The relative risk of second primary cancers in Queensland, Australia: A retrospective cohort study

Background Cancer survivors face an increased likelihood of being subsequently diagnosed with another cancer. The aim of this study was to quantify the relative risk of survivors developing a second primary cancer in Queensland, Australia. Methods Standardised incidence rates stratified by type of first primary cancer, type of second primary cancer, sex, age at first diagnosis, period of first diagnosis and follow-up interval were calculated for residents of Queensland, Australia, who were diagnosed with a first primary invasive cancer between 1982 and 2001 and survived for a minimum of 2 months. Results A total of 23,580 second invasive primary cancers were observed over 1,370,247 years of follow-up among 204,962 cancer patients. Both males (SIR = 1.22; 95% CI = 1.20-1.24) and females (SIR = 1.36; 95% CI = 1.33-1.39) within the study cohort were found to have a significant excess risk of developing a second cancer relative to the incidence of cancer in the general population. The observed number of second primary cancers was also higher than expected within each age group, across all time periods and during each follow-up interval. Conclusions The excess risk of developing a second malignancy among cancer survivors can likely be attributed to factors including similar aetiologies, genetics and the effects of treatment, underlining the need for ongoing monitoring of cancer patients to detect subsequent tumours at an early stage. Education campaigns developed specifically for survivors may be required to lessen the prevalence of known cancer risk factors.

Validation of de-identified record linkage to ascertain hospital admissions in a cohort study

Background Cohort studies can provide valuable evidence of cause and effect relationships but are subject to loss of participants over time, limiting the validity of findings. Computerised record linkage offers a passive and ongoing method of obtaining health outcomes from existing routinely collected data sources. However, the quality of record linkage is reliant upon the availability and accuracy of common identifying variables. We sought to develop and validate a method for linking a cohort study to a state-wide hospital admissions dataset with limited availability of unique identifying variables. Methods A sample of 2000 participants from a cohort study (n = 41 514) was linked to a state-wide hospitalisations dataset in Victoria, Australia using the national health insurance (Medicare) number and demographic data as identifying variables. Availability of the health insurance number was limited in both datasets; therefore linkage was undertaken both with and without use of this number and agreement tested between both algorithms. Sensitivity was calculated for a sub-sample of 101 participants with a hospital admission confirmed by medical record review. Results Of the 2000 study participants, 85% were found to have a record in the hospitalisations dataset when the national health insurance number and sex were used as linkage variables and 92% when demographic details only were used. When agreement between the two methods was tested the disagreement fraction was 9%, mainly due to "false positive" links when demographic details only were used. A final algorithm that used multiple combinations of identifying variables resulted in a match proportion of 87%. Sensitivity of this final linkage was 95%. Conclusions High quality record linkage of cohort data with a hospitalisations dataset that has limited identifiers can be achieved using combinations of a national health insurance number and demographic data as identifying variables.

The obesity-associated polymorphisms FTO rs9939609 and MC4R rs17782313 and endometrial cancer risk in non-Hispanic white women

Overweight and obesity are strongly associated with endometrial cancer. Several independent genome-wide association studies recently identified two common polymorphisms, FTO rs9939609 and MC4R rs17782313, that are linked to increased body weight and obesity. We examined the association of FTO rs9939609 and MC4R rs17782313 with endometrial cancer risk in a pooled analysis of nine case-control studies within the Epidemiology of Endometrial Cancer Consortium (E2C2). This analysis included 3601 non-Hispanic white women with histologically-confirmed endometrial carcinoma and 5275 frequency-matched controls. Unconditional logistic regression models were used to assess the relation of FTO rs9939609 and MC4R rs17782313 genotypes to the risk of endometrial cancer. Among control women, both the FTO rs9939609 A and MC4R rs17782313 C alleles were associated with a 16% increased risk of being overweight (p = 0.001 and p = 0.004, respectively). In case-control analyses, carriers of the FTO rs9939609 AA genotype were at increased risk of endometrial carcinoma compared to women with the TT genotype [odds ratio (OR) = 1.17; 95% confidence interval (CI): 1.03–1.32, p = 0.01]. However, this association was no longer apparent after adjusting for body mass index (BMI), suggesting mediation of the gene-disease effect through body weight. The MC4R rs17782313 polymorphism was not related to endometrial cancer risk (per allele OR = 0.98; 95% CI: 0.91–1.06; p = 0.68). FTO rs9939609 is a susceptibility marker for white non-Hispanic women at higher risk of endometrial cancer. Although FTO rs9939609 alone might have limited clinical or public health significance for identifying women at high risk for endometrial cancer beyond that of excess body weight, further investigation of obesity-related genetic markers might help to identify the pathways that influence endometrial carcinogenesis.

