Case Counts Research Articles

Study of significance of bone marrow microvessel density in myeloproliferative neoplasms in correlation with CD34 blasts, mast cell count and fibrosis

Background: Myeloproliferative neoplasms (MPN) are clonal hematopoietic stem cell diseases characterised by myeloid cell growth from one or more lineages. Angiogenesis, in contrast to other subtypes, plays a substantial role in the pathophysiology of primary myelofibrosis (PMF). Research expressing the correlation of microvessel density (MVD), blasts, fibrosis and mast cell count in MPN cases are rarely conducted. We aimed to study the significance of MVD in correlation with CD34 blasts, mast cells and fibrosis in bone marrow biopsies of MPN patients. Methods: The current research was a cross sectional study conducted on 66 cases diagnosed as MPN during a six-year period. This comprised of 32 chronic myeloid leukemia (CML), 31 PMF and three essential thrombocythemia (ET) cases. Routine staining along with reticulin stain to look for fibrosis and immunohistochemistry (IHC) using CD34 and mast cell tryptase (MCT) were performed. Results: We found increased MVD in PMF, when compared to CML and ET (p = 0.042). Further, mean MVD was observed to be increased with high blast counts (p = 0.036). On follow up, raised mean MVD was seen in those cases with relapse/deceased as compared to disease-free patients, which was highly significant (p = 0.000). Conclusions: Increased MVD score was mostly associated with PMF subtype among all the MPNs. Further, higher MVD was observed to be associated with increased blast count and poor prognosis. With angiogenesis playing a critical role in disease outcome, we now have drugs to regulate angiogenesis that are supported by contemporary research. However, further studies with larger cohorts to establish the theranostic role of MVD in MPNs is recommended.

Risk factors for SARS-CoV-2 seropositivity in a health care worker population during the early pandemic

BackgroundWhile others have reported severe acute respiratory syndrome-related coronavirus 2(SARS-CoV-2) seroprevalence studies in health care workers (HCWs), we leverage the use of a highly sensitive coronavirus antigen microarray to identify a group of seropositive health care workers who were missed by daily symptom screening that was instituted prior to any epidemiologically significant local outbreak. Given that most health care facilities rely on daily symptom screening as the primary method to identify SARS-CoV-2 among health care workers, here, we aim to determine how demographic, occupational, and clinical variables influence SARS-CoV-2 seropositivity among health care workers.MethodsWe designed a cross-sectional survey of HCWs for SARS-CoV-2 seropositivity conducted from May 15th to June 30th 2020 at a 418-bed academic hospital in Orange County, California. From an eligible population of 5,349 HCWs, study participants were recruited in two ways: an open cohort, and a targeted cohort. The open cohort was open to anyone, whereas the targeted cohort that recruited HCWs previously screened for COVID-19 or work in high-risk units. A total of 1,557 HCWs completed the survey and provided specimens, including 1,044 in the open cohort and 513 in the targeted cohort. Demographic, occupational, and clinical variables were surveyed electronically. SARS-CoV-2 seropositivity was assessed using a coronavirus antigen microarray (CoVAM), which measures antibodies against eleven viral antigens to identify prior infection with 98% specificity and 93% sensitivity.ResultsAmong tested HCWs (n = 1,557), SARS-CoV-2 seropositivity was 10.8%, and risk factors included male gender (OR 1.48, 95% CI 1.05–2.06), exposure to COVID-19 outside of work (2.29, 1.14–4.29), working in food or environmental services (4.85, 1.51–14.85), and working in COVID-19 units (ICU: 2.28, 1.29–3.96; ward: 1.59, 1.01–2.48). Amongst 1,103 HCWs not previously screened, seropositivity was 8.0%, and additional risk factors included younger age (1.57, 1.00-2.45) and working in administration (2.69, 1.10–7.10).ConclusionSARS-CoV-2 seropositivity is significantly higher than reported case counts even among HCWs who are meticulously screened. Seropositive HCWs missed by screening were more likely to be younger, work outside direct patient care, or have exposure outside of work.

