Sick Score Research Articles

Multiple Organ Dysfunction Syndrome in Malawian Children with Cerebral Malaria.

More than 1,000 children under 5 years of age die every day from malaria. Cerebral malaria (CM) is the most severe and deadly manifestation of the disease. The occurrence of multiple organ dysfunction syndrome (MODS) has been associated with increased mortality in adult patients with CM. However, little is known about the frequency and severity of MODS in children with CM. This was a retrospective study of 199 pediatric patients with CM admitted to a referral hospital in Blantyre, Malawi, between January 2019 and May 2023. Data were abstracted from charts to calculate scores using four established scoring systems: Pediatric Logistic Organ Dysfunction-2 (PELOD-2), Pediatric Sequential Organ Failure Assessment (pSOFA), Signs of Inflammation in Children that Can Kill (SICK), and Lambaréné Organ Dysfunction Score (LODS). Mortality was 16% (n = 32). All four scoring systems were predictive of mortality, but the PELOD-2 and pSOFA scores outperformed the others with area under the curve values of 0.75 and 0.67, respectively. Multiple organ dysfunction syndrome was diagnosed in 182 patients (91%) using the PELOD-2 score, 172 patients (86%) using the pSOFA score, 99 patients (50%) using the SICK score, and 30 patients (15%) using the LODS. The PELOD-2 and pSOFA identify MODS in children with CM but require laboratory-based testing that is often unavailable in malaria-endemic areas. Furthermore, these scoring systems may identify primary malarial disease pathology rather than true organ dysfunction. Simplified scoring systems designed to recognize and quantify MODS in this patient population may provide opportunities for improved resource allocation and timely, organ-specific treatment.

Sick Neonate Score as Predictor of Neonatal Mortality and Duration of Hospital Stay

Abstract Background: Sick neonate score (SNS) is a simple clinical score that requires minimal basic equipment and expertise. It is utilized for the prediction of mortality among neonates. Objective: The objective of this study was to study the correlation of SNS with mortality and duration of hospital stay of admitted neonates. Methodology: This hospital-based observational descriptive study was conducted in a tertiary care hospital in Jaipur, Rajasthan. A total of 404 outborn transported neonates were enrolled. Baseline characteristics and SNS were recorded and analyzed. Categorical data were presented in numbers and percentages, whereas continuous data in mean and standard deviation. The Chi-square test and unpaired t-test were used for analysis. Receiver operating characteristics curve analysis was used to estimate the cutoff value of SNS determining the mortality and duration of hospital stay of the patient. Results: Out of 404 neonates 243 (60%) were tachypneic, 242 (59%) had oxygen saturation <92% and 216 (53%) were hypothermic. A cutoff of SNS ≤8 with a sensitivity of 93% and specificity is 53.4% was found for prediction of mortality among neonates. A significantly higher number of patients with SNS score ≤8 had expired within 3 days. Among discharged patients, a significantly higher number of patients with SNS score ≤8 had a duration of stay in hospital >7 days. Conclusion: SNS has a good negative correlation with mortality. Neonate with SNS ≤8 either expired early or was discharged with a higher duration of hospital stay.

Identification of the Best Predictive Model for Mortality in Outborn Neonates-Retrospective Cohort Study.

