Hospital Surveillance Data Research Articles

Spatial and temporal clustering of patients hospitalized with laboratory-confirmed influenza in the United States

BackgroundTiming of influenza spread across the United States is dependent on factors including local and national travel patterns and climate. Local epidemic intensity may be influenced by social, economic and demographic patterns. Data are needed to better explain how local socioeconomic factors influence both the timing and intensity of influenza seasons to result in national patterns. MethodsTo determine the spatial and temporal impacts of socioeconomics on influenza hospitalization burden and timing, we used population-based laboratory-confirmed influenza hospitalization surveillance data from the CDC-sponsored Influenza Hospitalization Surveillance Network (FluSurv-NET) at up to 14 sites from the 2009/2010 through 2013/2014 seasons (n = 35,493 hospitalizations). We used a spatial scan statistic and spatiotemporal wavelet analysis, to compare temporal patterns of influenza spread between counties and across the country. ResultsThere were 56 spatial clusters identified in the unadjusted scan statistic analysis using data from the 2010/2011 through the 2013/2014 seasons, with relative risks (RRs) ranging from 0.09 to 4.20. After adjustment for socioeconomic factors, there were five clusters identified with RRs ranging from 0.21 to 1.20. In the wavelet analysis, most sites were in phase synchrony with one another for most years, except for the H1N1 pandemic year (2009–2010), wherein most sites had differential epidemic timing from the referent site in Georgia. ConclusionsSocioeconomic factors strongly impact local influenza hospitalization burden. Influenza phase synchrony varies by year and by socioeconomics, but is less influenced by socioeconomics than is disease burden.

Malaria-related hospitalization during childhood in Papua, Indonesia: A retrospective cohort study.

BackgroundIn endemic regions, the age distribution of malaria varies according to the infecting Plasmodium species. We aimed to delineate the pattern of malaria-related hospitalization from birth in Timika, Papua–an area co-endemic for P. falciparum and P. vivaxMethodsBetween April 2004 and December 2013, infants born at Mitra Masyarakat Hospital, or presenting within the first 7 days of life, were enrolled retrospectively into a cohort study and followed passively using routinely-collected hospital surveillance data. Outcomes were stratified by the presence or absence of Plasmodium parasitemia and included re-presentation to hospital, requirement for hospital admission and death.ResultsOverall, 11,408 infants were enrolled into the cohort. Median follow-up was 4.3 (maximum 9.7) years. In total, 7,847 (68.9%) infants made 90,766 re-presentations to hospital, 18,105 (19.9%) of which were associated with Plasmodium parasitemia. The incidence of re-presentations with malaria during the first year of life was 213 per 1,000 person-years (py) for P. vivax and 79 per 1,000py for P. falciparum (Incidence Rate Ratio (IRR) = 2.69, 95% Confidence Interval (95%CI): 2.48–2.92). After the age of 5 years, the incidence of P. vivax had fallen to 77/1,000py and the incidence of P. falciparum had risen to 95/1,000py (IRR = 0.80, 95%CI: 0.73–0.88). Overall, 79.7% (14,431/18,105) of malaria re-presentations were recurrences rather than initial infections. Malaria accounted for 31.7% (2,126/3,120) of all hospital admissions. The infant mortality rate in this study was 52 deaths per 1,000 live births. Beyond the early neonatal period, 13.4% of deaths were associated with Plasmodium parasitemia.ConclusionsIn Papua, Indonesia, malaria is a major cause of hospital presentation and admission in early life. The initial predominance of P. vivax over P. falciparum inverts after five years of age. Malaria is directly associated with nearly one in seven deaths after the early neonatal period.

