Family Practice Advance Access published October 31, 2014 Family Practice, 2014, 1–6 doi:10.1093/fampra/cmu072
Epidemiology
Downloaded from http://fampra.oxfordjournals.org/ at Health Sciences Library on November 13, 2014
Primary care utilization among patients with influenza during the 2009 pandemic. Does risk for severe influenza disease or prior contact with the general practitioner have any influence? Kristian A Simonsena,b, Steinar Hunskaara,c, Hogne Sandvikc and Guri Rortveita Research Group for General Practice, Department of Global Public Health and Primary Care, University of Bergen, Bergen, bResearch Unit for General Practice, Uni Research Health, Bergen and cNational Centre for Emergency Primary Health Care, Uni Research Health, Bergen, Norway.
a
*Correspondence to Kristian A Simonsen, Research Group for General Practice, Department of Global Public Health and Primary Care, University of Bergen, Kalfarveien 31, N-5018 Bergen, Norway; E-mail: [email protected]
Abstract Background. Little is known about how patients belonging to risk groups for influenza used the primary care system during the influenza pandemic. Aims. To investigate the use of general practice and out-of-hours (OOH) services in patients with influenza-like illness (ILI) according to (i) risk for severe influenza disease and (ii) the number of regular general practitioner (GP) visits before the pandemic. Method. Observational study of all ILI patients during the 2009 pandemic. Data were recorded prospectively and collected after the pandemic. Patients at risk were identified during an 18-month period by diagnoses from GPs’ billing claims. Associations between risk factors for severe influenza disease and utilization of primary care were analysed using bivariate and multivariate regression analyses. Similar analyses were used for the association between number of GP visits before the pandemic and the primary care utilization during the pandemic. Results. ILI patients who were pregnant [odds ratio (OR) 1.70; 95% confidence interval (CI) 1.52, 1.89], had diabetes (OR 1.68; 95% CI 1.49, 1.89) or chronic lung disease (OR 1.44; 95 CI 1.34, 1.55) had increased risk of attending OOH services compared with patients with no risk factor. ILI patients with at least one GP visit prior to the pandemic used OOH services less during the pandemic compared with those with no GP visit. Conclusion. An increased use of OOH services was found in ILI patients who were pregnant, with diabetes or with chronic lung disease. Having visited the GP before the pandemic was associated with less use of OOH services among ILI patients. Key words: After-hours care, general practitioners, influenza, pandemics, primary health care.
Introduction Continuity of care in general practice has been shown to decrease mortality, hospitalization and use of emergency services in patients with chronic diseases (1,2). Medical treatment, care and a proactive
follow-up approach for people with chronic disease are preferably managed during office hours by the regular general practitioners (GPs). However, patients with chronic diseases are frequent attenders of out-of-hours (OOH) services (3). Also, a higher incidence of acute respiratory infections is seen in people with chronic diseases
© The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected].
1
Family Practice, 2014, Vol. 00, No. 00
2
Method
of Diseases (ICD-10) during daytime, although being part of the community health services. Activities at office hours from these services are not included in this material due to lack of available data. The Norwegian Health Economics Administration (HELFO) registers all activities in general practice and OOH services by electronic billing claims. The HELFO material is found to be reliable for contacts between the patients and the primary care doctor, constituting >95% of all billing claims in 2009 (7). This study is based on data recorded prospectively in real time from the 2009 pandemic in Norway, examining billing claims from all consultations with GPs and OOH doctors (n = 26 591 484 consultations) in the period from 1 January 2008 to 31 December 2009. The data were obtained and prepared for analyses after this period. Claims with missing personal identity number for the patient were excluded from the material (n = 879 409; 3% of the material). Of these, 701 153 consultations took place in the prepandemic period (see below; 268 647 consultations in general practice), whereas 178 256 consultations were performed in the pandemic period (72 390 consultations in general practice). The final sample consisted of 25 712 075 claims. We defined the pandemic period from week 30 to 51, 2009, according to the definition by Norwegian Institute of Public Health, as described previously (6). The period for inclusion of risk patients in this study (Table 1) was set from week 1, 2008 to week 29, 2009 based on available data, defined as the prepandemic period.
