全科医生数据采集和空间分析在理解COPD和哮喘方面的实用价值
2019/02/19
摘要
背景：以全科医生（GP）为基础的医疗保健数据有希望在系统性收集时用于支持估计慢性阻塞性肺疾病(COPD)和哮喘的局部发病率、疾病负担的变化、危险因素和共病条件、疾病管理和护理质量。但是，在数据的范围和质量方面，利用全科医生信息系统来改善健康是有限的。这项研究评估在估计慢性阻塞性肺病和哮喘的局部发病率方面识别临床数据库的实用价值。我们将慢性阻塞性肺病和哮喘发病率与国家基准进行了比较，检查与慢性阻塞性肺病和哮喘相关的健康相关危险因素和共病发病率，并在小区域水平上评估患病率估计中的空间模式。
方法：从南澳大利亚阿德莱德西部的5个GP数据库中提取数据，选取2012年至2014年期间居住在该地区的患者。采用经验贝叶斯估计方法，在统计区域1 (SA1)空间单元水平上计算评估患病率。描述性分析包括汇总统计、空间指标和地理格局映射。评估双变量相关性，并调查疾病概况以确定共病发病率。考虑到包含共病条件数量在内的个体共变量，采用多水平逻辑回归模型以评估区域水平社会经济地位（SES）的影响。
结果：在33,725名患者中，COPD估计患病率为3.4%，哮喘估计患病率为10.3%，与2014-2015年国民健康调查(NHS)的基准相比，COPD估计患病率高了0.8%，哮喘估计患病率低了0.5%。年龄特异性比较显示“64岁以下”和“65岁及以上”年龄组的COPD患者、“15-25岁”和“75岁及以上”年龄组的哮喘患者存在差异。分析证实个体水平因素、共病条件和区域水平的SES具有相关性。慢性阻塞性肺病和哮喘在地理聚集性，聚集在全科医生诊所和卫生保健中心周围。空间格局与区域水平的SES呈负相关。
结论：以全科医生为基础的数据采集和分析可以通过研究地理差异性和其相关因素，以及易感年龄组当地估计患病率与NHS基准之间的差异如何变化从而为改善COPD和哮喘患者预后的研究提供支持。
Practical utility of general practice data capture and spatial analysis for understanding COPD and asthma.
Niyonsenga T, Coffee NT, Del Fante P, Høj SB, Daniel M.
Abstract
BACKGROUND: General practice-based (GP) healthcare data have promise, when systematically collected, to support estimating local rates of chronic obstructive pulmonary disease (COPD) and asthma, variations in burden of disease, risk factors and comorbid conditions, and disease management and quality of care. The use of GP information systems for health improvement has been limited, however, in the scope and quality of data. This study assessed the practical utility of de-identified clinical databases for estimating local rates of COPD and asthma. We compared COPD and asthma rates to national benchmarks, examined health related risk factors and co-morbidities as correlates of COPD and asthma, and assessed spatial patterns in prevalence estimates at the small-area level.
METHODS: Data were extracted from five GP databases in western Adelaide, South Australia, for active patients residing in the region between 2012 and 2014. Prevalence estimates were computed at the statistical area 1 (SA1) spatial unit level using the empirical Bayes estimation approach. Descriptive analyses included summary statistics, spatial indices and mapping of geographic patterns. Bivariate associations were assessed, and disease profiles investigated to ascertain multi-morbidities. Multilevel logistic regression models were fitted, accounting for individual covariates including the number of comorbid conditions to assess the influence of area-level socio-economic status (SES).
RESULTS: For 33,725 active patients, prevalence estimates were 3.4% for COPD and 10.3% for asthma, 0.8% higher and 0.5% lower for COPD and asthma, respectively, against 2014-15 National Health Survey (NHS) benchmarks. Age-specific comparisons showed discrepancies for COPD in the '64 years or less' and 'age 65 and up' age groups, and for asthma in the '15-25 years' and '75 years and up' age groups. Analyses confirmed associations with individual-level factors, co-morbid conditions, and area-level SES. Geographic aggregation was seen for COPD and asthma, with clustering around GP clinics and health care centres. Spatial patterns were inversely related to area-level SES.
CONCLUSION: GP-based data capture and analysis has a clear potential to support research for improved patient outcomes for COPD and asthma via knowledge of geographic variability and its correlates, and how local prevalence estimates differ from NHS benchmarks for vulnerable age-groups.
