Objective To understand the result coincidence of 4 hematology analyzers (LH780 ,LH750 ,XN-1000 ,and XS-1000i) .Methods 20 samples of routine blood test were selected and detected by different hematology analyzers whose within-run precision and between-run precision all met the requirements of WS/T 406 standard .LH750 was chose to be the reference instru-ment ,and its detection results were standard values .The relative deviations and coincidence rates of WBC ,RBC ,Hb ,HCT , PLT ,MCV ,MCH and MCHC of the other 3 hematology analyzers were calculated .The WBC and PLT coincidence results of 82 cases of low-WBC samples ,86 cases of low-PLT samples ,and 35 cases of platelet aggregation samples were investigated .Results The coincidence rates of all items were more than 90% between LH780 and LH750 .For XN-1000/XS-1000i ,the coincidence rate was 80% in HCT ,and which were less than 80% in MCV and MCHC ,and more than 85% in other items .After readjusting the calibration factors of HCT of XN-1000 and XS-1000i ,the coincidence rates of HCT ,MCV and MCHC were all increased to more than 85% .The coincidence rates of WBC and PLT were less than 70% in low-WBC samples (WBC<1 .0 × 109/L) and low-PLT samples (PLT<30 × 109/L) .The results of WBC and PLT could not reach the relative deviation standards in 35 platelet aggrega-tion samples .Conclusion The comparative tests should be carried out among different hematology analyzers in one laboratory .

OBJECTIVETo analyze the risk factors of nonspecific low back pain in community populations.

METHODSTwo community populations were investigated using questionnaires in this case-control study. The questionnaire was designed to collect data including age, gender, body weight, marriage, education, income, occupation, labor intensity, smoking, alcohol drinking and social mental status. The subjects with low back pain constituted the case group and those without low back pain served as the control group, and the data was analyzed by a Logistic regression model.

RESULTSA total of 1747 community residents participated in this survey, among whom 398 subjects had low back pain and 1126 subjects without low back pain were selected as the control group. Of all the latent risk factors of low back pain in Logistic regression model, gender was the most relevant factor (OR=3.5522) followed by education (OR=1.958), labor intensity (OR=1.956), marital status (OR=1.612), vibration source exposure (OR=1.491), BMI (OR=1.127) and age (OR=1.060).

CONCLUSIONGender, education, labor intensity, marriage, vibration source exposure and BMI are risk factors of nonspecific low back pain in community populations, and exercises and mental status can be protective factors against low back pain.

Case-Control Studies ; Humans ; Logistic Models ; Low Back Pain ; epidemiology ; Risk Factors ; Surveys and Questionnaires