中国西部农村贫困家庭健康风险模型与风险管理研究
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摘要
研究目的
分析西部农村贫困家庭健康风险特征,建立风险模型,为发展农村贫困家庭健康风险管理工具提供理论基础;理论上提出农村贫困家庭健康风险管理预防、缓和及应对策略,构建多方参与的社会干预模式,为农村贫困家庭健康风险管理的实践模式创造理论基础,并且为各级政府处理贫困家庭健康风险问题提供决策依据。
研究方法
数据来源:数据均来源于现场问卷调查。采取典型抽样的方式,在中国西部地区抽取两个国家级贫困县(区)作为样本点,在样本点采用随机抽样的方式抽取样本乡,将样本乡内的全部贫困户作为调查对象,并在贫困户所在自然村内随机选取相应数量的非贫困户作为对照调查对象,贫困户的确定以当地民政部门档案记载为依据。选择国家级贫困地区重庆市黔江区和贵州省贵定县作为样本点,在11个乡共抽取1109户农村家庭作为调查样本,其中贫困户559户,非贫困户550户,含4024名家庭成员。数据分析方法包括:描述性分析:计量资料的均值、标准差计算;计数资料和率、频数分布描述;单因素分析:计量资料的t检验和Mann- Whitney U检验;计数资料的卡方检验;多因素分析:非条件多元Logistic回归分析;两阶段聚类分析(Two Step Cluster Analysis)。所有数据分析采用SPSS for Windows 12.01专业统计软件处理。
研究结果
贫困家庭成员有着较高的健康需要并且被满足的程度较低,其两周患病率和慢性病患病率均显著性地高于非贫困家庭成员,但患病者治疗的比例显著性地低于非贫困家庭成员。贫困家庭用于两周患病诊治的就医花费、交通花费显著性地低于非贫困家庭。贫困家庭和非贫困家庭应对住院事件的筹资结构存在差别,贫困家庭成员住院时从积蓄中开支金额显著性地低于非贫困家庭成员;政府集体救助和债务支出显著性的高于非贫困家庭。在相对经济风险评估中,当健康支出占家庭总收入的比例超过20%时,无论是贫困家庭还是非贫困家庭,他们都会面临着超额健康经济风险(RR>1.0)。贫困家庭中灾害性健康支出发生率显著高于非贫困家庭。多元Logistic回归分析结果显示灾害性健康支出的发生同家庭人口结构、户主文化程度、慢性病等因素相关。
利用两阶段聚类分析方法,整个样本被分为两个类别:类别1为626个家庭,类别2为423个家庭。根据类别特征分析的结果,类别1被命名为强风险状态,类别2被命名为弱风险状态。多元Logistic回归分析结果显示强风险状态的发生其保护性因素包括:男性户主、户主高文化程度、户主职业为农民工和个体商户、较高的未成年人比例、较高的常住人口比例和外出务工人口比例;其危险因素包括:户主的婚姻状态为离婚和丧偶、高的家庭平均慢性病负荷、高家庭平均残疾负荷、高家庭人均两周治疗数和高家庭人均住院人次负荷。
健康风险认知与行为分析显示,贫困家庭户主认知到的风险原因,最主要的是“缺乏营养身体素质差”,其次是“运气不好、无法控制”。为了预防健康风险事件的发生,农村贫困家庭户主往往采取相应的预防措施,其中最常采用的是“不在高风险环境中作业”、“培养家庭成员的卫生习惯”以及“保障家庭成员的营养和休息”。88.9%的贫困家庭户主认为缺乏足够的资金来应对健康风险带来的消极影响是最为担心的情形,当健康风险支出占总收入的比例达到1/5时,累积有86.5%的贫困家庭户主表示无法承受。81.4%的户主对新型农村合作医疗项目的评价等于或超过“起一定作用”。
对188户一年内有住院史的家庭调查显示,用于住院的花费中,贫困家庭平均从储蓄中支出834.54元,非贫困家庭从储蓄中平均支出3161.30元,后者显著性地高于前者。应对健康风险,有69.3%的贫困家庭采用了借债手段,而57.6%的非贫困家庭也采用了借债手段,两者新借债率的差别无显著性统计学意义。针对借债家庭进一步分析发现,贫困家庭平均新增债款1424.69元,非贫困家庭为744.16元,两者的差别有极显著性统计学意义。贫困家庭和非贫困家庭的变卖率之间的差别有显著性的统计学意义,贫困家庭高于非贫困家庭。对于获得赠款和礼品者,贫困家庭可获得折合金额501.75元,非贫困家庭为1084.41元。两者差别有极显著性统计学意义。在贫困家庭中,家庭内成员花在照料病人的时间平均为14.56天,在非贫困家庭中平均为13.57天,两者差别有显著性统计学意义。