Early and late mortality after malaria in young children in Papua, Indonesia

BackgroundIn southern Papua, Indonesia, malaria is highly prevalent in young children and is a significant cause of morbidity and early mortality. The association between malaria and delayed mortality is unknown.MethodsRoutinely-collected hospital surveillance data from southern Papua, Indonesia, were used to assess the risk of recurrent malaria and mortality within 12 months of an initial presentation with malaria in all children younger than 5 years old attending the local hospital. Analysis was primarily by Kaplan Meier and Cox regression methods.ResultsIn total 15,716 children presenting with malaria between April 2004 and December 2013 were included in the analysis; 6184 (39.3%) with Plasmodium falciparum, 7499 (47.7%) with P. vivax, 203 (1.3%) with P. malariae, 3 with P. ovale and 1827 (11.6%) with mixed infections. Within 1 year, 48.4% (7620/15,716) of children represented a total of 16,957 times with malaria (range 1 to 11 episodes), with the incidence of malaria being greater in patients initially presenting with P. vivax infection (1334 [95%CI 1307–1361] per 1000 patient years) compared to those with P. falciparum infection (920 [896–944]). In total 266 (1.7%) children died within 1 year of their initial presentation, 129 (48.5%) within 30 days and 137 (51.5%) between 31 and 365 days. There was no significant difference in the mortality risk in patients infected with P. vivax versus P. falciparum either before 30 days (Hazard Ratio (HR) 1.02 [0.69,1.49]) or between 31 and 365 days (HR = 1.30 [0.90,1.88]). Children who died had a greater incidence of malaria, 2280 [95%CI 1946–2671] per 1000 patient years preceding their death, compared to 1141 [95%CI 1124–1158] per 1000 patient years in those surviving.ConclusionsChildren under-5 years old with P. vivax malaria, are at significant risk of multiple representations with malaria and of dying within 1 year of their initial presentation. Preventing recurrent malaria must be a public health priority in this vulnerable population.

National Surveillance for Health-Related Workplace Absenteeism, United States 2017-18

National Surveillance for Health-Related Workplace Absenteeism, United States 2017-18

Impact of Repeat Infection Timeframe on CLABSI Surveillance

BACKGROUND Mandatory reporting of central line-associated bloodstream infections (CLABSIs) to the National Healthcare Safety Network (NHSN) is used as a measure of healthcare quality and hospital reimbursement. State reporting laws mandate patient notification of each healthcare associated infection. In January 2015, NHSN updated surveillance procedures to include the implementation of the 14-day repeat infection timeframe (RIT), to clarify the definition of resolved versus continued infection. We describe the impact of the RIT on CLABSI surveillance at an academic, quaternary care pediatric hospital. METHODS We performed a retrospective, cohort evaluation of existing hospital surveillance data to identify repeat CLABSIs occurring within the first seven days after the CLABSI RIT from January 2015 to July 2018. Repeat CLABSI events were considered unresolved infections when the microorganisms grown were identical or closely related to those grown at onset or during the first 5 days of the index CLABSI. Characteristics of repeat CLABSI events were described and compared to all CLABSIs using Fisher's exact test. RESULTS We identified 13 new CLABSI events that occurred within seven days of the previous CLABSI's RIT (day 15-21). Seven of these CLABSIs were considered unresolved infections. The remaining 6 CLABSIs involved different microorganisms than identified at onset of the initial CLABSI. There was no difference in clinical service (p=0.89), patient immune status (p=0.25), line type (p=0.69), or presence of multiple central lines (p=0.27) when comparing unresolved CLABSIs (n=7) to the total number of CLABSIs identified during the time period (n=389). Conversely, 4 CLABSIs had new microorganisms added during the RIT that were not reported as a unique event. CONCLUSIONS The use of the RIT does not always appropriately identify new CLABSIs at our institution. Since CLABSI identification is used for improvement efforts, modification of the RIT may be needed to properly identify unique CLABSIs.

Birth Cohort Effects in Influenza Surveillance Data: Evidence That First Influenza Infection Affects Later Influenza-Associated Illness.

The evolution of influenza A viruses results in birth cohorts that have different initial influenza virus exposures. Historically, A/H3 predominant seasons have been associated with more severe influenza-associated disease; however, since the 2009 pandemic, there are suggestions that some birth cohorts experience more severe illness in A/H1 predominant seasons. United States influenza virologic, hospitalization, and mortality surveillance data during 2000-2017 were analyzed for cohorts born between 1918 and 1989 that likely had different initial influenza virus exposures based on viruses circulating during early childhood. Relative risk/rate during H3 compared with H1 predominant seasons during prepandemic versus pandemic and later periods were calculated for each cohort. During the prepandemic period, all cohorts had more influenza-associated disease during H3 predominant seasons than H1 predominant seasons. During the pandemic and later period, 4 cohorts had higher hospitalization and mortality rates during H1 predominant seasons than H3 predominant seasons. Birth cohort differences in risk of influenza-associated disease by influenza A virus subtype can be seen in US influenza surveillance data and differ between prepandemic and pandemic and later periods. As the population ages, the amount of influenza-associated disease may be greater in future H1 predominant seasons than H3 predominant seasons.

