Evaluation of the quality of type 2 diabetes care in North Karelia, Finland

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EVALUATION OF THE QUALITY OF TYPE 2 DIABETES CARE IN NORTH KARELIA, FINLAND

Nazma Akter Nazu Master’s thesis

Institute of Public Health and Clinical Nutrition School of Medicine

Faculty of Health Sciences University of Eastern Finland May 2017

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UNIVERSITY OF EASTERN FINLAND, Faculty of Health Sciences Institute of Public Health and Clinical Nutrition

Nazu, Nazma A.

: Evaluation of the quality of type 2 diabetes care in North Karelia, Finland.

Master’s thesis, 84 pages.

Instructors: Professor Tiina Laatikainen, MD, PhD and Professor Tomi-Pekka Tuomainen, DmedSc.

May 2017

Key words: Type 2 diabetes, Quality of Care, Age, Gender.

EVALUATION OF THE QUALITY OF TYPE 2 DIABETES CARE IN NORTH KARELIA, FINLAND.

Worldwide burden of type 2 diabetes (T2D) has increased in recent years. The consequence of T2D is extreme and might cause lethal conditions, lower the quality of life and put an additional economic burden on individual along with the society. In Finland, number of individuals with T2D is increasing.

Taking into account the impact of T2D on society, this study intended to provide follow-up data on quality of T2D care in North Karelia and to explore if there is an improvement in the level of adherence to the follow-ups and outcomes of care. We also aimed to investigate the socioeconomic factors that can affect the improvement of the intermediate outcomes.

This study is a retrospective cohort study of 9288 patients who were diagnosed with T2D in 2011- 12 in North Karelia, Finland. Baseline and follow-up information about patients’ and laboratory data were collected from the regional electronic patient database. We assessed the four quality indicators of diabetes care as the main outcome measures, such as measurement of HbA1c and LDL as process indicators and the management of HbA1c and LDL as intermediate outcome indicators. Chi square test, one sample t-test, one way ANOVA, age-standardized univariate ANOVA and multivariate logistic regression analyses were performed to obtain the results. We found that the measurement rate of HbA1c (F = 89.4% vs 79.3%, P < 0.001and M = 87.4% vs 76.1%, P < 0.001) and LDL (F = 82.5% vs 74.3%, P < 0.001 and M = 84.3% vs 73.6%, P < 0.001) has improved in 2013-14 compared with 2011-12. However, the management of HbA1c deteriorated (F = 66.1% vs 73.6%, P = 0.006 and M = 64.6% vs 70.9%, P = 0.008) in 2013-14 compared with 2011-12. An improvement in LDL management was observed only among males (61.9% vs 57.2%, P < 0.001) in 2013-14 compared with 2011-12.

This study shows that measurement rate of HbA1c and LDL has improved in North Karelia during the follow-up period. Older patients were more likely to be measured for HbA1c but less likely to show improvement in HbA1c management. Elderly patients were more likely to be measured for LDL and more likely to show improvement in LDL management. Gender disparity was observed only in the management of LDL. Variation in the measurement and management of HbA1c and LDL was observed between area level proportion of educated, unemployed, and median income of the citizens. However, they were not able to predict the improvement of follow-up or management of HbA1c and LDL.

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ACKNOWLEDGEMENT

First and foremost, I would like to thank my supervisor Tiina Laatikainen, MD, PhD, Professor of Health Promotion, for supervising me and giving me the opportunity to write this thesis. I am grateful for her patience, continuous support, and inspiration for the completion of this thesis and planning for the future. Secondly, I would like to thank my second supervisor Tomi-Pekka Tuomainen, DmedSc, Professor of Epidemiology for his supervision and valuable suggestions for the improvement of my thesis. I am grateful to Dr. Sohaib Khan, MBBS, RMP, MPH, PhD, Assistant Professor of International Health, for his guidance throughout the thesis writing. I would also like to thank Matti Estola, Senior Lecturer University of Eastern Finland, Faculty of Social Sciences and Business Studies, Joensuu for his support on data analyses.

Special thanks to my dear husband Kaiser, for his unconditional moral support to start and complete this work. I also thank my parents for their support and believe in me. Finally, I would like to thank the almighty Allah for giving me the strength to complete this work.

Kuopio, May 2017 Nazma Akter Nazu

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1 INTRODUCTION

Diabetes has become one of the most important health emergencies in recent years. Almost 415 million adults around the world are living with diabetes. The most prevalent form of diabetes is type 2 diabetes (T2D). In high income countries, among the total number of adults who are suffering from diabetes, almost 91% have T2D (International Diabetes Federation 2015). There is an increased risk of developing life-threatening microvascular and macrovascular conditions as well as pregnancy-related complications among T2D patients which subsequently reduce the quality of life of T2D patients (Seuring et al. 2015). In addition, there is a huge economic impact of T2D on patients, their families and national health care system of a country due to the cost of essential anti-diabetic medicines, more use of health care services, loss of productivity and the support needed to conquer the complications related to T2D. The global prevalence of T2D is expected to be increased from 8.8% in 2015 to 10.4% in 2040, along with the increase in global health expenditure due to T2D from 673 billion in 2015 to 802 billion in 2040. Superior preventive services and sufficient care can minimize the incidence and eliminate the additional complications related to T2D, thus can minimize the cost of T2D in the society (International Diabetes Federation 2015). There are clinical guidelines to support the health care professionals for the better management of T2D patients. Considering the impact of T2D on society, it has become essential to assess the quality of care of T2D delivered by the health care system and assess the level of adherence to the clinical guidelines (Parchman 2002).

One conventional approach to assess the quality of T2D care is the use of quality assessment indicators. A quality improvement program conducted in the US along with national diabetes quality improvement alliance (NDQIA) suggested nine indicators for the assessment of quality of T2D care (Four indicators for the measurement of care process and five for the outcomes of care), which have been accepted universally. Regular monitoring of glucose (HbA1c) and LDL cholesterol level are the indicators for the measurement of care process of T2D and achievement in the recommended glucose (HbA1c) and LDL cholesterol level are the indicators for the measurement of proximal outcome (Nicolucci et al. 2006).

