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BACKGROUND: Cancer incidence and mortality increase with age through much of adulthood, but earlier work has found that these rates decline among the very elderly. To compare incidence and mortality at the oldest ages, the authors investigated both in the same large population, which comprised 9.5% of the United States in 2000. The authors also report age-specific prevalence among the elderly, which has received little attention. METHODS: Twentythree cancer types were studied in men, and 24 cancer types were studied in women. Patient records were obtained from the SEER 9 cancer registries, and population figures were taken from the 2000 US Census. The authors explored the reliability of census data on the oldest old, which has been questioned. RESULTS: Age-specific incidence, prevalence, and mortality results are presented for the years 1998 to 2002. Incidence and mortality usually decreased or plateaued at very old ages. Prevalence usually decreased swiftly at ages >90 years. When there was statistical power, incidence normally peaked between ages 75 years and 90 years, dropping abruptly afterward. With several large exceptions, peak incidence and mortality coincided within AE5 years. Both rates often trended toward zero among centenarians, who may be asymptomatic or insusceptible. CONCLUSIONS: The current results were found to be consistent with autopsy and survival studies. Most age-specific models of carcinogenesis are based on cancer rate data for ages <85 years. The authors argue that these models could not fit the current results without fundamental modification and outline biologic mechanisms for such modification, mostly cellular and tissue senescence. They also recommend caution to researchers who use census data on the very elderly. Cancer 2012;118:1371-

Increased age is regularly linked with heightened cancer risk, but recent research suggests a flattening around age 80. We report that, independent of cancer site or time period, most incidence rates decrease in the more elderly and drop to or toward zero near the ceiling of human life span. For all major organ sites, male and female, we use 1979 to 2003 Surveillance, Epidemiology, and End Results registry records (8-26% of the U.S. population) to construct three sequential crosssections at 10-year intervals, totaling 129 sets of age-specific cancer data. To compute incidence rates, we estimate older populations at risk with census counts and NIH life tables. This article provides both a minimal and a more comprehensive extension of Surveillance, Epidemiology, and End Results cancer rates to those above 85. Almost all cancers peak at age f80. Generally, it seems that centenarians are asymptomatic or untargeted by cancers. We suggest that the best available justification for this pattern of incidence is a link between increased senescence and decreased proliferative potential among cancers. Then, thus far, as senescence may be a carcinogen, it might also be considered an anticarcinogen in the elderly. We model rising and falling incidence rates with a B curve obtained by appending a linearly decreasing factor to the well-known Armitage-Doll multistage model of cancer. Taken at face value, the B model implies that medical, diet, or lifestyle interventions restricting carcinogenesis ought to be examined for possible effects on longevity. [Cancer Res 2008;68(11):4465-78]

As a check for biases that could be introduced by the use of data from an emergency department, we repeated the study analyses after excluding all temperatures in the fever range (≥38.0°C, ≥100.4°F). The exclusion did not cause bathyphase (hollow points) or orthophase (solid points) to change significantly, though it widened their confidence intervals, especially for orthophase. Bathyphase refers to the time of the minimum value in the diurnal cycle of body temperature. Orthophase refers to the time of the maximum value. Confidence intervals are 95%. The displayed times of night, twilight, and day are the average values that occurred across the years of the study period (total study duration: 30 months). The times of night, twilight, and day shift sharply in middle March and early November because the daylight saving time system is used at the study location (Boston, United States). In the daylight saving time system, clock times are advanced by 1 hour on the second Sunday of March and this change is undone on the first Sunday of November. Consequently, times of sunrise and sunset increase by 1 hour in middle March and decrease by 1 hour in early November.