Laboratory-confirmed hospital-acquired infections: An analysis of a hospital's surveillance data in Nigeria

ObjectiveHospital-acquired infections (HAI) are a global problem and a major public health concern in hospitals throughout the world. Quantification of HAI is needed in developing countries; hence we describe the results of a 2-year surveillance data in a tertiary hospital in Nigeria. MethodologyThis study is a 2-year review using secondary data collected at a tertiary referral center in northwestern Nigeria. The data was collected using surveillance forms modeled based on the Centre for Disease Control (CDC) protocol. Descriptive statistics were used to present results as frequencies and percentages. Result518 patients developed HAI out of 8216 patients giving an overall prevalence of 6.3%. The mean age of the patients was 35.98 years (±15.92). Males constituted 281 (54.2%). UTI 223 (43.1%) was the most prevalent HAI. Overall, E. coli 207 (40.0%) was the most frequent isolates followed by P. aerugenosa 80 (15.4%). There was a high prevalence of cloxacillin resistant S. aureus (67.9%) and gram-negative rods resistant to third-generation cephalosporins. Trimethoprim-sulfamethoxazole resistance across the board was more than 90%. ConclusionThere is a high burden of HAI especially UTI in our hospital with resistance to commonly used antibiotics documented.

Incidence of human brucellosis in the Kilimanjaro Region of Tanzania in the periods 2007-2008 and 2012-2014.

BackgroundBrucellosis causes substantial morbidity among humans and their livestock. There are few robust estimates of the incidence of brucellosis in sub-Saharan Africa. Using cases identified through sentinel hospital surveillance and health care utilization data, we estimated the incidence of brucellosis in Moshi Urban and Moshi Rural Districts, Kilimanjaro Region, Tanzania, for the periods 2007–2008 and 2012–2014.MethodsCases were identified among febrile patients at two sentinel hospitals and were defined as having either a 4-fold increase in Brucella microscopic agglutination test titres between acute and convalescent serum or a blood culture positive for Brucella spp. Findings from a health care utilization survey were used to estimate multipliers to account for cases not seen at sentinel hospitals.ResultsOf 585 patients enrolled in the period 2007–2008, 13 (2.2%) had brucellosis. Among 1095 patients enrolled in the period 2012–2014, 32 (2.9%) had brucellosis. We estimated an incidence (range based on sensitivity analysis) of brucellosis of 35 (range 32–93) cases per 100 000 persons annually in the period 2007–2008 and 33 (range 30–89) cases per 100 000 persons annually in the period 2012–2014.ConclusionsWe found a moderate incidence of brucellosis in northern Tanzania, suggesting that the disease is endemic and an important human health problem in this area.

An ensemble approach to predicting the impact of vaccination on rotavirus disease in Niger.

Recently developed vaccines provide a new way of controlling rotavirus in sub-Saharan Africa. Models for the transmission dynamics of rotavirus are critical both for estimating current burden from imperfect surveillance and for assessing potential effects of vaccine intervention strategies. We examine rotavirus infection in the Maradi area in southern Niger using hospital surveillance data provided by Epicentre collected over two years. Additionally, a cluster survey of households in the region allows us to estimate the proportion of children with diarrhea who consulted at a health structure. Model fit and future projections are necessarily particular to a given model; thus, where there are competing models for the underlying epidemiology an ensemble approach can account for that uncertainty. We compare our results across several variants of Susceptible-Infectious-Recovered (SIR) compartmental models to quantify the impact of modeling assumptions on our estimates. Model-specific parameters are estimated by Bayesian inference using Markov chain Monte Carlo. We then use Bayesian model averaging to generate ensemble estimates of the current dynamics, including estimates of , the burden of infection in the region, as well as the impact of vaccination on both the short-term dynamics and the long-term reduction of rotavirus incidence under varying levels of coverage. The ensemble of models predicts that the current burden of severe rotavirus disease is 2.6–3.7% of the population each year and that a 2-dose vaccine schedule achieving 70% coverage could reduce burden by 39–42%.

Unsupervised primaquine for the treatment of Plasmodium vivax malaria relapses in southern Papua: A hospital-based cohort study.