Use of Community SARS-CoV-2 Case Counts and Instantaneous Reproductive Number to Predict Short-Term COVID-19 Hospital Admission Volumes.

The severe acute respiratory syndrome (SARS-CoV-2) pandemic and high hospitalization rates placed a tremendous strain on hospital resources, necessitating the use of models to predict hospital volumes and the associated resource requirements. Complex epidemiologic models have been developed and published, but many require continued adjustment of input parameters. We developed a simplified model for short-term bed need predictions that self-adjusts to changing patterns of disease in the community and admission rates. The model utilizes public health data on community new case counts for SARS-CoV-2 and projects anticipated hospitalization rates. The model was retrospectively evaluated after the second wave of SARS-CoV-2 in New York, New York (October 2020-April 2021) for its accuracy in predicting numbers of coronavirus disease 2019 (COVID-19) admissions 3, 5, 7, and 10 days into the future, comparing predicted admissions with actual admissions for each day at a large integrated health-care delivery network. The mean absolute percent error of the model was found to be low when evaluated across the entire health system, for a single region of the health system or for a single large hospital (6.1%-7.6% for 3-day predictions, 9.2%-10.4% for 5-day predictions, 12.4%-13.2% for 7-day predictions, and 17.1%-17.8% for 10-day predictions).

Tailoring capture-recapture methods to estimate registry-based case counts based on error-prone diagnostic signals.

Surveillance research is of great importance for effective and efficient epidemiological monitoring of case counts and disease prevalence. Taking specific motivation from ongoing efforts to identify recurrent cases based on the Georgia Cancer Registry, we extend recently proposed "anchor stream" sampling design and estimation methodology. Our approach offers a more efficient and defensible alternative to traditional capture-recapture (CRC) methods by leveraging a relatively small random sample of participants whose recurrence status is obtained through a principled application of medical records abstraction. This sample is combined with one or more existing signaling data streams, which may yield data based on arbitrarily non-representative subsets of the full registry population. The key extension developed here accounts for the common problem of false positive or negative diagnostic signals from the existing data stream(s). In particular, we show that the design only requires documentation of positive signals in these non-anchor surveillance streams, and permits valid estimation of the true case count based on an estimable positive predictive value (PPV) parameter. We borrow ideas from the multiple imputation paradigm to provide accompanying standard errors, and develop an adapted Bayesian credible interval approach that yields favorable frequentist coverage properties. We demonstrate the benefits of the proposed methods through simulation studies, and provide a data example targeting estimation of the breast cancer recurrence case count among Metro Atlanta area patients from the Georgia Cancer Registry-based Cancer Recurrence Information and Surveillance Program (CRISP) database.

Infant burn injuries related to water heating for powdered infant formula preparation.

Guidance for preparing powdered infant formula (PIF) helps to ensure it meets the nutritional needs of infants and is safe to consume. Among safety concerns is Cronobacter sakazakii contamination which can lead to serious infections and death. PIF preparation guidance varies; there is a lack of consensus on whether there is a need to boil water to inactivate potential Cronobacter and for how long to let the water cool before reconstitution. We sought to quantify the burden of burn injuries among infants related to water heating for PIF preparation. Estimating this burden may help inform preparation recommendations. Burn injuries among infants <18 months of age were identified from 2017 to 2019 National Electronic Injury Surveillance System data collected from sampled hospital emergency departments. Injuries were classified as related to PIF water heating, potentially related to PIF water heating but with undetermined causation, related to other infant feeding aspects, or unrelated to infant formula or breast milk feeding. Unweighted case counts for each injury classification were determined. Across sampled emergency departments, 7 PIF water heating injuries were seen among the 44,395 injuries reported for infants <18 months. No reported PIF water heating injuries were fatal, but 3 required hospitalization. Another 238 injuries potentially related to PIF water heating but with undetermined causation were also seen. Preparation guidance should consider both the potential risk for Cronobacter infection and the potential risk for burns.

