Public Health Surveillance Data Research Articles

Data Equity as a Building Block for Health Equity: Improving Surveillance Data for People With Disabilities, With Substance Use Disorder, or Experiencing Homelessness, United States.

People with disabilities, people experiencing homelessness, and people who have substance use disorders face unique health challenges. Gaps in public health surveillance data limit the identification of public health needs of these groups and data-driven action. This study aimed to identify current practices, challenges, and opportunities for collecting and reporting COVID-19 surveillance data for these populations. We used a rapid qualitative assessment to explore COVID-19 surveillance capacities. From July through October 2021, we virtually interviewed key informants from the Centers for Disease Control and Prevention, state and local health departments, and health care providers across the United States. We thematically analyzed and contextualized interview notes, peer-reviewed articles, and participant documents using a literature review. We identified themes centered on foundational structural and systems issues that hinder actionable surveillance data for these and other populations that are disproportionately affected by multiple health conditions. Qualitative data analysis of 61 interviews elucidated 4 primary challenges: definitions and policies, resources, data systems, and articulation of the purpose of data collection to these groups. Participants noted the use of multisector partnerships, automated data collection and integration, and data scorecards to circumvent challenges. This study highlights the need for multisector, systematic improvements in surveillance data collection and reporting to advance health equity. Improvements must be buttressed with adequate investment in data infrastructure and promoted through clear communication of how data are used to protect health.

Beyond “business as usual”: Lessons from FIFA for fair benefit-sharing in global health

Abstract While researchers and agencies from low- and middle-income countries often contribute significantly to public health surveillance data, which is crucial for effective pandemic prevention, preparedness, and response activities, they often do not receive adequate compensation for their contributions. Incentivizing data sharing is important for informing public health responses to pathogens with pandemic potential. However, existing data sharing legal frameworks have limitations. In this context, we looked beyond “business the usual suspects” to explore the applicability of a benefit-sharing model developed and implemented by the International Federation of Association Football (FIFA) in international association football. This model rewards grassroots contributions and redistributes benefits, promoting a fair balance of interests across diverse economic contexts. We discuss adapting FIFA's mechanisms, including training compensation and solidarity payments, to create a novel benefit-sharing framework in global health. Given the complexity of global health, we note ways in which components of the FIFA model would need to be adapted for global health. Challenges such as integrating into existing legal frameworks, ensuring broad international buy-in, and accommodating different pandemic periods are examined. While adapting the FIFA model presents challenges, it offers a promising approach to achieving more equitable data sharing and benefit distribution in global health.

Epidemiological anomaly detection in Philippine public health surveillance data through Newcomb-Benford analysis.

Public health surveillance is vital for monitoring and controlling disease spread. In the Philippines, an effective surveillance system is crucial for managing diverse infectious diseases. The Newcomb-Benford Law (NBL) is a statistical tool known for anomaly detection in various datasets, including those in public health. Using Philippine epidemiological data from 2019 to 2023, this study applied NBL analysis. Diseases included acute flaccid paralysis, diphtheria, measles, rubella, neonatal tetanus, pertussis, chikungunya, dengue, leptospirosis and others. The analysis involved Chi-square tests, Mantissa Arc tests, Mean Absolute Deviation (MAD) and Distortion Factor calculations. Most diseases exhibited nonconformity to NBL, except for measles. MAD consistently indicated nonconformity, highlighting potential anomalies. Rabies consistently showed substantial deviations, while leptospirosis exhibited closer alignment, especially in 2021. Annual variations in disease deviations were notable, with acute meningitis encephalitis syndrome in 2019 and influenza-like illness in 2023 having the highest deviations. The study provides practical insights for improving Philippine public health surveillance. Despite some diseases showing conformity, deviations suggest data quality issues. Enhancing the PIDSR, especially in diseases with consistent nonconformity, is crucial for accurate monitoring and response. The NBL's versatility across diverse domains emphasizes its utility for ensuring data integrity and quality assurance.