BackgroundPrimaquine is the only licensed drug for eradicating Plasmodium vivax hypnozoites and, therefore, preventing relapses of vivax malaria. It is a vital component of global malaria elimination efforts. Primaquine is efficacious when supervised in clinical trials, but its effectiveness in real-world settings is unknown. We aimed to determine whether unsupervised primaquine was effective for preventing re-presentation to hospital with vivax malaria in southern Papua, Indonesia.Methods and findingsRoutinely-collected hospital surveillance data were used to undertake a pragmatic comparison of the risk of re-presentation to hospital with vivax malaria in patients prescribed dihydroartemisinin-piperaquine (DHP) combined with primaquine versus those patients prescribed DHP alone. The omission of primaquine was predominantly due to 3 stock outages. Individual clinical, pharmacy, and laboratory data were merged using individual hospital identification numbers and the date of presentation to hospital. Between April 2004 and December 2013, there were 86,797 documented episodes of vivax malaria, of which 62,492 (72.0%) were included in the analysis. The risk of re-presentation with vivax malaria within 1 year was 33.8% (95% confidence Interval [CI] 33.1%–34.5%) after initial monoinfection with P. vivax and 29.2% (95% CI 28.1%–30.4%) after mixed-species infection. The risk of re-presentation with P. vivax malaria was higher in children 1 to <5 years of age (49.6% [95% CI 48.4%–50.9%]) compared to patients 15 years of age or older (24.2% [95% CI 23.4–24.9%]); Adjusted Hazard Ratio (AHR) = 2.23 (95% CI 2.15–2.31), p < 0.001. Overall, the risk of re-presentation was 37.2% (95% CI 35.6%–38.8%) in patients who were prescribed no primaquine compared to 31.6% (95% CI 30.9%–32.3%) in those prescribed either a low (≥1.5 mg/kg and <5 mg/kg) or high (≥5 mg/kg) dose of primaquine (AHR = 0.90 [95% CI 0.86–0.95, p < 0.001]). Limiting the comparison to high dose versus no primaquine in the period during and 12 months before and after a large stock outage resulted in minimal change in the estimated clinical effectiveness of primaquine (AHR 0.91, 95% CI 0.85–0.97, p = 0.003). Our pragmatic study avoided the clinical influences associated with prospective study involvement but was subject to attrition bias caused by passive follow-up.ConclusionsUnsupervised primaquine for vivax malaria, prescribed according to the current World Health Organization guidelines, was associated with a minimal reduction in the risk of clinical recurrence within 1 year in Papua, Indonesia. New strategies for the effective radical cure of vivax malaria are needed in resource-poor settings.

Modeling the Potential for Vaccination to Diminish the Burden of Invasive Non-typhoidal Salmonella Disease in Young Children in Mali, West Africa.

BackgroundIn sub-Saharan Africa, systematic surveillance of young children with suspected invasive bacterial disease (e.g., septicemia, meningitis) has revealed non-typhoidal Salmonella (NTS) to be a major pathogen exhibiting high case fatality (~20%). Where infant vaccination against Haemophilus influenzae type b (Hib) and Streptococcus pneumoniae has been introduced to prevent invasive disease caused by these pathogens, as in Bamako, Mali, their burden has decreased markedly. In parallel, NTS has become the predominant invasive bacterial pathogen in children aged <5 years. While NTS is believed to be acquired orally via contaminated food/water, epidemiologic studies have failed to identify the reservoir of infection or vehicles of transmission. This has precluded targeting food chain interventions to diminish disease transmission but conversely has fostered the development of vaccines to prevent invasive NTS (iNTS) disease. We developed a mathematical model to estimate the potential impact of NTS vaccination programs in Bamako.Methodology/Principal FindingsA Markov chain transmission model was developed utilizing age-specific Bamako demographic data and hospital surveillance data for iNTS disease in children aged <5 years and assuming vaccine coverage and efficacy similar to the existing, successfully implemented, Hib vaccine. Annual iNTS hospitalizations and deaths in children <5 years, with and without a Salmonella Enteritidis/Salmonella Typhimurium vaccine, were the model’s outcomes of interest. Per the model, high coverage/high efficacy iNTS vaccination programs would drastically diminish iNTS disease except among infants age <8 weeks.Conclusions/SignificanceThe public health impact of NTS vaccination shifts as disease burden, vaccine coverage, and serovar distribution vary. Our model shows that implementing an iNTS vaccine through an analogous strategy to the Hib vaccination program in Bamako would markedly reduce cases and deaths due to iNTS among the pediatric population. The model can be adjusted for use elsewhere in Africa where NTS epidemiologic patterns, serovar prevalence, and immunization schedules differ from Bamako.

