BackgroundIn 2011, 64% of children in Mozambique, ages 12–23 months, were fully immunized. Large provincial differences in vaccine uptake exist.MethodsWe conducted a survey of 1650 females with children aged 12–23 months in the districts of Gurùé and Milange. Implementation occurred from November to December 2014. Descriptive statistics and logistic regression using R-software 3.0.2 were used to examine factors associated with full vaccination status. ArcGIS version 10.3.1 (ESRI, Redlands, CA, USA) was used to map spatial patterns of vaccine uptake.ResultsFull vaccination was roughly 48%. Identifying ‘hospital’ as a location to get vaccinated was associated with having a fully vaccinated child (OR=1.87, 95% CI=1.02, 3.41, p=0.043). Households where health decisions are made solely by the male or the female had 38% (95% CI=0.32, 1.21) and 55% (95% CI=0.29, 0.69) lower odds, respectively, of their child being fully immunized. For every 10 km increase from the nearest health facility there was a 36% lower odds of the child being fully immunized (OR=0.64, 95% CI=0.44, 0.93, p<0.001).ConclusionZambézia Province, as a whole and the districts of Gurùé and Milange specifically, is falling short of vaccination targets. Intensified efforts focused on the least educated, most distant and which take a more family-centered approach are needed to improve vaccine uptake.

BackgroundMalaria is the leading cause of death among children in Mozambique. Prevalence and factors associated with malaria are not well studied among children in rural Zambézia Province. Whether prevalence of malaria varies across diverse districts within the province is unknown.MethodsA cross-sectional survey of female heads of household was conducted during April and May 2014, a period of peak malaria transmission. Data were collected on up to two randomly selected children aged 6–59 months per household. The outcome of interest was self-report of symptomatic malaria confirmed by diagnostic test in the past 30 days. Analyses accounted for the two-stage cluster sample design. Prevalence of symptomatic malaria was calculated for the province and three over-sampled focus districts—Alto Molócuè, Morrumbala, and Namacurra. Multivariable logistic regression of symptomatic malaria diagnosis included: district, age, sex, education, bed net use, urban setting, distance to health facility, income, roofing material, and pig farming.ResultsData were collected on 2540 children. Fifty percent were female, and the median age was 24 months. Sixty percent of children slept under bed nets the night prior to the survey, but utilization varied between districts (range 49–89%; p < 0.001). Forty-three percent of children reported fever in the past 30 days, 91% of those sought care at a health facility, 67% of those had either a malaria rapid diagnostic test or blood smear, and 67% of those had a positive test result and therefore met our case definition of self-reported symptomatic malaria. There were significant differences in prevalence of fever (p < 0.001), health-seeking (p < 0.001), and diagnostic testing (p = 0.003) between focus districts. Province-wide prevalence of symptomatic malaria was 13% and among focus districts ranged from 14% in Morrumbala to 17% in Namacurra (p < 0.001). Higher female caregiver education (OR 1.88; 95% CI 1.31–2.70), having fewer young children in the household (OR 1.25; 95% CI 1.01–1.56), and higher income (OR 1.56; 95% CI 1.11–2.22) were independently associated with having a child with symptomatic malaria.ConclusionsSelf-reported symptomatic malaria is highly prevalent among children in Zambézia Province, Mozambique and varies significantly between diverse districts. Factors facilitating access to health services are associated with symptomatic malaria diagnosis. These findings should inform resource allocation in the fight against malaria in Mozambique.

Objective To develop and validate algorithms for predicting 30-day fatal and nonfatal opioid-related overdose using statewide data sources including prescription drug monitoring program data, Hospital Discharge Data System data, and Tennessee (TN) vital records. Current overdose prevention efforts in TN rely on descriptive and retrospective analyses without prognostication. Materials and Methods Study data included 3 041 668 TN patients with 71 479 191 controlled substance prescriptions from 2012 to 2017. Statewide data and socioeconomic indicators were used to train, ensemble, and calibrate 10 nonparametric “weak learner” models. Validation was performed using area under the receiver operating curve (AUROC), area under the precision recall curve, risk concentration, and Spiegelhalter z-test statistic. Results Within 30 days, 2574 fatal overdoses occurred after 4912 prescriptions (0.0069%) and 8455 nonfatal overdoses occurred after 19 460 prescriptions (0.027%). Discrimination and calibration improved after ensembling (AUROC: 0.79–0.83; Spiegelhalter P value: 0–.12). Risk concentration captured 47–52% of cases in the top quantiles of predicted probabilities. Discussion Partitioning and ensembling enabled all study data to be used given computational limits and helped mediate case imbalance. Predicting risk at the prescription level can aggregate risk to the patient, provider, pharmacy, county, and regional levels. Implementing these models into Tennessee Department of Health systems might enable more granular risk quantification. Prospective validation with more recent data is needed. Conclusion Predicting opioid-related overdose risk at statewide scales remains difficult and models like these, which required a partnership between an academic institution and state health agency to develop, may complement traditional epidemiological methods of risk identification and inform public health decisions.