Seasonal variation in cardiovascular disease risk factors in a subarctic population : the Tromso Study 1979-2008

Background Seasonal changes in cardiovascular disease (CVD) risk factors may be due to exposure to seasonal environmental variables like temperature and acute infections or seasonal behavioural patterns in physical activity and diet. Investigating the seasonal pattern of risk factors should help determine the causes of the seasonal pattern in CVD. Few studies have investigated the seasonal variation in risk factors using repeated measurements from the same individual, which is important as individual and population seasonal patterns may differ. Methods The authors investigated the seasonal pattern in systolic and diastolic blood pressure, heart rate, body weight, total cholesterol, triglycerides, high-density lipoprotein cholesterol, C reactive protein and fibrinogen. Measurements came from 38 037 participants in the population-based cohort, the Tromsø Study, examined up to eight times from 1979 to 2008. Individual and population seasonal patterns were estimated using a cosinor in a mixed model. Results All risk factors had a highly statistically significant seasonal pattern with a peak time in winter, except for triglycerides (peak in autumn), C reactive protein and fibrinogen (peak in spring). The sizes of the seasonal variations were clinically modest. Conclusions Although the authors found highly statistically significant individual seasonal patterns for all risk factors, the sizes of the changes were modest, probably because this subarctic population is well adapted to a harsh climate. Better protection against seasonal risk factors like cold weather could help reduce the winter excess in CVD observed in milder climates.

Particulate air pollution and cardiorespiratory hospital admissions in a temperate Australian city: A case-crossover analysis

Although ambient air pollution exposure has been linked with poor health in many parts of the world, no previous study has investigated the effect on morbidity in the city of Adelaide, South Australia. To explore the association between particulate matter (PM) and hospitalisations, including respiratory and cardiovascular admissions in Adelaide, South Australia. Methods: For the study period September 2001 to October 2007, daily counts of all-cause, cardiovascular and respiratory hospital admissions were collected, as well as daily air quality data including concentrations of particulates, ozone and nitrogen dioxide. Visibility codes for presentweather conditions identified dayswhen airborne dust or smoke was observed. The associations between PM and hospitalisations were estimated using timestratified case-crossover analyses controlling for covariates including temperature, relative humidity, other pollutants, day of the week and public holidays. Mean PM10 concentrations were higher in the warm season, whereas PM2.5 concentrations were higher in the cool season. Hospital admissions were associated with PM10 in the cool season and with PM2.5 in both seasons. No significant effect of PM on all-age respiratory admissions was detected, however cardiovascular admissions were associated with both PM2.5 and PM10 in the cool season with the highest effects for PM2.5 (4.48%, 95% CI: 0.74%, 8.36% increase per 10 μg/m3 increase in PM2.5). These findings suggest that despite the city's relatively low levels of air pollution, PMconcentrations are associated with increases in morbidity in Adelaide. Further studies are needed to investigate the sources of PM which may be contributing to the higher cool season effects.

Cold and heat waves in the United States

Extreme cold and heat waves, characterised by a number of cold or hot days in succession, place a strain on people’s cardiovascular and respiratory systems. The increase in deaths due to these waves may be greater than that predicted by extreme temperatures alone. We examined cold and heat waves in 99 US cities for 14 years (1987–2000) and investigated how the risk of death depended on the temperature threshold used to define a wave, and a wave’s timing, duration and intensity. We defined cold and heat waves using temperatures above and below cold and heat thresholds for two or more days. We tried five cold thresholds using the first to fifth percentiles of temperature, and five heat thresholds using the ninety-fifth to ninety-ninth percentiles. The extra wave effects were estimated using a two-stage model to ensure that their effects were estimated after removing the general effects of temperature. The increases in deaths associated with cold waves were generally small and not statistically significant, and there was even evidence of a decreased risk during the coldest waves. Heat waves generally increased the risk of death, particularly for the hottest heat threshold. Cold waves of a colder intensity or longer duration were not more dangerous. Cold waves earlier in the cool season were more dangerous, as were heat waves earlier in the warm season. In general there was no increased risk of death during cold waves above the known increased risk associated with cold temperatures. Cold or heat waves earlier in the cool or warm season may be more dangerous because of a build up in the susceptible pool or a lack of preparedness for cold or hot temperatures.

Estimating the effects of environmental exposures using a weighted mean of monitoring stations

The health effects of environmental hazards are often examined using time series of the association between a daily response variable (e.g., death) and a daily level of exposure (e.g., temperature). Exposures are usually the average from a network of stations. This gives each station equal importance, and negates the opportunity for some stations to be better measures of exposure. We used a Bayesian hierarchical model that weighted stations using random variables between zero and one. We compared the weighted estimates to the standard model using data on health outcomes (deaths and hospital admissions) and exposures (air pollution and temperature) in Brisbane, Australia. The improvements in model fit were relatively small, and the estimated health effects of pollution were similar using either the standard or weighted estimates. Spatial weighted exposures would be probably more worthwhile when there is either greater spatial detail in the health outcome, or a greater spatial variation in exposure.