Critical condition classification of patients from ICCDR,B hospital surveillance data

During epidemic, when large number of patients appears in short interval, computer models could help in predicting the critical condition of newly admitted patients based on historical information of similar type of patients. In this research, we have developed two classification models by neural network and logistic regression to predict the critical condition of newly admitted patients. Three class labels, i.e., low, medium and high are used in this research to represent the critical condition of patients. However, due to class imbalance problem, the classifier performance was not good for high and mid classes. Therefore, a balancing technique is adopted by using synthetic minority over-sampling technique (SMOTE) algorithm coupled with locally linear embedding (LLE). Experimental results demonstrate that our balanced model outperforms other models by taking care the unbalance nature of ICDDR,B hospital surveillance data.

Critical condition classification of patients from ICCDR,B hospital surveillance data

During epidemic, when large number of patients appears in short interval, computer models could help in predicting the critical condition of newly admitted patients based on historical information of similar type of patients. In this research, we have developed two classification models by neural network and logistic regression to predict the critical condition of newly admitted patients. Three class labels, i.e., low, medium and high are used in this research to represent the critical condition of patients. However, due to class imbalance problem, the classifier performance was not good for high and mid classes. Therefore, a balancing technique is adopted by using synthetic minority over-sampling technique (SMOTE) algorithm coupled with locally linear embedding (LLE). Experimental results demonstrate that our balanced model outperforms other models by taking care the unbalance nature of ICDDR,B hospital surveillance data.

Use of prospective hospital surveillance data to define spatiotemporal heterogeneity of malaria risk in coastal Kenya.

BackgroundMalaria in coastal Kenya shows spatial heterogeneity and seasonality, which are important factors to account for when planning an effective control system. Routinely collected data at health facilities can be used as a cost-effective method to acquire information on malaria risk for large areas. Here, data collected at one specific hospital in coastal Kenya were used to assess the ability of such passive surveillance to capture spatiotemporal heterogeneity of malaria and effectiveness of an augmented control system.MethodsFever cases were tested for malaria at Msambweni sub-County Referral Hospital, Kwale County, Kenya, from October 2012 to March 2015. Remote sensing data were used to classify the development level of each monitored community and to identify the presence of rice fields nearby. An entomological study was performed to acquire data on the seasonality of malaria vectors in the study area. Rainfall data were obtained from a weather station located in proximity of the study area. Spatial analysis was applied to investigate spatial patterns of malarial and non-malarial fever cases. A space–time Bayesian model was performed to evaluate risk factors and identify locations at high malaria risk. Vector seasonality was analysed using a generalized additive mixed model (GAMM).ResultsAmong the 25,779 tested febrile cases, 28.7 % were positive for Plasmodium infection. Malarial and non-malarial fever cases showed a marked spatial heterogeneity. High risk of malaria was linked to patient age, community development level and presence of rice fields. The peak of malaria prevalence was recorded close to rainy seasons, which correspond to periods of high vector abundance. Results from the Bayesian model identified areas with significantly high malaria risk. The model also showed that the low prevalence of malaria recorded during late 2012 and early 2013 was associated with a large-scale bed net distribution initiative in the study area during mid-2012.ConclusionsThe results indicate that the use of passive surveillance was an effective method to detect spatiotemporal patterns of malaria risk in coastal Kenya. Furthermore, it was possible to estimate the impact of extensive bed net distribution on malaria prevalence among local fever cases over time. Passive surveillance based on georeferenced malaria testing is an important tool that control agencies can use to improve the effectiveness of interventions targeting malaria (and other causes of fever) in such high-risk locations.Electronic supplementary materialThe online version of this article (doi:10.1186/s12936-015-1006-7) contains supplementary material, which is available to authorized users.

Incidence of severe diarrhoea due to Vibrio cholerae in the catchment area of six surveillance hospitals in Bangladesh.