有小孩上学的家庭中,48.3%的贫困家庭中止了小孩上学,而22.4%的非贫困家庭中止了小孩上学,两者之间的差别有显著性的统计学意义。对于参加了合作医疗或者特困医疗救助的家庭,贫困家庭平均获得的经济补偿为269.10元,非贫困家庭平均为403.17元。贫困家庭认识的较高层次社会成员的比例低于非贫困家庭,意味着其社会资本的拥有量相对较低,188个调查家庭中,贫困家庭一年内平均用于礼金(礼品)的支出为433.95元,非贫困家庭的平均支出则为1136.50元,两者之间的差别有显著性的统计学意义。社会网络分析结果显示,贫困家庭应对住院事件时期社会网络功能低于非贫困家庭。
研究结论
1.在中国西部农村地区,贫困家庭成员的健康状态较非贫困家庭成员差,而贫困家庭因此有着更高的健康需要,贫困家庭的健康需要被满足的程度要低于非贫困家庭。这使得负性健康事件在贫困家庭中更容易累积下来,到达一定程度后,亦即,当疾病变得十分严重的时候,他们不得不寻求更加难以承担的住院医疗服务。这一情形使得疾病—贫困这一循环变得更加恶劣。
2.用频率、强度和相关性三个维度来描述中国西部农村贫困家庭的健康风险,其特征为:贫困家庭中负性健康事件的现患率和发生率均高于非贫困家庭,其中所占比重较大的是慢性病;贫困家庭在负性健康事件发生强度上的显著特征是:他们有着更大的支出收入比例,即相对强度高;整体而言,健康风险的相关性较弱,但在西部贫困地区传性疾病、地方病、自然疫源性疾病均存在散发和流行的可能。
3.从费用方面看,中国西部贫困家庭用于处理严重负性健康事件的家庭资源低于非贫困家庭。严重的负性健康风险事件给贫困家庭造成的经济冲击显著性地高于非贫困家庭。
4.中国西部农村地区强健康风险状态的发生与各种社会经济人口统计学因素以及健康需要和健康服务利用因素相关联。这包括了:家庭户主的性别、文化程度、职业、婚姻状态和家庭人口结构、慢性病和残疾以及两周诊疗量和住院服务量。
5.中国农村贫困家庭户主对健康风险事件产生原因的认识存在一定误区。农村贫困家庭健康风险分担模式中非正式机制与正式机制并存。
6.中国西部农村贫困家庭健康风险处理行为可以概括为一种“三级处理模式”。其中,一级处理指负性健康事件发生后家庭资源的重新分配及其储备的消耗,但这并未对其未来的生产活动产生实质性的影响;二级处理水平上,贫困家庭将要在放弃病人和降低家庭承受未来冲击的能力之间做出取舍平衡;三级处理要求贫困家庭要么为了获得食物或者工作而进行移民,要么重新组合家庭。
7.根据社会风险管理框架以及健康风险管理手段与策略的特点,中国西部农村贫困家庭健康风险管理需要构建一个面向贫困的综合性农村健康风险管理模式。这一模式,需要多方主体:政府、市场、非政府组织以及家庭共同参与,需要集成非正式和正式的风险管理手段,也需要综合预防、缓和和应对三种策略。其基本原则包括以贫困为中心、可持续性、参与性和动态性。这一模式充分考虑了不同策略与具体的风险管理手段之间的交互关系,同时能够反映随着主体的切换,处理健康风险的策略和手段发生的变化。在面向贫困的综合性农村健康风险管理模式中,卫生部门、农业部门、民政部门、社会保障部门、市场机构、非政府组织和家庭各自承担相应的责任并相互协调。
Objectives:
In analyzing the health risk features of impoverished rural families (IRFs) in Western China, setting up risk models and constructing a multi-party interactive social interference pattern therein, theoretical basis are furnished in this dissertation, for providing: management tools and practical paradigms; prevention, mitigation and coping strategies; and rationales for decision-making of governments at all levels for health risk management of IRFs.