Transportation of sick newborns is a major predictor of outcome. Prompt identification of the sickest newborns allows adequate intervention and outcome optimization. An optimal scoring system has not yet been identified. To identify a rapid, accurate, and easy-to-perform score predictive for neonatal mortality in outborn neonates. All neonates admitted by transfer in a level III regional neonatal unit between 1 January 2015 and 31 December 2021 were included. Infants with congenital critical abnormalities were excluded (N = 15). Gestational age (GA), birth weight (BW), Apgar score, place of birth, time between delivery and admission (AT), early onset sepsis, and sick neonatal score (SNS) were collected from medical records and tested for their association with mortality, including in subgroups (preterm vs. term infants); GA, BW, and AT were used to develop MSNS-AT score, to improve mortality prediction. The main outcome was all-cause mortality prediction. Univariable and multivariable analysis, including Cox regression, were performed, and odds ratio and hazard ratios were calculated were appropriate. 418 infants were included; 217/403 infants were born prematurely (53.8%), and 20 died (4.96%). Compared with the survivors, the non-survivors had lower GA, BW, and SNS scores (p < 0.05); only the SNS scores remained lower in the subgroup analysis. Time to admission was associated with an increased mortality rate in the whole group and preterm infants (p < 0.05). In multiple Cox regression models, a cut-off value of MSNS-AT score ≤ 10 was more precise in predicting mortality as compared with SNS (AUC 0.735 vs. 0.775) in the entire group and in the preterm infants group (AUC 0.885 vs. 0.810). The new MSNS-AT score significantly improved mortality prediction at admission in the whole study group and in preterm infants as compared with the SNS score, suggesting that, besides GA and BW, AT may be decisive for the outcome of outborn preterm infants.

Comparing neonatal intensive care unit outcomes of intramural and extramural neonates using the modified sick neonate score

Background: India contributes to one-fifth of the world’s annual childbirths and one-fourth of the neonatal mortality burden, with rural areas having higher mortality rates. Several scoring systems have been used to independently predict neonatal outcomes in babies born elsewhere and transported to a facility (extramural) and in the inborn or intramural neonates. We used the modified sick neonate score (MSNS) to compare the outcomes in extramural and intramural neonates. The score considers eight parameters and quantitatively measures the neonates’ status. This study aimed to determine whether the MSNS effectively predicts mortality among the two study groups. Methods: 410 neonates were considered for this prospective analytical study conducted at GMERS Hospital, Vadodara, a tertiary care center- over a duration of 20 months. Neonates were followed during their NICU stay, and outcomes were correlated with their scores recorded on admission. Results: The intramural newborns had higher MSNS than the extramural neonates (13 and 12, respectively), with the difference statistically significant. At an optimal cut-off of &lt;11, MSNS has a sensitivity of 85.11% and a specificity of 75.76% in predicting neonatal outcomes. The AUC was 0.859 (95% CI:0.822 to 0.891) with a positive predictive value of 31.2%, a negative predictive value of 97.5%, and an accuracy of 76.83%. Conclusions: The MSNS effectively predicts mortality among intramural and extramural neonates, with the range being higher for inborn babies- indicating a better prognosis.

Modified Sick Neonatal Score (MSNS) in Predicting the Outcome of Neonates in Tertiary Care Hospital

Introduction: Neonatal disease severity scoring systems are widely used to predict severity of illness. Existing scoring systems contain variables like pH, PO2 / FiO2 ratio, and base excess, which are difficult to obtain in resource-limited settings. Modified sick neonatal score (MSNS) is based on eight clinical variables which are easy to calculate and are also accurate enough at the same time. This study was done to evaluate MSNS for predicting the outcome of neonates in our settings.&#x0D; Methods: This was prospective observational study done at neonatal intensive care unit (NICU) of tertiary care hospital in Nepal from February 2021 to January 2022. The parameters required for the score were recorded at admission. Total score was calculated and outcome was noted. Data collected was analyzed using SPSS Statistics for Windows, v21.0. Chi square test, Mann-Whitney U test and ROC analysis were used for statistical analysis.&#x0D; Results: Total of 195 neonates were discharged and 37 expired. The mean MSNS score among expired was 8.16 ± 1.625 and discharged was 10.99 ± 1.753. For a cutoff score of ≤ 10, sensitivity and specificity; Positive and negative predictive value were 89.2% and 60.5%; 30.7% and 98.3% respectively. The area under the curve (AUC) of MSNS was 0.875 (CI 95%; 0.817 - 0.934). Lower MSNS score was also associated with requirement of iontrope and ventilator support.&#x0D; Conclusions: MSNS can be used as an important clinical tool for predicting the severity of disease in neonates in resource limited settings.