What Extraordinary Times Tell Us About Ordinary Ones: A Multiple Case Study of Precariously Employed Food Retail and Service Workers in Two U.S. State Contexts during the COVID-19 Pandemic

Disease prevention relies on a complex interplay between social context and individual behaviors. Work and the employment conditions that shape it are key domains where this interplay occurs, a reality highlighted by the COVID-19 pandemic. To explore the links between employment quality and social context as drivers of disease prevention, we conducted a multiple case study of food retail and services workers during COVID-19 in two U.S. states – Indiana and Washington – with differing norms and policy landscape. We drew on public health surveillance data, government/NGO documents, and media sources to contextualize in-depth interviews with 26 precariously employed food workers. Analysis consisted of a within-case and a cross-case phase, each drawing on state contextual and interview data. Precariously employed food workers in Indiana and Washington had contrasting expectations of employers, government, and public health that paralleled policies and norms in their respective states. Ultimately, though reasons for it differed, workers in both states made choices about preventive behaviors in an individualized way that was shaped by their employment circumstances. Explicit consideration of employment and the social safety net in public health would better prepare us for the next crisis and ameliorate health inequities under ordinary circumstances.

196 COVID-19 Hospitalization and Death by Concentrated Racial/Ethnic and Economic Segregation: Los Angeles County, January 2020-June 2023

OBJECTIVES/GOALS: This study aims to assess associations over time between several area-based indices of concentration at the extremes and COVID-19 hospitalization and death in Los Angeles County, from January 2020 to June 2023. These measures reflect concentrations of racial/ethnic and economic segregation at the census tract level. METHODS/STUDY POPULATION: Indices of concentration at the extremes (ICEs) for economic segregation, racial/ethnic segregation, and a combination of the two were constructed for each Los Angeles County (LAC) census tract, using 2015-2019 ACS data. The index ranges from -1 to 1 and is the number of advantaged households (HH) minus the number of disadvantaged HH over the total number of HH measured. Economic segregation is HH income over $100,000 vs. below $25,000 per year and racial/ethnic segregation defined as White and Non-Hispanic vs. non-White or Hispanic HH. The distribution of index scores was divided into quintiles (Q1-Q5) for all LAC census tracts. Age-adjusted hospitalization and death rates were derived at the census tract level by quarter (QTR) based on Los Angeles County Department of Public Health surveillance data. RESULTS/ANTICIPATED RESULTS: Age-adjusted hospitalization and death rates were consistently higher across all quarters in Q1 (most deprived) vs. Q5 (most privileged) for all ICE measures. For ICE of economic segregation, the age-adjusted hospitalization and death rate ratios between Q1 and Q5 were 2.12 (range: 1.32 - 4.15; peak 2020 QTR2) and 2.02 (range: 1.46 - 3.21; peak 2021 QTR1), respectively. For ICE of racial segregation, the age-adjusted hospitalization and death rate ratio between Q1 and Q5 was 2.03 (range: 1.08 - 3.95; peak 2020 QTR3) and 1.77 (range: 1.03 - 2.80; peak 2021 QTR1). The ICE of economic/racial segregation combined was the highest, with averages of the age-adjusted hospitalization and death rate ratios between Q1 and Q5 being 2.26 (1.16 - 4.43; peak 2020 QTR2) and 1.99 (range: 1.22 - 3.32; peak 2021 QTR1). DISCUSSION/SIGNIFICANCE: This study assesses the impact of geographic segregation based on indices that quantify the concentration of both deprivation, privilege, and racial/ethnic group, demonstrating that segregation and economic deprivation are consistently associated with higher rates of age-adjusted hospitalization and death from COVID-19 in LAC.

Investigating epidemiological distribution (temporality and intensity) of respiratory pathogens following COVID-19 de-escalation process in Catalonia, September 2016-June 2021: Analysis of regional surveillance data.