Impact of the COVID-19 Vaccination on Bike Sharing Demand in NYC: ARIMAX and Bayesian Time Series Analyses

The objective of this study is to investigate the impact of the COVID-19 vaccine on bike sharing demand in New York City (NYC). The vaccination’s impact was also compared with other known influences (COVID-19 cases counts, COVID-19 deaths, weather data, trip purpose, and more) to help improve bike share demand modeling in a pandemic setting. Autoregressive integrated moving average (ARIMAX) time series models were estimated for Brooklyn and Manhattan in both the pre-vaccine and post-vaccine periods. Mean absolute percentage error (MAPE) was used for model evaluation, and the average MAPE was below 5, suggesting a high-level accuracy in modeling. The Bayesian time series analysis results were very similar to the ARIMAX model results (with the exception of some parameters being significantly underestimated). The results of the time series analyses showed that vaccination did not have a significant effect on bike sharing demand in Manhattan but had a significant effect leading to increases in bike sharing demand in Brooklyn. Despite this, vaccination was not the main influence on bike sharing demand. For instance, in Brooklyn, grocery and retail/recreational shopping were strong influences both pre- and post-vaccine, indicating that regardless of vaccination, shopping is essential. COVID-19 cases counts had opposing effects in Brooklyn and Manhattan. Other findings include higher temperatures leading to increased bike demand, and precipitation and stronger winds leading to decreased bike sharing demand.

SARS-CoV-2 Seroprevalence Compared with Confirmed COVID-19 Cases among Children, Colorado, USA, May–July 2021

To compare SARS-CoV-2 antibody seroprevalence among children with seropositive confirmed COVID-19 case counts (case ascertainment by molecular amplification) in Colorado, USA, we conducted a cross-sectional serosurvey during May-July 2021. For a convenience sample of 829 Colorado children, SARS-CoV-2 seroprevalence was 36.7%, compared with prevalence of 6.5% according to individually matched COVID-19 test results reported to public health. Compared with non-Hispanic White children, seroprevalence was higher among Hispanic, non-Hispanic Black, and non-Hispanic other race children, and case ascertainment was significantly lower among Hispanic and non-Hispanic Black children. This serosurvey accurately estimated SARS-CoV-2 prevalence among children compared with confirmed COVID-19 case counts and revealed substantial racial/ethnic disparities in infections and case ascertainment. Continued efforts to address racial and ethnic differences in disease burden and to overcome potential barriers to case ascertainment, including access to testing, may help mitigate these ongoing disparities.

The variability of client, therapist and clinic in psychotherapy outcomes: A three‐level hierarchical model

AbstractThis article investigates the percentage of variance in psychotherapy outcomes explained by the case‐mix variables for individual cases, by the therapist (therapist effects), and what additional variance is explained by the clinic with which the therapist is affiliated. While there has been substantial recent research regarding therapist effects, very little has been published regarding clinic‐level effects after controlling for therapist effects. The study utilised the largest sample reported to date, using data from 28 clinics with a minimum of 2,000 cases in a clinical range of severity of symptoms with pre‐post change scores on an outcome questionnaire. Only cases treated by a therapist with at least 30 cases were included. These selection criteria resulted in a case count of 156,258 clients treated by 874 therapists located at 28 clinics. After controlling for differences in case mix using diagnosis and intake score (severity of symptoms), the resulting analyses indicated that 5.21% of the variance in treatment outcome was explained by the therapist, while another 1.13% of the variance was associated with the clinic. Findings are discussed with implications for practice and policy.