Several factors have been identified previously that affect the quality of care of T2D. Age, socioeconomic status (SES), ethnicity, geographical variation, and gender disparities are found to

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have association with monitoring and control of HbA1c and LDL level (Wallace & Colsher 1992, Suh et al. 2008, Sikio et al. 2014, Toivakka et al. 2015). Study by Suh et al. (2008) showed that older people are more likely to measure their blood sugar and receive better diabetes care than the youngers. However older people with T2D were less likely to achieve the target HbA1c level (Bruce et al. 2000). In addition, gender disparities in the achievement of recommended level of HbA1c and LDL also observed among T2D patient. Being female was found to be associated with higher probability of achieving recommended levels of HbA1c (Sikio et al. 2014) and a study in USA, showed that LDL management is better among males compared with females (Wallace &

Colsher 1992). Individual and area level socio-economic status (SES) were found to have influence on T2D care (Grintsova et al. 2014). Patients with lower social status were found to have a higher HbA1c level and worse quality of diabetes care (Geraghty et al. 2010, Baz et al. 2012). A systematic literature review investigating the inequalities in health care among T2D patients found that the achievement in the glycemic control targets are less often among patients living in deprived areas (Grintsova et al. 2014). Some studies in Finland also showed that there is variation in the quality of T2D care by age, gender, SES and geographical area (Sikio et al. 2014, Toivakka et al. 2015).

Understanding the SES differences in process and outcome of care is important for the policymakers to find out the possible problem related to the access to health care and the care process.

In Finland, over 500000 individuals are estimated to be suffering from diabetes and this number is increasing rapidly. At present, the share of the direct costs of diabetes care in Finland is about 15%

of the total health expenditure of Finland and the costs are also growing rapidly (Current Care Guideline 2016). A study in Finland found that complications arising from T2D cause twenty-fold increment in the expenses of care of the people with T2D (Kangas 2002). The Finnish current care guidelines recommend that the patient should be monitored at least every 6-12 months including HbA1c measurements and LDL level should be monitored at least once in 1-3 years (Current Care Guideline 2016). At present, it is not clear how well the guidelines are followed and whether the treatment targets are achieved.

The aim of this study is to provide the follow up data of T2D care in North Karelia. The particular objective of this study was to explore if there are improvements in the level of adherence to the

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follow ups and outcomes of care. We also intended to investigate the socioeconomic factors that can affect the improvement of the intermediate outcomes.

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2 LITERATURE REVIEW

2.1 Diabetes

Diabetes mellitus has become a major global health burden. An increasing prevalence trend has been observed around the world. Globally, the number of individuals living with diabetes mellitus was 422 million in 2014. Among them a dominant part experience T2D. In spite of the fact that T2D increases with age and is still more common among adults, it is now additionally influencing children (WHO 2016a).

2.1.1 Definition

“The term diabetes mellitus describes a metabolic disorder of multiple etiology characterized by chronic hyperglycemia with disturbances of carbohydrate, fat and protein metabolism resulting from defects in insulin secretion, insulin action or both”. Manifestations of diabetes mellitus may incorporate polyuria, polydipsia, weight reduction, polyphagia, and obscured vision (WHO 2016a).

2.1.2 Classification

Diabetes is mainly classified as type 1 (T1D), type 2 (T2D) and gestational diabetes (GDM). In addition, there are smaller groups of patients with more rare types of diabetes such as latent autoimmune diabetes in adults (LADA), maturity-onset diabetes of young (MODY), mitochondrial diabetes and neonatal diabetes (Current Care Guideline 2016). Diabetes can also occur as a secondary cause of some chronic diseases (American Diabetes Association 2014).

T1D is caused by β-cell destruction, which leads to absolute insulin deficiency. Among all diabetes patients, only 5–10 % suffer from this type of diabetes. It was previously named as insulin- dependent diabetes or juvenile onset diabetes because it mainly affected children and adolescents.

However, it can also occur in adults. This kind of diabetes patient is rarely obese and may not have any known etiology (American Diabetes Association 2014).

T2D, also called non–insulin dependent diabetes or adult-onset diabetes, is caused by insulin resistance or a defect in insulin secretion leading to a relative insulin deficiency. Almost 90–95%

of diabetes patients suffer from T2D. Most of the patients with T2D are obese or have an increased percentage of body fat around abdominal region and do not need to have insulin treatment initially for survival. Gestational diabetes is mainly the glucose intolerance that is first recognized during

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pregnancy. It might resolve with delivery or may persist and lead to T2D (American Diabetes Association 2014).

2.1.3 Risk factors of T2D

Almost all populations share same risk factors for the development of type 2 diabetes. These risk factors are broadly categorized as non-modifiable risk factors and modifiable risk factors. Age, gender and heredity are non-modifiable risk factors. Modifiable risk factors include obesity, diet, physical inactivity, smoking, alcohol consumption, psychological stress and depression (Zimmet 2011). In addition, low birth weight is associated with T2D (Vaag et al. 2012) and those persons who develop any impairment in glucose metabolism, such as gestational diabetes, impaired fasting glucose (IFG) or impaired glucose tolerance (IGT) are at a very high risk of developing T2D in future (Nathan et al. 2007).

Numerous studies have found that the risk of diabetes increases with age. The majority of people with diabetes in developed countries are more than 64 years old, whereas in the developing countries most of the patients are between 45 to 64 years of age. A study estimating the global prevalence of diabetes found that the prevalence of T2D is higher in men than in women (Wild et al. 2004). A Swedish study found that men tend to have T2D at a lower BMI and 3-4 years earlier than women. Unhealthy lifestyles and tendency of developing abdominal obesity, partly explains this gender difference (Wandell & Carlsson 2014).