Following the low incidence rates of non-SARS-CoV-2 respiratory viruses registered during the strict lockdown enforced in the pandemic, a resurgence of several endemic viruses in Catalonia (Spain) was noted during the early summer of 2021. In this study, we investigated whether the circulation of non-SARS-CoV-2 respiratory viruses in Catalonia, assessed by Microbiological Reporting System of Catalonia (MRSC) and the Epidemiological Surveillance Network of Catalonia, was affected by the strict lockdown measures, as well as, the implication of the Coronavirus Disease 19 (COVID-19) de-escalation process in the late season outbreaks registered during the 2020-2021 season. A retrospective comparison of epidemic patterns in the respiratory viruses' incidence, using regional public health surveillance data from MRSC, was performed between weeks 26/2016 to week 27/2021. Data were expressed as the weekly total number of test positivity for individual viruses. A segmented negative binomial regression model was conducted, with two parameters included (level and trend) for each segment of the time series (2020 pre-lockdown, 2020 post-lockdown and 2021). Results were reported as a unit changed in the strict lockdown. A total of 51588 confirmed cases of the different respiratory viruses were included in the analysis, the majority were influenza cases (63.7%). An immediate reduction in the weekly number of cases was observed in 2020 after the COVID-19 outbreak for human adenovirus virus (HAdV) (β2 = -2.606; P <0.01), human parainfluenza virus (HPIV) (β2 = -3.023; P <0.01), influenza virus (IFV) (β2 = -1.259; P <0.01), but not for respiratory syncytial virus (RSV), where the number of cases remained unchanged. During 2020, a significant negative trend was found for RSV (β3 = -0.170, P <0.01), and a positive trend for HAdV (β3 = 0.075, P <0.01). During 2021, a significant reduction in the weekly number of cases was also observed for all respiratory viruses, and a borderline non-significant reduction for HPIV (β3 = -0.027; P = 0.086). Moreover, significant positive trends were found for each viral pathogen, except for influenza during 2020-2021 season, where cases remained close to zero. The respiratory viruses increased activity and their late season epidemic start particularly affected children under 6 years old. Our data not only provides evidence that occurrence of different respiratory virus infections was affected by the strict lockdown taken against SARS-CoV-2 but it also shows a late resurgence of seasonal respiratory viruses' cases during the 2020-2021 season following the relaxation of COVID-19-targeted non-pharmaceutical interventions.

Syphilis Diagnosis After a Chlamydia, Gonorrhea, or HIV Diagnosis Among Reproductive-Aged Women in Baltimore, MD.

Syphilis incidence is increasing among reproductive-aged women, and previous sexually transmitted infections (STIs) are a risk factor for subsequent STIs. This study aimed to determine syphilis incidence after a chlamydia, gonorrhea, or HIV diagnosis, and identify characteristics associated with higher syphilis incidence rates among reproductive-aged women in 1 mid-Atlantic city. A retrospective cohort of 85,113 chlamydia, gonorrhea, and HIV diagnoses occurring between 2009 and 2021 and among women aged 13 to 50 years was constructed using public health surveillance data. Cumulative incidence curves were estimated to examine time to early syphilis (i.e., primary, secondary, or early latent) diagnosis, and multivariable analyses determined incidence rate ratios by age (<25 vs. ≥25 years) and number of prior STI diagnoses (0 vs. ≥1) during the study period, stratified by STI. There were 85,113 reportable STI diagnoses and 646 syphilis diagnoses in the cohort. Approximately 1 of 150 chlamydia, 1 of 100 gonorrhea, and 1 of 50 HIV diagnoses were followed by a syphilis diagnosis within 5 years. Cumulative incidence of syphilis differed significantly by STI diagnosis ( P < 0.001). In multivariable analysis, syphilis incidence rates were higher among women diagnosed with ≥1 (vs. 0) prior STI regardless of STI type ( P < 0.05) and among women ≥25 (vs. <25) years old diagnosed with gonorrhea ( P < 0.05). There were significant differences in syphilis incidence by prior STI type, number of STIs, and age. Our data support targeted screening for syphilis among women with a history of STIs, parwomen with ≥1 prior STI diagnosis, and older women diagnosed with gonorrhea.

Association of Tecovirimat Therapy With Mpox Symptom Improvement: A Cross-sectional Study-King County, Washington, May-October 2022.

