The Circulatory Risk in Communities Study (CIRCS) is an ongoing community-based epidemiological study of lifestyle-related disease involving dynamic prospective cohorts of approximately 12,000 adults from five communities of Japan: Ikawa, Ishizawa and Kita-Utetsu (Akita Prefecture), Minami-Takayasu (Osaka Prefecture), Noichi (Kochi Prefecture), and Kyowa (Ibaraki Prefecture). One of the most notable features of CIRCS is that it is not only an observational cohort study to identify risk factors for cardiovascular diseases (CVD), such as stroke, coronary heart disease, and sudden cardiac death, but it also involves prevention programs for CVD. Using basic, clinical, epidemiological, and statistical techniques, CIRCS has clarified characteristics of CVD and the related risk factors to develop specific methodologies towards CVD prevention in Japanese middle-aged or older adults for more than half a century.
Background: Previous studies have identified poor dietary intake as a health risk affecting survivors of the 2011 Great East Japan Earthquake and Tsunami. We examined the association between different social factors (eg, living conditions and perceptions of community social capital) and dietary intakes among disaster-affected survivors.
Methods: We studied 6,724 survivors in four municipalities of Iwate Prefecture 3 years after the disaster. Social capital was assessed via four items inquiring about respondents’ perceptions of social cohesion in their communities. Good dietary intake was defined according to the following criteria: intake of staple food ≥three times a day; intake of meat, fish and shellfish eggs, or soybean products ≥twice a day; vegetable intake ≥twice a day; and intake of fruit or dairy products ≥once a day. An individual who did not meet any of these criteria was defined as having poor dietary intake. We adjusted for covariates, including socioeconomic status, marital status, and residential area.
Results: Poor dietary intake was reported by 31.6% of respondents. Poisson regression analyses revealed that the following factors were related to poor dietary intake: age <65 years (men: prevalence ratio [PR] 1.48; 95% confidence interval [CI], 1.29–1.71 and women: PR 1.55; 95% CI, 1.36–1.77), difficulties in living conditions (men: PR 1.18; 95% CI, 1.00–1.39 and women: PR 1.19; 95% CI, 1.01–1.40), and low perceptions of community social capital (women: PR 1.20; 95% CI, 1.04–1.38).
Conclusions: Our findings suggest that social capital plays a role in promoting healthy dietary intake among women in disaster-affected areas.
Background: Epidemiological evidence of dyslipidemia in Pacific Island countries is limited despite the knowledge that non-communicable diseases have a high burden in the region. We aimed to examine the prevalence and correlates of dyslipidemia among residents of Palau.
Methods: The Palau STEPwise approach to Surveillance (STEPS), which was conducted from 2011 through 2013, comprised three parts: behavioral risk factors; physical measurements; and biochemical tests, covering areas such as blood lipids. We used STEPS-generated data to perform a cross-sectional study of 2,184 randomly selected Palau residents, comprising Palauans and non-Palauans aged 25–64 years.
Results: The age-adjusted mean BMI was 29.3 kg/m2 in men and 29.9 kg/m2 in women; age-adjusted mean triglycerides value was 182 mg/dL in men and 166 mg/dL in women; and age-adjusted mean cholesterol was 178 mg/dL in men and 183 mg/dL in women. The prevalence of overweight/obesity (BMI ≥25 kg/m2) was 75% in men and 76% in women, and those of hypertriglyceridemia (triglycerides ≥150 mg/dL) and hypercholesterolemia (total cholesterol ≥200 mg/dL) were 48% in men and 41% in women and 18% in men and 23% in women, respectively. Mean values of total cholesterol were 177 mg/dL in Palauan men and 182 mg/dL in non-Palauan men. Mean values of triglycerides were 171 mg/dL in Palauan women and 150 mg/dL in non-Palauan women. Women living in rural areas showed a higher mean value of total cholesterol than those in urban areas.
Conclusion: We found a high mean BMI and high prevalence of overweight/obesity and hypertriglyceridemia, but low mean total cholesterol and a low prevalence of hypercholesterolemia in Palau. Lipid profiles varied by age, ethnicity, and living area.
Background: Limited evidence is available on the association of insulin-like growth factors (IGFs) and risk of heart failure in population-based samples. We investigated whether serum IGFs concentrations can predict mortality from heart failure.