Heredity is related to the development of T2D (Poulsen et al. 1999). It is established earlier that family history of T2D increases the risk of T2D. The increased risk with the positive family history is around 2.4-fold (Wada et al. 2006, Valdez et al. 2007). Association of genetic variation with diabetes has been well established and various susceptible genes that contribute to the development of diabetes have been identified. Gene-environment interaction is found to modulate the risk of T2D (Franks 2011). This is seen as environment induced chemical modifications of the genome, which is known as epigenetic modification. Gene expression is found to be regulated by the influence of epigenetic modifications, DNA methylation and histone modifications, so any alteration in these processes is believed to have influence on phenotype transmission and the development of T2D (Ling & Groop 2009). Epigenetic modifications are found to be associated with exposure to heavy metals, smoking, folate deficiency and methionine deficiency during

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embryogenesis and thus suspected to influence the development of T2D in later life (Dayeh et al.

2014).

Obesity is one of the most important modifiable factors related to the risk of T2D. Researchers have found that unhealthy lifestyles and obesity contribute most to the increase in the prevalence of T2D. Studies have found a parallel increase of obesity and the prevalence of T2D in the societies (Berghofer et al. 2008, Hu 2011, Hill et al. 2013). After the age of 18, weight gain raises the risk of T2D for both sexes approximately by 25% for an additional unit of BMI over 22 kg/m² (Colditz et al. 1990, Chan et al. 1994). A 13-year follow-up study conducted among 27270 US male health professionals examined the relationship of abdominal adiposity and general obesity with the risk of T2D. The study concluded that both overall obesity and central obesity are strongly associated with T2D (Wang et al. 2005). Another study conducted among female nurses to observe the association between diet, lifestyle, and the risk of T2D found that, overweight and obesity were the most important predictors of T2D (Hu et al. 2001).

According to world health organization (WHO), every 1 in 4 adults around the world are not physically active enough which may be a major risk factor for non-communicable diseases (NCDs) such as diabetes (WHO 2016b). A Chinese study investigated the relationship between physical activity, smoking and alcohol consumption with the incidence of T2D among 51464 middle-aged and elderly men. They found that physical activity and moderate alcohol intake are inversely related to the risk of T2D (Shi et al. 2013). It is also observed in many studies that physical inactivity increases the risk of T2D (Hu et al. 2004, Waller et al. 2010).

Western dietary pattern, including high fat and sugar intake are found to increase the risk of diabetes. A prospective cohort study with 12 years follow up was conducted among 42504 male health professionals, to assess the role of major dietary patterns with the risk of T2D. The study found that the western dietary pattern was connected with a considerably elevated risk for the development of T2D (Van Dam et al. 2002). A study conducted among 4304 Finnish men and women aged between 40 to 69 years to observe the association between dietary patterns and the incidence of T2D found that the food rich in butter, potatoes, red meat, and whole milk is associated with a higher risk of T2D (Montonen et al. 2005). Intake of saturated fat or trans fatty acids has been linked with the risk of T2D in several studies (Van Dam et al. 2002). A prospective cohort

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study conducted in USA among 69554 women aged between 38 to 63 years to examine the association between dietary patterns and risk of T2D found that higher intake of processed meats was associated with increased risk of T2D (Fung et al. 2004). Other important macronutrients that play vital role in the development of diabetes are carbohydrates. Quality of carbohydrates has been found to be associated with the risk of diabetes (AlEssa et al. 2015). A study in USA found that the diet with high glycemic load increases the risk of T2D (Hu 2011).

Cigarette smoking is found to be an independent risk factor for the development of T2D (Foy et al.

2005, Patja et al. 2005, Willi et al. 2007). A prospective study showed a strong association of smoking with the risk of T2D, even after controlling for age and other major risk factors (Patja et al. 2005). A systemic review and meta-analysis reported that, active smoking is associated with an increased risk of T2D (Willi et al. 2007). It is observed that the risk of developing T2D in smokers is 45% higher than in nonsmokers (Hu 2011). Not only active, but also passive smoking is found to increase the risk of T2D (Hayashino et al. 2008).

Psychosocial stress is also found to be associated with the risk of developing T2D (Eriksson et al.

2008, Mantyselka et al. 2011). A cohort study of US men and women found a modest association between depressive symptoms and incidence of T2D (Golden et al. 2008).

A substantial number of women have gestational diabetes during their pregnancy. Subsequently, gestational diabetes inclines women to the development of T2D (Kaaja et al. 1996, Baptiste et al.

2009). It is observed that women with GDM and their offspring, have an increased risk for the development of metabolic syndrome and T2D after delivery (Kaaja & Ronnemaa 2008). A study in Iran examined the risk factors and incidence of abnormal glucose level and metabolic syndrome (MetS) in women with a history of GDM. The study found that within 1 to 6 years after delivery 32.7% women developed T2D, 10% had impaired fasting glucose (IFG) or impaired glucose tolerance (IGT) and 20% had developed Metabolic syndrome (Valizadeh et al. 2015).

Intrauterine growth retardation and low birth weight (< 2.5 kg) are connected with the development of T2D in offspring (Wei et al. 2003, Whincup et al. 2008). A systematic review on the association of birth weight and risk of T2D reported that the birth weight is inversely associated with the risk of T2D (Whincup et al. 2008). Two genes among the 45 known T2D susceptible genes were found to be associated with low birthweight (Vaag et al. 2012). It is also observed that there is an

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association between preterm birth and risk of T2D (Kajantie et al. 2010). A systematic review and meta-analysis was conducted to assess the relationship between preterm birth and both types of diabetes (T1D and T2D). The study found that, preterm birth is a significant and independent risk factor for both T1D and T2D (Li et al. 2014).

2.1.4 Complications of T2D

Diabetes is a major concern in public health mainly because of the severe complications associated with this disease. Complications are common among diabetic population and they can triple the annual cost of diabetes management (Bate & Jerums 2003). T2D can bring life-threatening microvascular and macrovascular complications (Stone et al. 2013). Microvascular complications include diabetic retinopathy, neuropathy and nephropathy. On the other hand, macrovascular complications include cerebrovascular diseases (stroke), cardiovascular diseases and peripheral vascular diseases (Bate & Jerums 2003, Fowler 2008). Macrovascular complications are found to be the major cause of morbidity and mortality in T2D. Microvascular complications, which may be recognized during the diagnosis of T2D or even among people with no symptoms, may decrease the quality of life of a diabetic patient and can lead to severe complications and even to death (Bate

& Jerums 2003).

