Publications by authors named "Elena N Naumova"

148 Publications

The Seasonal Microbial Ecology of Plankton and Plankton-Associated Vibrio parahaemolyticus in the Northeast United States.

Appl Environ Microbiol 2021 Jul 13;87(15):e0297320. Epub 2021 Jul 13.

Northeast Center for Vibrio Disease and Ecology, University of New Hampshire, Durham, New Hampshire, USA.

Microbial ecology studies have proven to be important resources for improving infectious disease response and outbreak prevention. Vibrio parahaemolyticus is an ongoing source of shellfish-borne food illness in the Northeast United States, and there is keen interest in understanding the environmental conditions that coincide with V. parahaemolyticus disease risk, in order to aid harvest management and prevent further illness. Zooplankton and chitinous phytoplankton are associated with V. parahaemolyticus dynamics elsewhere; however, this relationship is undetermined for the Great Bay estuary (GBE), an important emerging shellfish growing region in the Northeast United States. A comprehensive evaluation of the microbial ecology of V. parahaemolyticus associated with plankton was conducted in the GBE using 3 years of data regarding plankton community, nutrient concentration, water quality, and V. parahaemolyticus concentration in plankton. The concentrations of V. parahaemolyticus associated with plankton were highly seasonal, and the highest concentrations of V. parahaemolyticus cultured from zooplankton occurred approximately 1 month before the highest concentrations of V. parahaemolyticus from phytoplankton. The two V. parahaemolyticus peaks corresponded with different water quality variables and a few highly seasonal plankton taxa. Importantly, V. parahaemolyticus concentrations and plankton community dynamics were poorly associated with nutrient concentrations and chlorophyll , commonly applied proxy variables for assessing ecological health risks and human health risks from harmful plankton and V. parahaemolyticus elsewhere. Together, these statistical associations (or lack thereof) provide valuable insights to characterize the plankton-V. parahaemolyticus dynamic and inform approaches for understanding the potential contribution of plankton to human health risks from V. parahaemolyticus for the Northeast United States. The -plankton interaction is a focal relationship in disease research; however, little is known about this dynamic in the Northeast United States, where V. parahaemolyticus is an established public health issue. We integrated phototactic plankton separation with seasonality analysis to determine the dynamics of the plankton community, water quality, and V. parahaemolyticus concentrations. Distinct bimodal peaks in the seasonal timing of V. parahaemolyticus abundance from phyto- versus zooplankton and differing associations with water quality variables and plankton taxa indicate that monitoring and forecasting approaches should consider the source of exposure when designing predictive methods for V. parahaemolyticus. Helicotheca tamensis has not been previously reported in the GBE. Its detection during this study provides evidence of the changes occurring in the ecology of regional estuaries and potential mechanisms for changes in V. parahaemolyticus populations. The monitoring approaches can be translated to aid other areas facing similar public health challenges.
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http://dx.doi.org/10.1128/AEM.02973-20DOI Listing
July 2021

Together with the public health world.

Authors:
Elena N Naumova

J Public Health Policy 2021 Mar 5;42(1):1-5. Epub 2021 Mar 5.

Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Avenue, Boston, MA, 02111, USA.

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http://dx.doi.org/10.1057/s41271-021-00278-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7934346PMC
March 2021

Fecal Indicator Bacteria Data to Characterize Drinking Water Quality in Low-Resource Settings: Summary of Current Practices and Recommendations for Improving Validity.

Int J Environ Res Public Health 2021 02 28;18(5). Epub 2021 Feb 28.

Department of Civil and Environmental Engineering, School of Engineering, Tufts University, Medford, MA 02155, USA.

Fecal indicator bacteria (FIB) values are widely used to assess microbial contamination in drinking water and to advance the modeling of infectious disease risks. The membrane filtration (MF) testing technique for FIB is widely adapted for use in low- and middle-income countries (LMICs). We conducted a systematic literature review on the use of MF-based FIB data in LMICs and summarized statistical methods from 172 articles. We then applied the commonly used statistical methods from the review on publicly available datasets to illustrate how data analysis methods affect FIB results and interpretation. Our findings indicate that standard methods for processing samples are not widely reported, the selection of statistical tests is rarely justified, and, depending on the application, statistical methods can change risk perception and present misleading results. These results raise concerns about the validity of FIB data collection, analysis, and presentation in LMICs. To improve evidence quality, we propose a FIB data reporting checklist to use as a reminder for researchers and practitioners.
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http://dx.doi.org/10.3390/ijerph18052353DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7957662PMC
February 2021

How Seasonality of Malnutrition Is Measured and Analyzed.

Int J Environ Res Public Health 2021 02 13;18(4). Epub 2021 Feb 13.

Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA 02111, USA.

Seasonality is a critical source of vulnerability across most human activities and natural processes, including the underlying and immediate drivers of acute malnutrition. However, while there is general agreement that acute malnutrition is highly variable within and across years, the evidence base is limited, resulting in an overreliance on assumptions of seasonal peaks. We review the design and analysis of 24 studies exploring the seasonality of nutrition outcomes in Africa's drylands, providing a summary of approaches and their advantages and disadvantages. Over half of the studies rely on two to four time points within the year and/or the inclusion of time as a categorical variable in the analysis. While such approaches simplify interpretation, they do not correspond to the climatic variability characteristic of drylands or the relationship between climatic variability and human activities. To better ground our understanding of the seasonality of acute malnutrition in a robust evidence base, we offer recommendations for study design and analysis, including drawing on participatory methods to identify community perceptions of seasonality, use of longitudinal data and panel analysis with approaches borrowed from the field of infectious diseases, and linking oscillations in nutrition data with climatic data.
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http://dx.doi.org/10.3390/ijerph18041828DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7918225PMC
February 2021

Joint Associations of Multiple Dietary Components With Cardiovascular Disease Risk: A Machine-Learning Approach.

