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Summary: Background: In June 2018, a type 1 circulating vaccine-derived poliovirus (cVDPV1) outbreak was declared in Papua New Guinea (PNG), resulting in a total of 26 paralytic confirmed cases. Eight vaccination campaign rounds with bivalent oral poliovirus vaccine (bOPV) were carried out in response. Prevalence of neutralizing polio antibodies in children was assessed two years after the outbreak response was completed. Methods: We conducted a cross-sectional serological survey among children aged 6 months–10 years selected from six provinces in PNG to evaluate seroprevalence of neutralizing polio antibodies to the three poliovirus serotypes and analyse sociodemographic risk factors. Findings: We included 984 of 1006 enrolled children in the final analysis. The seroprevalence of neutralizing polio antibodies for serotype 1, 2 and 3 was 98.3% (95% CI: 97.4–98.9), 63.1% (95% CI: 60.1–66.1) and 95.0% (95% CI: 93.6–96.3), respectively. Children

Introduction: Posterior circulation stroke (PCS) is often diagnosed late and long-term outcomes have not been described in South India. We wished to study clinical features, long-term outcomes, and independent predictors of the same. Materials and Methods: We conducted a prospective cohort study in PCS patients from January 2014 to May 2017. Morbidity and mortality at discharge, at 3 months and 6 months was studied. Univariate and multivariate analysis of predictors of poor outcome as well as the survival analysis was computed. Results: We recruited 291 PCS during the study period; prevalence of PCS among all strokes was 12.9% in 2014. The mean age was 53.34 years (standard deviation 13.34). Young strokes constituted 28.8%. The common comorbidities were hypertension 72%, diabetes mellitus 52.6%, smoking 38.5%, and dyslipidemia 32.6%. The common presenting symptoms were giddiness 79%, unsteadiness 75.35%, ataxia 56%, motor deficits 48.8% and nausea and vomiting 43%. Most were Ischemic strokes (86.5%); 10.3% gave a history of preceding TIAs. Common arteries involved were posterior cerebral 45%, posterior inferior cerebellar 38%, and basilar artery 19.2%. Among ischemic strokes, large artery atherosclerosis was 50.8%. Dysphagia was seen in 32.6% and sepsis in 19.2%; post stroke pain in 23%, followed by cognition decline in 10.3%. Bad outcome (modifies Rankins score 4–6) was 16.8% at discharge, 16.4% at 1 month, 14.4% at 3 months, and 13.8% at 6 months. Independent predictors of bad outcomes at 6 months were baseline National Institute of Health Stroke Scale (NIHSS) score (Odds ratio [OR] 1615.59 confidence interval [CI] 27.64–94447.7), invasive ventilation (OR 7.77 CI 1.57–38.43), sepsis (OR 17.22 CI 1.45–204.08), and basilar artery involvement (OR 19.98 CI 1.67–238.81). Baseline NIHSS scores between 0 and 5 suggest a good outcome while scores more than 13 suggest a bad outcome at 6 months. At 6 months, half are unemployed. Conclusions: The prevalence, clinical features, and comorbidities were also similar to previous studies. There was good survival at 6 months. NIHSS score is useful in predicting poor outcomes.

Introduction: In children with Systemic Lupus Erythematosus (SLE), autoantibodies are considered as biomarkers for specific organ involvement or tissue damage. Some autoantibodies are used for diagnosis, disease activity, and few for disease characterisation. By using cluster analysis, we identified antibody clustering in children with SLE with specific subsets of clinical manifestations at the time of diagnosis. Materials and Methods: All pediatric SLE (pSLE) who fulfilled 4/11 ACR criteria were included in this study. Their autoantibodies profiles were noted. We recruited 212 patients with newly diagnosed pSLE and cluster analysis was done. We identified 3 clusters which were used for analysis. Results: Cluster 1 had ANA and anti-dsDNA antibodies a low prevalence of all other antibodies. Children in cluster 2 had autoantibodies such as ANA, anti-dsDNA, anti-RNP, and anti-Sm. Cluster 3 was had autoantibodies such as dsDNA, ANA, anti-cardiolipin and anti-SSA antibodies. On analysis, there was statistically significant difference among the 3 clusters for hair loss (P = 0.006), oral ulcers (P = 0.024), arthritis (P = 0.025), neurological symptoms (P = 0.037), renal manifestations (P = 0.003), AIHA (P = 0.012). Conclusion: In pSLE, autoantibodies clusters with distinct clinical phenotypes. Hence, all autoantibodies should be done at time of diagnosis. It will help in predicting the clinical course of pSLE and also to identify patients at risk of developing major organ involvement later.

