An era of expanded information capacity poses new challenges to data utilization capabilities of all practitioners. The true relationship between variables can only be revealed by identifying the type and characteristics of the data, then analyzed using the appropriate statistical analysis methods. Most available studies focuson the analysis of the rate of due/non-hospitalization, and there are few articles analyzing the factors influencing the number of hospitalizations using the countdata.

To fit an optimal model suitable for the annual number of hospitalizations of residents, and to analyze its influencing factors.

Data came from the health service survey database of the project "innovating payment system toimprove health benefits" carried out from July to August 2019. A total of 27 196 residents from four mountainous counties (Yanchi, Haiyuan, Pengyang and Xiji) in southern Ningxia were selected to attend a questionnaire survey by use of multistage stratified random sampling, and 22 427 (82.46%) of them with complete and clear key information who returned responsive questionnaires were included for analysis. Four models (Possion regression, negative binomial regression, zero-inflated Poisson regression and zero-inflated negative binomial regression) were established with annual number of hospitalizations as the dependent variable, and demographic factors (sex, age, marital status, education level and occupation) , household characteristics (household size, annual household income per capita, prevalence of poverty-stricken household/household living on minimum subsistence allowances) , social feature (distance to the nearest township health center) as independent variables, then the model with the best fitting performance was selected to explore the factors associated with the annual number of hospitalizations.

The percentage of residents with zero annual hospitalizations was 88.30% (19 802/22 427) . The negative binomial regression model was examined to have better fitting performance than the Possion regression model by discrete choice experiments (O=87.665, P<0.01) . The statistic value of Vuong's test was greater than 1.96, indicating that the data were zero-inflated. The zero-inflated negative binomial regression model had the smallest goodness of fit (18 331.87) measured by the Akaike information criterion. And the analysis using this model revealed that sex, education level, occupation, household size, annual household income per capita, an prevalence of poverty-stricken household/household living on minimum subsistence allowances were associated with annual number of hospitalizations (P<0.05) . And age, marital status, education level and occupation were associated with the zero-inflation in annual number of hospitalizations (P<0.05) .

The zero-inflated negative binomial regression model fitted the data of annual hospitalizations of the participants best. Being female, unemployed, or poverty-stricken household/household living on minimum subsistence allowances was associated with higher possibility of seeking hospitalization care, while higher education level, or a larger household size was associated with lower possibility of seeking hospitalization care. A higher potential demand for hospitalization care was found in 40-59-year-olds and the married, which could be partially lowered by improving education level and reducing physical labor intensity level.