Am J Epidemiol 2021 Jul;190(7):1353-1365

The human diet consists of a complex mixture of components. To realistically assess dietary impacts on health, new statistical tools that can better address nonlinear, collinear, and interactive relationships are necessary. Using data from 1,928 healthy participants in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort (1985-2006), we explored the association between 12 dietary factors and 10-year predicted risk of atherosclerotic cardiovascular disease (ASCVD) using an innovative approach, Bayesian kernel machine regression (BKMR). Employing BKMR, we found that among women, unprocessed red meat was most strongly related to the outcome: An interquartile range increase in unprocessed red meat consumption was associated with a 0.07-unit (95% credible interval: 0.01, 0.13) increase in ASCVD risk when intakes of other dietary components were fixed at their median values (similar results were obtained when other components were fixed at their 25th and 75th percentile values). Among men, fruits had the strongest association: An interquartile range increase in fruit consumption was associated with -0.09-unit (95% credible interval (CrI): -0.16, -0.02), -0.10-unit (95% CrI: -0.16, -0.03), and -0.11-unit (95% CrI: -0.18, -0.04) lower ASCVD risk when other dietary components were fixed at their 25th, 50th (median), and 75th percentile values, respectively. Using BKMR to explore the complex structure of the total diet, we found distinct sex-specific diet-ASCVD relationships and synergistic interaction between whole grain and fruit consumption.
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http://dx.doi.org/10.1093/aje/kwab004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8245893PMC
July 2021

Completeness of open access FluNet influenza surveillance data for Pan-America in 2005-2019.

Sci Rep 2021 01 12;11(1):795. Epub 2021 Jan 12.

Tufts University Friedman School of Nutrition Science and Policy, Boston, USA.

For several decades, the World Health Organization has collected, maintained, and distributed invaluable country-specific disease surveillance data that allow experts to develop new analytical tools for disease tracking and forecasting. To capture the extent of available data within these sources, we proposed a completeness metric based on the effective time series length. Using FluNet records for 29 Pan-American countries from 2005 to 2019, we explored whether completeness was associated with health expenditure indicators adjusting for surveillance system heterogeneity. We observed steady improvements in completeness by 4.2-6.3% annually, especially after the A(H1N1)-2009 pandemic, when 24 countries reached > 95% completeness. Doubling in decadal health expenditure per capita was associated with ~ 7% increase in overall completeness. The proposed metric could navigate experts in assessing open access data quality and quantity for conducting credible statistical analyses, estimating disease trends, and developing outbreak forecasting systems.
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http://dx.doi.org/10.1038/s41598-020-80842-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7804328PMC
January 2021

Seasonality of diet costs reveals food system performance in East Africa.

Sci Adv 2020 Dec 4;6(49). Epub 2020 Dec 4.

Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA 02111, USA.

Seasonal fluctuations in food prices reflect interactions between climate and society, measuring the degree to which predictable patterns of crop growth and harvest are offset by storage and trade. Previous research on seasonality in food systems has focused on specific commodities. This study accounts for substitution between items to meet nutritional needs, computing seasonal variation in local food environments using monthly retail prices for 191 items across Ethiopia, Malawi, and Tanzania from 2002 through 2016. We computed over 25,000 least-cost diets meeting nutrient requirements at each market every month and then measured the magnitude and timing of seasonality in diet costs. We found significant intensity in Malawi, Tanzania, and Ethiopia (10.0, 6.3, and 4.0%, respectively), driven primarily by synchronized price rises for nutrient-dense foods. Results provide a metric to map nutritional security, pointing to opportunities for more targeted investments to improve the year-round delivery of nutrients.
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http://dx.doi.org/10.1126/sciadv.abc2162DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7821891PMC
December 2020

Sustained nutrition impact of a multisectoral intervention program two years after completion.

Matern Child Nutr 2021 04 3;17(2):e13103. Epub 2020 Nov 3.

Friedman School of Nutrition Science and Policy, Tufts University, Boston, Massachusetts, USA.

Progress on the nutrition Sustainable Development Goals has been slow. More attention is needed on the 'sustainable' part, focused on impact lasting beyond programme implementation. To determine sustained impact of a multisectoral nutrition intervention that provided water, sanitation, hygiene, livelihood, health and nutrition support (2013-2015) in eastern Chad, we utilize longitudinal household data collected 2 years (2017) after the intervention ended. Between 2013 and 2015, children (6-59 months) in the multisectoral intervention were less likely to be severely wasted, underweight and had a higher weight-for-height z-score (WHZ) compared with the control. To measure sustained programme impact, we use data on six nutrition indicators from 517 children between 2015 and 2017. We ran three models: a generalized linear model on cross-sectional child cohorts; a mixed-effects model on household panel data; and a mixed-effects model on child panel data. For children who were born during the programme, we saw significant improvement in underweight, weight for age z-scores (WAZs) and height-for-age z-scores (HAZs). Boys 6-23 months born after the end of the programme, on the other hand, were significantly more likely to be underweight or wasted and had lower WHZ and WAZ compared with boys born during the programme and girls born during and after the programme. Corresponding to the literature from sub-Saharan Africa, boys appear to be more vulnerable to malnutrition, which might be why they are more sensitive to programme cessation. Future monitoring, evaluations and research need to consider impact sustainability and that it might not be homogeneous across age and gender.
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http://dx.doi.org/10.1111/mcn.13103DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7988880PMC
April 2021

Seasonal synchronization of foodborne outbreaks in the United States, 1996-2017.

Sci Rep 2020 10 15;10(1):17500. Epub 2020 Oct 15.

Tufts University Friedman School of Nutrition Science and Policy, Boston, USA.