Diabetic retinopathy is the most common microvascular complication of diabetes. In the United States, every year almost 10000 new cases of blindness are diagnosed among diabetic patients. The risk of diabetic retinopathy depends on both the duration and the severity of hyperglycemia. It is found that retinopathy may start to develop 7 years before the diagnosis of T2D (Fowler 2008).

There are several pathological mechanisms by which diabetes may lead to the development of retinopathy. It is observed that with poor glucose control long-term damage occurs in the small blood vessels in the retina of a diabetes patient causing retinopathy and subsequent loss of vision.

Several mechanisms are found by which hyperglycemia causes retinal capillary damage, for instance increased polyol pathway, activation of protein kinase C, increased non-enzymatic glycation and generation of reactive oxygen species (Cai & Boulton 2002). Almost 2.6% of global blindness are caused because of diabetes (WHO 2016a).

Almost all individuals with T2D experience renal dysfunction during his or her lifetime. Almost 7% of patients with T2D may already have microalbuminuria at the time of their diagnosis.

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Diabetic nephropathy is found to be the leading cause of renal failure in the United States. If not treated, microalbuminuria usually progresses to proteinuria and then turn into diabetic nephropathy (Fowler 2008). UK Prospective Diabetes Study (UKPDS) observed systematic progression of nephropathy from diagnosis of T2D among 5097 subjects in the UK. The study found that within 10 years after diagnosis of T2D, almost 24.9 % of patients developed microalbuminuria, 5.3 % developed macro-albuminuria and 0.8 % developed end-stage renal disease (ESRD) which was assessed by elevated plasma creatinine (>250 μmol/l) or the need for renal replacement therapy.

The rate of deterioration of nephropathy from one stage to the next stage was about 2 to 3% per year. The mortality rate was found to increase with the advancing stages of nephropathy. Probably because of this progressive manner, both in diabetes and nephropathy, T2D has become the leading cause of ESRD in the modern world (Adler et al. 2003). It is found that T2D, hypertension or both together are responsible for around 80% cases of ESRD (WHO 2016a).

Diabetic neuropathy is another important microvascular complication of T2D patients. The risk of developing diabetic neuropathy is also dependent on the magnitude and duration of hyperglycemia.

However, genetic variation may also play a role in the development of such a complication.

Diabetic neuropathy can manifest in different forms, such as sensory, focal/multifocal, and autonomic neuropathies. Chronic sensorimotor distal symmetric polyneuropathy is found to be the common form of neuropathy in diabetes. In such condition, patient may experience burning, tingling, and electrical sensation or pain or even simple numbness. Patients who lose 10-g monofilament sensation has an increased risk for developing foot ulceration. Pure sensory neuropathy is found to be relatively rare, but it is related with poor glycemic control. Onset of mononeuropathies may be sudden and can affect any nerve, but median, ulnar, and radial nerves are affected most (Fowler 2008). Neuropathy is found to be an independent risk factor for the development of ulcer in a diabetic patient. The combination of diminished blood flow and neuropathy in feet, increase the possibility of infection and foot ulcers which in turn may lead to limb amputation (Adler et al. 1999). Many studies have shown that the rate of leg or thigh amputation is five times higher in persons with diabetes compared with non-diabetic persons (Melliere et al. 1999).

Coronary heart disease is the most common macrovascular complication of T2D. Coronary heart disease has been associated with diabetes in several studies (Kannel & Mc Gee 1979). The main

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reason of macrovascular disease is stiffness of the arterial wall and narrowing of the arterial lumen which is mainly caused by atherosclerosis. Atherosclerosis occurs due to chronic inflammation and injury to the arterial lumen for example in the peripheral or the coronary vascular system. This endothelial injury and inflammation causes oxidation of lipids from LDL particles, which in turn accumulate in the endothelial wall of arteries. After that monocytes infiltrate into the arterial wall and form macrophages, which then form foam cells from oxidized lipids. Then the foam cells attract and stimulate T-lymphocytes. T-lymphocytes then increase the proliferation of smooth muscle and collagen in the arterial walls. Finally, the whole process leads to the formation of a lipid-rich atherosclerotic lesion known as atheroma. When this atheroma ruptures, it causes acute vascular infarction. There is also evidence of platelet adhesion and hypercoagulability in T2D.

Increased coagulability and impaired fibrinolysis may increase the risk of vascular occlusion and cardiovascular disease in T2D (Fowler 2008). People with diabetes have a higher risk of developing cardiovascular disease. There is two to three times higher risk of developing cardiovascular disease in an adult with diabetes compared with people without the disease. With the rise of fasting plasma glucose level the risk of developing CVD rise parallelly (WHO 2016a).

2.2 Prevention, early diagnosis and treatment of T2D 2.2.1 Prevention of T2D

The long time distance from high risk to the onset of clinically diagnosed T2D offers an opportunity to prevent the disease. The Finnish Diabetes Prevention Study (DPS) showed the first time that T2D can be prevented by lifestyle changes, which included increased physical activity, dietary changes and weight reduction (Tuomilehto et al. 1997). Several studies have identified that healthier lifestyle could prevent T2D in people at risk (Yamaoka & Tango 2005, WHO 2016c).

Some randomized controlled clinical trials observed that both lifestyle modification and pharmacological treatment may reduce the incidence of T2D among the people with IGT (Gillies et al. 2007). Because obesity is one of the most important risk factors for the development of diabetes, most of the studies included individuals with obesity and IGT as their study group and the majority of them suggested lifestyle intervention based on diet and exercise for the prevention of T2D. These studies proved that T2D can be prevented or at least the onset can be delayed by lifestyle modification (Saaristo et al. 2010) and pharmacological treatment (Gillies et al. 2007).

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Lifestyle intervention seems to have a larger effect compared with pharmacological treatment (Knowler et al. 2002). It is also found that the impact of genetic and familial risk factors of diabetes could be overcome by lifestyle changes (Uusitupa et al. 2011). Weight reduction should be the primary target intervention for the reduction of diabetes risk (Hamman et al. 2006) as obesity is the strongest predictor of diabetes (Wang et al. 2005, Narayan et al. 2007). For severely obese individuals, along with lifestyle and pharmacological interventions, surgical intervention has been found to be effective. It is observed that bariatric surgery is highly effective for extremely obese individuals (Merlotti et al. 2014).

