American Indian/Alaska Native Birth and Maternal Health Outcomes in Louisiana: Investigating Regional Disparities.

OBJECTIVES: To report regional maternal and infant health characteristics in the Louisiana American Indian/Alaska Native (AIAN) population. METHODS: This was a cross-sectional descriptive analysis using 2016-2019 Louisiana birth certificate data (N = 242,359; 3205 AIAN births). Prevalence of health characteristics and disparities from population averages were calculated. Health characteristics included low birth weight, preterm birth, very low birth weight, very preterm birth, high birth weight, NICU admission, breastfeeding at discharge, rurality, cesarean section, inadequate prenatal care, and maternal smoking. RESULTS: Low birth weight prevalence ranged from 5.7% in central Louisiana to 20.7% in northeast Louisiana. Most other infant outcomes followed a similar pattern. Disparities from regional averages also varied. AIAN infants had 8% higher risk of LBW than the northeast Louisiana average and 4% lower risk in central Louisiana. Maternal smoking was most prevalent in northern and western Louisiana. Across Louisiana, maternal smoking was as or more prevalent in mothers of AIAN infants compared to regional averages. CONCLUSIONS FOR PRACTICE: There is substantial regional variability in maternal and infant health characteristics in Louisiana AIAN populations, motivating emphasis on locally focused epidemiology to better serve Indigenous communities.

COVID-19 Mortality Among American Indian and Alaska Native Persons - 14 States, January-June 2020.

American Indian/Alaska Native (AI/AN) persons experienced disproportionate mortality during the 2009 influenza A(H1N1) pandemic (1,2). Concerns of a similar trend during the coronavirus disease 2019 (COVID-19) pandemic led to the formation of a workgroup* to assess the prevalence of COVID-19 deaths in the AI/AN population. As of December 2, 2020, CDC has reported 2,689 COVID-19-associated deaths among non-Hispanic AI/AN persons in the United States.† A recent analysis found that the cumulative incidence of laboratory-confirmed COVID-19 cases among AI/AN persons was 3.5 times that among White persons (3). Among 14 participating states, the age-adjusted AI/AN COVID-19 mortality rate (55.8 deaths per 100,000; 95% confidence interval [CI] = 52.5-59.3) was 1.8 (95% CI = 1.7-2.0) times that among White persons (30.3 deaths per 100,000; 95% CI = 29.9-30.7). Although COVID-19 mortality rates increased with age among both AI/AN and White persons, the disparity was largest among those aged 20-49 years. Among persons aged 20-29 years, 30-39 years, and 40-49 years, the COVID-19 mortality rates among AI/AN were 10.5, 11.6, and 8.2 times, respectively, those among White persons. Evidence that AI/AN communities might be at increased risk for COVID-19 illness and death demonstrates the importance of documenting and understanding the reasons for these disparities while developing collaborative approaches with federal, state, municipal, and tribal agencies to minimize the impact of COVID-19 on AI/AN communities. Together, public health partners can plan for medical countermeasures and prevention activities for AI/AN communities.

Evidence for Limited Early Spread of COVID-19 Within the United States, January-February 2020.

From January 21 through February 23, 2020, public health agencies detected 14 U.S. cases of coronavirus disease 2019 (COVID-19), all related to travel from China (1,2). The first nontravel-related U.S. case was confirmed on February 26 in a California resident who had become ill on February 13 (3). Two days later, on February 28, a second nontravel-related case was confirmed in the state of Washington (4,5). Examination of four lines of evidence provides insight into the timing of introduction and early transmission of SARS-CoV-2, the virus that causes COVID-19, into the United States before the detection of these two cases. First, syndromic surveillance based on emergency department records from counties affected early by the pandemic did not show an increase in visits for COVID-19-like illness before February 28. Second, retrospective SARS-CoV-2 testing of approximately 11,000 respiratory specimens from several U.S. locations beginning January 1 identified no positive results before February 20. Third, analysis of viral RNA sequences from early cases suggested that a single lineage of virus imported directly or indirectly from China began circulating in the United States between January 18 and February 9, followed by several SARS-CoV-2 importations from Europe. Finally, the occurrence of three cases, one in a California resident who died on February 6, a second in another resident of the same county who died February 17, and a third in an unidentified passenger or crew member aboard a Pacific cruise ship that left San Francisco on February 11, confirms cryptic circulation of the virus by early February. These data indicate that sustained, community transmission had begun before detection of the first two nontravel-related U.S. cases, likely resulting from the importation of a single lineage of virus from China in late January or early February, followed by several importations from Europe. The widespread emergence of COVID-19 throughout the United States after February highlights the importance of robust public health systems to respond rapidly to emerging infectious threats.

How Good Is Your Rule of Thumb? Validating Male-to-Female Case Ratio as a Proxy for Men Who Have Sex With Men Involvement in N. gonorrhoeae Incidence at the County Level.