Variables
Data source The Norwegian primary care system is organized with GPs taking care of patients during office hours. Almost every citizen in Norway is registered with a GP. Diagnoses are made based on the International Classification of Primary Care (ICPC-2). Emergency medical service is usually provided by the patient’s GP during office hours and by OOH services run by GPs on duty. In some of the larger cities, the 24-hour emergency service uses the coding system International Classification
The outcome in this study was use of OOH services, with use of general practice as reference category. Exposure variables were risk factors for severe influenza outcome (aim 1) and the number of consultations with the GP in the pre-pandemic period (aim 2). Potential confounding variables were age, gender and centrality. An ILI case was defined as a consultation with the primary care doctor when the diagnosis (ICPC-2) R80 influenza/ILI was given.
Table 1. Clinical risk groups for influenza according to ICPC-2 Risk factora
ICPC-2 codes
Chronic hepatic disease Chronic cardiovascular disease
D72 viral hepatitis, D97 liver disease K71 rheumatic fever/heart disease, K74 ischaemic heart disease with angina, K75 acute myocardial infarction, K76 Ischaemic heart disease without angina, K77 heart failure, K78 atrial fibrillation/flutter, K82 pulmonary heart disease, K83 heart valve disease, K84 heart disease other, K89 transient cerebral ischaemia, K90 stroke/cerebrovascular accident, K91 cerebrovascular disease, K92 atherosclerosis/ peripheral vascular disease, K93 pulmonary embolism N70 poliomyelitis, N85 congenital anomaly neurological, N86 multiple sclerosis, N87 Parkinsonism, N88 epilepsy R79 chronic bronchitis, R95 chronic obstructive pulmonary disease, R96 asthma T82 obesity, T83 overweight T89 diabetes insulin dependent, T90 diabetes non-insulin dependent A79 malignancy, B72 Hodgkin’s disease/lymphoma, B73 leukaemia, B78 hereditary haemolytic anaemia, B90 human immunodeficiency virus infection/AIDS, B74 malignant neoplasm blood other, D74 malignant neoplasm stomach, D75 malignant neoplasm colon/rectum, D76 malignant neoplasm pancreas, D77 malignant neoplasm digest other/not otherwise specified, L71 malignant neoplasm musculoskeletal, L88 rheumatoid/seropositive arthritis, N74 malignant neoplasm nervous system, R84 malignant neoplasm bronchus/lung, R85 malignant neoplasm respiratory other, T71 malignant neoplasm thyroid, U75 malignant neoplasm of kidney, U76 malignant neoplasm of bladder, U77 malignant neoplasm urinary other, X75 malignant neoplasm cervix, X76 malignant neoplasm breast female, X77 malignant neoplasm genital other (f), Y77 malignant neoplasm prostate, Y78 malignant neoplasm male genital other W03 antepartum bleeding, W05 pregnancy vomiting/nausea, W78/W781 pregnancy, W81 toxaemia of pregnancy, W84 pregnancy high risk, W85 gestational diabetes
Neurological disease Chronic pulmonary disease Obesity Diabetes Cancer and immunodeficiency
Pregnancyb
Chronic renal disease was omitted due to lack of relevant and specific ICPC-2 codes to recognize that condition. Pregnancy was defined as having at least one consultation with the GP in the pandemic period with pregnancy-specific ICPC-2 codes.
