OBJECTIVES: To examine the relationship between specific types of morbidity, measured by validated survey questions, and hospital service use and mortality to see if the latter two could act as a proxy in health needs assessment, health service planning, and resource allocation in a typical health district. DESIGN: A postal questionnaire was used to provide information about depression, digestive disorders, musculo-skeletal disorders, obesity, respiratory disease, and hip and knee pain. The questions were from survey instruments that have been widely used to derive information about these conditions. The relationships between the prevalence of these specific types of morbidity and appropriate admission and mortality rates were explored using linear regression and Pearson correlation analysis. SETTING: The population of Rotherham health district, England. SUBJECTS: A simple random sample of the residents of each of the 22 electoral wards in Rotherham health district. RESULTS: Responses were obtained from 78% of the 5000 sampled (82% after excluding people who had moved house or died). Significant, positive correlations were found between the prevalence of respiratory disease and the hospital admission and mortality rates for respiratory problems (r = 0.68, p < 0.01 and r = 0.54, p < 0.01) and the prevalence of depression and the admission rate for depression (r = 0.52, p < 0.05). No such relations were found for digestive disease, musculo-skeletal disease, and obesity. For the conditions examined here, hospital service use was a more useful measure than mortality. CONCLUSIONS: Only two diseases (respiratory disease and depression) out of the seven diseases or procedures investigated showed a positive correlation between hospital admission and disease prevalence. But even for these two, the correlations explained less than 50% of the variance. Caution must be exercised when hospital service use is being considered as a proxy for morbidity.
OBJECTIVES: To examine the relationship between specific types of morbidity, measured by validated survey questions, and hospital service use and mortality to see if the latter two could act as a proxy in health needs assessment, health service planning, and resource allocation in a typical health district. DESIGN: A postal questionnaire was used to provide information about depression, digestive disorders, musculo-skeletal disorders, obesity, respiratory disease, and hip and knee pain. The questions were from survey instruments that have been widely used to derive information about these conditions. The relationships between the prevalence of these specific types of morbidity and appropriate admission and mortality rates were explored using linear regression and Pearson correlation analysis. SETTING: The population of Rotherham health district, England. SUBJECTS: A simple random sample of the residents of each of the 22 electoral wards in Rotherham health district. RESULTS: Responses were obtained from 78% of the 5000 sampled (82% after excluding people who had moved house or died). Significant, positive correlations were found between the prevalence of respiratory disease and the hospital admission and mortality rates for respiratory problems (r = 0.68, p < 0.01 and r = 0.54, p < 0.01) and the prevalence of depression and the admission rate for depression (r = 0.52, p < 0.05). No such relations were found for digestive disease, musculo-skeletal disease, and obesity. For the conditions examined here, hospital service use was a more useful measure than mortality. CONCLUSIONS: Only two diseases (respiratory disease and depression) out of the seven diseases or procedures investigated showed a positive correlation between hospital admission and disease prevalence. But even for these two, the correlations explained less than 50% of the variance. Caution must be exercised when hospital service use is being considered as a proxy for morbidity.
Authors: J G Williams; S E Roberts; M F Ali; W Y Cheung; D R Cohen; G Demery; A Edwards; M Greer; M D Hellier; H A Hutchings; B Ip; M F Longo; I T Russell; H A Snooks; J C Williams Journal: Gut Date: 2007-02 Impact factor: 23.059