Background: This study assessed seroprevalence of poliovirus antibodies in children from selected poliovirus high-risk areas of the Far North region of Cameroon which serves to monitor polio immunization program. Methods: This was a community-based cross-sectional seroprevalence survey involving collection of dried blood specimens (DBS) among children aged 12–59 months (n = 401). Multi-stage cluster sampling using GIS was applied to select the study sample. Collected DBS were analysed with microneutralization assays for poliovirus neutralizing antibody levels. Results: The overall seroprevalence of types 1, 2 and 3 neutralizing antibodies were 86.8 % (95 % confidence interval [CI]: 83.1–89.8), 74.6 % (95 % CI: 70.1–78.6) and 79.3 % (95 % CI: 75.1–83.0), respectively. Median titers (log2 scale) for type 1, 2 and 3 were 7.17 (6.5–7.5), 5.17 (4.83–5.5), and 6.17 (5.5–6.5), respectively. There was an increasing trend in median titers and seroprevalence with age, statistically significant between the youngest and oldest age groups (p

Background: Count data represents the number of occurrences of an event within a fixed period of time. In count data modelling, overdispersion is inevitable. Sometimes, this overdispersion may not be just due to the excess zeros but may be due to the presence of two or more mixtures. Hence the main objective is to examine for the presence of mixtures if any, with excess zeros and compare Generalized Poisson model, Mixture models with other count data models using real time and simulated data. Methods: Three real time over-dispersed datasets were used for the comparison of the models. The real time data models were compared using information criteria like AIC and BIC and regression coefficients. Data was also simulated using mixture Poisson with excess zeros. The simulation was repeated for different sample sizes were used to identify the better model. Results: Generalized Poisson showed consistently lower bias and MSE when compared to the other models for varying sample of sizes. AIC and BIC values were almost similar for Generalized Poisson, ZIP and Mixture Poisson model. Similar findings were also obtained from real time data. Conclusion: Generalized Poisson models provides a better fit for overdispersed data due to excess zeros, consistently in real time and simulated with varying sample sizes. Negative Binomial models can be redistricted or re-evaluated against Generalized Poisson model.

Childhood pneumonia accounts for 17% of IMR in India, posing a major health burden. With cultural beliefs influencing care seeking behaviour and disparities existing in health infrastructure across the country, an understanding of the underlying issues merits exploration. Study assessed prevalence of probable pneumonia and examined care seeking behaviour of mothers in three states, Madhya Pradesh (MP), Uttar Pradesh (UP) and Tamil Nadu (TN). This mixed methods study involved a household survey and qualitative interviews with mothers in three districts from each state. Households with children aged 2-59 months were screened to identify those with probable pneumonia; sub-sample of mothers participated in qualitative interviews. Care seeking behaviour was explored in the context of recognition of symptoms, nature of first care provided, time when care was sought outside the home and choice of health provider. Overall 17,442 children from 13,544 households were screened, of which 729 (MP), 752 (UP) and 713 (TN) children respectively, were identified with probable pneumonia; 72 mothers participated in the qualitative interviews. Three months period prevalence was estimated in study districts at 22.2%-MP 13.3%-UP and 8.4%-TN. Most mothers in MP and UP were not perceptive to severity of illness; type of care sought was often inappropriate, delayed, with home remedies and visits to unqualified care providers being their first response. In contrast, in TN, use of home remedies was minimal, going to untrained care providers, non-existent and more than 90% mothers sought appropriate care. Private doctors were the preferred choice among all mothers but utilization of government care was highest in TN (20%). Community health workers were underutilized, with less than 10% mothers consulting them. Need for educating mothers about appropriate care seeking and development of good health infrastructure as essential to attainment of better child health indices are advocated.

Antibiotic pressure contributes to rising antibiotic resistance. Policy guidelines encourage rational prescribing behavior, but effectiveness in containing antibiotic use needs further assessment. This study therefore assessed the patterns of antibiotic use over a decade and analyzed the impact of different modes of guideline development and dissemination on inpatient antibiotic use.Antibiotic use was calculated monthly as defined daily doses (DDD) per 100 bed days for nine antibiotic groups and overall. This time series compared trends in antibiotic use in five adjacent time periods identified as 'Segments,' divided based on differing modes of guideline development and implementation: Segment 1--Baseline prior to antibiotic guidelines development; Segment 2--During preparation of guidelines and booklet dissemination; Segment 3--Dormant period with no guidelines dissemination; Segment 4--Booklet dissemination of revised guidelines; Segment 5--Booklet dissemination of revised guidelines with intranet access. Regression analysis adapted for segmented time series and adjusted for seasonality assessed changes in antibiotic use trend.Overall antibiotic use increased at a monthly rate of 0.95 (SE = 0.18), 0.21 (SE = 0.08) and 0.31 (SE = 0.06) for Segments 1, 2 and 3, stabilized in Segment 4 (0.05; SE = 0.10) and declined in Segment 5 (-0.37; SE = 0.11). Segments 1, 2 and 4 exhibited seasonal fluctuations. Pairwise segmented regression adjusted for seasonality revealed a significant drop in monthly antibiotic use of 0.401 (SE = 0.089; p