Methods:
Data Source: All data originate from field investigation and questionnaires. Typical sampling method is employed to locate two National-level poverty counties (areas), within which sample villages are selected randomly. All the IRFs thereof (as confirmed by the documentations of local Civil Sectors) are subjected to investigation and control groups are equally and randomly drawn from Non- IRFs within the same natural villages. Accordingly Qianjiang Area of Chongqing City and Guiding County of Guizhou Province are located as sample areas, 1109 rural families are sampled within 11 villages, which consists of 559 IRFs, 550 Non- IRFs, and a grand sum of 4024 family members.
Statistical Analysis methods include—Descriptive Statistics:Mean and S.D. for interval scaled data; nominal scales’frequency and descriptives; Univariate Analysis:T-test and Mann- Whitney U-test for interval scaled data; Chi-square for variable data; Multivariate Analysis:Unconditional Multiple Logistic Regression; Two Step Cluster Analysis, etc. All data are processed by SPSS for Windows 12.01.
Results:
Fortnight and chronic disease Morbidity-rate of IRFs outpace significantly that of Non- IRFs, whereas the doctor-seeking behavior of the former is significantly lower in proportion than that of the latter, therefore the comparatively high health demands of IRFs are under-satisfied. Medical expenditure and hospital-going transportation fares of the IRFs are significantly lower than that of the Non- IRFs. Significant difference emerges between financing structure of IRFs and Non- IRFs, IRFs significantly draw fewer savings and heavier government and collective relief funds and debts. In comparative economic risk evaluation, when the proportion of health expenditure accounts for more than 20% of the total family avenue, both IRFs and Non- IRFs are faced with excessive health economic risk(RR>1.0). Occurrence rate of catastrophic health expenditure in IRFs is significantly higher than that of Non- IRFs. Multiple Logistic Regressions have shown that the occurrence of catastrophic health expenditure is significantly correlated with factors like family structure, householder literacy, and chronic disease, etc.
Through Two Step Cluster Analysis, the whole sample is divided into 2 clusters: Cluster 1 consists of 626 families and, 434 for Cluster 2. Results of the Cluster feature analysis have nominated Cluster 1 as high-stake risk group, Cluster 2 as low-stake risk group. Multiple Logistic Regressions further elaborates that protective factors for high- stake risk status include: male householder, householder literacy, householder profession as farmer or private entrepreneur, high minor-person proportion, high normal residents proportion and outflow of laborers; risk factors include: divorce or spouse bereavement, high average chronic disease burden, high average disability burden, high Fortnight Morbidity-rate and per diem/ per capita in-hospital disease burden.
Health risk cognition and behavior analysis reflect that IRF householders recognize risks’causes mainly as“malnutrition, poor physiological condition”, followed only by“bad luck or predestinarianism”. To prevent health risk events, commonly adopted measures are“prohibiting from working under high risk environment, cultivating hygienic habits, securing sufficient nutrition and rest for family members”. 88.9 percent of IRF householders regard the negative impact of scanty capital to cope with health risk as most worrisome: when health risk accounts for more than 1/5 of total family revenue, a total of 86.5% of IRF householders proclaim this as insufferable. 81.4% of householders evaluate the New Rural Cooperative Medical scheme (RCMS) as“has some effect”or even be of greater help.
