Schreiber S, Zielinski T
New York State Department of Health, Albany 12237, USA.
J Rural Health. 1997 Fall;13(4):276-84. doi: 10.1111/j.1748-0361.1997.tb00970.x.
Ambulatory care sensitive admission rates have been proposed as measures of access to health care. To test this, admissions for ambulatory care sensitive conditions (ACSC) were analyzed by multiple linear regression. The percentage of population below 200 percent of the federally defined poverty level, the percentage of black people, and the number of primary care providers per 1,000 population were found to be positively associated with ACSC admissions. Population density was negatively associated with ACSC admissions. There was no association between the location of the ZIP code in a health professional shortage area and ACSC admissions. Proximity to the hospital was found to be positively associated with ACSC admissions but was examined only in the most rural ZIP code group. The significant independent variables and the direction of their effects were the same across all ZIP code groups. The analysis suggests that high ACSC admissions may be a reflection of deficits in one or more of the following areas: primary care availability, accessibility, or appropriateness. In-depth study is needed to determine the relative importance of these factors in a given geographical area. There also may be environmental and social factors external to the health care system that contribute to ACSC admissions. The findings suggest that ACSC should be used cautiously as a measure of primary care system needs, and in conjunction with other health, demographic, or service utilization data.
门诊护理敏感型住院率已被提议作为衡量获得医疗保健服务的指标。为了对此进行检验，通过多元线性回归分析了门诊护理敏感型疾病（ACSC）的住院情况。研究发现，低于联邦定义贫困水平200%的人口百分比、黑人人口百分比以及每千人口中初级保健提供者的数量与ACSC住院呈正相关。人口密度与ACSC住院呈负相关。邮政编码所在地区是否为卫生专业人员短缺地区与ACSC住院之间没有关联。研究发现，距离医院的远近与ACSC住院呈正相关，但仅在最偏远的邮政编码组中进行了考察。所有邮政编码组中显著的自变量及其影响方向均相同。分析表明，ACSC住院率高可能反映了以下一个或多个方面的不足：初级保健的可及性、可达性或适当性。需要进行深入研究以确定这些因素在特定地理区域的相对重要性。医疗保健系统外部的环境和社会因素也可能导致ACSC住院。研究结果表明，ACSC作为衡量初级保健系统需求的指标应谨慎使用，并应结合其他健康、人口或服务利用数据。
