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.

信息容量膨胀的时代对各方从业者数据驾驭能力提出新的挑战，明确资料类型和特征，适配相应的统计分析方法，才能揭示真实的变量关系。目前，关于居民住院情况的学术研究多集中在应/未住院率的分析，以计数资料角度，剖析次数影响因素的研究相对较少。

拟合适用于居民年住院次数的最优模型，并分析居民年住院次数的影响因素。

本研究资料来源于2019年7—8月开展的"创新支付制度，提高卫生效益"项目卫生服务调查数据库，该调查采用多阶段分层整群随机抽样法调查了宁夏南部山区4县（盐池县、海原县、彭阳县、西吉县）的27 196例居民，本研究选取关键信息完整且明确的22 427份（82.46%）问卷进行分析。以居民年住院次数为因变量，以个人基本特征（性别、年龄、婚姻状况、文化程度、职业）、家庭特征（常住人口规模、家庭年人均收入、贫困户/低保户情况）、社会特征（离家最近的乡镇卫生院距离）为自变量，构建Possion回归、负二项回归、零膨胀Possion回归、零膨胀负二项回归4种模型，比较后得出最优模型，并据此开展影响因素分析。

居民年住院次数为0次者达19 802例（88.29%）。过离散检验结果显示，O=87.665、P<0.01，表明负二项回归模型的拟合效果优于Possion回归模型。Vuong检验的统计量值>1.96，表明数据存在零膨胀现象。零膨胀负二项回归模型的赤池信息准则（AIC）值最小（为18 331.87），模型分析结果显示：性别、文化程度、职业、常住人口规模、家庭年人均收入、贫困户/低保户情况是居民年住院次数的影响因素（P<0.05）；年龄、婚姻状况、文化程度、职业对居民年住院次数出现零膨胀现象有影响（P<0.05）。

宁夏南部山区居民的年住院次数拟合零膨胀负二项回归模型效果最好。女性、无业者、贫困户/低保户的住院行为相对较多，文化程度较高、家庭人口规模较大者寻求住院就医的行为较少。40~59岁、在婚人群的潜在住院需求更大，提升文化程度、降低体力劳动强度可在一定程度上降低此类需求。