Investigations for 188 families that has annual hospitalization record has shown that IRFs draw $834.54 in average from savings while Non- IRFs draw $3161.30, which is significantly higher than that of IRFs. Facing health risks, 69.3% IRFs and 57.6% Non- IRFs resort to borrowing, no difference of statistical significance is detectable. As for newly-added average debt, IRFs and Non- IRFs have $ 1424.69 and $ 744.16 respectively, the difference of which enjoys highly marked statistical significance. Markedness of difference applies also to selling-off rate for curing disease, with IRFs resorting to this approach more heavily. While in receiving donations and presents, IRFs and Non- IRFs amount to $501.75 and $1084.41 respectively(Highly significant difference). As for time taken for family members to look after the patient, IRFs and Non- IRFs have 14.56 days and 13.57 days respectively in average (with Marked statistical significance). 48.3% of the IRFs quit their Children from schooling, contrasted by 22.4% of the Non- IRFs (with Marked statistical significance). To families participated in New RCMS (Rural Cooperative Medical Scheme) and Medicaid for Destitute Families, IRFs can receive an average reimbursement of $269.10, compared with $403.17 that of the Non- IRFs. Meanwhile, IRFs have fewer acquaintances of higher social status than that of the Non- IRFs, this is a refraction of their comparatively lower possession of“social capital”. In 188 families investigated, IRFs spend annually $433.95 in present-giving, Non- IRFs spend $1136.50(with Marked statistical significance). The result of social network analysis has shown that IRFs’social network function falls far behind that of the Non- IRFs while coping with hospitalization.
Conclusions:
1. The health status of IRFs members is worse than that of the Non- IRFs, as a result the IRFs call for much greater health demands. On the contrary, the comparatively high health demands of IRFs are under-satisfied. As an aftermath the negative health events of IRFs are prone to be postponed and, in accumulation to a certain degree, i.e., when diseases were dragged to most severe status, IRFs are obliged to resort to in-hospital services that are even more unaffordable. This prevailing phenomenon has deploringly aggravated the vicious circle of disease-poverty transmutation.
2. As described in three dimensions: frequency, intensity and correlation, health risk of IRFs in Western China has the following characteristics: current morbidity rate and morbidity occurrence rate of IRFs > Non- IRFs, with chronic disease taken the lion’s share; significant features of the aforesaid negative health events in IRFs are: they have greater expenditure/income ratio, i.e., higher comparative intensity; as a whole, correlations among health risks are moderate and even weak, but in impoverished areas of Western China, infectious, endemic and natural epidemic diseases still pose great threats of breaking out and spreading.
3. From the economic perspective, IRFs of Western China have more scanty financial resources than that of the Non- IRFs in dealing with grave negative health events, which in turn has significantly more smashing impact on IRFs than on Non- IRFs.
4. The occurrence of severe health risk status in Western Rural areas of China has apparent correlation with various socio-economic, ethnographic factors, health demands and health service utilization factors, which encompass: gender of householders, literacy and education, profession, marital status, family structure, chronic disease, the handicapped and disabled, fortnight outpatient and in-hospital medical treatment utilizations, etc.
5. IRF householders do harbor misconceptions with respect of the causality of health risk events, informal and formal health risk-sharing modes co-exist in IRFs.
6. Handling behaviors of Health risks in IRFs of Western China could be generalized into a“Tripartite Handling Mode”in which: First Handling denotes the reshuffling of family resources and the consumption of savings after the occurrence of negative health events, this process has no substantial impact on the family’s future productive activities; on the level of the Secondary Handling process, IRFs have to trade-off between giving up the patient and buffering the future impact of diseases; Tertiary Handling requires the IRFs to either migrate to obtain sustenance and work, or reset the family in disregard of the suffered.
7. In light of the Framework Health Risk Management and the characteristics of Health Risk Management strategies and approaches, a poverty-relief-oriented, comprehensive Health Risk Management Model is in great need for the IRFs in Western China. This pattern calls for multiple parties or bodies: government, market, NGOs as well as families; informal and formal Health Risk Management approaches, and a combination of prevention, mitigation and coping strategies. The primary principles of this model or paradigm are poverty-reduction, sustainability, interactivity and dynamic development. Taking into full consideration of the interactions among management strategies and concrete approaches, this framework enjoys high flexibility in accommodating the shift of active parties and changing management strategies and approaches. In this framework, health, agricultural, civil, social security sectors, together with market institutions, NGOs and families will shoulder their corresponding responsibilities and maintain a harmonious coordination.