Cholera is an important public health problem in Bangladesh. Interventions to prevent cholera depend on their cost-effectiveness which in turn depends on cholera incidence. Hospital-based diarrhoeal disease surveillance has been ongoing in six Bangladeshi hospitals where a systematic proportion of patients admitted with diarrhoea were enrolled and tested for Vibrio cholerae. However, incidence calculation using only hospital data underestimates the real disease burden because many ill persons seek treatment elsewhere. We conducted a healthcare utilization survey in the catchment areas of surveillance hospitals to estimate the proportion of severe diarrhoeal cases that were admitted to surveillance hospitals and estimated the population-based incidence of severe diarrhoea due to V. cholerae by combining both hospital surveillance and catchment area survey data. The estimated incidence of severe diarrhoea due to cholera ranged from 0.3 to 4.9/1000 population in the catchment area of surveillance hospitals. In children aged <5 years, incidence ranged from 1.0 to 11.0/1000 children. Diarrhoeal deaths were most common in the Chhatak Hospital's catchment area (18.5/100 000 population). This study provides a credible estimate of the incidence of severe diarrhoea due to cholera in Bangladesh, which can be used to assess the cost-effectiveness of cholera prevention activities.

The Impact of Bile Duct Cultures on Surgical Site Infections in Pancreatic Surgery.

In pancreatic surgery pre-operative biliary drainage (PBD) is associated with bacteribilia, which increases the risk for surgical site infections (SSIs). This study is a retrospective observational cohort design that compared micro-organisms of intra-operative bile duct cultures with micro-organisms of SSIs after pancreaticoduodenectomy. From January 2004 until December 2010, 887 patients underwent pancreaticoduodenectomy or hepaticojejunostomy for benign and malignant peri-ampullary lesions. Surgical site infections occurred in 10% (87/887). Cultures of SSIs with corresponding intra-operative bile duct cultures were available for 59 patients. Sixty-four percent (38/59) had undergone PBD. Pre-operative biliary drainage was associated with positive intra-operative bile duct cultures in 95% (36/38), versus 48% (10/21; p≤0.001). The correlation of SSIs with intra-operative bile duct cultures was 59% (35/59). There was a significant association between the micro-organisms cultured from SSIs and the corresponding bile duct cultures for Enterococcus spp., Escherichia coli, Klebsiella pneumoniae, methicillin-resistant Staphylococcus aureus (MRSA), Enterobacteriaceae with extended spectrum ß-lactamase (ESBL), and Candida spp. After pancreaticoduodenectomy, SSIs are often caused by the same micro-organisms that are present on intra-operative bile duct cultures, especially after PBD. Therefore, intra-operative bile duct cultures should be performed routinely to adjust the antibiotic prophylaxis according to the local hospital surveillance data.

Mining ICDDR, B Hospital Surveillance Data and Exhibiting Strategies for Balancing Large Unbalanced Datasets

This research uses a number of classifier models on Hospital Surveillance data to classify admitted patients according to their critical conditions. Three class labels were used to distinguish the criticality of the admitted patients. Furthermore, set forth are two distinct approaches to address the over-fitting problem in the unbalanced dataset since the frequency of instances of the class ‘low' is significantly higher than the other two classes. Apart from trimming the dataset to balance the classes, this work has dealt with the over-fitting problem by introducing the ‘Synthetic Minority Over-sampling Technique' (SMOTE) algorithm coupled with Locally Linear Embedding (LLE). It has constructed three models that applied the neural, and multinomial logistic regression classifications and finally compared the performance of the work's models with the models developed by Rahman and Hasan (2011) where they used several decision tree models to classify the same dataset using tenfold cross validation. Additionally, for a comprehensive comparative analysis, this work has compared the classification performance of the authors' novel third model using support vector machine (SVM). After comparison, the work shows that one of the authors' models surpasses all prior models in terms of classification performance, taking into account the performance time trade-off, giving them an efficient model that handles large scale unbalanced datasets efficiently with standard classification performance. The models developed in this research can become imperative tools to doctors when large numbers of patients arrive in a short interval especially during epidemics. Since, intervention of machines become a necessity when doctors are scarce, computer applications powered by these models are helpful to diagnose and measure the criticality of the newly arrived patients with the help of the historical data kept in the surveillance database.

Statin treatment and mortality: propensity score-matched analyses of 2007-2008 and 2009-2010 laboratory-confirmed influenza hospitalizations.

