Objectives We describe clinical characteristics, pregnancy, and infant outcomes in pregnant people with laboratory‐confirmed SARS‐CoV‐2 infection by trimester of infection. Study Design We analyzed data from the Surveillance for Emerging Threats to Mothers and Babies Network and included people with infection in 2020, with known timing of infection and pregnancy outcome. Outcomes are described by trimester of infection. Pregnancy outcomes included live birth and pregnancy loss (<20 weeks and ≥20 weeks gestation). Infant outcomes included preterm birth (<37 weeks gestation), small for gestational age, birth defects, and neonatal intensive care unit admission. Adjusted prevalence ratios (aPR) were calculated for pregnancy and selected infant outcomes by trimester of infection, controlling for demographics. Results Of 35,200 people included in this analysis, 50.8% of pregnant people had infection in the third trimester, 30.8% in the second, and 18.3% in the first. Third trimester infection was associated with a higher frequency of preterm birth compared to first or second trimester infection combined (17.8% vs. 11.8%; aPR 1.44 95% CI: 1.35–1.54). Prevalence of birth defects was 553.4/10,000 live births, with no difference by trimester of infection. Conclusions There were no signals for increased birth defects among infants in this population relative to national baseline estimates, regardless of timing of infection. However, the prevalence of preterm birth in people with SARS‐CoV‐2 infection in pregnancy in our analysis was higher relative to national baseline data (10.0–10.2%), particularly among people with third trimester infection. Consequences of COVID‐19 during pregnancy support recommended COVID‐19 prevention strategies, including vaccination.
Background Multiple reports have described neonatal SARS‐CoV‐2 infection, including likely in utero transmission and early postnatal infection, but published estimates of neonatal infection range by geography and design type. Objectives To describe maternal, pregnancy and neonatal characteristics among neonates born to people with SARS‐CoV‐2 infection during pregnancy by neonatal SARS‐CoV‐2 testing results. Methods Using aggregated data from the Surveillance for Emerging Threats to Mothers and Babies Network (SET‐NET) describing infections from 20 January 2020 to 31 December 2020, we identified neonates who were (1) born to people who were SARS‐CoV‐2 positive by RT‐PCR at any time during their pregnancy, and (2) tested for SARS‐CoV‐2 by RT‐PCR during the birth hospitalisation. Results Among 28,771 neonates born to people with SARS‐CoV‐2 infection during pregnancy, 3816 (13%) underwent PCR testing and 138 neonates (3.6%) were PCR positive. Ninety‐four per cent of neonates testing positive were born to people with infection identified ≤14 days of delivery. Neonatal SARS‐CoV‐2 infection was more frequent among neonates born preterm (5.7%) compared to term (3.4%). Neonates testing positive were born to both symptomatic and asymptomatic pregnant people. Conclusions Jurisdictions reported SARS‐CoV‐2 RT‐PCR results for only 13% of neonates known to be born to people with SARS‐CoV‐2 infection during pregnancy. These results provide evidence of neonatal infection identified through multi‐state systematic surveillance data collection and describe characteristics of neonates with SARS‐CoV‐2 infection. While perinatal SARS‐CoV‐2 infection was uncommon among tested neonates born to people with SARS‐CoV‐2 infection during pregnancy, nearly all cases of tested neonatal infection occurred in pregnant people infected around the time of delivery and was more frequent among neonates born preterm. These findings support the recommendation for neonatal SARS‐CoV‐2 RT‐PCR testing, especially for people with acute infection around the time of delivery.
Objective We examined the relationship between trimester of SARS-CoV-2 infection, illness severity, and risk for preterm birth. Study design We analyzed data for 6336 pregnant persons with SARS-CoV-2 infection in 2020 in the United States. Risk ratios for preterm birth were calculated for illness severity, trimester of infection, and illness severity stratified by trimester of infection adjusted for age, selected underlying medical conditions, and pregnancy complications. Result Pregnant persons with critical COVID-19 or asymptomatic infection, compared to mild COVID-19, in the second or third trimester were at increased risk of preterm birth. Pregnant persons with moderate-to-severe COVID-19 did not show increased risk of preterm birth in any trimester. Conclusion Critical COVID-19 in the second or third trimester was associated with increased risk of preterm birth. This finding can be used to guide prevention strategies, including vaccination, and inform clinical practices for pregnant persons.