Modern food systems represent complex dynamic networks vulnerable to foodborne infectious outbreaks difficult to track and control. Seasonal co-occurrences (alignment of seasonal peaks) and synchronization (similarity of seasonal patterns) of infections are noted, yet rarely explored due to their complexity and methodological limitations. We proposed a systematic approach to evaluate the co-occurrence of seasonal peaks using a combination of L-moments, seasonality characteristics such as the timing (phase) and intensity (amplitude) of peaks, and three metrics of serial, phase-phase, and phase-amplitude synchronization. We used public records on counts of nine foodborne infections abstracted from CDC's FoodNet Fast online platform for the US and ten representative states from 1996 to 2017 (264 months). Based on annualized and trend-adjusted Negative Binomial Harmonic Regression (NBHR) models augmented with the δ-method, we determined that seasonal peaks of Campylobacter, Salmonella, and Shiga toxin-producing Escherichia Coli (STEC) were tightly clustered in late-July at the national and state levels. Phase-phase synchronization was observed between Cryptosporidium and Shigella, Listeria, and Salmonella (ρ = 0.51, 0.51, 0.46; p < 0.04). Later peak timing of STEC was associated with greater amplitude nationally (ρ = 0.50, p = 0.02) indicating phase-amplitude synchronization. Understanding of disease seasonal synchronization is essential for developing reliable outbreak forecasts and informing stakeholders on mitigation and preventive measures.
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http://dx.doi.org/10.1038/s41598-020-74435-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7562704PMC
October 2020

An analecta of visualizations for foodborne illness trends and seasonality.

Sci Data 2020 10 13;7(1):346. Epub 2020 Oct 13.

Tufts University Friedman School of Nutrition Science and Policy, Boston, USA.

Disease surveillance systems worldwide face increasing pressure to maintain and distribute data in usable formats supplemented with effective visualizations to enable actionable policy and programming responses. Annual reports and interactive portals provide access to surveillance data and visualizations depicting temporal trends and seasonal patterns of diseases. Analyses and visuals are typically limited to reporting the annual time series and the month with the highest number of cases per year. Yet, detecting potential disease outbreaks and supporting public health interventions requires detailed spatiotemporal comparisons to characterize spatiotemporal patterns of illness across diseases and locations. The Centers for Disease Control and Prevention's (CDC) FoodNet Fast provides population-based foodborne-disease surveillance records and visualizations for select counties across the US. We offer suggestions on how current FoodNet Fast data organization and visual analytics can be improved to facilitate data interpretation, decision-making, and communication of features related to trend and seasonality. The resulting compilation, or analecta, of 436 visualizations of records and codes are openly available online.
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http://dx.doi.org/10.1038/s41597-020-00677-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7553952PMC
October 2020

Public health response to COVID-19: the forecaster's dilemma.

Authors:
Elena N Naumova

J Public Health Policy 2020 Dec;41(4):395-398

Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Avenue, Boston, MA, 02111, USA.

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http://dx.doi.org/10.1057/s41271-020-00252-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7480629PMC
December 2020

Effects of Data Aggregation on Time Series Analysis of Seasonal Infections.

Int J Environ Res Public Health 2020 08 13;17(16). Epub 2020 Aug 13.

Division of Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA 02111, USA.

Time series analysis in epidemiological studies is typically conducted on aggregated counts, although data tend to be collected at finer temporal resolutions. The decision to aggregate data is rarely discussed in epidemiological literature although it has been shown to impact model results. We present a critical thinking process for making decisions about data aggregation in time series analysis of seasonal infections. We systematically build a harmonic regression model to characterize peak timing and amplitude of three respiratory and enteric infections that have different seasonal patterns and incidence. We show that irregularities introduced when aggregating data must be controlled during modeling to prevent erroneous results. Aggregation irregularities had a minimal impact on the estimates of trend, amplitude, and peak timing for daily and weekly data regardless of the disease. However, estimates of peak timing of the more common infections changed by as much as 2.5 months when controlling for monthly data irregularities. Building a systematic model that controls for data irregularities is essential to accurately characterize temporal patterns of infections. With the urgent need to characterize temporal patterns of novel infections, such as COVID-19, this tutorial is timely and highly valuable for experts in many disciplines.
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http://dx.doi.org/10.3390/ijerph17165887DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7460497PMC
August 2020

The International Diet-Health Index: a novel tool to evaluate diet quality for cardiometabolic health across countries.

BMJ Glob Health 2020 07;5(7)

Division of Nutrition Epidemiology and Data Science, Tufts University Friedman School of Nutrition Science and Policy, Boston, Massachusetts, USA

Introduction: Diet is a major modifiable risk factor for cardiometabolic disease; however, interpretable measures capturing impacts of overall diet on health that can be easily used by policymakers at the global/national levels are not readily available.

Methods: We developed the International Diet-Health Index (IDHI) to measure health impacts of dietary intake across 186 countries in 2010, using age-specific and sex-specific data on country-level dietary intake, effects of dietary factors on cardiometabolic diseases and country-specific cardiometabolic disease profiles. The index encompasses the impact of 11 foods/nutrients on 12 cardiometabolic diseases, the mediation of health effects of specific dietary intakes through blood pressure and body mass index and background disease prevalence in each country-age-sex group. We decomposed the index into IDHI for risk-reducing factors, and IDHI for risk-increasing factors. The flexible functional form of the IDHI allows inclusion of additional risk factors and diseases as data become available.

Results: By sex, women experienced smaller detrimental cardiometabolic effects of diet than men: (females IDHI range: -0.480 (5th percentile, 95th percentile: -0.932, -0.300) to -0.314 (-0.543, -0.213); males IDHI range: (-0.617 (-1.054, -0.384) to -0.346 (-0.624, -0.222)). By age, middle-aged adults had highest IDHI (females: 0.392 (0.235, 0.763); males: 0.415 (0.243, 0.949)) and younger adults had most extreme IDHI (females: -0.480 (-0.932, -0.300); males: -0.617 (-1.054, -0.384)). Regionally, Central Latin America had the lowest IDHI (-0.466 (-0.892, -0.159)), while Southeast Asia had the highest IDHI (0.272 (-0.224, 0.903)). IDHI was highest in low-income countries and lowest in upper middle-income countries (-0.039 (-0.317, 0.227) and -0.146 (-0.605, 0.303), respectively). Among 186 countries, Honduras had lowest IDHI (-0.721 (-0.916, -0.207)), while Malaysia had highest IDHI (0.904 (0.435, 1.190)).