背景：以全科医生（GP）为基础的医疗保健数据有希望在系统性收集时用于支持估计慢性阻塞性肺疾病(COPD)和哮喘的局部发病率、疾病负担的变化、危险因素和共病条件、疾病管理和护理质量。但是，在数据的范围和质量方面，利用全科医生信息系统来改善健康是有限的。这项研究评估在估计慢性阻塞性肺病和哮喘的局部发病率方面识别临床数据库的实用价值。我们将慢性阻塞性肺病和哮喘发病率与国家基准进行了比较，检查与慢性阻塞性肺病和哮喘相关的健康相关危险因素和共病发病率，并在小区域水平上评估患病率估计中的空间模式。
方法：从南澳大利亚阿德莱德西部的5个GP数据库中提取数据，选取2012年至2014年期间居住在该地区的患者。采用经验贝叶斯估计方法，在统计区域1 (SA1)空间单元水平上计算评估患病率。描述性分析包括汇总统计、空间指标和地理格局映射。评估双变量相关性，并调查疾病概况以确定共病发病率。考虑到包含共病条件数量在内的个体共变量，采用多水平逻辑回归模型以评估区域水平社会经济地位（SES）的影响。
结果：在33,725名患者中，COPD估计患病率为3.4%，哮喘估计患病率为10.3%，与2014-2015年国民健康调查(NHS)的基准相比，COPD估计患病率高了0.8%，哮喘估计患病率低了0.5%。年龄特异性比较显示“64岁以下”和“65岁及以上”年龄组的COPD患者、“15-25岁”和“75岁及以上”年龄组的哮喘患者存在差异。分析证实个体水平因素、共病条件和区域水平的SES具有相关性。慢性阻塞性肺病和哮喘在地理聚集性，聚集在全科医生诊所和卫生保健中心周围。空间格局与区域水平的SES呈负相关。
结论：以全科医生为基础的数据采集和分析可以通过研究地理差异性和其相关因素，以及易感年龄组当地估计患病率与NHS基准之间的差异如何变化从而为改善COPD和哮喘患者预后的研究提供支持。
（中日友好医院呼吸与危重症医学科 禹汶伯 摘译 林江涛 审校)
(BMC Health Serv Res. 2018 Nov 26;18(1):897.)
(BMC Health Serv Res. 2018 Nov 26;18(1):897.)
Practical utility of general practice data capture and spatial analysis for understanding COPD and asthma.
Niyonsenga T, Coffee NT, Del Fante P, Høj SB, Daniel M.
Abstract
BACKGROUND: General practice-based (GP) healthcare data have promise, when systematically collected, to support estimating local rates of chronic obstructive pulmonary disease (COPD) and asthma, variations in burden of disease, risk factors and comorbid conditions, and disease management and quality of care. The use of GP information systems for health improvement has been limited, however, in the scope and quality of data. This study assessed the practical utility of de-identified clinical databases for estimating local rates of COPD and asthma. We compared COPD and asthma rates to national benchmarks, examined health related risk factors and co-morbidities as correlates of COPD and asthma, and assessed spatial patterns in prevalence estimates at the small-area level.
METHODS: Data were extracted from five GP databases in western Adelaide, South Australia, for active patients residing in the region between 2012 and 2014. Prevalence estimates were computed at the statistical area 1 (SA1) spatial unit level using the empirical Bayes estimation approach. Descriptive analyses included summary statistics, spatial indices and mapping of geographic patterns. Bivariate associations were assessed, and disease profiles investigated to ascertain multi-morbidities. Multilevel logistic regression models were fitted, accounting for individual covariates including the number of comorbid conditions to assess the influence of area-level socio-economic status (SES).
RESULTS: For 33,725 active patients, prevalence estimates were 3.4% for COPD and 10.3% for asthma, 0.8% higher and 0.5% lower for COPD and asthma, respectively, against 2014-15 National Health Survey (NHS) benchmarks. Age-specific comparisons showed discrepancies for COPD in the '64 years or less' and 'age 65 and up' age groups, and for asthma in the '15-25 years' and '75 years and up' age groups. Analyses confirmed associations with individual-level factors, co-morbid conditions, and area-level SES. Geographic aggregation was seen for COPD and asthma, with clustering around GP clinics and health care centres. Spatial patterns were inversely related to area-level SES.
CONCLUSION: GP-based data capture and analysis has a clear potential to support research for improved patient outcomes for COPD and asthma via knowledge of geographic variability and its correlates, and how local prevalence estimates differ from NHS benchmarks for vulnerable age-groups.
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