Data on tecovirimat effectiveness for human mpox are limited. We conducted a retrospective cross-sectional interview-based study to identify associations between tecovirimat treatment and the mpox clinical course. Using public health surveillance data from King County, Washington, we recruited and interviewed persons diagnosed with mpox during May-October 2022. We calculated descriptive statistics on demographics, vaccination status, comorbidities, and symptoms including 3 self-reported dates (symptom onset, first date of symptom improvement, and illness resolution). We used multivariable linear regression, stratified by illness severity, to evaluate the association of tecovirimat treatment with time to symptom improvement and time to illness resolution. We compared individuals who did not receive tecovirimat to participants who started tecovirimat early (≤5 days from symptom onset) and late (>5 days and ≤28 days from symptom onset) in their illness. Of 465 individuals diagnosed with mpox, 115 (25%) participated in this study. Eighty participants (70%) received tecovirimat and 43 (37%) initiated tecovirimat early. Sixty-eight (59%) reported severe symptoms during their illness, including proctitis (n = 38 [33%]), rectal bleeding (n = 27 [24%]), or severe pain (n = 24 [21%]). In the multivariable analysis, early tecovirimat was associated with shorter time to symptom improvement (-5.5 days, P = .04) among participants with severe illness but not among those with nonsevere illness (0.9 day, P = .66). Early tecovirimat was not associated with faster illness resolution, regardless of severity. Our small study suggests that early tecovirimat initiation may hasten subjective symptomatic improvement in people with severe mpox. Larger randomized trials are needed to evaluate this finding.

Structural Bias in the Completeness of Death Investigations for Sudden Unexpected Infant Deaths (SUIDs).

To assess sudden unexpected infant death (SUID) investigations for structural inequities by race/ethnicity and geography. The SUID Case Registry compiles data on death investigations. We analyzed cases from 2015 to 2018 (N = 3847) to examine likelihood of an incomplete death investigation, defined as missing autopsy, missing scene investigation, or missing detailed information about where and how the body was found. We also analyzed which specific components of death investigations led to the greatest number of incomplete investigations. Twenty-four percent of SUIDs had incomplete death investigations. Death scenes in rural places had 1.51 times the odds of incomplete death investigations (95% confidence interval [CI], 1.19-1.92) compared with urban areas. Scene investigations led by law enforcement were more likely to result in incomplete death investigations (odds ratio [OR] = 1.49; 95% CI, 1.18-1.88) than those led by medical examiners. American Indian/Alaska Native SUIDs were more likely than other racial groups to have an incomplete investigation (OR = 1.49; 95% CI, 0.92-2.42), more likely to occur in rural places ( P = .055), and more likely to be investigated by law enforcement ( P < .001). If doll reenactments had been performed, 358 additional cases would have had complete investigations, and if SUID investigation forms had been performed, 243 additional cases would have had complete investigations. American Indian/Alaska Native SUIDs were also more likely to be missing specific components of death investigations. To produce equitable public health surveillance data used in prevention efforts, it is crucial to improve SUID investigations, especially in rural areas and among American Indian/Alaska Native babies.

The Arbovirus Mapping and Prediction (ArboMAP) system for West Nile virus forecasting.

West Nile virus (WNV) is the most common mosquito-borne disease in the United States. Predicting the location and timing of outbreaks would allow targeting of disease prevention and mosquito control activities. Our objective was to develop software (ArboMAP) for routine WNV forecasting using public health surveillance data and meteorological observations. ArboMAP was implemented using an R markdown script for data processing, modeling, and report generation. A Google Earth Engine application was developed to summarize and download weather data. Generalized additive models were used to make county-level predictions of WNV cases. ArboMAP minimized the number of manual steps required to make weekly forecasts, generated information that was useful for decision-makers, and has been tested and implemented in multiple public health institutions. Routine prediction of mosquito-borne disease risk is feasible and can be implemented by public health departments using ArboMAP.

Comparison of statistical approaches to predicting norovirus laboratory reports before and during COVID-19: insights to inform public health surveillance

Social distancing interrupted transmission patterns of contact-driven infectious agents such as norovirus during the Covid-19 pandemic. Since routine surveillance of norovirus was additionally disrupted during the pandemic, traditional naïve forecasts that rely only on past public health surveillance data may not reliably represent norovirus activity. This study investigates the use of statistical modelling to predict the number of norovirus laboratory reports in England 4-weeks ahead of time before and during Covid-19 pandemic thus providing insights to inform existing practices in norovirus surveillance in England. We compare the predictive performance from three forecasting approaches that assume different underlying structure of the norovirus data and utilized various external data sources including mobility, air temperature and relative internet searches (Time Series and Regularized Generalized Linear Model, and Quantile Regression Forest). The performance of each approach was evaluated using multiple metrics, including a relative prediction error against the traditional naive forecast of a five-season mean. Our data suggest that all three forecasting approaches improve predictive performance over the naïve forecasts, especially in the 2020/21 season (30–45% relative improvement) when the number of norovirus reports reduced. The improvement ranged from 7 to 22% before the pandemic. However, performance varied: regularized regression incorporating internet searches showed the best forecasting score pre-pandemic and the time series approach achieved the best results post pandemic onset without external data. Overall, our results demonstrate that there is a significant value for public health in considering the adoption of more sophisticated forecasting tools, moving beyond traditional naïve methods, and utilizing available software to enhance the precision and timeliness of norovirus surveillance in England.