Impact of public health policy and mobility change on transmission potential of severe acute respiratory syndrome coronavirus 2 in Rhode Island, March 2020 – November 2021

ABSTRACT To study the SARS-CoV-2 transmission potential in Rhode Island (RI) and its association with policy changes and mobility changes, the time-varying reproduction number, Rt, was estimated. The daily incident case counts (16 March 2020, through 30 November 2021) were bootstrapped within a 15-day sliding window and multiplied by Poisson-distributed multipliers (λ = 4, sensitivity analysis: 11) to generate 1000 estimated infection counts, to which EpiEstim was applied to generate Rt time series. The median Rt percentage change when policies changed was estimated. The time lag correlations were assessed between the 7-day moving average of the relative changes in Google mobility data in the first 90 days, and Rt and estimated infection count, respectively. There were three major pandemic waves in RI in 2020–2021: spring 2020, winter 2020–2021 and fall-winter 2021. The median Rt fluctuated within the range of 0.5–2 from April 2020 to November 2021. Mask mandate (18 April 2020) was associated with a decrease in Rt (−25.99%, 95% CrI: −37.42%, −14.30%). Termination of mask mandates on 6 July 2021 was associated with an increase in Rt (36.74%, 95% CrI: 27.20%, 49.13%). Positive correlations were found between changes in grocery and pharmacy, Rt retail and recreation, transit, and workplace visits, for both Rt and estimated infection count, respectively. Negative correlations were found between changes in residential area visits for both Rt and estimated infection count, respectively. Public health policies enacted in RI were associated with changes in the pandemic trajectory. This ecological study provides further evidence of how non-pharmaceutical interventions and vaccination slowed COVID-19 transmission in RI.

Spatial Analysis of Greenspace and Mental Health in North Carolina: Consideration of Rural and Urban Communities.

Greenspace positively impacts mental health. Previous research has focused on the greenspace-mental health relationship in urban areas. Yet, little work has looked at rural areas despite rural communities reporting similar rates of poor mental health outcomes and higher rates of suicide mortality compared with urban areas. This ecological research study examined the following research questions: (1) Do public and/or private greenspaces affect the spatial distribution of mental health outcomes in North Carolina? (2) Does this relationship change with rurality? Emergency department data for 6 mental health conditions and suicide mortality data from 2009 to 2018 were included in this analysis. Spatial error and ordinary least squares regressions were used to examine the influence of public and private greenspace quantity on mental health in rural and urban communities. Results suggest greenspace benefits mental health in rural and urban communities. The strength of this relationship varies with urbanity and between public and private greenspaces, suggesting a more complex causal relationship. Given the high case counts and often lower density of mental health care facilities in rural areas, focusing attention on low-cost mental health interventions, such as greenspace, is important when considering rural mental health care.

A Systematic Review of Inflammatory Bowel Disease Epidemiology and Health Outcomes in Sexual and Gender Minority Individuals

A Systematic Review of Inflammatory Bowel Disease Epidemiology and Health Outcomes in Sexual and Gender Minority Individuals

Analysis of google trends and search results for ophthalmic symptoms of monkeypox.

In July2022, the World Health Organization (WHO) declared monkeypox virus's global spread a "public health emergency of international concern." About a quarter of monkeypox cases feature ophthalmic symptoms. We assessed trends in worldwide search interest in monkeypox ophthalmic involvement and inclusion in online search engine queries. The following keywords were searched on Google Trends from April 1, 2022, to August 12, 2022: monkeypox + eye, pink eye, eye infection, eyelid, vision, blurry vision, vision loss, blindness, eye symptoms, eye problems, eye pain, eye redness, conjunctivitis, conjunctiva, cornea, keratitis, corneal ulcer, and blepharitis. We analyzed trends, correlated search interest with case count data, and compared popularity of search terms via nonparametric Mann-Whitney-U analysis. Inclusion of ophthalmic symptoms in Google search results for "monkeypox symptoms" was assessed. "Monkeypox eye" had the highest averagesearch interest worldwide and in the United States. Search interest peaked between mid-May and late July 2022. When compared to interest in "monkeypox rash," the most searched monkeypox symptom, the average interest in "monkeypox eye" was lower (p < 0.01). Of the first 50 results from the Google search of "monkeypox symptoms," 10/50 (20%) mentioned ophthalmic symptoms. 6/50 (12%) mentioned the eye as a route of virus transmission. Search interest in monkeypox ophthalmic symptoms corresponds with geographic and temporal trends, i.e., timing and location of the first reported non-endemic cases and WHO announcement. Although ophthalmic symptoms are not as widely searched currently, inclusion in public health messaging is key for diagnosis, appropriate management, and reduction of further transmission.