Methods: We conducted a nested case-control study of 39,242 subjects aged 40–79 years who participated in the JACC study, a large Japanese prospective cohort study; participants provided serum samples and were followed up for 9 years. In heart failure cases and age-, sex-, community-, and year of blood withdrawal-matched controls, we measured serum concentrations of IGF-I, IGF-II, and IGF binding protein 3 (IGFBP3) and transforming growth factor (TGF-β1).
Results: During the follow-up, there were 88 heart failure deaths (44 men and 44 women). Each increment of 1 standard deviation [SD] of IGF-II (120.0 ng/mL in women and 143.7 ng/mL in men) was associated with a 47% reduced risk of mortality from heart failure; multivariable odds ratio was 0.53 (95% confidence interval [CI], 0.30–0.94, P-trend = 0.03). The multivariable odds ratio in the highest quartile of IGFBP3 serum concentrations (≥3.29 µg/mL in women and ≥3.31 µg/mL in men) compared with the lowest (<2.11 µg/mL in women and <2.56 µg/mL in men) was 0.24 (95% CI, 0.05–1.11; P-trend = 0.12). No association was found between serum concentrations of IGF-I or TGF-β1 and risk of heart failure.
Conclusions: Higher serum concentrations of IGF-II were associated with lower mortality from heart failure, which might suggest a possible role of IGF-II in the occurrence or prognosis of heart failure.
Background: The presence of comorbidities in cancer patients may influence treatment decisions and prognoses. This study aimed to examine the impact of comorbidities on overall survival in Japanese patients diagnosed with major solid tumors.
Methods: To obtain patient-level information on clinical conditions and vital status, we performed a record linkage of population-based cancer registry data from Osaka Prefecture, Japan and administrative data produced under the Diagnosis Procedure Combination (DPC) system. The study population comprised patients who received a primary diagnosis of gastric, colorectal, or lung cancer between 2010 and 2012 at any of five cancer centers. We employed the Charlson Comorbidity Index (CCI) score to quantify the impact of comorbidities on survival. The association between CCI score and survival for each cancer site was analyzed using Cox proportional hazards regression models for all-cause mortality, after adjusting for patient sex, age at cancer diagnosis, and cancer stage.
Results: A total of 2,609 patients with a median follow-up duration of 1,372 days were analyzed. The most frequent CCI score among the patients was 0 (77.7%), followed by 2 (14.3%). After adjusting for the covariates, we detected a significant association between CCI score and all-cause mortality. The hazard ratios per one-point increase in CCI score were 1.12 (95% confidence interval [CI], 1.02–1.23), 1.20 (95% CI, 1.08–1.34), and 1.14 (95% CI, 1.04–1.24) for gastric, colorectal, and lung cancer, respectively.
Conclusions: Comorbidities have a negative prognostic impact on overall survival in cancer patients, and should be assessed as risk factors for mortality when reporting outcomes.
Background: The effects, in terms of bias and precision, of omitting non-confounding predictive covariates from generalized linear models have been well studied, and it is known that such omission results in attenuation bias but increased precision with logistic regression. However, many epidemiologic risk analyses utilize alternative models that are not based on a linear predictor, and the effect of omitting non-confounding predictive covariates from such models has not been characterized.
Methods: We employed simulation to study the effects on risk estimation of omitting non-confounding predictive covariates from an excess relative risk (ERR) model and a general additive-multiplicative relative-risk mixture model for binary outcome data in a case-control setting. We also compared the results to the effects with ordinary logistic regression.
Results: For these commonly employed alternative relative-risk models, the bias was similar to that with logistic regression when the risk was small. More generally, the bias and standard error of the risk-parameter estimates demonstrated patterns that are similar to those with logistic regression, but with greater magnitude depending on the true value of the risk. The magnitude of bias and standard error had little relation to study size or underlying disease prevalence.
Conclusions: Prior conclusions regarding omitted covariates in logistic regression models can be qualitatively applied to the ERR and the general additive-multiplicative relative-risk mixture model without substantial change. Quantitatively, however, these alternative models may have slightly greater omitted-covariate bias, depending on the magnitude of the true risk being estimated.