分析西部农村贫困家庭健康风险特征,建立风险模型,为发展农村贫困家庭健康风险管理工具提供理论基础;理论上提出农村贫困家庭健康风险管理预防、缓和及应对策略,构建多方参与的社会干预模式,为农村贫困家庭健康风险管理的实践模式创造理论基础,并且为各级政府处理贫困家庭健康风险问题提供决策依据。
研究方法
数据来源:数据均来源于现场问卷调查。采取典型抽样的方式,在中国西部地区抽取两个国家级贫困县(区)作为样本点,在样本点采用随机抽样的方式抽取样本乡,将样本乡内的全部贫困户作为调查对象,并在贫困户所在自然村内随机选取相应数量的非贫困户作为对照调查对象,贫困户的确定以当地民政部门档案记载为依据。选择国家级贫困地区重庆市黔江区和贵州省贵定县作为样本点,在11个乡共抽取1109户农村家庭作为调查样本,其中贫困户559户,非贫困户550户,含4024名家庭成员。数据分析方法包括:描述性分析:计量资料的均值、标准差计算;计数资料和率、频数分布描述;单因素分析:计量资料的t检验和Mann- Whitney U检验;计数资料的卡方检验;多因素分析:非条件多元Logistic回归分析;两阶段聚类分析(Two Step Cluster Analysis)。所有数据分析采用SPSS for Windows 12.01专业统计软件处理。
研究结果
贫困家庭成员有着较高的健康需要并且被满足的程度较低,其两周患病率和慢性病患病率均显著性地高于非贫困家庭成员,但患病者治疗的比例显著性地低于非贫困家庭成员。贫困家庭用于两周患病诊治的就医花费、交通花费显著性地低于非贫困家庭。贫困家庭和非贫困家庭应对住院事件的筹资结构存在差别,贫困家庭成员住院时从积蓄中开支金额显著性地低于非贫困家庭成员;政府集体救助和债务支出显著性的高于非贫困家庭。在相对经济风险评估中,当健康支出占家庭总收入的比例超过20%时,无论是贫困家庭还是非贫困家庭,他们都会面临着超额健康经济风险(RR>1.0)。贫困家庭中灾害性健康支出发生率显著高于非贫困家庭。多元Logistic回归分析结果显示灾害性健康支出的发生同家庭人口结构、户主文化程度、慢性病等因素相关。
利用两阶段聚类分析方法,整个样本被分为两个类别:类别1为626个家庭,类别2为423个家庭。根据类别特征分析的结果,类别1被命名为强风险状态,类别2被命名为弱风险状态。多元Logistic回归分析结果显示强风险状态的发生其保护性因素包括:男性户主、户主高文化程度、户主职业为农民工和个体商户、较高的未成年人比例、较高的常住人口比例和外出务工人口比例;其危险因素包括:户主的婚姻状态为离婚和丧偶、高的家庭平均慢性病负荷、高家庭平均残疾负荷、高家庭人均两周治疗数和高家庭人均住院人次负荷。
健康风险认知与行为分析显示,贫困家庭户主认知到的风险原因,最主要的是“缺乏营养身体素质差”,其次是“运气不好、无法控制”。为了预防健康风险事件的发生,农村贫困家庭户主往往采取相应的预防措施,其中最常采用的是“不在高风险环境中作业”、“培养家庭成员的卫生习惯”以及“保障家庭成员的营养和休息”。88.9%的贫困家庭户主认为缺乏足够的资金来应对健康风险带来的消极影响是最为担心的情形,当健康风险支出占总收入的比例达到1/5时,累积有86.5%的贫困家庭户主表示无法承受。81.4%的户主对新型农村合作医疗项目的评价等于或超过“起一定作用”。
对188户一年内有住院史的家庭调查显示,用于住院的花费中,贫困家庭平均从储蓄中支出834.54元,非贫困家庭从储蓄中平均支出3161.30元,后者显著性地高于前者。应对健康风险,有69.3%的贫困家庭采用了借债手段,而57.6%的非贫困家庭也采用了借债手段,两者新借债率的差别无显著性统计学意义。针对借债家庭进一步分析发现,贫困家庭平均新增债款1424.69元,非贫困家庭为744.16元,两者的差别有极显著性统计学意义。贫困家庭和非贫困家庭的变卖率之间的差别有显著性的统计学意义,贫困家庭高于非贫困家庭。对于获得赠款和礼品者,贫困家庭可获得折合金额501.75元,非贫困家庭为1084.41元。两者差别有极显著性统计学意义。在贫困家庭中,家庭内成员花在照料病人的时间平均为14.56天,在非贫困家庭中平均为13.57天,两者差别有显著性统计学意义。有小孩上学的家庭中,48.3%的贫困家庭中止了小孩上学,而22.4%的非贫困家庭中止了小孩上学,两者之间的差别有显著性的统计学意义。对于参加了合作医疗或者特困医疗救助的家庭,贫困家庭平均获得的经济补偿为269.10元,非贫困家庭平均为403.17元。贫困家庭认识的较高层次社会成员的比例低于非贫困家庭,意味着其社会资本的拥有量相对较低,188个调查家庭中,贫困家庭一年内平均用于礼金(礼品)的支出为433.95元,非贫困家庭的平均支出则为1136.50元,两者之间的差别有显著性的统计学意义。社会网络分析结果显示,贫困家庭应对住院事件时期社会网络功能低于非贫困家庭。