EVALUATION OF SICK (SIGNS OF INFLAMATION THAT CAN KILL) SCORE IN PREDICTING MORTALITY AND DURATION OF STAY OF PATIENTS IN PICU IN RURAL TERTIARY CARE CENTRE

Background: Mortality in critically ill children occurs in first 24 hour of admission. Therefore, immediate assessment of children at admission with a rapid clinical score is required. Early identification and proper triage of patients, allocation of available resources based on severity of illness is essential for effective management of critical illness. In this study SICK SCORE is used to predict mortality at admission. Methods: This is a prospective observational study of 100 patients over a period of two years at a rural tertiary center. The assessment using SICK SCORE was done on arrival prior to initiation of treatment for children admitted in PICU. A SICK SCORE variable (temperature, heart rate, respiratory rate, systolic blood pressure, saturation, capillary refill time, sensorium by AVPU scale) was measured using standard guidelines. Each variable is scored 0 to 1, higher the score higher is the prediction of mortality. Results: The study included 100 children admitted in PICU, children between 1 month to 17yr were included in this study, there were 10 deaths (all three had SICK SCORE&gt;6, with 70% sensitivity and specificity 5 %) during study period of 2 years. The mortality risk and duration of stay was found to be increasing with increase in SICK score. Conclusion: Substantially increased rate of mortality and lengthy duration of hospital stay was observed among SICK children who had higher sick score at admission. Hence, we conclude, SICK score is extremely useful in predicting mortality and lengthy stay on admission to hospital and it is also simple and cost-effective scoring system for triaging in resource limited settings.

The Effectiveness of the ‘Extended Sick Neonatal Score in Predicting Mortality in a ResourceConstrained Neonatal Care Uni

Objectives: To determine the diagnostic accuracy of the extended sick neonatal score (ESNS) in neonates admitted in a resource-limited neonatal intensive care unit (NICU) at Peshawar.&#x0D; Study Design: Cross-sectional analytical study&#x0D; Place and Duration of Study: Neonatal Intensive Care Unit, CMH Peshawar Pakistan, from Mar to May 2020.&#x0D; Methodology: Primary data was collected from 60 neonates admitted to NICU after taking consent from the parents. The receiver operating characteristic curve (ROC) was plotted to determine the clinical score (ESNS) cut-off value in predicting mortality.&#x0D; Result: The sensitivity and specificity of the Extended sick neonatal score to predict mortality among neonates was 93.3% and 97%, respectively, for a cut-off of 12.5. The area under the ROC curve was 0.990 (95% CI: 0.971–1.000). This was statistically significant with a p-value of &lt;0.001&#x0D; Conclusion: Extended Sick Neonatal score is an important tool that helps predict the risk of mortality of a neonate without the help of any invasive diagnostic procedure, thus enhancing the prioritization of health care to the most deserving neonates.

Role of modified sick neonatal score in predicting the neonatal mortality at limited-resource setting of central India

Background: The targets of Sustainable Development Goals cannot be achieved without reducing mortality of inborn as well as outborn neonates. Objective: The objective was to predict the mortality of neonates by applying Modified Sick Neonatal Score (MSNS). Material and Methods: Parameters of MSNS scoring system were applied to 450 neonates at the time of admission and followed up prospectively till discharge or death. The score and individual parameters were correlated with outcome. A receiver operating curve was plotted to determine the cutoff value for score to predict the mortality. Results: The common clinical diagnoses were respiratory distress (38%), sepsis (26%), and jaundice (14.44%). Sixty-two percent neonates were born at term and 38% were preterm while 55.56% neonates were low birth weight and 44.4% were weighted more than 2500 gms. Neonates who died were older at the time of admission, and duration of hospital stay was shorter compared to alive neonates (P < 0.001) with a mortality rate of 23.11%. The total MSNS for neonates who died was statistically significantly low, (mean ± standard deviation) 7.93 ± 2.70, compared to alive neonates, 12.02 ± 1.84 (P < 0.0001). With optimum cutoff score of ≤10, the sensitivity was 79.80% and the specificity was 82.37% while the positive predictive value and negative predictive value was 57.64% and 93.14% respectively with the area under curve was 0.89 (odds ratio-18.46, 95% confidence interval 10.3-33.64, P < 0.0001). Conclusion: The MSNS of ≤10 has a better sensitivity and specificity in predicting neonatal mortality and is easy to use with minimal resources to both preterm and term neonates.