a
b
Downloaded from http://fampra.oxfordjournals.org/ at Health Sciences Library on November 13, 2014
in most age groups (4). Possibly, increased attention to such patients from GPs may improve the risk of complications for patients as well as reduce the pressure on the OOH services. The winter season coincides with an increase of workload in primary care and the majority of influenza patients are treated by GPs (5). The 2009 influenza pandemic caused a more than 3-fold increase of influenza-like illness (ILI) consultations in general practice in Norway compared with a normal season (6). GPs play a key role in recognizing patients at risk of severe outcomes of influenza because they have knowledge of vaccination status and morbidity in patients registered to their lists. Knowledge about how patients with risk conditions for influenza use GPs and OOH services during an influenza pandemic is lacking. Also, it is of interest to know whether prior visit to the GP influences the use of primary care services during outbreaks. To our knowledge, this is the first study to explore primary care utilization during a pandemic among ILI patients with risk factors for severe influenza. The first aim of this study was to explore how patients at risk for severe influenza used primary care services for ILI during the pandemic in comparison with low-risk patients. The second aim was to investigate how patients with prior GP consultations used primary care services for ILI in comparison with patients who had no such consultations before the pandemic.
Primary care utilization
3
Statistics The data were analysed in SPSS Statistics version 21 with frequency analyses and bivariate analyses, as well as multivariate logistic regression analyses. In the frequency tables, age was divided into eight strata (0–4, 5–9, 10–14, 15–19, 20–29, 30–39, 40–49 and ≥50 years of age). Age, gender and centrality were considered as potential confounders and effect modifiers. Effect modification was tested by the Breslow–Day test for homogeneity between odds ratios (OR) after stratified analyses. Confounding was evaluated by Mantel–Haenszel common ORs and logistic regression analyses. Multivariate logistic regression analyses were performed to adjust for the confounders. We used multivariate logistic regression analyses with primary care service type (general practice as reference category) as dependent variable and risk factor (no risk as reference category) as explanatory variable. In the analyses of association between primary care utilization and number of GP visits in the pre-pandemic period, the explanatory variable was number of GP visits (0 visits as reference category). Service type was dichotomized to general practice and OOH services. Data from ILI patients visiting both types of services were excluded from the multivariate analyses. The multivariate analyses were adjusted for age, gender and centrality. Age was
transformed to a categorical variable divided in 5-year strata in the multivariate analyses (0–5, 6–10, 11–15, (…) and >90 years of age). Significance was accepted at the 5% level (P
Epidemiology
Downloaded from http://fampra.oxfordjournals.org/ at Health Sciences Library on November 13, 2014
Primary care utilization among patients with influenza during the 2009 pandemic. Does risk for severe influenza disease or prior contact with the general practitioner have any influence? Kristian A Simonsena,b, Steinar Hunskaara,c, Hogne Sandvikc and Guri Rortveita Research Group for General Practice, Department of Global Public Health and Primary Care, University of Bergen, Bergen, bResearch Unit for General Practice, Uni Research Health, Bergen and cNational Centre for Emergency Primary Health Care, Uni Research Health, Bergen, Norway.
a
*Correspondence to Kristian A Simonsen, Research Group for General Practice, Department of Global Public Health and Primary Care, University of Bergen, Kalfarveien 31, N-5018 Bergen, Norway; E-mail: [email protected]
Abstract Background. Little is known about how patients belonging to risk groups for influenza used the primary care system during the influenza pandemic. Aims. To investigate the use of general practice and out-of-hours (OOH) services in patients with influenza-like illness (ILI) according to (i) risk for severe influenza disease and (ii) the number of regular general practitioner (GP) visits before the pandemic. Method. Observational study of all ILI patients during the 2009 pandemic. Data were recorded prospectively and collected after the pandemic. Patients at risk were identified during an 18-month period by diagnoses from GPs’ billing claims. Associations between risk factors for severe influenza disease and utilization of primary care were analysed using bivariate and multivariate regression analyses. Similar analyses were used for the association between number of GP visits before the pandemic and the primary care utilization during the pandemic. Results. ILI patients who were pregnant [odds ratio (OR) 1.70; 95% confidence interval (CI) 1.52, 1.89], had diabetes (OR 1.68; 95% CI 1.49, 1.89) or chronic lung disease (OR 1.44; 95 CI 1.34, 1.55) had increased risk of attending OOH services compared with patients with no risk factor. ILI patients with at least one GP visit prior to the pandemic used OOH services less during the pandemic compared with those with no GP visit. Conclusion. An increased use of OOH services was found in ILI patients who were pregnant, with diabetes or with chronic lung disease. Having visited the GP before the pandemic was associated with less use of OOH services among ILI patients. Key words: After-hours care, general practitioners, influenza, pandemics, primary health care.