Asthma and clinical outcomes of COVID-19 in a community setting

ObjectivesThe association between asthma and COVID-19 mortality remains inconclusive. We examined the association between asthma and clinical outcomes of patients with COVID-19. Study designA case–control study based on a surveillance cohort in Harris County, Texas. MethodsUsing the data of 21,765 patients who reported having at least one chronic health condition, we investigated the association between asthma and COVID-19 severity, characterized primarily by hospitalization and death. Unconditional logistic regression models were used to estimate the multivariable odds ratio (mOR) and its 95 % confidence interval (CI) of COVID-19 severity associated with asthma and other chronic lung diseases, adjusting for demographic and other comorbidities. A P-value < 0.005 was considered statistically significant after correcting multiple testing. ResultsIn total, 3034 patients (13.9 %) had asthma, and 774 (3.56 %) had other chronic lung diseases. The case death rate among patients with asthma and other chronic lung diseases was 0.75 % and 19.0 %, respectively. Compared to patients without the respective conditions, patients with asthma had lower odds of death (mOR = 0.44, 95 % CI: 0.27–0.69), while patients with other chronic lung diseases had higher odds of hospitalization (mOR = 2.02, 95 % CI: 1.68–2.42) and death (mOR = 1.95, 95 % CI: 1.52–2.49) (P-values < 0.005). Risk factors for COVID-19 mortality included older age, male gender, diabetes, obesity, hypertension, cardiovascular disease, active cancer, and chronic kidney disease. ConclusionsThe public health surveillance data suggested that preexisting asthma was inversely associated with COVID-19 mortality.

Effectiveness of the Modified Vaccinia Ankara Vaccine Against Mpox in Men Who Have Sex With Men: A Retrospective Cohort Analysis, Seattle, Washington.

Data on modified Vaccinia Ankara (MVA) vaccine effectiveness against mpox in real-world settings are limited. We performed a retrospective cohort analysis using Cox proportional hazards regression to estimate the association between vaccination and laboratory-confirmed mpox incidence. Study subjects included all men who have sex with men seen in a sexual health clinic in Seattle, Washington, between 1 January 2020 and 31 December 2022. Subjects' receipt of vaccine and diagnosis with mpox were ascertained from public health vaccine registry and surveillance data. Analyses were adjusted for demographic factors, human immunodeficiency virus (HIV) status, and sexual risk behaviors. The incidence of mpox per 100 person-years was 8.83 among patients with 0 doses, 3.32 among patients with 1 dose, and 0.78 among patients with 2 doses of MVA vaccine. Mpox diagnosis was significantly associated with age category 30-39 and 40-51 years, HIV positivity, syphilis diagnosis in the prior year, >10 sex partners in the last year, and having a clinic visit in the last year. In the multivariate model adjusting for these factors, vaccine effectiveness was 81% for 1 dose and 83% for 2 doses. These data support the effectiveness of the MVA vaccine-including a single dose of the vaccine-in preventing mpox disease and highlight the appropriateness of risk factor-based prioritization of immunization early in the epidemic. The durability of MVA vaccine-induced immunity is unknown, and at-risk persons should receive 2 doses of MVA.