Conclusion: IDHI encompasses dietary intakes, health effects and country disease profiles into a single index, allowing policymakers a useful means of assessing/comparing health impacts of diet quality between populations.
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http://dx.doi.org/10.1136/bmjgh-2019-002120DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7375435PMC
July 2020

The traps of calling the public health response to COVID-19 "an unexpected war against an invisible enemy".

Authors:
Elena N Naumova

J Public Health Policy 2020 Sep;41(3):233-237

Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Avenue, Boston, MA, 02111, USA.

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http://dx.doi.org/10.1057/s41271-020-00237-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7330530PMC
September 2020

Multivariate time-series analysis of biomarkers from a dengue cohort offers new approaches for diagnosis and prognosis.

PLoS Negl Trop Dis 2020 06 16;14(6):e0008199. Epub 2020 Jun 16.

E25Bio Inc., Cambridge, Massachusetts, United States of America.

Dengue is a major public health problem worldwide with distinct clinical manifestations: an acute presentation (dengue fever, DF) similar to other febrile illnesses (OFI) and a more severe, life-threatening form (severe dengue, SD). Due to nonspecific clinical presentation during the early phase of dengue infection, differentiating DF from OFI has remained a challenge, and current methods to determine severity of dengue remain poor early predictors. We present a prospective clinical cohort study conducted in Caracas, Venezuela from 2001-2005, designed to determine whether clinical and hematological parameters could distinguish DF from OFI, and identify early prognostic biomarkers of SD. From 204 enrolled suspected dengue patients, there were 111 confirmed dengue cases. Piecewise mixed effects regression and nonparametric statistics were used to analyze longitudinal records. Decreased serum albumin and fibrinogen along with increased D-dimer, thrombin-antithrombin complex, activated partial thromboplastin time and thrombin time were prognostic of SD on the day of defervescence. In the febrile phase, the day-to-day rates of change in serum albumin and fibrinogen concentration, along with platelet counts, were significantly decreased in dengue patients compared to OFI, while the day-to-day rates of change of lymphocytes (%) and thrombin time were increased. In dengue patients, the absolute lymphocytes to neutrophils ratio showed specific temporal increase, enabling classification of dengue patients entering the critical phase with an area under the ROC curve of 0.79. Secondary dengue patients had elongation of Thrombin time compared to primary cases while the D-dimer formation (fibrinolysis marker) remained always lower for secondary compared to primary cases. Based on partial analysis of 31 viral complete genomes, a high frequency of C-to-T transitions located at the third codon position was observed, suggesting deamination events with five major hot spots of amino acid polymorphic sites outside in non-structural proteins. No association of severe outcome was statistically significant for any of the five major polymorphic sites found. This study offers an improved understanding of dengue hemostasis and a novel way of approaching dengue diagnosis and disease prognosis using piecewise mixed effect regression modeling. It also suggests that a better discrimination of the day of disease can improve the diagnostic and prognostic classification power of clinical variables using ROC curve analysis. The piecewise mixed effect regression model corroborated key early clinical determinants of disease, and offers a time-series approach for future vaccine and pathogenesis clinical studies.
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http://dx.doi.org/10.1371/journal.pntd.0008199DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7380649PMC
June 2020

Sensitivity of Nutrition Indicators to Measure the Impact of a Multi-Sectoral Intervention: Cross-Sectional, Household, and Individual Level Analysis.

Int J Environ Res Public Health 2020 04 30;17(9). Epub 2020 Apr 30.

Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA 02111, USA.

Interventions tackling multiple drivers of child malnutrition have potential, yet the evidence is limited and draws on different analysis and nutrition outcomes, reducing comparability. To better understand the advantages and disadvantages of three different analytical approaches on seven common nutrition indicators, we use panel data (2012, 2014, 2015) on 1420 households from a randomized control study of a multi-sectoral intervention in Chad. We compare program impact using three types of analysis: a cross-sectional analysis of non-matched children; a panel analysis on longitudinal outcomes following the worst-off child in the household; and a panel analysis on longitudinal outcomes of matched children. We find that the sensitivity of the nutrition outcomes to program impact increases with each subsequent analytical approach, despite the reduction in sample size, as the analysis is able to control for more non-measured child and household characteristics. In the matched child panel analysis, the odds of a child being severely wasted were 76% lower (CI: 0.59-0.86, = 0.001), the odds of being underweight were 33% lower (CI: 0.15-0.48, = 0.012), and weight-for-height z-score was 0.19 standard deviations higher (CI: 0.09-0.28, = 0.022) in the treatment versus control group. The study provides evidence for multi-sectoral interventions to tackle acute malnutrition and recommends the best practice analytical approach.
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http://dx.doi.org/10.3390/ijerph17093121DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7246654PMC
April 2020

Rural Ghanaian households are more likely to use alternative unimproved water sources when water from boreholes has undesirable organoleptic characteristics.

Int J Hyg Environ Health 2020 06 2;227:113514. Epub 2020 Apr 2.

Tufts University School of Arts and Sciences, Medford, MA, USA.