Purpose Opioid overdose death rates rose 36% from 2015 to 2016 in Missouri, indicating a worsening of the opioid overdose epidemic. To better understand urban and rural differences in nonfatal opioid overdoses treated in Missouri emergency departments, this paper analyzed hospital billing data from emergency departments due to opioid overdose from 2012 to 2016. Methods Emergency department records meeting the opioid overdose case definition were aggregated into 6 progressively rural groups using the National Center for Health Statistics (NCHS) urban‐rural county classification from 2013. These data were analyzed to determine significant trends amongst and between the geographic groups. Findings Generally, the magnitude of opioid overdose morbidity decreased as levels of rurality increased, using annual percentage change as the metric of change. Over the study period, Missouri's most urban counties had significantly higher rates of opioid overdose and saw larger percentage increases in rates compared to more rural areas. Statewide, all rural‐urban classifications experienced increases in heroin overdose morbidity; however, there was extreme variation in the trajectory of those increases. Heroin overdose rates were much higher in urban areas than rural areas. Conversely, rural and urban areas saw relatively similar patterns for non‐heroin opioid overdoses, though overall magnitude of these increases was more modest across all geographic groups. Conclusions The results from this analysis can help inform prioritization of strategies and resources to implement activities addressing the opioid overdose epidemic. Using a rich hospital discharge database could allow for further analysis of subpopulations to enhance personalization and customization of care.
Among 10,011 neonates of SARS-CoV-2-infected mothers, 1448 (14%) underwent PCR testing (and 1347 (95%) had mothers with third trimester infections). Fifty-nine (4%) were PCR-positive. Neonates testing positive were born to both symptomatic and asymptomatic women, and nearly all were born to women with infection identified near delivery.
ObjectiveIn this analysis we examine Missouri NAS discharge rates with special focus on the ICD-9-CM/ICD-10-CM transition and changes in code descriptions.IntroductionNeonatal Abstinence Syndrome (NAS) rates have tripled for Missouri residents in the past three years. NAS is a condition infants suffer soon after birth due to withdrawal after becoming opioid-dependent in the womb. NAS has significant immediate health concerns and can have long term effects on child development and quality of life.2 The Missouri Department of Health and Senior Services (MODHSS) maintains the Patient Abstract System (PAS), a database of inpatient, emergency room, and outpatient records collected from non-federal hospitals and ambulatory surgical centers throughout the state. PAS records contain extensive information about the visit, patient, and diagnosis. When examining 2015 annual PAS data for NAS-associated discharges, Missouri analysts noticed a greater than 50% increase in discharges, even larger than anticipated in light of the opioid epidemic. Provisional 2016 data produced similar high rates, dispelling the notion that the trend was a transitional problem. In fact, provisional 2016 rates are 115% higher than NAS rates in 2015. In contrast, percentage change of opioid misuse emergency department visits (as defined by MODHSS) for Missouri women age 18-44 was +13% in 2015 and -12% in 2016.MethodsNAS discharges for Missouri residents under the age of 1 were identified using all available diagnosis fields of the PAS record, using finalized data from 2014 and 2015 and provisional data from 2016. Results were stratified by quarter and ICD-CM code. Rates for each of these stratifications were calculated using Missouri resident live births as the denominator. Adhering to methodology used by MODHSS to calculate significance on its public data query tool, 95% confidence intervals were used to determine statistical significance. Depending on numerator size, either Poisson or the inverse gamma methodology was utilized to analyze changes in discharge rates over time. Two ICD-9-CM codes and four ICD-10-CM codes (identified as equivalents using an in-house crosswalk system) were used as NAS indicators (Figure 1).ResultsAn exploration of the data by quarter and diagnosis code (ICD-9-CM or ICD-10-CM), as well as supporting information from the Centers for Medicare & Medicaid Services, show that definitional changes to ICD-10-CM codes P044 and P0449, (previously 76072 in ICD-9-CM coding), was responsible for the majority of the NAS rate increase in Missouri. Annual rates for 76072 and its equivalents jumped significantly from a rate of 3.82 (per 1,000) to 8.22 Q3 to Q4-2015 (95% CI: 3.39-4.29, 7.57-8.87), while ICD-9-CM code 7795 and its equivalents had a more modest rise, from 5.57 to 6.17, which was not statistically significant (95% CI: 5.04-6.13, 5.62-6.76). Once this anomaly was identified, examination of the code’s description was conducted. This exposed a change in definition, with the words ‘suspected to be’ added to the ICD-10-CM long description, which were not present in the ICD-9-CM equivalent. Further complicating matters is a 2017 revision (effective Q3-2016) deleting the ‘suspected’ language from the description. This reversion to language more closely aligning with prior descriptions may be the reason for the slight decrease in discharges coded to P044 in the provisional Q4-2016 PAS data. Though this dataset is not finalized, there was a decrease in discharges that included code P044 from 27.50 in Q3-2016 to 23.15 in Q4-2016 (Figure 2, Figure 3).ConclusionsWhile NAS discharge rates are undoubtedly increasing in Missouri in tune with the opioid epidemic, the extreme escalation from 2014 to 2016 is, at least partially, the result of a definitional change that came with the transition from ICD-9-CM to ICD-10-CM and not a true indication of profound intensification. Indeed, the definitional change of a single ICD-CM code was responsible, in part, for a greater than three-fold increase in NAS discharge rates in Missouri. This analysis will allow public health program planners to better understand NAS trends and adjust intervention strategies accordingly. Further analysis exploring quarterly trends associated with the 2017 ICD-10-CM revision are ongoing.References1. Centers for Medicare & Medicaid Services. ICD-9-CM and ICD-10. https://www.cms.gov/Medicare/Coding/ICD9ProviderDiagnosticCodes/index.html.2. Stanford Children’s Health. Neonatal Abstinence Syndrome. http://www.stanfordchildrens.org/en/topic/default?id=neonatal-abstinence-syndrome-90-P02387.
