on HIV case-based surveillance dataset (July 2019 to June 2022) from the Southwest Ethiopia Regional State. Recent HIV infection is an infection that acquired within the last 12 months as diagnosed by Asante recency test kits. Data were analyzed using SPSS version 26. ArcGIS version 10.8 was used for mapping recent infections. Logistic regression was employed to identify factors associated with recent infections. In multivariable logistic regression analysis, variables with p-value < 0.05 and an adjusted odds ratio with 95% confidence interval were considered to declare significant association.ResultsA total of 1,167 newly diagnosed HIV cases (eligible cases) were identified. Among these, 786 (67.3%) recency tests were performed. The mean age of individuals with recent infection was 28.4 years. The proportion of recent infection is 89 (11.3%, 95% CI: 11.2, 11.5%). The highest proportion of recent infection is reported from the West Omo zone (42.9%), whereas 13.2% in Bench Sheko zone. Recent infection is significantly associated with age 15–24 years [AOR = 7.14, 95%CI: 2.89,17.57], age 25–34 years [AOR = 5.34, 95%CI: 2.20,12.94], females [AOR = 2.03, 95%CI: 1.26,3.25], and contact history with the index case [AOR = 0.48, 95%CI: 0.28, 0.83]. The incidence of recent infection increased from 86 (in 2019/20) to 132 (in 2022) recent infections per 1,000 newly diagnosed cases.ConclusionsRecent HIV infection is a public health concern in the Southwest Ethiopia Regional State with an increasing incidence. Targeted prevention efforts are necessary, especially for females and younger people.

Multiple Imputation of Race and Hispanic Ethnicity in National Surveillance Data for Chlamydia, Gonorrhea, and Syphilis.

Disease burden of sexually transmitted infections such as chlamydia, gonorrhea, and syphilis is often compared across age categories, sex categories, and race and ethnicity categories. Missing data may prevent researchers from accurately characterizing health disparities between populations. This article describes the methods used to impute race and Hispanic ethnicity in a large national surveillance data set. All US cases of chlamydia, gonorrhea, and syphilis (excluding congenital syphilis) reported through the National Notifiable Diseases Surveillance System from the year 2019 were included in the analyses. We used fully conditional specification to impute missing race and Hispanic ethnicity data. After imputation, reported case rates were calculated, by disease, for each race and Hispanic ethnicity category using Vintage 2019 Population and Housing Unit Estimates from the US Census. We then used case counts from subsets that contained only complete race and Hispanic ethnicity information to investigate if the confidence intervals from the multiply imputed data included the observed number of cases in each race and Hispanic ethnicity category. Among the 2,553,038 cases reported in 2019, race and Hispanic ethnicity were multiply imputed for 9% of syphilis cases, 22% of gonorrhea cases, and 33% of chlamydia cases. In the subset analyses, every nonzero rate of reported cases was contained within the confidence intervals that were calculated from multiply imputed data. Confidence intervals that account for the uncertainty of the predictions are an advantage of multiple imputation over complete-case analysis because a realistic variance estimate allows for valid hypothesis testing results.