In this observational study, we evaluated time-of-day variation in the incidence of fever that is seen at triage. The observed incidence of fever could change greatly over the day because body temperatures generally rise and fall in a daily cycle, yet fever is identified using a temperature threshold that is unchanging, such as ≥38.0° Celsius (C) (≥100.4° Fahrenheit [F]). Methods: We analyzed 93,225 triage temperature measurements from a Boston emergency department (ED) (2009-2012) and 264,617 triage temperature measurements from the National Hospital Ambulatory Medical Care Survey (NHAMCS, 2002-2010), making this the largest study of body temperature since the mid-1800s. Boston data were investigated exploratorily, while NHAMCS was used to corroborate Boston findings and check whether they generalized. NHAMCS results are nationally representative of United States EDs. Analyses focused on adults. Results: In the Boston ED, the proportion of patients with triage temperatures in the fever range (≥38.0°C, ≥100.4°F) increased 2.5-fold from morning to evening (7:00-8:59 PM vs 7:00-8:59 AM: risk ratio [RR] 2.5, 95% confidence interval [CI], 2.0-3.3). Similar time-of-day changes were observed when investigating alternative definitions of fever: temperatures ≥39.0°C (≥102.2°F) and ≥40.0°C (≥104.0°F) increased 2.4-and 3.6-fold from morning to evening (7:00-8:59 PM vs 7:00-8:59 AM: RRs [95% CIs] 2.4 [1.5-4.3] and 3.6 [1.5-17.7], respectively). Analyses of adult NHAMCS patients provided confirmation, showing mostly similar increases for the same fever definitions and times of day (RRs [95% CIs] 1.8 [1.6-2.1], 1.9 [1.4-2.5], and 2.8 [0.8-9.3], respectively), including after adjusting for 12 potential confounders using multivariable regression (adjusted RRs [95% CIs] 1.8 [1.5-2.1], 1.8 [1.3-2.4], and 2.7 [0.8-9.2], respectively), in age-group analyses (18-64 vs 65+ years), and in several sensitivity analyses. The patterns observed for fever mirror the circadian rhythm of body temperature, which reaches its highest and lowest points at similar times. Conclusion: Fever incidence is lower at morning triages than at evening triages. High fevers are especially rare at morning triage and may warrant special consideration for this reason. Studies should examine whether fever-causing diseases are missed or underappreciated during mornings, especially for sepsis cases and during screenings for infectious disease outbreaks. The daily cycling of fever incidence may result from the circadian rhythm. [

The immediate effect on dairy cow mobility, daily activity and milk yield following treatment for claw horn disease was examined in 306 lame cows located on four Cheshire dairy farms over twelve months. The daily activity and milk yield of all cows in these herds was recorded on computer using pedometers and in-parlour milk flow meters. Lame cows identified by stockmen were assessed subjectively by locomotion score, then restrained and their claws examined to identify the predominant lesion present. Those with locomotion scores ≥2.5 that presented with sole ulcer, haemorrhage and bruising, or white line disease were studied. Claws of the affected limb were trimmed by one paraprofessional claw trimmer using the five-step Dutch method and the affected claw unloaded either by trimming or application of a block to the healthy digit: those on the contra-lateral limb were trimmed similarly. The same observer repeated the locomotion score assessment seven days later: trimming reduced the proportion of lame cows (score ≥3) by 55% and those with poor gait (score ≤3 ≥2.5) by 49%, and the proportion of all cows not lame after trimming was 51% (χ 2 4.94: P≤0.001). Night time activity levels increased from 76 to 81 steps/hour on day 2 after treatment (P<0.05) but this was not maintained: daily milk yields fell by 2%. Using univariate mixed models, year and season, parity and farm all had significant effects on locomotion and activity levels. This treatment for claw horn disease in lame dairy cows improved their immediate health and welfare.

BackgroundThe emergency department (ED) increasingly acts as a gateway to the evaluation and treatment of acute illnesses. Consequently, it has also become a key testing ground for systems that monitor and identify outbreaks of disease. Here, we describe a new technology that automatically collects body temperatures during triage. The technology was tested in an ED as an approach to monitoring diseases that cause fever, such as seasonal flu and some pandemics.MethodsTemporal artery thermometers that log temperature measurements were placed in a Boston ED and used for initial triage vital signs. Time-stamped measurements were collected from the thermometers to investigate the performance a real-time system would offer. The data were summarized in terms of rates of fever (temperatures ≥100.4 °F [≥38.0 °C]) and were qualitatively compared with regional disease surveillance programs in Massachusetts.ResultsFrom September 2009 through August 2011, 71,865 body temperatures were collected and included in our analysis, 2073 (2.6 %) of which were fevers. The period of study included the autumn–winter wave of the 2009–2010 H1N1 (swine flu) pandemic, during which the weekly incidence of fever reached a maximum of 5.6 %, as well as the 2010–2011 seasonal flu outbreak, during which the maximum weekly incidence of fever was 6.6 %. The periods of peak fever rates corresponded with the periods of regionally elevated flu activity.ConclusionsTemperature measurements were monitored at triage in the ED over a period of 2 years. The resulting data showed promise as a potential surveillance tool for febrile disease that could complement current disease surveillance systems. Because temperature can easily be measured by non-experts, it might also be suitable for monitoring febrile disease activity in schools, workplaces, and transportation hubs, where many traditional syndromic indicators are impractical. However, the system’s validity and generalizability should be evaluated in additional years and settings.Electronic supplementary materialThe online version of this article (doi:10.1186/s12873-016-0080-7) contains supplementary material, which is available to authorized users.

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