研究结论
1.在中国西部农村地区,贫困家庭成员的健康状态较非贫困家庭成员差,而贫困家庭因此有着更高的健康需要,贫困家庭的健康需要被满足的程度要低于非贫困家庭。这使得负性健康事件在贫困家庭中更容易累积下来,到达一定程度后,亦即,当疾病变得十分严重的时候,他们不得不寻求更加难以承担的住院医疗服务。这一情形使得疾病—贫困这一循环变得更加恶劣。
2.用频率、强度和相关性三个维度来描述中国西部农村贫困家庭的健康风险,其特征为:贫困家庭中负性健康事件的现患率和发生率均高于非贫困家庭,其中所占比重较大的是慢性病;贫困家庭在负性健康事件发生强度上的显著特征是:他们有着更大的支出收入比例,即相对强度高;整体而言,健康风险的相关性较弱,但在西部贫困地区传性疾病、地方病、自然疫源性疾病均存在散发和流行的可能。
3.从费用方面看,中国西部贫困家庭用于处理严重负性健康事件的家庭资源低于非贫困家庭。严重的负性健康风险事件给贫困家庭造成的经济冲击显著性地高于非贫困家庭。
4.中国西部农村地区强健康风险状态的发生与各种社会经济人口统计学因素以及健康需要和健康服务利用因素相关联。这包括了:家庭户主的性别、文化程度、职业、婚姻状态和家庭人口结构、慢性病和残疾以及两周诊疗量和住院服务量。
5.中国农村贫困家庭户主对健康风险事件产生原因的认识存在一定误区。农村贫困家庭健康风险分担模式中非正式机制与正式机制并存。
6.中国西部农村贫困家庭健康风险处理行为可以概括为一种“三级处理模式”。其中,一级处理指负性健康事件发生后家庭资源的重新分配及其储备的消耗,但这并未对其未来的生产活动产生实质性的影响;二级处理水平上,贫困家庭将要在放弃病人和降低家庭承受未来冲击的能力之间做出取舍平衡;三级处理要求贫困家庭要么为了获得食物或者工作而进行移民,要么重新组合家庭。
7.根据社会风险管理框架以及健康风险管理手段与策略的特点,中国西部农村贫困家庭健康风险管理需要构建一个面向贫困的综合性农村健康风险管理模式。这一模式,需要多方主体:政府、市场、非政府组织以及家庭共同参与,需要集成非正式和正式的风险管理手段,也需要综合预防、缓和和应对三种策略。其基本原则包括以贫困为中心、可持续性、参与性和动态性。这一模式充分考虑了不同策略与具体的风险管理手段之间的交互关系,同时能够反映随着主体的切换,处理健康风险的策略和手段发生的变化。在面向贫困的综合性农村健康风险管理模式中,卫生部门、农业部门、民政部门、社会保障部门、市场机构、非政府组织和家庭各自承担相应的责任并相互协调。
Objectives:
In analyzing the health risk features of impoverished rural families (IRFs) in Western China, setting up risk models and constructing a multi-party interactive social interference pattern therein, theoretical basis are furnished in this dissertation, for providing: management tools and practical paradigms; prevention, mitigation and coping strategies; and rationales for decision-making of governments at all levels for health risk management of IRFs.