Risk assessment of novel coronavirus COVID-19 outbreaks in the border areas of southwest China.

This study aimed to assess the risk of coronavirus disease 2019 in the border areas of southwest China, so as to provide guidance to targeted prevention and control measures in the border areas of different risk levels.We assessed the dependence of the risk of an outbreak in the southwest China from imported cases on key parameters such as the cumulative number of infectious diseases in the border area of southwest China in the past 3 years; the connectivity of the neighboring countries with China’s Southwest border, including baseline travel numbers, travel frequencies, the effect of travel restrictions, and the length of borders with neighboring countries; the cumulative number of close contacts of coronavirus disease 2019 patients; (iv) the population density in border areas; the efficacy of control measures in border areas; experts estimated risks in border areas based on experience and then given a score; Spearman correlation and Logistic regression models were used to analyze the associated factors of novel coronavirus. According to the correlation of various factors, we assigned values to each parameter, calculated the risk score of each county, and then divided each county into high, medium, and low risk according to the sick score and took different control measure according to different risk levels. Finally, the total risk level was evaluated according to the Harvard disease risk index model.The number of infectious diseases in the past 3 years, travel numbers, travel frequencies, experts estimated risk score, effect of travel restrictions, and the number of close contacts were associated with the incidence of new coronary pneumonia. It is concluded that bilateral transportation convenience is a risk factor for new coronary pneumonia, (odds ratio = 9.23, 95% confidence interval, 1.99–42.73); the number of observers is a risk factor for new coronary pneumonia (odds ratio = 1.04, 95% confidence interval, 1.00–1.08).We found that in countries with travel numbers, travel frequencies, and experts' estimated risk scores were the influencing factors of novel coronavirus. The effect of travel restrictions and the cumulative number of close contacts of the case are risk factors for novel coronavirus.

Evaluation of Modified Extended Sick Neonate Score to Predict In-Hospital Mortality among Newborns Admitted to Resource-Poor Settings in Rural India.

To devise a score based on eight extended sick neonatal score (ESNS) variables by adding two more variables, birth weight, and gestational age to check its efficacy. A retrospective study was conducted on 521 neonates admitted from January 2018 to June 2020 in the neonatal intensive care unit (NICU) of a tertiary care center. The proposed score has the following components, respiratory effort, heart rate, axillary temperature, capillary refill time, random blood sugar, pulse oximeter saturation, Moro reflex, modified Downes score, gestation age, and birth weight. Each was scored as 0, 1, and 2. The total score for each neonate was calculated from the records available and outcome documented. Receiver operating characteristic (ROC) curve was generated; and a cutoff score was derived to predict mortality and compared with modified sick neonatal score (MSNS). Modified ESNS has a strong correlation with the outcome. Area under the ROC curve was 0.995 (95% CI: 0.925-0.985) for modified ESNS and 0.933 (95% CI: 0.901-0.976) for MSNS. The optimum cutoff values for predicting mortality were 15.5 for modified ESNS and 12.5 for MSNS. For a cutoff score of ≤ 15, sensitivity and specificity were 86.27% and 86.60% for modified ESNS and 90.20% and 84.89%, respectively, for MSNS, in predicting mortality. Positive and negative predictive values were 41.12% and 98.31% for modified ESNS and 39.32% and 98.76%, respectively for MSNS. The modified ESNS can predict in-hospital mortality among neonates, with good sensitivity and specificity.