Sustainable Development Goal (SDG) 6 aims to achieve universal access to safe drinking water sources. However, the health benefits of meeting this goal will only be fully realized if improved sources are used to the exclusion of unimproved sources. Very little is known about how rural African households balance the use of improved and unimproved water sources when multiple options are present. We assessed parallel use of untreated surface water and unimproved hand-dug wells (HDWs) in the presence of boreholes (BHs) using a semi-quantitative water use survey among 750 residents of 15 rural Ghanaian communities, distributed across three BH water quality clusters: control, high salinity, and high iron. Multivariate mixed effects logistic regression models were used to assess the impact of water quality cluster on the use of BHs, HDWs, and surface water, controlling for distance to the nearest source of each type. Reported surface water use was significantly higher in the high salinity and high iron clusters than in the control cluster, especially for water-intensive activities. Respondents in the non-control clusters had approximately eight times higher odds of clothes washing with surface water (p < 0.01) than in the control. Respondents in the high salinity cluster also had 4.3 times higher odds of drinking surface water (p < 0.05). BH use was high in all clusters, but decreased substantially when distance to the nearest BH exceeded 300 m (OR = 0.17-0.25, p < 0.001). Water use from all sources was inversely correlated with distance, with the largest effect observed on HDW use in multivariate models (OR = 0.02, p < 0.001). Surface water and HDW use will likely continue despite the presence of BHs when perceived groundwater quality is poor and other water sources are in close proximity. It is essential to account for naturally-occurring but undesirable groundwater quality parameters in rural water planning to ensure that SDG 6 is met and health benefits are realized.
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http://dx.doi.org/10.1016/j.ijheh.2020.113514DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7387189PMC
June 2020

Profile of social self-management practices in daily life with Parkinson's disease is associated with symptom severity and health quality of life.

Disabil Rehabil 2020 Apr 1:1-13. Epub 2020 Apr 1.

The Gerald J. and Dorothy R. Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA.

Social participation is a key determinant of healthy aging, yet little is known about how people with Parkinson's disease manage social living. This study describes individual differences in social self-management practices and their association with symptom severity and health quality of life. People with Parkinson's disease ( = 90) completed measures of healthy routines, activities and relationships, symptom severity, and health related quality of life. Cluster analysis identified profiles of social self-management practices. Analysis of variance tested differences between profiles in symptom severity and health quality of life. Participants clustered into one of seven groups according to different combinations of three practices: , , and . The healthiest cluster engaged equally in all three practices at above sample average degree of engagement. Four clusters that engaged at or above sample average in experienced less health problems than three clusters that engaged below average. Variation in aspects of social lifestyle unrelated to health appeared also to contribute to profile diversity. Findings provide insight into similarity and variation in how people with Parkinson's disease engage with social self-management resources and point to person-centered interventions.Implications for RehabilitationSocial self-management is a biopsychosocial construct to identify and describe self-care practices that engage one's social resources for managing healthful daily living.People with Parkinson's disease vary in their profiles of engaging in social self-management practices in daily living, and this variability relates to severity of symptoms and health quality of life.Learning how to identify health-centered social self-management practices may help people with Parkinson's disease to focus on the healthfulness of their own practices.Learning how to strategically engage one's social resources as part of self-care may help people with Parkinson's disease to master managing their health and well-being in daily life.
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http://dx.doi.org/10.1080/09638288.2020.1741035DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7529710PMC
April 2020

Beat osteoporosis - nourish and exercise skeletons (BONES): a group randomized controlled trial in children.

BMC Pediatr 2020 02 21;20(1):83. Epub 2020 Feb 21.

Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Avenue, Boston, MA, 02111, USA.

Background: Lifelong healthy habits developed during childhood may prevent chronic diseases in adulthood. Interventions to promote these habits must begin early. The BONES (Beat Osteoporosis - Nourish and Exercise Skeletons) project assessed whether early elementary school children participating in a multifaceted health behavior change, after-school based intervention would improve bone quality and muscular strength and engage in more bone-strengthening behaviors.

Methods: The 2-year BONES (B) intervention included bone-strengthening physical activity (85 min/week), educational materials (2 days/week), and daily calcium-rich snacks (380 mg calcium/day) delivered by after-school program leaders. BONES plus Parent (B + P) included an additional parent education component. From 1999 to 2004, n = 83 after-school programs (N = 1434 children aged 6-9 years) in Massachusetts and Rhode Island participated in a group randomized trial with two intervention arms (B only, n = 25 programs; B + P, n = 33) and a control arm (C, n = 25). Outcome measures (primary: bone quality (stiffness index of the calcaneus) and muscular strength (grip strength and vertical jump); secondary: bone-strengthening behaviors (calcium-rich food knowledge, preference, and intake; and physical activity level (metabolic equivalent time (MET) score, and weight-bearing factor (WBF) score)) were recorded at baseline, and after years one and two. Analyses followed an intent-to-treat protocol, and focused on individual subjects' trajectories along the three time points adjusting for baseline age and race via a mixed-effects regression framework. Analyses were performed with and without sex stratification.

Results: Children in B + P increased bone stiffness compared to C (p = 0.05); No significant changes were observed in muscle strength, food knowledge, or vertical jump. Children in B + P showed significant improvement in their MET and WBF scores compared to C (p < 0.01) with a stronger effect in boys in both B and B + P (all p < 0.01).

Conclusion: After-school programs, coupled with parental engagement, serving early elementary school children are a potentially feasible platform to deliver bone-strengthening behaviors to prevent osteoporosis in adulthood, with some encouraging bone and physical activity outcomes.

Trial Registration: ClinicalTrials.gov NCT00065247. Retrospectively registered. First posted July 22, 2003.
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http://dx.doi.org/10.1186/s12887-020-1964-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038625PMC
February 2020

Assessing Seasonality Variation with Harmonic Regression: Accommodations for Sharp Peaks.

Int J Environ Res Public Health 2020 02 18;17(4). Epub 2020 Feb 18.

Department of Biostatistics, Christian Medical College, Vellore 632002, India.