A systematic review examined the association between physical activity of moderate intensity and the risk of T2D. The study concluded that physical activities of moderate intensity, such as brisk walking, can reduce the risk of T2D (Jeon et al. 2007). Evidence suggests that moderate to intensive level exercise is effective for the reduction of abdominal obesity, which in turn will reduce the risk of T2D and CVD (Shi et al. 2013, Palermo et al. 2014). Regular, at least 30 minutes moderate intensity activity is recommended for the prevention of diabetes (WHO 2016c). A prospective cohort study was conducted among 4554 women with the history of GDM from the Nurses' Health Study II. The intention of the study was to examine the role of physical activity and other sedentary behaviors in the progression from GDM to T2D and the study found that physical activity reduced the risk of T2D, even in highly susceptible individuals (Bao et al. 2014).

Several studies have investigated the effectiveness of the drug therapy, including oral hypoglycemic agents, anti-obesity agents, antihypertensive agents, statins, fibrates, and estrogen replacement agents on either delay or prevention of T2D. It is observed that only oral hypoglycemic and anti-obesity agents have a significant effect on T2D (Lauritzen et al. 2007). Orchard et al.

(2005) found that lifestyle intervention and metformin therapy have a potential to reduce the onset of metabolic syndrome and T2D.

2.2.2 Diagnosis of T2D

In Finland, the Current Care Guidelines give instructions for prevention and screening of diabetes as well as for appropriate treatment. The Finnish Diabetes Risk Test (FINDRISK) is used to distinguish the high-risk individuals from the target population. All high-risk individuals should be screened for T2D to make the diagnosis and to start the treatment as early as possible (Finnish

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Diabetes Association 2003). It is observed that the difference between diagnosis and original onset of diabetes is on average 7 years (Saudek et al. 2008). According to the Finnish Diabetes Association, in 2016 there were almost 150000 people undiagnosed in Finland.

The most utilized method to diagnose the diabetes is the estimation of fasting glucose level and glucose level two hours after a glucose load, but WHO has also recommended glycated hemoglobin (HbA1c) measurement (HbA1c > 7%) for the confirmation of diagnosis of diabetes (WHO 2011).

According to the American Diabetes Association (2016) there are 4 options for the diagnosis of diabetes (Table 1) : 1) Fasting plasma glucose (FPG) ≥ 126 mg/dl (7.0 mmol/l), where fasting means no caloric intake for at least 8 hours, 2) Measurement of 2-h postprandial glucose (PG) ≥ 200 mg/dl (11.1mmol/l) during an OGTT, 3) Measurement of HbA1c ≥ 6.5% (48 mmol/l) and 4) In a patient with classic symptoms of hyperglycemia or hyperglycemic crisis, a random plasma glucose ≥ 200 mg/dl (11.1 mmol/l) (American Diabetes Association 2016). The diagnostic criteria of T2D in Finland are in accordance with the criteria of the American Diabetes Association (ADA) 2016 guidelines (Current Care Guideline 2016).

Table 1: Criteria for Diabetes Diagnosis (American Diabetes Association 2016).

Parameters Unit

Fasting plasma glucose (FPG) ≥ 126 mg/dl or 7.0 mmol/l

2-h Postprandial glucose (PG) ≥ 200 mg/dl or 11.1mmol/l

HbA1c ≥ 6.5%

Random plasma glucose ≥ 200 mg/dl or 11.1 mmol/l

2.2.3 Treatment of T2D

To prevent the complications of T2D and to ensure good quality of life, it is important to implement a suitable treatment and systematically monitor patient’s condition. A prospective observational study was conducted among 23 hospital based clinics in England, Scotland, and Northern Ireland to examine the association between glycaemia over time and the risk of macrovascular or microvascular complications in patients with T2D. The study found that there is a strong association between incidence of clinical complications and glycaemia. According to the study, each 1% reduction in updated mean HbA1c was associated with 37% reductions in risk for microvascular complications (95% CI 33% to 41%, P < 0.0001) and 14% for myocardial infarction

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(95% CI 8% to 21%, P < 0.0001) (Stratton et al. 2000). Another study examined the relationship between diabetic complications and age, sex, duration, mode of therapy, body weight, control of blood glucose, blood pressure, and serum triglycerides and cholesterol among T2D patient in Japan.

The study found that control of blood glucose, mode of therapy, and duration were correlated with microvascular complications and macrovascular complications were strongly related to aging and blood pressure. According to the study along with good glycemic control, sufficient antihypertensive therapy is also necessary for treating T2D patients (Meeuwisse et al. 2008).

According to the Finnish Current Care Guidelines (2016), lifestyle changes are the mainstream treatment of T2D (Figure 1). Starting with lifestyle change and medication such as metformin is suggested for early-diagnosed diabetes patient. The treatment is tailored according to the individual patient’s life situation, such as early DM, chronic DM > 10 years, obese patient, elderly patient, professional drivers or patient with kidney impairment. Insulin therapy is also appropriate as a temporary first line therapy if hyperglycemia is found to be causing severe symptoms of T2D. The insulin dosage can be adjusted based on plasma glucose concentration in the morning or evening, the goal is to get the concentration of fasting plasma glucose value at the right level using either intermediate acting insulin such as, neutral protamine hagedorn (NPH) or long acting insulin 1-2 times a day. When treating elderly diabetic patient, the possibility of drug interaction and hypoglycemia needs to be considered (Current Care Guideline 2016).

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Figure 1. An example of treatment plan for early diabetes mellitus by Finnish Current Care Guidelines 2016 (Current Care Guideline 2016).

According to the Current Care Guidelines, the treatment goals in Finland for T2D are HbA1c below 7%, fasting glucose concentration less than 7 mmol/l, 2-hour post-prandial glucose level below 10 mmol/l and LDL cholesterol less than 2.5 mmol/l (Table 2) (Current Care Guideline 2016). The glycemic control targets by the Finnish Current Care Guidelines are in accordance with the recommendations of the American Diabetes Association (ADA) 2016 guidelines.