Validation of modified sick neonatal score, a simple clinical score for assessment of severity of illness and outcome in new-borns for resource poor settings

Background: India contributes to 25% of the neonatal deaths worldwide each year. Neonatal disease severity scoring systems are needed to make standardized comparison between performances of different units and also give prognostic information. Existing scoring systems are unsuitable for resource-limited settings which lack investigations like pH, pO2/FiO2 ratio and base excess. Modified sick neonatal score (MSNS) is based on eight routinely measured clinical variables in NICUs namely respiratory effort, heart rate, axillary temperature, capillary refill time, random blood sugar, pulse oximeter saturation, gestational age and birth weight found to be useful in resource poor settings. The aim of the study was to validate MSNS score for its clinical utility in predicting mortality.Methods: This was a cross sectional study done at NICU of Mamata Medical College Hospital. The parameters required for the score were recorded immediately at admission in NICU from 1 January 2020 to 1 January 2021 and scored using Modified sick neonatal score (MSNS). The total score was calculated and outcome was noted. The data collected were coded and analzed using SPSS Statistics for Windows, v21.0 Chi square test, Mann-Whitney U test and ROC analysis.Results: Total of 355 neonates got discharged, while 45 neonates expired. For a cutoff score of ≤10, sensitivity and specificity were 85.9% and 51.1%, respectively. Positive predictive value and negative predictive value were 93.3% and 31.5%, respectively. The Area under the curve (AUC) was 0.811 (95%CI: 0.788-0.835), which indicates the accuracy of 81.1%.Conclusions: MSNS is a better suited neonatal disease severity score for resource poor settings.

Assessment of severity of illness on admission using SICK score and evaluation of the risk factors associated with mortality in children admitted in a paediatric urban tertiary care centre in south India

Background: “Signs of inflammation that can kill” (SICK) score is one of the severities scoring systems used for predicting outcome of children at admission. The aim of the present study was to study the clinical and demographic profile of children admitted to Paediatric ward, to assess the usefulness of SICK score in predicting the mortality and evaluate the risk factors in predicting mortality. Methods: SICK scoring was done for 369 children on admission. The outcome was recorded as death or discharge. The associated factors were analysed using SPSS software package analysis. Receiver operating curve was used to arrive at the cut-off point of SICK score for predicting mortality. Quantitative data differences between children who died and children who were discharged from the hospital were analysed using student independent t test. Need for assisted ventilation, presence of shock, age less than 3 years, and SICK score&gt;2 were studied to find their association with mortality. Statistical analysis was done using univariate analysis and those factors that were significantly associated with mortality were subjected multivariate logistic regression analysis.Results: The performance of SICK score was “excellent” in discriminating between death and survival with area under the receiver operating characteristics curve 0.94. Age&lt;3-year presence of shock, need for mechanical ventilation and SICK score&gt;2 showed statistically significant association with mortality as evidenced by multivariate logistic regression model. Conclusions: SICK score performed extremely well in predicting mortality on admission. Age&lt;3 years, SICK score&gt;2, Presence of Shock and need for assisted ventilation showed statistically significant association with mortality.

Study of platelet parameters in sick children

Background: Prediction of outcome of a patient plays a key role in the management of PICU. Studies have predicted that platelet parameters are novel predictors of mortality. Hence, present study was designed to assess the accuracy of platelet parameters (platelet count, MPV (mean platelet volume), PDW (platelet distribution width), PCT (Plateletcrit), MPV/PCT, PDW/Platelet count, MPV/platelet count in prediction of mortality and morbidity. Methods: Total 66 children requiring admission to PICU, were divided as sick (with sick score≥3) and non-sick (with sick score&lt;3). Their platelet parameters were compared. Platelet parameters were further studied with respect to morbidity and mortality. Statistical analysis was done using SPSS 22 version software. Chi-square test was used as test of significance for qualitative data. Continuous data was represented as mean and standard deviation. Independent t test was used as test of significance to identify the mean difference between two quantitative variables. Results: Both groups were comparable in terms of age and gender. Significant difference in all platelet parameters were noted in between groups (p&lt;0.001). Platelet parameters and their ratios were significant predictors of mortality and morbidity, with negative correlation to platelet count and plateletcrit (PCT), and positive correlation to the rest.Conclusions: Simple analysis of platelet indices at admission by haematology counter analysers could give us a great idea in predicting the mortality and morbidity of the children.