Segregated mobility patterns amplify neighborhood disparities in the spread of COVID-19

AbstractThe global and uneven spread of COVID-19, mirrored at the local scale, reveals stark differences along racial and ethnic lines. We respond to the pressing need to understand these divergent outcomes via neighborhood level analysis of mobility and case count information. Using data from Chicago over 2020, we leverage a metapopulation Susceptible-Exposed-Infectious-Removed model to reconstruct and simulate the spread of SARS-CoV-2 at the ZIP Code level. We demonstrate that exposures are mostly contained within one’s own ZIP Code and demographic group. Building on this observation, we illustrate that we can understand epidemic progression using a composite metric combining the volume of mobility and the risk that each trip represents, while separately these factors fail to explain the observed heterogeneity in neighborhood level outcomes. Having established this result, we next uncover how group level differences in these factors give rise to disparities in case rates along racial and ethnic lines. Following this, we ask what-if questions to quantify how segregation impacts COVID-19 case rates via altering mobility patterns. We find that segregation in the mobility network has contributed to inequality in case rates across demographic groups.

Monkeypox in Latin America and the Caribbean: assessment of the first 100 days of the 2022 outbreak

ABSTRACT During the 2022 monkeypox (mpox) epidemic’s first 100 days, 99 non-endemic countries, including 25 Latin American and Caribbean (LAC) countries, reported >64,000 cases. We aim to assess the cases’ introduction, epidemiological profile, initial response, transmission dynamics, and main challenges ahead among LAC countries during the first 100 days of the mpox 2022 epidemic. We used mixed methods, including desktop research and open data analysis. The 2022 mpox epidemic has progressed consistently through LAC, with Brazil and Peru combining for over 80% of the confirmed LAC cases. Although Brazil reports the highest mpox case counts (n = 4472), Peru reports the highest incidence (41 confirmed cases per 1 million inhabitants). Initially, LAC missed the opportunity to focus on the high-risk population, including the people living with HIV (PLHIV) and gay, bisexual, and men who have sex with men (GBMSM). Moreover, the main challenges ahead include stigmatization, vaccine inequity, barriers to accessing diagnostics, and complete isolation. Furthermore, we estimated that Colombia, Brazil, the United States, and Peru are the world frontrunners in mpox duplication time (estimated between 6.4 and 8.8) and effective reproductive number (estimated between 2.7 and 3.8). In addition, Brazil reported its first case of inverse zoonosis in a dog and Peru its first autochthonous MPXV lineage, B.1.6. LAC has become the epicenter of the 2022 mpox epidemic, with Brazil and Peru emerging as the new mpox hot zones. Therefore, LAC countries must join efforts to control this epidemic and overcome the challenges of vaccine inequity and stigmatization.

Online Health Information Seeking for Mpox in Endemic and Nonendemic Countries: Google Trends Study.