Methods:
Data Source: All data originate from field investigation and questionnaires. Typical sampling method is employed to locate two National-level poverty counties (areas), within which sample villages are selected randomly. All the IRFs thereof (as confirmed by the documentations of local Civil Sectors) are subjected to investigation and control groups are equally and randomly drawn from Non- IRFs within the same natural villages. Accordingly Qianjiang Area of Chongqing City and Guiding County of Guizhou Province are located as sample areas, 1109 rural families are sampled within 11 villages, which consists of 559 IRFs, 550 Non- IRFs, and a grand sum of 4024 family members.
Statistical Analysis methods include—Descriptive Statistics:Mean and S.D. for interval scaled data; nominal scales’frequency and descriptives; Univariate Analysis:T-test and Mann- Whitney U-test for interval scaled data; Chi-square for variable data; Multivariate Analysis:Unconditional Multiple Logistic Regression; Two Step Cluster Analysis, etc. All data are processed by SPSS for Windows 12.01.
Results:
Fortnight and chronic disease Morbidity-rate of IRFs outpace significantly that of Non- IRFs, whereas the doctor-seeking behavior of the former is significantly lower in proportion than that of the latter, therefore the comparatively high health demands of IRFs are under-satisfied. Medical expenditure and hospital-going transportation fares of the IRFs are significantly lower than that of the Non- IRFs. Significant difference emerges between financing structure of IRFs and Non- IRFs, IRFs significantly draw fewer savings and heavier government and collective relief funds and debts. In comparative economic risk evaluation, when the proportion of health expenditure accounts for more than 20% of the total family avenue, both IRFs and Non- IRFs are faced with excessive health economic risk(RR>1.0). Occurrence rate of catastrophic health expenditure in IRFs is significantly higher than that of Non- IRFs. Multiple Logistic Regressions have shown that the occurrence of catastrophic health expenditure is significantly correlated with factors like family structure, householder literacy, and chronic disease, etc.
Through Two Step Cluster Analysis, the whole sample is divided into 2 clusters: Cluster 1 consists of 626 families and, 434 for Cluster 2. Results of the Cluster feature analysis have nominated Cluster 1 as high-stake risk group, Cluster 2 as low-stake risk group. Multiple Logistic Regressions further elaborates that protective factors for high- stake risk status include: male householder, householder literacy, householder profession as farmer or private entrepreneur, high minor-person proportion, high normal residents proportion and outflow of laborers; risk factors include: divorce or spouse bereavement, high average chronic disease burden, high average disability burden, high Fortnight Morbidity-rate and per diem/ per capita in-hospital disease burden.
Health risk cognition and behavior analysis reflect that IRF householders recognize risks’causes mainly as“malnutrition, poor physiological condition”, followed only by“bad luck or predestinarianism”. To prevent health risk events, commonly adopted measures are“prohibiting from working under high risk environment, cultivating hygienic habits, securing sufficient nutrition and rest for family members”. 88.9 percent of IRF householders regard the negative impact of scanty capital to cope with health risk as most worrisome: when health risk accounts for more than 1/5 of total family revenue, a total of 86.5% of IRF householders proclaim this as insufferable. 81.4% of householders evaluate the New Rural Cooperative Medical scheme (RCMS) as“has some effect”or even be of greater help.