Rapid vs. Slow Rewarming for Management of Moderate to Severe Hypothermia in Low-Birth-Weight Pre-term Neonates—An Open Label Randomized Controlled Trial

Evidence is lacking regarding the optimal method of rewarming hypothermic low-birth-weight (LBW) pre-term neonates. We aim to evaluate the effect of rapid vs. slow rewarming in the management of moderate to severe hypothermia in LBW pre-term neonates. In this open label, randomized controlled trial, 100 LBW (<2.5 kg), pre-term (<37 weeks) neonates with moderate to severe hypothermia (<36°C) was randomized to two groups of 50 each and received either rapid (at >0.5°C/h) or slow (at ≤0.5°C/h) rewarming rate till normothermia. The primary outcome was stabilization score [TOPS (temperature, oxygenation, perfusion and saturation) and MSNS (modified sick neonatal score)] at baseline, 6 and 24 h and mortality until discharge. Other neonatal morbidities were assessed as secondary outcomes. Mean TOPS score and MSNS score at baseline, 6 and 24 h of admission as well as change in score from baseline were similar between the two groups. The median rewarming rate [interquartile range (IQR)] was higher in rapid rewarming group than in the slow rewarming group [5.05°C/h (3.54-7.71) vs. 0.71°C/h (0.60-0.90); p < 0.001]. The median rewarming time taken in rapid rewarming group was lesser compared with that in the slow rewarming group [0.31 h (IQR 0.13-0.75) vs. 2.05 h (IQR 1.11-3.03); p < 0.001]. Mortality in rapid rewarming and slow rewarming group was similar [7/50 vs. 5/50; OR 1.46 (0.43-4.97), p = 0.538]. Rapid rewarming was as effective and safe as slow rewarming in the management of moderate to severe hypothermia in LBW pre-term neonates with similar short-term neonatal outcomes. CTRI/2018/01/011187.

Extended sick neonatal score in prediction of mortality of neonates transported to tertiary healthcare centre and its comparison with sick neonatal score and temperature, oxygenation, perfusion and blood sugar score

Background: India contributes up to 25% of neonatal mortality worldwide. Most of the times, the patient deteriorates in-transit from periphery to tertiary healthcare facility. There is a need for reliable scoring system in analysis of neonates transferred to tertiary healthcare centers in prediction of in-hospital mortality. The aim of the research was to evaluate the efficiency of extended sick neonatal score in ‘in-hospital’ mortality prediction of neonates referred to tertiary healthcare institutes and to compare it with that of other scoring systems like sick neonatal score and temperature, oxygenation, perfusion and blood sugar (TOPS) score.Methods: The research was a retrospective observational study. All extra-mural neonates admitted over a period of one month at neonatal intensive care unit (NICU) of our tertiary healthcare centre were considered after informed consent and the required parameters for scoring were assessed. Data was collected according to a pre-formed proforma.Results: Over a period of one month, 145 neonates attended on arrival were analyzed according to the mentioned systems. Various cohorts were considered. Of the systems analyzed, extended sick neonatal score had the highest sensitivity and specificity, regardless of the cohort.Conclusions: Extended sick neonatal score was more efficient than sick neonatal score and TOPS score in mortality prediction of sick neonates.

Do Social Determinants Define "Too Sick" to Transplant in Patients With End-stage Liver Disease?