Introduction Continuity of care in general practice has been shown to decrease mortality, hospitalization and use of emergency services in patients with chronic diseases (1,2). Medical treatment, care and a proactive
follow-up approach for people with chronic disease are preferably managed during office hours by the regular general practitioners (GPs). However, patients with chronic diseases are frequent attenders of out-of-hours (OOH) services (3). Also, a higher incidence of acute respiratory infections is seen in people with chronic diseases
© The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected].
1
Family Practice, 2014, Vol. 00, No. 00
2
Method
of Diseases (ICD-10) during daytime, although being part of the community health services. Activities at office hours from these services are not included in this material due to lack of available data. The Norwegian Health Economics Administration (HELFO) registers all activities in general practice and OOH services by electronic billing claims. The HELFO material is found to be reliable for contacts between the patients and the primary care doctor, constituting >95% of all billing claims in 2009 (7). This study is based on data recorded prospectively in real time from the 2009 pandemic in Norway, examining billing claims from all consultations with GPs and OOH doctors (n = 26 591 484 consultations) in the period from 1 January 2008 to 31 December 2009. The data were obtained and prepared for analyses after this period. Claims with missing personal identity number for the patient were excluded from the material (n = 879 409; 3% of the material). Of these, 701 153 consultations took place in the prepandemic period (see below; 268 647 consultations in general practice), whereas 178 256 consultations were performed in the pandemic period (72 390 consultations in general practice). The final sample consisted of 25 712 075 claims. We defined the pandemic period from week 30 to 51, 2009, according to the definition by Norwegian Institute of Public Health, as described previously (6). The period for inclusion of risk patients in this study (Table 1) was set from week 1, 2008 to week 29, 2009 based on available data, defined as the prepandemic period.
Variables
Data source The Norwegian primary care system is organized with GPs taking care of patients during office hours. Almost every citizen in Norway is registered with a GP. Diagnoses are made based on the International Classification of Primary Care (ICPC-2). Emergency medical service is usually provided by the patient’s GP during office hours and by OOH services run by GPs on duty. In some of the larger cities, the 24-hour emergency service uses the coding system International Classification
The outcome in this study was use of OOH services, with use of general practice as reference category. Exposure variables were risk factors for severe influenza outcome (aim 1) and the number of consultations with the GP in the pre-pandemic period (aim 2). Potential confounding variables were age, gender and centrality. An ILI case was defined as a consultation with the primary care doctor when the diagnosis (ICPC-2) R80 influenza/ILI was given.