Background Geospatial technology has facilitated the discovery of disease distributions and etiology and helped target prevention programs. Globally, gastric cancer is the leading infection-associated cancer, and third leading cause of cancer mortality worldwide, with marked geographic variation. Central and South America have a significant burden, particularly in the mountainous regions. In the context of an ongoing population-based case-control study in Central America, our aim was to examine the spatial epidemiology of gastric cancer subtypes and H. pylori virulence factors. Methods Patients diagnosed with gastric cancer from 2002 to 2013 in western Honduras were identified in the prospective gastric cancer registry at the principal district hospital. Diagnosis was based on endoscopy and confirmatory histopathology. Geospatial methods were applied using the ArcGIS v10.3.1 and SaTScan v9.4.2 platforms to examine regional distributions of the gastric cancer histologic subtypes (Lauren classification), and the H. pylori CagA virulence factor. Getis-Ord-Gi hot spot and Discrete Poisson SaTScan statistics, respectively, were used to explore spatial clustering at the village level (30–50 rural households), with standardization by each village’s population. H. pylori and CagA serologic status was determined using the novel H. pylori multiplex assay (DKFZ, Germany). Results Three hundred seventy-eight incident cases met the inclusion criteria (mean age 63.7, male 66.3%). Areas of higher gastric cancer incidence were identified. Significant spatial clustering of diffuse histology adenocarcinoma was revealed both by the Getis-Ord-GI* hot spot analysis ( P -value < 0.0015; range 0.00003–0.0014; 99%CI), and by the SaTScan statistic ( P- value < 0.006; range 0.0026–0.0054). The intestinal subtype was randomly distributed. H. pylori CagA had significant spatial clustering only in association with the diffuse histology cancer hot spot (Getis-Ord-Gi* P value ≤0.001; range 0.0001–0.0010; SaTScan statistic P value 0.0085). In the diffuse gastric cancer hot spot, the lowest age quartile range was 21–46 years, significantly lower than the intestinal cancers ( P = 0.024). Conclusions Geospatial methods have identified a significant cluster of incident diffuse type adenocarcinoma cases in rural Central America, suggest of a germline genetic association. Further genomic and geospatial analyses to identify potential spatial patterns of genetic, bacterial, and environmental risk factors may be informative.

Nepal has a diverse geographic landscape that could potentially create clustered subpopulations with regional socio-cultures that could result in differential health outcomes. With an alarming rise in married male populations migrating for work, it is possible that these migrants are engaged in risky sexual behaviour, putting their wives at risk for infectious disease outcomes, including reproductive tract infections (RTI), when they return home. The prevalence of male migration varies by geographic region in Nepal and this variation could potentially contribute to different RTI rates. Using a cross-sectional dataset (the 2011 Nepal Demographic and Health Survey) including 9607 married women, we investigated geospatial and socio-cultural factors associated with the symptoms of RTIs with a focus on the husbands' migration status. Choropleth maps were created to illustrate areas with high percentages of RTIs that correlated with migration patterns. Overall, 31.9% of the husbands were migrating for work. After adjusting for wealth, contraception use, age at first marriage, urban/rural status and husband's education, women whose husbands had been absent for a year or more in Nepal's Mid-West region (OR 1.93 95%, CI 1.02-3.67) or Far-West region (OR 2.89 95%, CI 1.24-6.73) were more likely to report RTI-like symptoms than others. Our results suggest a potential association between husbands' migration status and Nepali women reporting RTI symptoms by geographic regions. However, further research is needed to put this outcome on a stronger footing with respect to this under-studied population, specifically in the context of geographical variation.