BACKGROUND: Lacking information on men who have sex with men (MSM) for most reported cases, sexually transmitted disease (STD) programs in the United States have used crude measures such as male-to-female case ratios (MFCR) as a rule of thumb to gauge MSM involvement at the local level, primarily with respect to syphilis cases in the past. Suitability of this measure for gonorrhea incidence has not previously been investigated. METHODS: A random sample of gonorrhea cases reported from January 2010 through June 2013 were interviewed in selected counties participating in the STD Surveillance Network to obtain gender of sex partners and history of transactional sex. Weighted estimates of proportion of cases among MSM and proportion reporting transactional sex were developed; correlation between MFCR and proportion MSM was assessed. RESULTS: Male-to-female case ratio ranged from 0.66 to 8.7, and the proportion of cases occurring among MSM varied from 2.5% to 62.3%. The MFCR was strongly correlated with proportion of cases among MSM after controlling for transactional sex (Pearson partial r = 0.754, P < 0.0001). CONCLUSIONS: Male-to-female case ratio for gonorrhea at the county level is a reliable proxy measure indicating MSM involvement in gonorrhea case incidence and should be used by STD programs to tailor their programmatic mix to include MSM-specific interventions.

Severe Acute Respiratory Illness (SARI) Surveillance in Louisiana, 2013-2014.

The objectives of this article are to describe the severe acute respiratory illness (SARI) surveillance implemented in Louisiana during the 2013-2014 influenza season, present the epidemiology of reported SARI cases, and identify ways to improve this system by incorporating formal SARI surveillance into the influenza surveillance program. Of the 212 SARI cases, 181 (85%) had at least one underlying medical condition, 54 (25.7%) had two conditions, 43 (20.3%) had three conditions, and 25 (11.8%) reported four or more. The most common four underlying conditions were: obesity (43.4%), chronic cardiac conditions (39.6%), diabetes (29.7%), and chronic pulmonary conditions (26.9%). While obesity was the most reported underlying condition, it was three times more likely to be reported in less than 65 years old rather than those >65. Continuation of SARI data collection in future seasons will allow comparisons regarding severity, populations affected, and identify risk factors most commonly associated with severe illness. Reporting of SARI cases also increased influenza-associated adult mortality reporting to the Office of Public Health's Office of Infectious Diseases Epidemiology (ID Epi). Though all influenza-associated mortality is reportable in Louisiana, adult mortality was reported rarely prior to the 2013-2014 season.

Extragenital gonorrhea and chlamydia testing and infection among men who have sex with men--STD Surveillance Network, United States, 2010-2012.

BACKGROUND: Gonorrhea (GC) and chlamydia (CT) are the most commonly reported notifiable diseases in the United States. The Centers for Disease Control and Prevention recommends that men who have sex with men (MSM) be screened for urogenital GC/CT, rectal GC/CT, and pharyngeal GC. We describe extragenital GC/CT testing and infections among MSM attending sexually transmitted disease (STD) clinics. METHODS: The STD Surveillance Network collects patient data from 42 STD clinics. We assessed the proportion of MSM attending these clinics during July 2011-June 2012 who were tested and positive for extragenital GC/CT at their most recent visit or in the preceding 12 months and the number of extragenital infections that would have remained undetected with urethral screening alone. RESULTS: Of 21 994 MSM, 83.9% were tested for urogenital GC, 65.9% for pharyngeal GC, 50.4% for rectal GC, 81.4% for urogenital CT, 31.7% for pharyngeal CT, and 45.9% for rectal CT. Of MSM tested, 11.1% tested positive for urogenital GC, 7.9% for pharyngeal GC, 10.2% for rectal GC, 8.4% for urogenital CT, 2.9% for pharyngeal CT, and 14.1% for rectal CT. More than 70% of extragenital GC infections and 85% of extragenital CT infections were associated with negative urethral tests at the same visit and would not have been detected with urethral screening alone. CONCLUSIONS: Extragenital GC/CT was common among MSM attending STD clinics, but many MSM were not tested. Most extragenital infections would not have been identified, and likely would have remained untreated, with urethral screening alone. Efforts are needed to facilitate implementation of extragenital GC/CT screening recommendations for MSM.

Predicting state level suicide fatalities in the united states with realtime data and machine learning.

Digital trace data and machine learning techniques are increasingly being adopted to predict suicide-related outcomes at the individual level; however, there is also considerable public health need for timely data about suicide trends at the population level. Although significant geographic variation in suicide rates exist by state within the United States, national systems for reporting state suicide trends typically lag by one or more years. We developed and validated a deep learning based approach to utilize real-time, state-level online (Mental Health America web-based depression screenings; Google and YouTube Search Trends), social media (Twitter), and health administrative data (National Syndromic Surveillance Program emergency department visits) to estimate weekly suicide counts in four participating states. Specifically, per state, we built a long short-term memory (LSTM) neural network model to combine signals from the real-time data sources and compared predicted values of suicide deaths from our model to observed values in the same state. Our LSTM model produced accurate estimates of state-specific suicide rates in all four states (percentage error in suicide rate of -2.768% for Utah, -2.823% for Louisiana, -3.449% for New York, and -5.323% for Colorado). Furthermore, our deep learning based approach outperformed current gold-standard baseline autoregressive models that use historical death data alone. We demonstrate an approach to incorporate signals from multiple proxy real-time data sources that can potentially provide more timely estimates of suicide trends at the state level. Timely suicide data at the state level has the potential to improve suicide prevention planning and response tailored to the needs of specific geographic communities.