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According to the Centers for Disease Control and Prevention (CDC), from 2000 to 2014, reported cases of legionellosis per 100 000 population increased by 300% in the USA, although reports on disease seasonality are inconsistent. Using two national databases, we assessed seasonal patterns of legionellosis in the USA. We created a monthly time series from 1993 to 2015 of reported cases of legionellosis from the CDC, and from 1997 to 2006 of medical claims of legionellosis-related hospitalisation in older adults from the Centers for Medicaid and Medicare Services (CMS). We split the study time interval into two segments (before and after 2003), and applied a Poisson harmonic regression model to each dataset and each segment. The time series of monthly counts exhibited a significant shift of seasonal peaks from mid-September (9.676 ± 0.164 months) before 2003 to mid-August (8.452 ± 0.042 months) after 2003, along with an alarming increase in the amplitude of seasonal peaks in both CDC and CMS data. The lowest monthly reported cases of legionellosis in 2015 (281) exceed the maximum value reported before 2003 (206). We also observed a discrepancy between CDC and CMS data, suggesting that not all cases of legionellosis diagnosed by hospital-based laboratories were reported to the CDC. Improved reporting of legionellosis is required to better inform the public and organise disease prevention.
Improving understanding of the pathogen-specific seasonality of enteric infections is critical to informing policy on the timing of preventive measures and to forecast trends in the burden of diarrhoeal disease. Data obtained from active surveillance of cohorts can capture the underlying infection status as transmission occurs in the community. The purpose of this study was to characterise rotavirus seasonality in eight different locations while adjusting for age, calendar time and within-subject clustering of episodes by applying an adapted Serfling model approach to data from a multi-site cohort study. In the Bangladesh and Peru sites, within-subject clustering was high, with more than half of infants who experienced one rotavirus infection going on to experience a second and more than 20% experiencing a third. In the five sites that are in countries that had not introduced the rotavirus vaccine, the model predicted a primary peak in prevalence during the dry season and, in three of these, a secondary peak during the rainy season. The patterns predicted by this approach are broadly congruent with several emerging hypotheses about rotavirus transmission and are consistent for both symptomatic and asymptomatic rotavirus episodes. These findings have practical implications for programme design, but caution should be exercised in deriving inferences about the underlying pathways driving these trends, particularly when extending the approach to other pathogens.
Diarrhoeal diseases are major causes of morbidity and mortality in developing countries. This longitudinal study aimed to identify controllable environmental drivers of intestinal infections amidst a highly contaminated drinking water supply in urban slums and villages of Vellore, Tamil Nadu in southern India. Three hundred households with children (<5 years) residing in two semi-urban slums and three villages were visited weekly for 12–18 months to monitor gastrointestinal morbidity. Households were surveyed at baseline to obtain information on environmental and behavioural factors relevant to diarrhoea. There were 258 diarrhoeal episodes during the follow-up period, resulting in an overall incidence rate of 0·12 episodes/person-year. Incidence and longitudinal prevalence rates of diarrhoea were twofold higher in the slums compared to rural communities (P < 0·0002). Regardless of study site, diarrhoeal incidence was highest in infants (<1 year) at 1·07 episodes/person-year, and decreased gradually with increasing age. Increasing diarrhoeal rates were associated with presence of children (<5 years), domesticated animals and low socioeconomic status. In rural communities, open-field defecation was associated with diarrhoea in young children. This study demonstrates the contribution of site-specific environmental and behavioural factors in influencing endemic rates of urban and rural diarrhoea in a region with highly contaminated drinking water.
HIV-positive persons and the elderly have increased risk for influenza-related complications, including pneumonia. Using claims data for pneumonia and influenza (P&I) hospitalization in the USA, we described the temporo-demographic trends and in-patient case-fatality in persons aged ⩾65 years by HIV status. Our results showed a near doubling in the fraction of P&I admissions representing HIV-positive persons between 1991 and 2004 [relative risk (RR) 1·95, 95% confidence interval (CI) 1·80–2·13]. HIV-positive adults were younger (70·3 vs. 79·9 years, P<0·001), and had higher case-fatality (18·0% vs. 12·6%, P<0·001). Adjusting for other variables, case-fatality decreased by 5·8% in HIV-positive persons with the availability of highly active antiretroviral therapy (P=0·032). However, HIV-positive seniors were still 51% more likely to die during hospitalization than HIV-negative persons in 2004 (OR 1·51, 95% CI 1·23–1·85). HIV-infected persons represent a growing fraction of the elderly population hospitalized with P&I. Additional measures are needed to reduce case-fatality associated with P&I in this population.
Birth cohort has been shown to be related to morbidity and mortality from other diseases and conditions, yet little is known about the potential for birth cohort in its relation to pneumonia and influenza (P&I) outcomes. This issue is particularly important in older adults, who experience the highest disease burden and most severe complications from these largely preventable diseases. The objective of this analysis is to assess P&I patterns in US seniors with respect to age, time, and birth cohort. All Medicare hospitalizations due to P&I (ICD-9CM codes 480-487) were abstracted and categorized by single-year of age and influenza year. These counts were then divided by intercensal estimates of age-specific population levels extracted from the US Census Bureau to obtain age- and season-specific rates. Rates were log-transformed and linear models were used to assess the relationships in P&I rates and age, influenza year, and cohort. The increase in disease rates with age accounted for most of the variability by age and influenza season. Consistent relationships between disease rates and birth cohorts remained, even after controlling for age. Seasonal associations were stronger for influenza than for pneumonia. These findings suggest that there may be a set of unmeasured characteristics or events people of certain ages experienced contemporaneously that may account for the observed differences in P&I rates in birth cohorts. Further understanding of these circumstances and those resulting age and cohort groups most vulnerable to P&I may help to target health services towards those most at risk of disease.
We propose an analytical and conceptual framework for a systematic and comprehensive assessment of disease seasonality to detect changes and to quantify and compare temporal patterns. To demonstrate the proposed technique, we examined seasonal patterns of six enterically transmitted reportable diseases (EDs) in Massachusetts collected over a 10-year period (1992–2001). We quantified the timing and intensity of seasonal peaks of ED incidence and examined the synchronization in timing of these peaks with respect to ambient temperature. All EDs, except hepatitis A, exhibited well-defined seasonal patterns which clustered into two groups. The peak in daily incidence of Campylobacter and Salmonella closely followed the peak in ambient temperature with the lag of 2–14 days. Cryptosporidium, Shigella, and Giardia exhibited significant delays relative to the peak in temperature (~40 days, P<0·02). The proposed approach provides a detailed quantification of seasonality that enabled us to detect significant differences in the seasonal peaks of enteric infections which would have been lost in an analysis using monthly or weekly cumulative information. This highly relevant to disease surveillance approach can be used to generate and test hypotheses related to disease seasonality and potential routes of transmission with respect to environmental factors.
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