Festivals following the easing of COVID-19 restrictions: Prevalence of new psychoactive substances and illicit drugs

The market for illicit drugs and new psychoactive substances (NPS) has grown significantly and people attending festivals have been identified as being at high risk (high extent and frequency of substance use). Traditional public health surveillance data sources have limitations (high costs, long implementation times, and ethical issues) and wastewater-based epidemiology (WBE) can cost-effectively support surveillance efforts. Influent wastewater samples were analyzed for NPS and illicit drug consumption collected during New Year period (from 29-Dec-2021 to 4-Jan-2022) and a summer Festival (from 29-June-2022 to 12-July-2022) in a large city in Spain. Samples were analyzed for phenethylamines, cathinones, opioids, benzodiazepines, plant-based NPS, dissociatives, and the illicit drugs methamphetamine, MDA, MDMA, ketamine, heroin, cocaine, and pseudoephedrine by liquid chromatography mass spectrometry. High consumption rates of specific NPS and established illicit drugs were identified at the peak of each event. Furthermore, a dynamic change in NPS use (presence and absence of substances) was detected over a period of six months. Eleven NPS, including synthetic cathinones, benzodiazepines, plant-based NPS and dissociatives, and seven illicit drugs were found across both the New Year and summer Festival. Statistically significant differences (p < 0.05) were seen for 3-MMC (New Year vs summer Festival), eutylone (New Year vs summer Festival), cocaine (summer Festival vs normal week and summer Festival vs New Year), MDMA (New Year vs normal week and summer Festival vs normal week), heroin (summer Festival vs New Year) and pseudoephedrine (summer Festival vs New Year). This WBE study assessed the prevalence of NPS and illicit drugs at festivals following the reduction of the COVID-19 pandemic restrictions highlighting the high use of specific substances at the peak of each event. This approach identified in a cost-effective and timely manner without any ethical issues the most used drugs and changes in use patterns and, thus, can complement public health information.

Machine learning clinical decision support systems for surveillance: a case study on pertussis and RSV in children.

We tested the performance of a machine learning (ML) algorithm based on signs and symptoms for the diagnosis of RSV infection or pertussis in the first year of age to support clinical decisions and provide timely data for public health surveillance. We used data from a retrospective case series of children in the first year of life investigated for acute respiratory infections in the emergency room from 2015 to 2020. We collected data from PCR laboratory tests for confirming pertussis or RSV infection, clinical symptoms, and routine blood testing results, which were used for the algorithm development. We used a LightGBM model to develop 2 sets of models for predicting pertussis and RSV infection: for each type of infection, we developed one model trained with the combination of clinical symptoms and results from routine blood test (white blood cell count, lymphocyte fraction and C-reactive protein), and one with symptoms only. All analyses were performed using Python 3.7.4 with Shapley values (Shap values) visualization package for predictor visualization. The performance of the models was assessed through confusion matrices. The models were developed on a dataset of 599 children. The recall for the pertussis model combining symptoms and routine laboratory tests was 0.72, and 0.74 with clinical symptoms only. For RSV infection, recall was 0.68 with clinical symptoms and laboratory tests and 0.71 with clinical symptoms only. The F1 score for the pertussis model was 0.72 in both models, and, for RSV infection, it was 0.69 and 0.75. ML models can support the diagnosis and surveillance of infectious diseases such as pertussis or RSV infection in children based on common symptoms and laboratory tests. ML-based clinical decision support systems may be developed in the future in large networks to create accurate tools for clinical support and public health surveillance.

Correlations and Timeliness of COVID-19 Surveillance Data Sources and Indicators - United States, October 1, 2020-March 22, 2023.