Background. Annual influenza epidemics are responsible for substantial morbidity and mortality. The use of immunomodulatory agents such as statins to target host inflammatory responses in influenza virus infection has been suggested as an adjunct treatment, especially during pandemics, when antiviral quantities are limited or vaccine production can be delayed.Methods. We used population-based, influenza hospitalization surveillance data, propensity score-matched analysis, and Cox regression to determine whether there was an association between mortality (within 30 days of a positive influenza test) and statin treatment among hospitalized cohorts from 2 influenza seasons (October 1, 2007 to April 30, 2008 and September 1, 2009 to April 31, 2010).Results. Hazard ratios for death within the 30-day follow-up period were 0.41 (95% confidence interval [CI], .25–.68) for a matched sample from the 2007–2008 season and 0.77 (95% CI, .43–1.36) for a matched sample from the 2009 pandemic.Conclusions. The analysis suggests a protective effect against death from influenza among patients hospitalized in 2007–2008 but not during the pandemic. Sensitivity analysis indicates the findings for 2007–2008 may be influenced by unmeasured confounders. This analysis does not support using statins as an adjunct treatment for preventing death among persons hospitalized for influenza.

Outcomes of Patients With Anemia and Acute Decompensated Heart Failure With Preserved Versus Reduced Ejection Fraction (from the ARIC Study Community Surveillance)

Anemia is associated with poor prognosis in patients hospitalized with acute decompensated heart failure (ADHF). Whether the impact of anemia differs by heart failure with preserved ejection fraction (HFpEF) or heart failure with reduced ejection fraction (HFrEF) is uncertain. We examined hospital surveillance data captured by the Atherosclerosis Risk in Communities Study from January 1, 2005, to December 31, 2010. Diagnoses of ADHF were validated by standardized physician review of the medical record. Anemia was classified using the World Health Organization criteria (<12g/dl for women and <13g/dl for men), and HF type was determined by the ejection fraction (<40% for HFrEF and ≥40% for HFpEF). Hospital length of stay and 1-year mortality outcomes were analyzed by multivariable regression, weighted to account for the sampling design, and adjusted for demographics and clinical covariates. Over 6years, 15,461 (weighted) hospitalized events for ADHF (59% HFrEF) occurred in the catchment of the Atherosclerosis Risk in Communities, based on 3,309 sampled events. Anemia was associated with a mortality hazard ratio of 2.1 (95% confidence interval [CI] 1.6 to 2.7) in patients classified with HFpEF and 1.4 (95% CI 1.1 to 1.7) in those with HFrEF; p for interaction= 0.05. The mean increase in length of hospital stay associated with anemia was 3.5days (95% CI 3.4 to 3.6) for patients with HFpEF, compared with 1.8days (95% CI 1.7 to 1.9) for those with HFrEF; p for interaction <0.0001. In conclusion, the incremental risks of death and lengthened hospital stay associated with anemia are more pronounced in ADHF patients classified with HFpEF than HFrEF.

The Burden of Influenza Hospitalizations in Infants From 2003 to 2012, United States

Little information is available describing the epidemiology and clinical characteristics of those <12 months hospitalized with influenza, particularly at a population level. We used population-based, laboratory-confirmed influenza hospitalization surveillance data from 2003 to 2012 seasons to describe the impact of influenza by age category (<3, 3 to <6 and 6 to <12 months). Logistic regression was used to explore risk factors for intensive care unit (ICU) admission. Adjusted age-specific, influenza-associated hospitalization rates were calculated and applied to the number of US infants to estimate national numbers of hospitalizations. Influenza was associated with an annual average of 6514 infant hospitalizations (range 1842-12,502). Hospitalization rates among infants <3 months were substantially higher than the rate in older infants. Most hospitalizations occurred in otherwise healthy infants (75%) among whom up to 10% were admitted to the ICU and up to 4% had respiratory failure. These proportions were 2-3 times higher in infants with high risk conditions. Infants <6 months were 40% more likely to be admitted to the ICU than older infants. Lung disease (adjusted odds ratio 1.80; 95% confidence interval 1.22-2.67), cardiovascular disease (adjusted odds ratio: 4.16; 95% confidence interval: 2.65-6.53), and neuromuscular disorder (adjusted odds ratio: 2.99; 95% confidence interval: 1.87-4.78) were risk factors for ICU admission among all infants. The impact of influenza on infants, particularly those very young or with high risk conditions, underscores the importance of influenza vaccination, especially among pregnant women and those in contact with young infants not eligible for vaccination.