The use of the harmonic regression model is well accepted in the epidemiological and biostatistical communities as a standard procedure to examine seasonal patterns in disease occurrence. While these models may provide good fit to periodic patterns with relatively symmetric rises and falls, for some diseases the incidence fluctuates in a more complex manner. We propose a two-step harmonic regression approach to improve the model fit for data exhibiting sharp seasonal peaks. To capture such specific behavior, we first build a basic model and estimate the seasonal peak. At the second step, we apply an extended model using sine and cosine transform functions. These newly proposed functions mimic a quadratic term in the harmonic regression models and thus allow us to better fit the seasonal spikes. We illustrate the proposed method using actual and simulated data and recommend the new approach to assess seasonality in a broad spectrum of diseases manifesting sharp seasonal peaks.
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http://dx.doi.org/10.3390/ijerph17041318DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7068504PMC
February 2020

Ultrafine Particle Number Concentration Model for Estimating Retrospective and Prospective Long-Term Ambient Exposures in Urban Neighborhoods.

Environ Sci Technol 2020 02 24;54(3):1677-1686. Epub 2020 Jan 24.

Department of Civil and Environmental Engineering , Tufts University , 200 College Avenue , Medford , Massachusetts 02155 , United States.

Short-term exposure to ultrafine particles (UFP; <100 nm in diameter), which are present at high concentrations near busy roadways, is associated with markers of cardiovascular and respiratory disease risk. To date, few long-term studies (months to years) have been conducted due to the challenges of long-term exposure assignment. To address this, we modified hybrid land-use regression models of particle number concentrations (PNCs; a proxy for UFP) for two study areas in Boston (MA) by replacing the measured PNC term with an hourly model and adjusting for overprediction. The hourly PNC models used covariates for meteorology, traffic, and sulfur dioxide concentrations (a marker of secondary particle formation). We compared model performance against long-term PNC data collected continuously from 9 years before and up to 3 years after the model-development period. Model predictions captured the major temporal variations in the data and model performance remained relatively stable retrospectively and prospectively. The Pearson correlation of modeled versus measured hourly log-transformed PNC at a long-term monitoring site for 9 years prior was 0.74. Our results demonstrate that highly resolved spatial-temporal PNC models are capable of estimating ambient concentrations retrospectively and prospectively with generally good accuracy, giving us confidence in using these models in epidemiological studies.
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http://dx.doi.org/10.1021/acs.est.9b03369DOI Listing
February 2020

Prediction of hookworm prevalence in southern India using environmental parameters derived from Landsat 8 remotely sensed data.

Int J Parasitol 2020 01 20;50(1):47-54. Epub 2019 Nov 20.

Division of Gastrointestinal Sciences, Christian Medical College, Vellore, Tamil Nadu, India.

Soil-transmitted helminth infections propagate poverty and slow economic growth in low-income countries. As with many other neglected tropical diseases, environmental conditions are important determinants of soil-transmitted helminth transmission. Hence, remotely sensed data are commonly utilised in spatial risk models intended to inform control strategies. In the present study, we build upon the existing modelling approaches by utilising fine spatial resolution Landsat 8 remotely sensed data in combination with topographic variables to predict hookworm prevalence in a hilly tribal area in southern India. Hookworm prevalence data collected from two field surveys were used in a random forest model to investigate the predictive capacity of 15 environmental variables derived from two remotely sensed images acquired during dry and rainy seasons. A variable buffer radius (100-1000 m) was applied to the point-prevalence locations in order to integrate environmental conditions around the village centroids into the modelling approach and understand where transmission is more likely. Elevation and slope were the most important variables in the models, with lower elevation and higher slope correlating with higher transmission risk. A modified normalised difference water index was among other recurring important variables, likely responsible for some seasonal differences in model performance. The 300 m buffer distance produced the best model performance in this setting, with another spike at 700 m, and a marked drop-off in R values at 1000 m. In addition to assessing a large number of environmental correlates with hookworm transmission, the study contributes to the development of standardised methods of spatial linkage of continuous environmental data with point-based disease prevalence measures for the purpose of spatially explicit risk profiling.
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http://dx.doi.org/10.1016/j.ijpara.2019.10.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6981258PMC
January 2020

Forecasting Seasonal Concentrations in New England Shellfish.

Int J Environ Res Public Health 2019 11 7;16(22). Epub 2019 Nov 7.

Northeast Center for Vibrio Disease and Ecology, University of New Hampshire, Durham, NH 03824, USA.

Seafood-borne illness is a global public health issue facing resource managers and the seafood industry. The recent increase in shellfish-borne illnesses in the Northeast United States has resulted in the application of intensive management practices based on a limited understanding of when and where risks are present. We aim to determine the contribution of factors that affect concentrations in oysters () using ten years of surveillance data for environmental and climate conditions in the Great Bay Estuary of New Hampshire from 2007 to 2016. A time series analysis was applied to analyze concentrations and local environmental predictors and develop predictive models. Whereas many environmental variables correlated with concentrations, only a few retained significance in capturing trends, seasonality and data variability. The optimal predictive model contained water temperature and pH, photoperiod, and the calendar day of study. The model enabled relatively accurate seasonality-based prediction of concentrations for 2014-2016 based on the 2007-2013 dataset and captured the increasing trend in extreme values of concentrations. The developed method enables the informative tracking of concentrations in coastal ecosystems and presents a useful platform for developing area-specific risk forecasting models.
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http://dx.doi.org/10.3390/ijerph16224341DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6888421PMC
November 2019

Rotavirus Seasonality: An Application of Singular Spectrum Analysis and Polyharmonic Modeling.

Int J Environ Res Public Health 2019 11 6;16(22). Epub 2019 Nov 6.

Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA 02111, USA.