The recent global outbreak of mpox (monkeypox) has already been declared a public health emergency of international concern by the World Health Organization. Given the health, social, and economic impacts of the COVID-19 pandemic, there is understandable concern and anxiety around the emergence of another infectious disease-especially one about which little is known. We used Google Trends to explore online health information seeking patterns for mpox in endemic and nonendemic countries and investigated the impact of the publication of the first in-country case on internet search volume. Google Trends is a publicly accessible and free data source that aggregates worldwide Google search data. Google search data were used as a surrogate measure of online health information seeking for 178 days between February 18 and August 18, 2022. Searching data were downloaded across this time period for nonendemic countries with the highest case count (United States, Spain, Germany, United Kingdom, and France) and 5 endemic countries (Democratic Republic of Congo, Nigeria, Ghana, Central African Republic, and Cameroon). Joinpoint regression analysis was used to measure changes in searching trends for mpox preceding and following the announcement of the first human case. Online health information seeking significantly increased after the publication of the first case in all the nonendemic countries-United States, Spain, Germany, United Kingdom, and France, as illustrated by significant joinpoint regression models. Joinpoint analysis revealed that models with 3 significant joinpoints were the most appropriate fit for these data, where the first joinpoint represents the initial rise in mpox searching trend, the second joinpoint reflects the start of the decrease in the mpox searching trend, and the third joinpoint represents searching trends' return to searching levels prior to the first case announcement. Although this model was also found in 2 endemic countries (ie, Ghana and Nigeria), it was not found in Central African Republic, Democratic Republic of Congo, or Cameroon. Findings demonstrate a surge in online heath information seeking relating to mpox after the first in-country case was publicized in all the nonendemic countries and in Ghana and Nigeria among the endemic counties. The observed increases in mpox searching levels are characterized by sharp but short-lived periods of searching before steep declines back to levels observed prior to the publication of the first case. These findings emphasize the importance of the provision of accurate, relevant online public health information during disease outbreaks. However, online health information seeking behaviors only occur for a short time period, and the provision of accurate information needs to be timely in relation to the publication of new case-related information.

Mpox in the United States: Current implications for public health nursing.

In 2022, mpox, an orthopoxvirus first isolated in 1958 in cynomolgus monkeys, became a global public health threat. While the virus can be communicated through skin-to-skin contact from any infected person to non-infected person, most cases in the United States have been in gay and bisexual men. Consequently, early public health and community-based efforts concentrated on reducing infections in this population. This article explores current mpox case count epidemiologic data and trends. In addition, vaccination indications, contraindications, adverse events, and national administration data are provided along with directions for nurses and other clinicians moving forward in the outbreak.

Are at-risk sociodemographic attributes stable across COVID-19 transmission waves?

COVID-19 health impacts and risks have been disproportionate across social, economic, and racial gradients (Chen et al., 2021; Thompson et al., 2021; Mamuji et al., 2021; COVID-19 and Ethnicity, 2020). By examining the first five waves of the pandemic in Ontario, we identify if Forward Sortation Area (FSAs)based measures of sociodemographic status and their relationship to COVID-19 cases are stable or vary by time. COVID-19 waves were defined using a time-series graph of COVID-19 case counts by epi-week. Percent Black visible minority, percent Southeast Asian visible minority and percent Chinese visible minority at the FSA level were then integrated into spatial error models with other established vulnerability characteristics. The models indicate that area-based sociodemographic patterns associated with COVID-19 infection change over time. If sociodemographic characteristics are identified as high risk (increased COVID-19 case rates) increased testing, public health messaging, and other preventative care may be implemented to protect populations from the inequitable burden of disease.

Sensitivity and specificity in signal detection with the reporting odds ratio and the information component.