Investigations for 188 families that has annual hospitalization record has shown that IRFs draw $834.54 in average from savings while Non- IRFs draw $3161.30, which is significantly higher than that of IRFs. Facing health risks, 69.3% IRFs and 57.6% Non- IRFs resort to borrowing, no difference of statistical significance is detectable. As for newly-added average debt, IRFs and Non- IRFs have $ 1424.69 and $ 744.16 respectively, the difference of which enjoys highly marked statistical significance. Markedness of difference applies also to selling-off rate for curing disease, with IRFs resorting to this approach more heavily. While in receiving donations and presents, IRFs and Non- IRFs amount to $501.75 and $1084.41 respectively(Highly significant difference). As for time taken for family members to look after the patient, IRFs and Non- IRFs have 14.56 days and 13.57 days respectively in average (with Marked statistical significance). 48.3% of the IRFs quit their Children from schooling, contrasted by 22.4% of the Non- IRFs (with Marked statistical significance). To families participated in New RCMS (Rural Cooperative Medical Scheme) and Medicaid for Destitute Families, IRFs can receive an average reimbursement of $269.10, compared with $403.17 that of the Non- IRFs. Meanwhile, IRFs have fewer acquaintances of higher social status than that of the Non- IRFs, this is a refraction of their comparatively lower possession of“social capital”. In 188 families investigated, IRFs spend annually $433.95 in present-giving, Non- IRFs spend $1136.50(with Marked statistical significance). The result of social network analysis has shown that IRFs’social network function falls far behind that of the Non- IRFs while coping with hospitalization.
Conclusions:
1. The health status of IRFs members is worse than that of the Non- IRFs, as a result the IRFs call for much greater health demands. On the contrary, the comparatively high health demands of IRFs are under-satisfied. As an aftermath the negative health events of IRFs are prone to be postponed and, in accumulation to a certain degree, i.e., when diseases were dragged to most severe status, IRFs are obliged to resort to in-hospital services that are even more unaffordable. This prevailing phenomenon has deploringly aggravated the vicious circle of disease-poverty transmutation.
2. As described in three dimensions: frequency, intensity and correlation, health risk of IRFs in Western China has the following characteristics: current morbidity rate and morbidity occurrence rate of IRFs > Non- IRFs, with chronic disease taken the lion’s share; significant features of the aforesaid negative health events in IRFs are: they have greater expenditure/income ratio, i.e., higher comparative intensity; as a whole, correlations among health risks are moderate and even weak, but in impoverished areas of Western China, infectious, endemic and natural epidemic diseases still pose great threats of breaking out and spreading.
3. From the economic perspective, IRFs of Western China have more scanty financial resources than that of the Non- IRFs in dealing with grave negative health events, which in turn has significantly more smashing impact on IRFs than on Non- IRFs.
4. The occurrence of severe health risk status in Western Rural areas of China has apparent correlation with various socio-economic, ethnographic factors, health demands and health service utilization factors, which encompass: gender of householders, literacy and education, profession, marital status, family structure, chronic disease, the handicapped and disabled, fortnight outpatient and in-hospital medical treatment utilizations, etc.
5. IRF householders do harbor misconceptions with respect of the causality of health risk events, informal and formal health risk-sharing modes co-exist in IRFs.
6. Handling behaviors of Health risks in IRFs of Western China could be generalized into a“Tripartite Handling Mode”in which: First Handling denotes the reshuffling of family resources and the consumption of savings after the occurrence of negative health events, this process has no substantial impact on the family’s future productive activities; on the level of the Secondary Handling process, IRFs have to trade-off between giving up the patient and buffering the future impact of diseases; Tertiary Handling requires the IRFs to either migrate to obtain sustenance and work, or reset the family in disregard of the suffered.
7. In light of the Framework Health Risk Management and the characteristics of Health Risk Management strategies and approaches, a poverty-relief-oriented, comprehensive Health Risk Management Model is in great need for the IRFs in Western China. This pattern calls for multiple parties or bodies: government, market, NGOs as well as families; informal and formal Health Risk Management approaches, and a combination of prevention, mitigation and coping strategies. The primary principles of this model or paradigm are poverty-reduction, sustainability, interactivity and dynamic development. Taking into full consideration of the interactions among management strategies and concrete approaches, this framework enjoys high flexibility in accommodating the shift of active parties and changing management strategies and approaches. In this framework, health, agricultural, civil, social security sectors, together with market institutions, NGOs and families will shoulder their corresponding responsibilities and maintain a harmonious coordination.
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