Background: Pregnant persons with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection are at increased risk of preterm birth, and evidence suggests this risk may be higher among pregnant persons with severe coronavirus disease 2019 (COVID-19) or among those infected later in pregnancy. However, the relationship between trimester of SARS-CoV-2 infection, severity of COVID-19, and preterm birth is not fully understood.Objective: This study examined the relationship between trimester of SARS-CoV-2 infection, illness severity, and risk for preterm birth after adjusting for maternal age, selected underlying conditions, and pregnancy complications.Study Design: Using a cohort of 6,396 pregnant persons with SARS-CoV-2 infection in 2020 identified through the Surveillance for Emerging Threats to Mothers and Babies Network, we analyzed data for those with infection at <37 weeks gestation who delivered a singleton liveborn infant. Illness severity groups (asymptomatic infection, mild, moderate-to-severe, and critical) were adapted from National Institutes of Health and World Health Organization criteria. Risk ratios for preterm birth (<37 weeks) were calculated for illness severity categories (referent=mild), trimester of SARS-CoV-2 infection (referent=first trimester), and illness severity stratified by trimester of infection adjusted for age, selected underlying medical conditions, and pregnancy complications.Results: Pregnant persons with critical COVID-19, compared to mild COVID-19, in the second (aRR 3.9; 95% CI: 1.7-9.0) or third (aRR 4.6; 95% CI: 3.2-6.6) trimester were at increased risk of preterm birth. Among persons infected in the second or third trimester, those with critical COVID-19 delivered sooner after infection compared with persons with mild COVID-19 (p<0.001 for second trimester and p=0.02 for third trimester). Nearly half of those with moderate-to-severe or critical COVID-19 delivered by cesarean, with most critical COVID-19 cesarean deliveries as emergent (76.6% weighted [65/96 unweighted]).Conclusion: When infection occurred in the second or third trimester, critical COVID-19 was associated with increased risk of preterm birth, and those with critical COVID-19 delivered sooner after infection compared to those with mild COVID-19. These findings can be used to guide prevention strategies, including vaccination, and inform clinical practices for pregnant persons, particularly those presenting with critical COVID-19 later in pregnancy.
ObjectiveTo evaluate the relationship between heroin and non-heroin opioid seizures reported by law enforcement and the number of ER visits due to heroin and non-heroin opioid poisoning in selected counties in Missouri.IntroductionIn 2016, there were approximately 63,000 deaths nationally due to drug overdose. This trend continues to increase with the provisional number of US deaths for 2017 being approximately 72,000 (1). This increase in overdose deaths is fueled largely by the opioid class of drugs. The opioid epidemic began in the 1990s with a steady rise in prescription opioid overdoses. However, after 2010 a rise in heroin overdose deaths also began to occur. In addition to the heroin deaths increasing, there was a sharp rise in overdose deaths due to synthetic opioids including illicitly manufactured fentanyl beginning in 2013 (2). In Missouri, ER visits follow similar trends with heroin overdose visits greatly increasing after 2011. While PDMPs help function as data sources that provide information on the licit drug supply, they cannot give much knowledge on the illicit supply. Because of this, drug seizure data from law enforcement can provide a much-needed tool in understanding the supply of illicit substances and their impact on a county’s morbidity.MethodsData sources used in this analysis include the El Paso Intelligence Center (EPIC) drug seizure database thanks to cooperation by the Midwest HIDTA (High Intensity Drug Trafficking Area) office and Missouri Highway Patrol. ER Visit Data was retrieved from the Missouri Patient Abstract System, which includes ER visits for non-federal hospitals. Data was aggregated on a quarterly basis from 2014-2016 resulting in 12 observations (n) for every county observed.A subset of counties were selected and reviewed based on both high counts and high rates of ED visits for opioid overdoses (3). The counties reviewed were Franklin, Greene, Jefferson, St. Francois, St. Louis City and St. Louis County. The majority of these counties were located in the greater St. Louis Are with Greene and St. Francois counties being notable exceptions. Greene County contains the city of Springfield and is located in southwest Missouri. St. Francois is the most rural county in our subset and is located south of the St. Louis area. For each county, the number of ER Visits were compared to the number of drug seizures