Delisting for being "too sick" to be transplanted is subjective. Previous work has demonstrated that the mortality of patients delisted for "too sick" is unexpectedly low. Transplant centers use their best clinical judgment for determining "too sick," but it is unclear how social determinants influence decisions to delist for "too sick." We hypothesized that social determinants and Donor Service Area (DSA) characteristics may be associated with determination of "too sick" to transplant. Data were obtained from the Scientific Registry of Transplant Recipients for adults listed and removed from the liver transplant waitlist from 2002 to 2017. Patients were included if delisted for "too sick." Our primary outcome was Model for End-Stage Liver Disease (MELD) score at waitlist removal for "too sick." Regression assessed the association between social determinants and MELD at removal for "too sick." We included 5250 delisted for "too sick" at 127 centers, in 53 DSAs, over 16 years. The mean MELD at delisting for "too sick" was 25.8 (SD ± 11.2). On adjusted analysis, social determinants including age, race, sex, and education predicted the MELD at delisting for "too sick" (P < 0.05). There is variation in delisting MELD for "too sick" score across DSA and time. While social determinants at the patient and system level are associated with delisting practices, the interplay of these variables warrants additional research. In addition, center outcome reports should include waitlist removal rate for "too sick" and waitlist death ratios, so waitlist management practice at individual centers can be monitored.

Evaluating Twitter for Foodborne Illness Outbreak Detection in New York City

ObjectiveTo incorporate data from Twitter into the New York City Department of Health and Mental Hygiene foodborne illness surveillance system and evaluate its utility and impact on foodborne illness complaint and outbreak detection.IntroductionAn estimated one in six Americans experience illness from the consumption of contaminated food (foodborne illness) annually; most are neither diagnosed nor reported to health departments1. Eating food prepared outside of the home is an established risk factor for foodborne illness2. New York City (NYC) has approximately 24,000 restaurants and &gt;8.5 million residents, of whom 78% report eating food prepared outside of the home at least once per week3. Residents and visitors can report incidents of restaurant-associated foodborne illness to a citywide non-emergency information service, 311. In 2012, the NYC Department of Health and Mental Hygiene (DOHMH) began collaborating with Columbia University to improve the detection of restaurant-associated foodborne illness complaints using a machine learning algorithm and a daily feed of Yelp reviews to identify reports of foodborne illness4. Annually, DOHMH manages over 4,000 restaurant-associated foodborne illness reports received via 311 and identified on Yelp which lead to the detection of about 30 outbreaks associated with a restaurant in NYC. Given the small number of foodborne illness outbreaks identified, it is probable that many restaurant-associated foodborne illness incidents remain unreported. DOHMH sought to incorporate and evaluate an additional data source, Twitter, to enhance foodborne illness complaint and outbreak detection efforts in NYC.MethodsDOHMH epidemiologists continue to collaborate with computer scientists at Columbia University who developed a text mining algorithm that identifies tweets indicating foodborne illness. Twitter data are received via a targeted application program interface query that searches for foodborne illness key words and uses metadata to select for tweets with a possible NYC location. Each tweet is assigned a sick score between 0–1; those meeting a threshold value of 0.5 are manually reviewed by an epidemiologist, and a survey link is tweeted to users who have tweeted about foodborne illness, requesting more information regarding the date and time of the foodborne illness event, restaurant details, and user contact information. Survey data are used to validate complaints and are incorporated in a daily analysis using all sources of complaint data to identify restaurants with multiple foodborne illness complaints within a 30-day period. This system was launched on November 29, 2016.ResultsDuring November 29, 2016–September 27, 2017, 12,015 tweets qualified for review (39/day on average); 2,288 (19.0%) indicated foodborne illness in NYC, and 1,778 (14.8%) were tweeted a survey link (510 foodborne illness tweets were either deleted by the Twitter user or were tweets from a user who was already sent a survey for the same foodborne illness incident). The survey tweets resulted in 92 likes, 12 retweets, 65 replies, 232 profile views and 348 survey link clicks. Of the 1,778 surveys sent, 27 were completed (response rate 1.5%), of which 20 (74.7%) confirmed foodborne illness associated with a NYC restaurant; none had been reported via 311/Yelp. Of those, 11 (55%) provided a phone number, of which 10 (90.9%) completed phone interviews. The completed surveys contributed to the identification of two restaurants with multiple foodborne illness complaints within a 30-day period.ConclusionsThe utility of Twitter for foodborne illness outbreak detection continues to be evaluated. While the survey response rate has been low, the identification of new complaints not otherwise reported to 311 and Yelp suggests this will be a useful tool. Future plans include using feedback data collected by DOHMH epidemiologist review to increase the sensitivity and specificity of the text mining algorithm and improve the location detection for Twitter users. In addition, we plan to implement enhancements to the survey and create a web page to promote survey responses. Furthermore, we intend to share this system with other health departments so that they might incorporate Twitter in their outbreak detection and public health surveillance activities.

