The California Office of Statewide Health Planning and Development (Sacramento, CA) provided patient discharge data and ED visit files. Diagnoses in both sources are coded according to the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM; Centers for Disease Control and Prevention 1979 ). Of the hospital admissions during this period, 41% occurred among those people who had been admitted from an initial ED visit. The ED data, on the other hand, included all ED visits, regardless of whether patients were subsequently admitted to the hospital (of which 16% were). Thus, our resulting data sets for all incidents of ED visits and inpatient hospitalizations were not mutually exclusive.
Heat waves exacerbate a wide range of preexisting illnesses, and excess morbidity and mortality associated with heat waves go beyond that classified formally as “heat related.” With this in mind, we classified morbidity outcome data into categories consistent with the major groups applied in previous heat wave hospitalization studies (notably Semenza et al. 1999 (link)) and included all diagnoses combined, all internal causes (ICD-9-CM codes 001–799.9), diabetes mellitus (250), disorders of fluid and electrolyte balance (276), cardiovascular diseases (390–398, 402, 404–429, 440–448), acute myocardial infarction (MI; 410), cerebrovascular diseases (430–438), respiratory illnesses (460–519), nephritis and nephrotic syndrome and nephrosis (580–589), acute renal failure (584), and heat-related effects (992).
Because risk appears to be greatest in very young and old persons, we used three age categories in the analysis: 0–4 years, 5–64 years, and ≥ 65 years of age. We used several race and ethnicity categories that have been previously applied in health vulnerability analyses in California: Asian/Pacific Islander, African American, Latino/Hispanic, Native American/Alaska Native, other, unreported race/ethnicity, and non-Hispanic white.
Some studies of heat wave–related hospitalizations have found that using primary discharge diagnoses alone can underestimate increases in some admissions (Kilbourne 1999 (link); Semenza et al. 1999 (link)). To calculate rate ratios (RRs) among ED visits and hospitalizations, we combined the primary and the first nine secondary diagnoses listed in the discharge record; for example, we classified admissions that included a nephritis code, regardless of whether primary or secondary, as nephritis in this analysis. Because there may be appreciable variation in the order of the various cause codes in the primary (the condition that prompted the admission or visit, not necessarily the most severe condition) and secondary diagnoses, we combined the primary and the first nine secondary codes to lend more consistency to the heat-wave versus non-heat-wave descriptive epidemiology.
To estimate the number of excess hospital admissions and ED visits during the heat wave and describe the principal disease process treated, we evaluated primary discharge diagnoses separately to tabulate changes in the total numbers of individuals seeking treatment.
Heat waves exacerbate a wide range of preexisting illnesses, and excess morbidity and mortality associated with heat waves go beyond that classified formally as “heat related.” With this in mind, we classified morbidity outcome data into categories consistent with the major groups applied in previous heat wave hospitalization studies (notably Semenza et al. 1999 (link)) and included all diagnoses combined, all internal causes (ICD-9-CM codes 001–799.9), diabetes mellitus (250), disorders of fluid and electrolyte balance (276), cardiovascular diseases (390–398, 402, 404–429, 440–448), acute myocardial infarction (MI; 410), cerebrovascular diseases (430–438), respiratory illnesses (460–519), nephritis and nephrotic syndrome and nephrosis (580–589), acute renal failure (584), and heat-related effects (992).
Because risk appears to be greatest in very young and old persons, we used three age categories in the analysis: 0–4 years, 5–64 years, and ≥ 65 years of age. We used several race and ethnicity categories that have been previously applied in health vulnerability analyses in California: Asian/Pacific Islander, African American, Latino/Hispanic, Native American/Alaska Native, other, unreported race/ethnicity, and non-Hispanic white.
Some studies of heat wave–related hospitalizations have found that using primary discharge diagnoses alone can underestimate increases in some admissions (Kilbourne 1999 (link); Semenza et al. 1999 (link)). To calculate rate ratios (RRs) among ED visits and hospitalizations, we combined the primary and the first nine secondary diagnoses listed in the discharge record; for example, we classified admissions that included a nephritis code, regardless of whether primary or secondary, as nephritis in this analysis. Because there may be appreciable variation in the order of the various cause codes in the primary (the condition that prompted the admission or visit, not necessarily the most severe condition) and secondary diagnoses, we combined the primary and the first nine secondary codes to lend more consistency to the heat-wave versus non-heat-wave descriptive epidemiology.
To estimate the number of excess hospital admissions and ED visits during the heat wave and describe the principal disease process treated, we evaluated primary discharge diagnoses separately to tabulate changes in the total numbers of individuals seeking treatment.
Free full text: Click here