When the U.S. COVID-19 public health emergency declaration expires on May 11, 2023, national reporting of certain categories of COVID-19 public health surveillance data will be transitioned to other data sources or will be discontinued; COVID-19 hospitalization data will be the only data source available at the county level (1). In anticipation of the transition, national COVID-19 surveillance data sources and indicators were evaluated for purposes of ongoing monitoring. The timeliness and correlations among surveillance indicators were analyzed to assess the usefulness of COVID-19-associated hospital admission rates as a primary indicator for monitoring COVID-19 trends, as well as the suitability of other replacement data sources. During April 2022-March 2023, COVID-19 hospital admission rates from the National Healthcare Safety Network (NHSN)† lagged 1 day behind case rates and 4 days behind percentages of positive test results and COVID-19 emergency department (ED) visits from the National Syndromic Surveillance Program (NSSP). In the same analysis, National Vital Statistics System (NVSS) trends in the percentage of deaths that were COVID-19-associated, which is tracked by date of death rather than by report date, were observable 13 days earlier than those from aggregate death count data, which will be discontinued (1). During October 2020-March 2023, strong correlations were observed between NVSS and aggregate death data (0.78) and between the percentage of positive SARS-CoV-2 test results from the National Respiratory and Enteric Viruses Surveillance System (NREVSS) and COVID-19 electronic laboratory reporting (CELR) (0.79), which will also be discontinued (1). Weekly COVID-19 Community Levels (CCLs) will be replaced with levels of COVID-19 hospital admission rates (low, medium, or high) which demonstrated >99% concordance by county during February 2022-March 2023. COVID-19-associated hospital admission levels are a suitable primary metric for monitoring COVID-19 trends, the percentage of COVID-19 deaths is a timely disease severity indicator, and the percentages of positive SARS-CoV-2 test results from NREVSS and ED visits serve as early indicators for COVID-19 monitoring. Collectively, these surveillance data sources and indicators can support monitoring of the impact of COVID-19 and related prevention and control strategies as ongoing public health priorities.

Fatal drug use in the COVID-19 pandemic response: Changing trends in drug-involved deaths before and after stay-at-home orders in Louisiana.

The effect of disaster events on increasing drug-involved deaths has been clearly shown in previous literature. As the COVID-19 pandemic led to stay-at-home orders throughout the United States, there was a simultaneous spike in drug-involved deaths around the country. The landscape of a preexisting epidemic of drug-involved deaths in the United States is one which is not geographically homogenous. Given this unequal distribution of mortality, state-specific analysis of changing trends in drug use and drug-involved deaths is vital to inform both care for people who use drugs and local policy. An analysis of public health surveillance data from the state of Louisiana, both before and after the initial stay-at-home order of the COVID-19 pandemic, was used to determine the effect the pandemic may have had on the drug-involved deaths within this state. Using the linear regression analysis of total drug-involved deaths, as well as drug-specific subgroups, trends were measured based on quarterly (Qly) deaths. With the initial stay-at-home order as the change point, trends measured through quarter 1 (Q1) of 2020 were compared to trends measured from quarter 2 (Q2) of 2020 through quarter 3 (Q3) of 2021. The significantly increased rate of change in Qly drug-involved deaths, synthetic opioid-involved deaths, stimulant-involved deaths, and psychostimulant-involved deaths indicates a long-term change following the initial response to the COVID-19 pandemic. Changes in the delivery of mental health services, harm reduction services, medication for opioid use disorder (MOUD), treatment services, withdrawal management services, addiction counseling, shelters, housing, and food supplies further limited drug-involved prevention support, all of which were exacerbated by the new stress of living in a pandemic and economic uncertainty.

Changes in the Demographic Distribution of Chicago Gun-Homicide Decedents From 2015-2021: Violent Death Surveillance Cross-sectional Study

Homicide is one of the 5 leading causes of death in the United States for persons aged 1 to 44 years. In 2019, 75% of US homicides were by gun. Chicago has a gun-homicide rate 4 times the national average, and 90% of all homicides are by gun. The public health approach to violence prevention calls for a 4-step process, beginning with defining and monitoring the problem. Insight into the characteristics of gun-homicide decedents can help frame next steps, including identifying risk and protective factors, developing prevention and intervention strategies, and scaling effective responses. Although much is known about gun homicide because it is a long-standing, entrenched public health problem, it is useful to monitor trends to update ongoing prevention efforts. This study aimed to use public health surveillance data and methods to describe changes in the race/ethnicity, sex, and age of Chicago gun-homicide decedents from 2015-2021, in the context of year-to-year variation and an overall increase in the city's gun-homicide rate. We calculated the distribution of gun-related homicide deaths by 6 race/ethnicity and sex groups (non-Hispanic Black female, non-Hispanic White female, Hispanic female, non-Hispanic Black male, non-Hispanic White male, and Hispanic male), age in years, and age by age group. We used counts, percentages, and rates per 100,000 persons to describe the distribution of deaths among these demographic groups. Comparisons of means and column proportions with tests of significance set at P≤.05 were used to describe changes in the distribution of gun-homicide decedents over time by race-ethnicity-sex and age groups. The comparison of mean age by race-ethnicity-sex group is done using 1-way ANOVA with significance set at P≤.05. The distribution of gun-homicide decedents in Chicago by race/ethnicity and sex groups had been relatively stable from 2015 to 2021 with 2 notable exceptions: a more than doubling of the proportion of gun-homicide decedents who were non-Hispanic Black female (3.6% in 2015 to 8.2% in 2021) and an increase of 3.27 years in the mean age of gun-homicide decedents. The increase in mean age coincided with a decrease in the proportion of non-Hispanic Black male gun-homicide decedents between the ages of 15-19 and 20-24 years and, conversely, an increase in the proportion of non-Hispanic Black male gun-homicide decedents aged 25-34 years. The annual gun-homicide rate in Chicago had been increasing since 2015 with year-to-year variation. Continued monitoring of trends in the demographic makeup of gun-homicide decedents is necessary to provide the most relevant and timely information to help shape violence prevention efforts. We detected several changes that suggest a need for increased outreach and engagement marketed toward non-Hispanic Black female and non-Hispanic Black male individuals between the ages of 25-34 years.