Higher parental occupational social contact is associated with a reduced risk of incident pediatric type 1 diabetes: Mediation through molecular enteroviral indices.

We aimed to examine the association between parental occupational social contact and hygiene factors on type 1 diabetes (T1D) risk and possible mediation of these effects through child enteroviral infection. We interviewed 333 incident T1D cases and 660 controls from 2008–2011 in Melbourne, Australia. Enteroviral indices (ribonucleic acid by reverse transcription polymerase chain reaction and Coxsackie B virus antibody levels) in peripheral blood were measured in nested case control samples. Parent occupational social contact was assessed by the number of well or sick children, adults or animals contacted daily through work. Higher parental occupational social contact was strongly associated with reduced T1D risk with evidence of dose response (contact with the well or sick score, Adjusted odds ratio (AOR) per category: 0.73 (95% Confidence Interval (CI): 0.66, 0.81); P<0.001 or AOR 0.63 (95% CI: 0.53, 0.75); P<0.001) respectively). Nine of the ten parental social contact indices, were significant mediated through one or more enteroviral indices. The strength of association between enterovirus presence and T1D onset increased with child age (1.2 fold increase per year; P = 0.05). Lower child hand hygiene enhanced the adverse effect of low parental occupational contact with the sick; Synergy Index 5.16 (95% CI: 3.61, 7.36). The interaction between hand washing and parental occupational contact is more consistent with protection against parental enteroviral shedding than the sharing of a protective infectious agent or microbiome.

Validation of SICK score - A severity of illness scoring system at a tertiary care centre in Puducherry

Objectives: Validation of SICK score for its ability to predict out come. Validation based on predominant system involved. Methods: Prospective study done at tertiary care centre in puducherry. Inclusion criteria: All children admitted in PICU between 1 month & 12 year in 6 month period. Exclusion criteria: Patients with surgical illness, congenital anomalies, non availability of consent. Physical variables of Temperature, Heart Rate, Respiratory Rate, Blood pressure, Capiallary Refill time, Oxygen saturation & sensoriurn (using AVPU scale) were recorded at the time of admission. SICK score was calculated and outcome was recorded as survived or expired. Association of patients outcome with total score was done using logistic regression analysis. The receiver operating characteristic (ROC) curve analysis was performed to decide the optimum cut off point in terms of total score that provides maximum sensitivity & specificity. Area under ROC curve (AUC) was used to compare the predictive power of total score. All analysis was carried out using SPPS software version 13.0. Results: Total of 354 Children were analyzed. For SICK score of <1 mortality was 0%, which gradually increased to 100% with a score of 7 or more. The area under the ROC curve was 93% (95%: CI: 89.8% - 97.3%) indicating good predictive ability of the score. Conclusion: SICK score has good predictive ability and uses only physical criteria. Further SICK score is not influenced by predominant system involved.

Neonatal transport in resource restricted settings: a simple clinical score at arrival and its role in predicting mortality

Methods Setting: JIPMER, a tertiary care teaching hospital, south India. Number of subjects: extramural neonates (<28 days) transported to JIPMER NICU (n=303). Study design: descriptive study. Transport and clinical details were recorded at arrival to NICU and all neonates were evaluated using a simple clinical score sick neonate score (SNS) as described below in Table 1. All neonates were followed up until discharge or expiry. To determine the association of clinical factors with outcome Chi-square and Fisher exact test were used. All statistical analyses were carried out at 5% level of significance and p value <0.05 was considered significant. To determine the cutoff value for the clinical score (SNS), receiver operated curve was plotted.