The dynamics of many viral infections, including rotaviral infections (RIs), are known to have a complex non-linear, non-stationary structure with strong seasonality indicative of virus and host sensitivity to environmental conditions. However, analytical tools suitable for the identification of seasonal peaks are limited. We introduced a two-step procedure to determine seasonal patterns in RI and examined the relationship between daily rates of rotaviral infection and ambient temperature in cold climates in three Russian cities: Chelyabinsk, Yekaterinburg, and Barnaul from 2005 to 2011. We described the structure of temporal variations using a new class of singular spectral analysis (SSA) models based on the "Caterpillar" algorithm. We then fitted Poisson polyharmonic regression (PPHR) models and examined the relationship between daily RI rates and ambient temperature. In SSA models, RI rates reached their seasonal peaks around 24 February, 5 March, and 12 March (i.e., the 55.17 ± 3.21, 64.17 ± 5.12, and 71.11 ± 7.48 day of the year) in Chelyabinsk, Yekaterinburg, and Barnaul, respectively. Yet, in all three cities, the minimum temperature was observed, on average, to be on 15 January, which translates to a lag between the peak in disease incidence and time of temperature minimum of 38-40 days for Chelyabinsk, 45-49 days in Yekaterinburg, and 56-59 days in Barnaul. The proposed approach takes advantage of an accurate description of the time series data offered by the SSA-model coupled with a straightforward interpretation of the PPHR model. By better tailoring analytical methodology to estimate seasonal features and understand the relationships between infection and environmental conditions, regional and global disease forecasting can be further improved.
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http://dx.doi.org/10.3390/ijerph16224309DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6888479PMC
November 2019

Spatiotemporal Patterns of Cholera Hospitalization in Vellore, India.

Int J Environ Res Public Health 2019 11 2;16(21). Epub 2019 Nov 2.

Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA 02111, USA.

Systematically collected hospitalization records provide valuable insight into disease patterns and support comprehensive national infectious disease surveillance networks. Hospitalization records detailing patient's place of residence (PoR) can be utilized to better understand a hospital's case load and strengthen surveillance among mobile populations. This study examined geographic patterns of patients treated for cholera at a major hospital in south India. We abstracted 1401 laboratory-confirmed cases of cholera between 2000-2014 from logbooks and electronic health records (EHRs) maintained by the Christian Medical College (CMC) in Vellore, Tamil Nadu, India. We constructed spatial trend models and identified two distinct clusters of patient residence-one around Vellore (836 records (61.2%)) and one in Bengal (294 records (21.5%)). We further characterized differences in peak timing and disease trend among these clusters to identify differences in cholera exposure among local and visiting populations. We found that the two clusters differ by their patient profiles, with patients in the Bengal cluster being most likely older males traveling to Vellore. Both clusters show well-aligned seasonal peaks in mid-July, only one week apart, with similar downward trend and proportion of predominant O1 serotype. Large hospitals can thus harness EHRs for surveillance by utilizing patients' PoRs to study disease patterns among resident and visitor populations.
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http://dx.doi.org/10.3390/ijerph16214257DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6862112PMC
November 2019

Spatiotemporal and Demographic Trends and Disparities in Cardiovascular Disease Among Older Adults in the United States Based on 181 Million Hospitalization Records.

J Am Heart Assoc 2019 11 29;8(21):e012727. Epub 2019 Oct 29.

Tufts Friedman School of Nutrition Science & Policy Boston MA.

Background The US population is aging, with concurrent increases in cardiovascular disease (CVD) burdens; however, spatiotemporal and demographic trends in CVD incidence in the US elderly have not been investigated in detail. This study aims to characterize trends from 1991 to 2014 in CVD hospitalizations among US Medicare beneficiaries, aged 65+ years, by single year of age/sex/race/state using records from the US Centers for Medicare & Medicaid, covering 98% of older Americans. Methods and Results We abstracted 181 202 758 US Centers for Medicare & Medicaid hospitalization records indicating CVD in any of 10 diagnosis codes; tabulated total cases of CVD by sex, age, race, state, and calendar year (1991-2014); and normalized hospitalization counts to standardize over data batches. Stratum-specific hospitalization rates were calculated using US Centers for Medicare & Medicaid records and US Census population counts; a cubic polynomial function was fit to year-specific distributions of rates by single year of age. Nationwide, CVD-related hospitalization rates increased from 1991 to 2014. Differences between hospitalization rates at age 65 and 66 years, representing magnitude of healthcare deferral until Medicare onset, increased by 7.49 per 100 people 1991 to 2006 overall, and were largest among blacks and Native Americans. Rates of CVD hospitalizations were consistently highest in the Midwest/Deep South. Evidence of misclassification of race/ethnicity in US Centers for Medicare & Medicaid hospitalization records in the 1990s was noted. Conclusions Trends in CVD-related hospitalization rates among older Americans highlight the essential need for targeted policies to reduce CVD burdens, to improve reporting of race/ethnicity in large administrative databases, and to enhance access to affordable healthcare.
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http://dx.doi.org/10.1161/JAHA.119.012727DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6898811PMC
November 2019

Incorporating calendar effects to predict influenza seasonality in Milwaukee, Wisconsin.

Epidemiol Infect 2019 09 11;147:e268. Epub 2019 Sep 11.

Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA.

Social outings can trigger influenza transmission, especially in children and elderly. In contrast, school closures are associated with reduced influenza incidence in school-aged children. While influenza surveillance modelling studies typically account for holidays and mass gatherings, age-specific effects of school breaks, sporting events and commonly celebrated observances are not fully explored. We examined the impact of school holidays, social events and religious observances for six age groups (all ages, ⩽4, 5-24, 25-44, 45-64, ⩾65 years) on four influenza outcomes (tests, positives, influenza A and influenza B) as reported by the City of Milwaukee Health Department Laboratory, Milwaukee, Wisconsin from 2004 to 2009. We characterised holiday effects by analysing average weekly counts in negative binomial regression models controlling for weather and seasonal incidence fluctuations. We estimated age-specific annual peak timing and compared influenza outcomes before, during and after school breaks. During the 118 university holiday weeks, average weekly tests were lower than in 140 school term weeks (5.93 vs. 11.99 cases/week, P < 0.005). The dampening of tests during Winter Break was evident in all ages and in those 5-24 years (RR = 0.31; 95% CI 0.22-0.41 vs. RR = 0.14; 95% CI 0.09-0.22, respectively). A significant increase in tests was observed during Spring Break in 45-64 years old adults (RR = 2.12; 95% CI 1.14-3.96). Milwaukee Public Schools holiday breaks showed similar amplification and dampening effects. Overall, calendar effects depend on the proximity and alignment of an individual holiday to age-specific and influenza outcome-specific peak timing. Better quantification of individual holiday effects, tailored to specific age groups, should improve influenza prevention measures.
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http://dx.doi.org/10.1017/S0950268819001511DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6805754PMC
September 2019

Age-Based Dynamics of a Stable Circulating Cd8 T Cell Repertoire Component.