Table 1. Clinical risk groups for influenza according to ICPC-2 Risk factora
ICPC-2 codes
Chronic hepatic disease Chronic cardiovascular disease
D72 viral hepatitis, D97 liver disease K71 rheumatic fever/heart disease, K74 ischaemic heart disease with angina, K75 acute myocardial infarction, K76 Ischaemic heart disease without angina, K77 heart failure, K78 atrial fibrillation/flutter, K82 pulmonary heart disease, K83 heart valve disease, K84 heart disease other, K89 transient cerebral ischaemia, K90 stroke/cerebrovascular accident, K91 cerebrovascular disease, K92 atherosclerosis/ peripheral vascular disease, K93 pulmonary embolism N70 poliomyelitis, N85 congenital anomaly neurological, N86 multiple sclerosis, N87 Parkinsonism, N88 epilepsy R79 chronic bronchitis, R95 chronic obstructive pulmonary disease, R96 asthma T82 obesity, T83 overweight T89 diabetes insulin dependent, T90 diabetes non-insulin dependent A79 malignancy, B72 Hodgkin’s disease/lymphoma, B73 leukaemia, B78 hereditary haemolytic anaemia, B90 human immunodeficiency virus infection/AIDS, B74 malignant neoplasm blood other, D74 malignant neoplasm stomach, D75 malignant neoplasm colon/rectum, D76 malignant neoplasm pancreas, D77 malignant neoplasm digest other/not otherwise specified, L71 malignant neoplasm musculoskeletal, L88 rheumatoid/seropositive arthritis, N74 malignant neoplasm nervous system, R84 malignant neoplasm bronchus/lung, R85 malignant neoplasm respiratory other, T71 malignant neoplasm thyroid, U75 malignant neoplasm of kidney, U76 malignant neoplasm of bladder, U77 malignant neoplasm urinary other, X75 malignant neoplasm cervix, X76 malignant neoplasm breast female, X77 malignant neoplasm genital other (f), Y77 malignant neoplasm prostate, Y78 malignant neoplasm male genital other W03 antepartum bleeding, W05 pregnancy vomiting/nausea, W78/W781 pregnancy, W81 toxaemia of pregnancy, W84 pregnancy high risk, W85 gestational diabetes
Neurological disease Chronic pulmonary disease Obesity Diabetes Cancer and immunodeficiency
Pregnancyb
Chronic renal disease was omitted due to lack of relevant and specific ICPC-2 codes to recognize that condition. Pregnancy was defined as having at least one consultation with the GP in the pandemic period with pregnancy-specific ICPC-2 codes.
a
b
Downloaded from http://fampra.oxfordjournals.org/ at Health Sciences Library on November 13, 2014
in most age groups (4). Possibly, increased attention to such patients from GPs may improve the risk of complications for patients as well as reduce the pressure on the OOH services. The winter season coincides with an increase of workload in primary care and the majority of influenza patients are treated by GPs (5). The 2009 influenza pandemic caused a more than 3-fold increase of influenza-like illness (ILI) consultations in general practice in Norway compared with a normal season (6). GPs play a key role in recognizing patients at risk of severe outcomes of influenza because they have knowledge of vaccination status and morbidity in patients registered to their lists. Knowledge about how patients with risk conditions for influenza use GPs and OOH services during an influenza pandemic is lacking. Also, it is of interest to know whether prior visit to the GP influences the use of primary care services during outbreaks. To our knowledge, this is the first study to explore primary care utilization during a pandemic among ILI patients with risk factors for severe influenza. The first aim of this study was to explore how patients at risk for severe influenza used primary care services for ILI during the pandemic in comparison with low-risk patients. The second aim was to investigate how patients with prior GP consultations used primary care services for ILI in comparison with patients who had no such consultations before the pandemic.
Primary care utilization
3
Statistics The data were analysed in SPSS Statistics version 21 with frequency analyses and bivariate analyses, as well as multivariate logistic regression analyses. In the frequency tables, age was divided into eight strata (0–4, 5–9, 10–14, 15–19, 20–29, 30–39, 40–49 and ≥50 years of age). Age, gender and centrality were considered as potential confounders and effect modifiers. Effect modification was tested by the Breslow–Day test for homogeneity between odds ratios (OR) after stratified analyses. Confounding was evaluated by Mantel–Haenszel common ORs and logistic regression analyses. Multivariate logistic regression analyses were performed to adjust for the confounders. We used multivariate logistic regression analyses with primary care service type (general practice as reference category) as dependent variable and risk factor (no risk as reference category) as explanatory variable. In the analyses of association between primary care utilization and number of GP visits in the pre-pandemic period, the explanatory variable was number of GP visits (0 visits as reference category). Service type was dichotomized to general practice and OOH services. Data from ILI patients visiting both types of services were excluded from the multivariate analyses. The multivariate analyses were adjusted for age, gender and centrality. Age was
transformed to a categorical variable divided in 5-year strata in the multivariate analyses (0–5, 6–10, 11–15, (…) and >90 years of age). Significance was accepted at the 5% level (P
