Classification of cross-sectional studies in cross-sectional surveys

General survey refers to the investigation or examination of everyone within a certain range in a certain period of time to understand the epidemic situation of a certain disease or the health status of a certain group of people. The emphasis here is on every member of a certain range of people. For example, all residents of residential areas. A certain time can be 1-2 days or 1-2 weeks, and a large-scale census can also be completed within 2-3 months. The time of the census should not be delayed for too long, so as to avoid changes in the diseases or health status of the population and affect the quality of the census. China has rich experience in conducting population census. A large-scale survey was conducted on tumors, cardiovascular diseases, goiter, hepatitis B and tuberculosis. Through early treatment and repeated prevention and treatment, some diseases have been controlled or basically controlled, and remarkable results have been achieved. The main purpose of the census is to find cases early and give them timely treatment. It is best to have a high disease prevalence rate in the general survey, so that enough cases can be obtained in a short time. Because the census is to investigate all the members of a certain population, it is relatively simple to determine the object of investigation; Moreover, the obtained data can understand the three distribution characteristics of the disease, so it can give some enlightenment to the epidemic factors of the disease. But for diseases with short course of disease, low prevalence rate or complicated examination methods, it is not suitable for general survey. Due to the large number of census objects, it is inevitable to miss diagnosis and misdiagnosis; Due to the large number of staff involved in the census, their proficiency in investigation techniques and inspection methods is different, and the quality of investigators is difficult to control; At the same time, due to the heavy workload, it is difficult to conduct in-depth and detailed investigations. In practical work, sampling survey is usually to randomly select some observation units (statistically called samples) from the population, which is called sampling survey. Sampling survey is to estimate some characteristics of the population represented by the sample according to the results of sampling survey, so sampling survey must follow the principle of randomization in order to obtain better representative samples. Sampling survey can save manpower, material resources and time. Because of its small scope of investigation, the investigation work is easy to do in detail. However, the design, implementation and data analysis of sampling survey are complicated, and duplication and omission are not easy to find, so it is not suitable for the research objects with excessive variation. The commonly used random sampling methods are as follows: 1. Simplerandomsampling is to number all the observation units in the survey population first, and then randomly select some observation units to form samples through random number table or lottery. At present, in the cross-sectional study, because there are too many observation units, it is difficult to number all the observation units, so there are not many opportunities to use simple random sampling, but it is the basis for implementing other sampling methods. 2. Systematic sampling is also called equidistant sampling or mechanical sampling. That is to say, the whole observation unit is divided into n parts according to a certain sequence number, and then k observation units are randomly selected from the first part, and then one observation unit is mechanically extracted from each part at equal intervals to form a sample. Example 1 To know the HBSAg positive rate of employees in a certain unit, there are 1000 employees in this unit. Try to take a sample of 100 according to the systematic sampling method. The current population is N= 1000, the sample number is = 100, and the sampling interval is =100/10. First, randomly determine a number between 1 and 10, such as 4. 3. Stratified sampling is also called classified sampling. That is to say, the population is divided into several types or groups (called "strata" statistically) according to a certain characteristic that has a great influence on the observed values, and then a certain number of observation units (which can be determined by proportion or optimal distribution) are randomly selected from each layer to form samples. For example, the people surveyed are divided into different levels according to their age, gender or disease severity, and then randomly sampled at each level. Stratified sampling can reduce the sampling error caused by different characteristics of each layer. 4. clustersampling In cluster sampling, not an individual is sampled, but several groups (groups) composed of an individual. Cluster sampling is to divide the crowd into k groups (such as k regions, etc.). ), and each group includes several observation units. Then several groups are randomly selected from K groups, and all observation units in each group are sampled. For example, the survey of hookworm disease randomly selected all villagers in several towns of a county. When doing a family planning survey, check all the residents of several neighborhood committees in the city. Because the sampling survey randomly selects some observation units from the whole population as the survey objects, it will inevitably produce sampling errors, and the size of sampling errors varies with different sampling methods. Generally speaking, the order of sampling error from small to large is stratified sampling, systematic sampling, simple random sampling and cluster sampling.