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Table 2. Blood Glucose & LDL Cholesterol Targets for Adults with Diabetes (American Diabetes Association 2016, Current Care Guideline 2016).

Parameters Target

HbA1c ˂ 7%

Fasting plasma glucose (FPG) ˂ 7 mmol/l

2-h Postprandial glucose (PG) ˂ 10 mmol/l

LDL cholesterol ˂ 2.5 mmol/l

However, both according to the guidelines of ADA and the Finnish Current Care Guidelines, targets can be tailored based on age, life expectancy, comorbid conditions, diabetes duration and hypoglycemic status. In case of patient aged more the 75 years, the main goal of treatment is to improve the quality of life, to promote self-reliance and to get free of symptoms. Although targets can be flexible, it needs to be remembered that keeping HbA1c level below or around 7% reduces the risk of microvascular complications (American Diabetes Association 2016, Current Care Guideline 2016).

2.3 Epidemiology of T2D

When describing the prevalence and trends of diabetes it is not always possible to distinguish between T1D and T2D. In many countries, the registration or surveys do not provide information separately for different types of diabetes. Because of this, in this descriptive epidemiological summary the general term “diabetes” is used in many places.

Diabetes has become one of the main health burdens of the 21st century. About 415 million people aged 20 to 79 years are living with diabetes around the world and 12% of total global health expenditure is found to be spent on it. It has been estimated that one adult out of ten will have diabetes by 2040 (International Diabetes Federation 2015). In 1995, it was estimated that the total number of people with diabetes will reach over 300 million by 2025 and the major change in the number of people with diabetes will be seen in developing countries. Several studies have proven the trustworthiness of these predictions (King et al. 1998). The rise in the prevalence of diabetes observed in low-income and middle-income countries is larger than in high-income countries. It may be due to population growth, aging, change in diet and genetic susceptibility. In Europe, a

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reasonably steady trend is observed. Trends in Europe might be explained by better-resourced and utilized health care systems (NCD Risk Factor Collaboration 2016).

2.3.1 Prevalence and trends of T2D

Studies have found that the prevalence of T2D is greatly influenced by socio-demographic factors (Veghari et al. 2010). Age, gender, living area and socio-economic status (SES) are found to have remarkable impact over T2D.

2.3.1.1 Prevalence and trends by age and gender

Age has a great influence on T2D. Chance of the development of T2D increases with age. The most critical demographic change in diabetes globally appears to be the expansion in the proportion of individuals more than 65 years of age (Wild et al. 2004). In spite of the fact that T2D increases with age and is still more common among adults, it is now additionally influencing children (WHO 2016a). Increasing obesity is found to be the reason for this trend (American Diabetes Association 2000). From 1980 to 2014 the prevalence of diabetes has ascended from 4.7% to 8.5% in adults (WHO 2016a).

The gender distribution of T2D has also changed over the time. Increase in T2D has shifted towards men from women. There are around 15.6 million more men living with diabetes in contrast with women (International Diabetes Federation 2015). It is found that men are diagnosed with T2D 3-4 year earlier, even with lower BMI, compared with women. Unhealthy life-styles and the tendency of developing abdominal obesity are said to be the reason behind this phenomenon (Wandell &

Carlsson 2014). Although men are more susceptible to diabetes, there are also an alarming number of women living with diabetes (Wild et al. 2004).

Patients with T2D have also gender difference in mortality. However, this difference varies among regions. In North America and Caribbean and Western Pacific Regions, the mortality rate due to diabetes is higher in men compared with women. On the other hand, scenario is opposite in Africa, Europe, Middle East and North Africa, South-East Asia, and South and Central America Regions (International Diabetes Federation 2015).

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2.3.1.2 Prevalence and trends by area

Difference in the prevalence of diabetes has been observed between countries, within regions and between urban and rural areas depending on the urbanization and mechanization level, which affect the lifestyle of the population (Cockram 2000). Differences in diabetes prevalence are observed within the countries also in Europe. For instance, in Germany, variation in the prevalence of T2D is observed from southwest to northeast, the regional standardized prevalence was highest in the east being 12.0% (10.3–13.7%) and lowest in the south being 5.8% (4.9–6.7%). This difference might be explained by the differences in the distribution of risk factors of T2D among the regions (Schipf et al. 2012).

Regional differences in the prevalence of T2D are also found in Finland. A study examined the prevalence of T2D, IGT and IFG among Finnish adults aged between 45 to 64 years within eastern, southwestern and southern Finland (Helsinki-Vantaa region). The study found that the prevalence of any form of abnormal glucose regulation was lowest in the eastern Finland and highest in the Helsinki-Vantaa region (31% vs. 38% in men, respectively, P = 0.004 and 19% vs. 26% in women, P < 0.001). This difference might be due to the differences in lifestyle within an ethnically homogenous population (Yliharsila et al. 2005). A study in England observed regional variation in the prevalence of T2D and found that, at strategic health authority (StHA) level, prevalence of T2D varied from 2.4% in Thames Valley to 4 % in North East London (Congdon 2006). Living in deprived area, individual socioeconomic status and ethnicity may explain the regional difference partially (Maier et al. 2013).

2.3.1.3 Prevalence and trends by Socio-economic status

The prevalence of T2D is shown higher among lower socioeconomic groups especially in high income countries (Agardh et al. 2004, Espelt et al. 2008). Sedentary lifestyle and obesity are suggested to be the reasons for this (Zimmet et al. 2001). A systematic review and meta-analysis was conducted to observe the worldwide associations between T2D incidence and socio-economic position (SEP) which was expressed as educational level, occupation and income. The study found that the overall risk of T2D was associated with low SEP in high, middle and low-income countries (Agardh et al. 2011). A study in Germany analyzed the relation between prevalence of T2D and area deprivation at municipality level. The study found that area deprivation is significantly associated with the prevalence of T2D at municipality level (Grundmann 2014). Access to health-

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care services and information, availability of healthy foods and availability of places for exercise, economic opportunities, occupational opportunities and individual lifestyle may be the underlying mechanism by which SEP influence in the development of T2D (Brown et al. 2004).