Front Immunol 2019 6;10:1717. Epub 2019 Aug 6.

Versiti Wisconsin, Blood Research Institute, Milwaukee, WI, United States.

T-cell memory to pathogens can be envisioned as a receptor-based imprint of the pathogenic environment on the naïve repertoire of clonotypes. Recurrent exposures to a pathogen inform and reinforce memory, leading to a mature state. The complexity and temporal stability of this system in man is only beginning to be adequately described. We have been using a rank-frequency approach for quantitative analysis of CD8 T cell repertoires. Rank acts as a proxy for previous expansion, and rank-frequency, the number of clonotypes at a particular rank, as a proxy for abundance, with the relation of the two estimating the diversity of the system. Previous analyses of circulating antigen-experienced cytotoxic CD8 T-cell repertoires from adults have shown a complex two-component clonotype distribution. Here we show this is also the case for circulating CD8 T cells expressing the BV19 receptor chain from five adult subjects. When the repertoire characteristic of clonotype stability is added to the analysis, an inverse correlation between clonotype rank frequency and stability is observed. Clonotypes making up the second distributional component are stable; indicating that the circulation can be a depot of selected clonotypes. Temporal repertoire dynamics was further examined for influenza-specific T cells from children, middle-aged, and older adults. Taken together, these analyses describe a dynamic process of system development and aging, with increasing distributional complexity, leading to a stable circulating component, followed by loss of both complexity and stability.
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http://dx.doi.org/10.3389/fimmu.2019.01717DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6691812PMC
October 2020

Effects of Air Pollution on Lung Innate Lymphoid Cells: Review of In Vitro and In Vivo Experimental Studies.

Int J Environ Res Public Health 2019 07 2;16(13). Epub 2019 Jul 2.

Department of Immunology, Erasmus Medical Center, 3015GD Rotterdam, The Netherlands.

Outdoor air pollution is associated with respiratory infections and allergies, yet the role of innate lymphoid cells (ILCs) in pathogen containment and airway hyperresponsiveness relevant to effects of air pollutants on ILCs is poorly understood. We conducted a systematic review to evaluate the available evidence on the effect of outdoor air pollutants on the lung type 1 (ILC1) and type 2 ILCs (ILC2) subsets. We searched five electronic databases (up to Dec 2018) for studies on the effect of carbon monoxide (CO), sulfur dioxide (SO), nitrogen dioxide (NO), diesel exhaust particles (DEP), ozone (O), and particulate matter (PM) on respiratory ILCs. Of 2209 identified citations, 22 full-text papers were assessed for eligibility, and 12 articles describing experimental studies performed in murine strains (9) and on human blood cells (3) were finally selected. Overall, these studies showed that exposure to PM, DEP, and high doses of O resulted in a reduction of interferon gamma (IFN-γ) production and cytotoxicity of ILC1. These pollutants and carbon nanotubes stimulate lung ILC2s, produce high levels of interleukin (IL)-5 and IL-13, and induce airway hyperresponsiveness. These findings highlight potential mechanisms by which human ILCs react to air pollution that increase the susceptibility to infections and allergies.
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http://dx.doi.org/10.3390/ijerph16132347DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6650824PMC
July 2019

Seasonality of Rotavirus Hospitalizations at Costa Rica's National Children's Hospital in 2010-2015.

Int J Environ Res Public Health 2019 06 30;16(13). Epub 2019 Jun 30.

Servicio de Gastroenterología y Nutrición, Hospital Nacional de Niños "Dr. Carlos Sáenz Herrera", Centro de Ciencias Médicas, Caja Costarricense de Seguro Social (CCSS), San José 10103, Costa Rica.

Rotavirus is a leading cause of acute diarrhea in children worldwide. Costa Rica recently started universal rotavirus vaccinations for infants with a two-dose schedule in February 2019. We aimed to study the seasonality of rotavirus during the pre-vaccination era. We retrospectively studied a six-year period of hospital admissions due to rotavirus gastroenteritis. We estimated seasonal peak timing and relative intensities using trend-adjusted negative binomial regression models with the δ-method. We assessed the relationship between rotavirus cases and weather characteristics and estimated their effects for the current month, one-month prior and two months prior, by using Pearson correlation coefficients. A total of 798 cases were analyzed. Rotavirus cases predominated in the first five months of the year. On average, the peak of admissions occurred between late-February and early-March. During the seasonal peaks, the monthly count tended to increase 2.5-2.75 times above the seasonal nadir. We found the strongest negative association of monthly hospitalizations and joint percentiles of precipitation and minimal temperature at a lag of two months (R = -0.265, = 0.027) and we detected correlations of -0.218, -0.223, and -0.226 ( < 0.05 for all three estimates) between monthly cases and the percentile of precipitation at lags 0, 1, and 2 months. In the warm tropical climate of Costa Rica, the increase in rotavirus hospitalizations coincided with dry and cold weather conditions with a two-month lag. The findings serve as the base for predictive modeling and estimation of the impact of a nation-wide vaccination campaign on pediatric rotaviral infection morbidity.
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http://dx.doi.org/10.3390/ijerph16132321DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6651376PMC
June 2019