reported by law enforcement facilities in EPIC. Numbers were compared for both heroin and non-heroin opioids. Records were identified as a heroin overdose or non-heroin opioid overdose based on CDC drug poisoning guidance (4). If an ER discharge record contained codes for both heroin and a non-heroin opioid, the record was counted in the heroin column only. This method avoided counting records twice.The Spearman correlation coefficient was calculated in SAS to determine if there was a possible relationship between seizures and ED visits at the county level due to the relatively few data points, the presence of outlier observations in the seizure numbers, as well as violations of statistical normality among the county seizure data. The Spearman Correlation Coefficient is a better alternative in this case to the commonly used Pearson Correlation Coefficient due to its ability to handle skewed data and outliers (5). As with the Pearson Correlation Coefficient, a score of 0 is read as the variables have no discernable relationships, and scores of 1 or -1 denote a perfect linear relationship between the observed variables (positive and negative respectively).ResultsInitial results showed correlational effects between ED visits and seizures to be generally moderate or weak on the county level. The strongest relationships observed were found in St. Louis City for both heroin (R=-0.455) and non-heroin opioids (R=-0.51) as well as Jefferson County for both heroin (R=0.536) and non-heroin (R=0.50). St. Louis County also had a notable relationship for heroin seizures and heroin ED visits with R=-0.55. P values were also calculated to test if correlation values differed significantly from the null hypothesis of R=0 (i.e. no correlation). In all examined cases, there was no p value that was less than the standard cutoff of 0.05 which indicates none of the results are markedly different given the null hypothesis of R=0 is true (6).Of particular interest is the contrast in results between St. Louis City and Jefferson County. St. Louis City had a moderate negative relationship with seizures and ED visits with ED Visits tending to decrease as drug seizures increased. Whereas, Jefferson County had a moderate positive relationship with ED Visits increasing alongside drug seizures. Due to their close geographic proximity, it is likely that both counties influence one another. Further evaluation is required to gauge regional effects.ConclusionsDue to the complexity of the opioid epidemic, the value of having varied data sources cannot be understated. While the correlational effects observed here are not indicative of a strong relationship between ED visits and drug seizures, further evaluation and research of both data sources is highly recommended. As additional data is gathered in the future, stronger analyses than the Spearman Correlation Coefficient may be used to further explore the relationship between drug overdose morbidity and law enforcement seizure data. Other relationships may also be explored such as drug seizures in relation to drug overdose mortality.References1. National Center for Health Statistics. (2018, September 12). Retrieved September 19, 2018, from <a href="https://www.cdc.gov/nchs/nvss/vsrr/drug-overdose-data.htm">https://www.cdc.gov/nchs/nvss/vsrr/drug-overdose-data.htm</a>2. Opioid Overdose. (2017, August 30). Retrieved September 19, 2018, from <a href="https://www.cdc.gov/drugoverdose/epidemic/index.html">https://www.cdc.gov/drugoverdose/epidemic/index.html</a>3. Bureau of Health Care Analysis and Data Dissemination, Missouri Department of Health and Senior Services. (2018, June 27). ER Visits Due to Opioid Misuse. Retrieved September 19, 2018 from <a href="https://health.mo.gov/data/opioids/pdf/opioid-dashboard-slide-16.pdf">https://health.mo.gov/data/opioids/pdf/opioid-dashboard-slide-16.pdf</a> 4. CDC Prescription Drug Overdose Team. (2013, August 12). GUIDE TO ICD-9-CM AND ICD-10 CODES RELATED TO POISONING AND PAIN. Retrieved September 2018 from <a href="https://www.cdc.gov/drugoverdose/pdf/pdo_guide_to_icd-9-cm_and_icd-10_codes-a.pdf">https://www.cdc.gov/drugoverdose/pdf/pdo_guide_to_icd-9-cm_and_icd-10_codes-a.pdf</a>5. Mukaka, M. (2012, September). A guide to appropriate use of Correlation coefficient in medical research. Retrieved September 20, 2018, from <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3576830/">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3576830/</a> 6. Ronald L. Wasserstein & Nicole A. Lazar (2016) The ASA's Statement on p-Values: Context, Process, and Purpose, The American Statistician, 70:2, 129-133, DOI:10.1080/00031305.2016.1154108 from <a href="https://amstat.tandfonline.com/doi/pdf/10.1080/00031305.2016.1154108">https://amstat.tandfonline.com/doi/pdf/10.1080/00031305.2016.1154108</a>
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