2.4 Impact of T2D on society 2.4.1 T2D and quality of life

Diabetes is greatly affecting people’s life, in spite of being a preventable disease. It is observed that the quality of life (QOL) scores of diabetic patients are significantly lower compared with the general population. Patients with T2D have significant impairment in all aspects of QOL. For instance, it influences physical, psychological and social aspects of QOL and put an extra burden on the affected individual. Glycemic control is essential for preventing long-term complications and improvement in QOL of diabetic patient (Porojan et al. 2012). A study assessed the effect of comorbidities, depression, treatment intensity, and demographic factors on QOL among patients with T2D, and found that the low socioeconomic status, cardiovascular disease, peripheral vascular disease and microvascular complications are associated with decreased QOL (Wexler et al. 2006).

Another study examining the health related quality of life (HRQOL) among 15926 individuals with T2D in Catalonia found that vascular disease, or risk factors for vascular disease are connected with the reduction of QOL in T2D patients (Oliva et al. 2012). Also, area based HRQOL among diabetic population has been investigated, and it was observed that the diabetic patients living in rural area reported the worst HRQOL scores (Thommasen et al. 2005). For a chronic illness like diabetes, which is not curable, it is fundamental to introduce a better management for the improvement of people’s QOL.

2.4.2 Cost of T2D

The economic burden of T2D has ascended in recent years and impacts of it on the labor market are demonstrating an upward trend (Seuring et al. 2015). As per the estimation of the international diabetes federation (IDF) complete worldwide healthcare expenditure on diabetes has tripled from 2003 to 2013 (International Diabetes Federation 2015). Some studies have assessed that worldwide, direct yearly cost of diabetes is more than US$ 827 billion (WHO 2016c). A study in Helsinki, estimated the health services and their costs among diabetes patients in Finland. The study estimated that the cost of diabetes care constitutes over 12 per cent of Finland’s health care expenditure. However, the costs of the treatment of complications arising from diabetes cause

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almost 90 percent of the total costs caused by diabetes. It is evaluated that complications cause twenty-fold increment in the expenses of care of the people with T2D (Kangas 2002).

2.5 Quality of care of T2D

Reduction in the quality of life of diabetic individuals, life threatening complications and extra economic burden for treating these complications address the importance of the quality of care of T2D. Measurement of the quality of care is a complex process. There is a quality evaluation hypothesis given by Donabedian called Donabedian's Triad Model, which includes three quality appraisal components such as structure, process and outcome. Structure portrays the facilities, financing, equipment and personnel, the process is related to giving and receiving the care or the implementation of interventions, and outcome depicts the impact of care or interventions (Donabedian 1966).

As indicated by Mainz (2003) study, outcome indicators can be separated into intermediate and end-result indicators. Intermediate outcome indicators portray the changes in biological status that are connected with the end-result outcomes, for instance changes in HbA1c or micro-albuminuria, and the end-result indicator such as survival. Outcome indicators appear to give the best perspective of quality performance and provide a quantitative base for organizations, planners and service providers to improve processes and care (Mainz 2003).

In order to evaluate the quality of diabetes care, several sets of measures have been developed. The Diabetes Quality Improvement Project (DQIP) that was initiated in 2001 included measures like rates of annual testing for HbA1c, screening for foot problems and retinal disease, and levels of HbA1c and cholesterol control. Another quality improvement program conducted in US along with national diabetes quality improvement alliance (NDQIA) have identified nine indicators among which four measurements were for care process and five for the outcomes of care. These indicators have been widely accepted internationally (Nicolucci et al. 2006). The study proposed three main groups of indicators such as one group of indicators for measuring the process of diabetes care and two groups of indicators for measuring the outcomes of diabetes care (Table 3). Process indicators consisted of, annual testing of HbA1c and low-density lipoprotein (LDL) followed by an annual screening for nephropathy and eye examination. Proximal outcome indicators comprised of HbA1c control and LDL cholesterol control. Rate of lower extremity amputation, kidney disease in persons

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with diabetes and mortality due to cardiovascular disease in patients with diabetes were included in distal outcome indicators (Nicolucci et al. 2006).

Table 3: Indicators for measuring the quality of T2D care (Nicolucci et al. 2006).

Area Indicator name

Processes of diabetes care Annual HbA1c testing

Annual LDL cholesterol testing Annual screening for nephropathy Annual eye examination

Proximal outcomes HbA1c control

LDL cholesterol control

Distal outcomes Lower-extremity amputation rates Kidney disease in persons with diabetes

Cardiovascular mortality in patients with diabetes A study in Kuwait approached to measure T2D care performance through the use of a diabetes quality indicator set (DQIS) in four major primary health care centers in Kuwait City including 3211 patients in 2010 and 4241 patients in 2012. They included five indicators developed by the NDQIA and IDF into their quality indicator set. The measures were blood glucose level measurement, cholesterol level measurement, blood pressure measurement, kidney function testing and smoking status check. According to their findings, primary health care centers in Kuwait have achieved noteworthy improvement in diabetes care between 2010 and 2012 and DQIS has the capacity to help policymakers to recognize performance gaps and to research key obstacles in diabetes care in Kuwait (Badawi et al. 2015).

Another study observing cardiovascular occasions in light of the score counted from the quality of T2D care among 5181 Italian diabetic patients found that there is a significant association of future cardiovascular outcomes and quality of diabetes care. The incidence of cardiovascular events was higher among people with a lower diabetes care score. Therefore, the authors concluded that the scores produced by the indicators could be useful for monitoring quality of care and comparing performance between different centers (De Berardis et al. 2008, Rossi et al. 2011).

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2.6 Variation in the quality of T2D care

It is observed in the past, that there is variation in quality of T2D care based on age, gender and socioeconomic status. A study in Finland examined the difference in achievement of control and treatment targets in T2D patients by different areas and the study found that area-level inequalities exist in the care of T2D by a detailed 7-class area classification. The study also analyzed the influence of demographic factors in achievements of control or treatment targets of T2D and found that gender, age, area-level education and the area class in which patient belongs to are associated with achievements in control and treatment targets (Toivakka et al. 2015).