As measures of association between an adverse drug reaction (ADR) and exposure to a drug the reporting odds ratio (ROR) and the information component (IC) can be used. We sought to test the reliability of signal detection with these. We simulated ADR counts as binomially distributed random numbers for different expected ADR frequencies and theoretical reporting odds ratios (RORs). We then calculated the empirical IC and the empirical ROR and their confidence intervals. The rate of signals that was detected despite a theoretical ROR of 1 represented the false positive rate, and represented the sensitivity if the ROR was >1. For expected case counts below 1 the false positive rate oscillates from 0.01 to 0.1 even though 0.025 were intended. Even beyond expected case counts of 5 oscillations can cover a range of 0.018 to 0.035. The first n oscillations with the largest amplitude are eliminated if a minimum case count of n is required. To detect an ROR of 2 with a sensitivity of 0.8, a minimum of 12 expected ADRs are required. In contrast, 2 expected ADRs suffice to detect an ROR of 4. Summaries of measures for disproportionality should include the expected number of cases in the group of interest if a signal was detected. If no signal was detected the sensitivity for the detection of a representative ROR or the minimum ROR that could be detected with probability 0.8 should be reported.

SARS-CoV-2 concentration in wastewater consistently predicts trends in COVID-19 case counts by at least two days across multiple WWTP scales.

Wastewater surveillance of SARS-CoV-2 has proven instrumental in mitigating the spread of COVID-19 by providing an economical and equitable approach to disease surveillance. Here, we analyze the correlation of SARS-CoV-2 RNA in influents of seven wastewater plants (WWTPs) across the state of South Carolina with corresponding daily case counts to determine whether underlying characteristics of WWTPs and sewershed populations predict stronger correlations. The populations served by these WWTPs have varying social vulnerability and represent 24% of the South Carolina population. The study spanned 15 months from April 19, 2020, to July 1, 2021, which includes the administration of the first COVID-19 vaccines. SARS-CoV-2 RNA concentrations were measured by either reverse transcription quantitative PCR (RT-qPCR) or droplet digital PCR (RT-ddPCR). Although populations served and average flow rate varied across WWTPs, the strongest correlation was identified for six of the seven WWTPs when daily case counts were lagged two days after the measured SARS-CoV-2 RNA concentration in wastewater. The weakest correlation was found for WWTP 6, which had the lowest ratio of population served to average flow rate, indicating that the SARS-CoV-2 signal was too dilute for a robust correlation. Smoothing daily case counts by a 7-day moving average improved correlation strength between case counts and SARS-CoV-2 RNA concentration in wastewater while dampening the effect of lag-time optimization. Correlation strength between cases and SARS-CoV-2 RNA was compared for cases determined at the ZIP-code and sewershed levels. The strength of correlations using ZIP-code-level versus sewershed-level cases were not statistically different across WWTPs. Results indicate that wastewater surveillance, even without normalization to fecal indicators, is a strong predictor of clinical cases by at least two days, especially when SARS-CoV-2 RNA is measured using RT-ddPCR. Furthermore, the ratio of population served to flow rate may be a useful metric to assess whether a WWTP is suitable for a surveillance program.

Changes in routine blood parameters of patients with generalized tonic clonic seizure: a retrospective study

To analyze the alterations in routine blood parameters in these patients. Generalized tonic clonic seizure (GTCS) is one main type of generalized epilepsy. Clinicians have recently found changes in routine blood parameters in GTCS patients. A retrospective study of 45 GTCS patients and 200 healthy controls was conducted. Blood routine tests in emergency ward and on the second day of admission were retrieved. Changes in routine blood parameters, including red blood cell (RBC) count, white blood cell (WBC) count, platelet count, lymphocyte count, neutrophil count, and neutrophil-to-lymphocyte ratio (NLR) on the seizure episode day and the second day after seizure were analyzed. The GTCS patients had increased platelet count, RBC count, WBC count, neutrophil count, lymphocyte count, and NLR, especially at onset of GTCS episode, compared to the controls. These parameters had decreased on the second day after seizure onset. The neutrophil count and WBC count in GTCS cases with seizure duration <5min were significantly lower than those with seizure duration >5 min. However, this phenomenon was not observed in other indices. The GTCS may induce a transient increase in RBC count, WBC count, and platelet count, and obvious elevation in neutrophil count. There is a certain correlation between WBC count and the duration of seizures. Changes in routine blood parameters observed in GTCS patients may provide useful information clinically.