Estimating the Under-ascertainment of COVID-19 cases in Toronto, Ontario, March to May 2020.

Public health surveillance data do not always capture all cases, due in part to test availability and health care seeking behaviour. Our study aimed to estimate under-ascertainment multipliers for each step in the reporting chain for COVID-19 in Toronto, Canada. We applied stochastic modeling to estimate these proportions for the period from March 2020 (the beginning of the pandemic) through to May 23, 2020, and for three distinct windows with different laboratory testing criteria within this period. For each laboratory-confirmed symptomatic case reported to Toronto Public Health during the entire period, the estimated number of COVID-19 infections in the community was 18 (5th and 95th percentile: 12, 29). The factor most associated with under-reporting was the proportion of those who sought care that received a test. Public health officials should use improved estimates to better understand the burden of COVID-19 and other similar infections.

51 Syphilis Incidence Following an STI Diagnosis Among Cisgender Women in Baltimore, MD, from 2009-2021

OBJECTIVES/GOALS: The primary objective of this study is to evaluate the risk of an early syphilis diagnosis following a chlamydia, gonorrhea, or HIV diagnosis, and to determine differences by race, repeat infection, diagnosing provider and STI/HIV risk behavior among cisgender women in Baltimore, MD. METHODS/STUDY POPULATION: Public health surveillance data from 2009-2021 was used to examine the overall incidence of syphilis infections among cisgender women ages 13-50 diagnosed with a reportable STI (chlamydia, gonorrhea, or HIV) and the percentage of total infections that were early infections (primary, secondary, or early latent syphilis) in Baltimore City. Data were collected on age, race, diagnosing location (i.e., STI clinic, private provider, etc.), preceding STI diagnoses, and sexual risk behaviors. STI-specific cumulative incidence and incidence rate ratios were used to compare syphilis diagnoses among Black vs. white women, women with repeat STI diagnoses vs. one STI diagnosis, women diagnosed at a public vs. private clinic, and commercial sex workers and substance users vs. those not reporting these risk behaviors. RESULTS/ANTICIPATED RESULTS: Based on recent surveillance data, we expect approximately 79,000 chlamydia, gonorrhea, and HIV diagnoses among cisgender women between 2009-2021. We hypothesize that 3% of chlamydia, gonorrhea, and HIV diagnoses among cisgender women will be followed by a syphilis diagnosis within the study period. Extrapolating from previous studies of early syphilis in men who have sex with men in Baltimore, we expect the rate of syphilis diagnosis following STI diagnosis will be higher in Black vs. white women, women with a prior gonorrhea or HIV diagnosis vs. chlamydia diagnosis, women with repeat STI diagnoses vs. one STI diagnosis, women diagnosed at public STI clinics vs. those diagnosed by private providers, and women reporting commercial sex work and/or substance use vs. those not reporting these risk behaviors. DISCUSSION/SIGNIFICANCE: Local healthcare providers should offer syphilis screening to any woman diagnosed with a chlamydia, gonorrhea, or HIV infection. The higher rates of early syphilis diagnosis among women with repeat STI diagnoses or a prior gonorrhea or HIV diagnosis suggests regular screening is critical in these populations.