2.6.1 Variation in the quality of T2D care by age

Quality of care is found to be associated with age. A study in Hong Kong examining the quality of care of patients with T2D in primary care setting found that older people are more likely to achieve the HbA1c target but less likely to have been followed up regularly (Wong et al. 2012). Another study investigating the quality of the process of diabetes care provided to the patients under universal health insurance coverage found that annual testing for HbA1c was less frequent among young diabetic individuals compared with elders (Tanaka et al. 2016). Also, a study among urban African Americans, after follow-up of 5 to 12 months from baseline, found that young adults had higher prevalence of poor glycemic control (El-Kebbi et al. 2003).

2.6.2 Variation in the quality of T2D care by gender

Many studies have observed the association between gender and the quality of T2D care along with age. Significant variation was found in the quality of care based on gender. A study investigating the literature about gender specific care differences, found that women were at higher risk than men in developing diabetes related complications. The study showed that women are at 4-6-fold higher risk for developing CVD and less likely to receive treatment for it compared with men. It was also observed that diabetic women have a higher risk for hypertension and dyslipidemia compared with men. Therefore, the author concluded that gender specific care should be highly considered during the planning of policies for diabetes care (Legato et al. 2006). Another study investigated the potential disparities in the quality of T2D care in Hong Kong by patient characteristics and clinics. The study found that women were 17% less likely to achieve the HbA1c target compared with men (Wong et al. 2012). A study investigating the racial and gender influence on diabetes care using health care effectiveness data and information set (HEDIS), medicare

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enrollment files and U.S. Census found that females have received more HbA1c screenings and eye examination compared with males and cholesterol screening was almost similar in males (91.4%) and females (91.9%) (Chou et al. 2007). A study conducted in Italy to investigate the quality of T2D care according to gender found that the proportion of patients achieving the HbA1c goal was higher among men in almost 80% of the diabetic centers and women were 14% more likely to have HbA1c > 9.0% in spite of insulin treatment, 42% more likely to have LDL cholesterol (LDL-C) ≥ 130 mg/dl in spite of lipid-lowering treatment and 50% more likely to have BMI ≥ 30 kg/m2 compared with men. Monitoring for foot and eye complications was also lower in women than men (Rossi et al. 2013). Franzini et al. (2013) also found that control of HbA1c and LDL is less satisfactory in women than men. A study in Finland found that found that gender, age, area- level education and the area class of the patient are associated with achievements in control and treatment targets (Sikio et al. 2014).

2.6.3 Variation in the quality of T2D care by area

Differences exist in the care of T2D according to the area. People living in rural area and having poor access to health care services were found to have deferred T2D care (Andrus 2004). A study in Finland aimed to investigate the area level differences in the achievement of the control and treatment targets of T2D patients using urban and rural classification and other area level classifications. The study found that area-level inequalities exist in the care of T2D by a detailed grid-based area classification instead of only by urban and rural area (Sikio et al. 2014).

2.6.4 Variation in the quality of T2D care by Socio-economic status

Individual socio-economic status (SES) and residential area deprivation have influence on T2D care and achievement of the treatment targets in T2D patient (Grintsova et al. 2014). A study in Germany examining the association between social status and quality of T2D care found that patients with lower social status have a higher HbA1c level and lower social status is associated with worse quality of diabetes care (Baz et al. 2012). Another registry based study in California assessing the disparities in the outcomes of T2D found that there is an association between neighborhood SES and HbA1c control, lower income neighborhoods having higher HbA1c level but no association was found with LDL control (Geraghty et al. 2010). Area-level socio-economic status is also found to have influence on diabetes care, a study analyzing data from regional electronic patient database during the years of 2011-12 to observe the influence of area-level socio-

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economic factors on the prevalence and outcomes of T2D in North Karelia, Finland, found that the low SES in the postal code area was related to poor HbA1c measurement rate and control (Sikio et al. 2014). A systematic literature review was conducted to investigate the inequalities in health care among patients with T2D by individual socio-economic status (SES) and regional deprivation found that patients living in deprived areas achieve the glycemic control targets less often and also worse lipid profile control was more prevalent (Grintsova et al. 2014). Another study in England, found that worse diabetes control and lower quality of care is associated with deprived regions (Bottle 2008).

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3 AIMS OF THE STUDY

To assess whether the follow-up and achievement of treatment targets of T2D patients have been improved from the period 2011-12 to 2013-14 in North Karelia.

 Assessment of the quality of T2D care in 2011–12 and 2013-14 in North Karelia, Finland using HbA1c and LDL as treatment outcome indicator.

 Assessment of adequate HbA1c and LDL follow-up

 Assessment of socioeconomic variables that can anticipate the change in follow-up rate or control of HbA1c and LDL.

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4 METHODOLOGY

4.1 Study design

This study is a register based retrospective cohort study.

4.2 Study population

North Karelia is a region in Eastern Finland, which is bordered by Kainuu, Northern Savonia, Southern Savonia and South Karelia, as well as Russia (Regional Council of North Karelia 2016).

There are 13 municipalities in the North Karelia region, among which 5 are towns (Joensuu, Kitee, Lieksa, Nurmes and Outokumpu). A total of 164755 people (81914 male and 82841 female) live within the area of 21585 km²in the North Karelia region (Statistics Finland 2016). All the municipalities in North Karelia have established a common electronic patient database system in order to keep the patient records centrally. Establishment of the database started in 2009 and was completed by 2011. All the municipalities of North Karelia started to use the common regional database (the Mediatri) by the end of year 2011. The North Karelia IT-center maintains the database. The information on T2D (based on ICD-10 code E11) patients has been received from this database. Place of residency of people with postal code, date of birth, date of diagnosis, gender, laboratory data (different tests and dates of the tests) and all confirmed diagnoses (based on ICD- 10 code) data are recorded in Mediatri. From the database, we collected data on above mentioned parameters for the years 2011-12 and 2013-14. The socioeconomic information based on postal code areas was collected from the Statistics Finland database (Statistics Finland 2016). To ensure the privacy of the patients, their personal identification number was not given to us.

At the end of year 2012, altogether 10204 patients had T2D (based on ICD-10 code E11). During the follow up, 909 patients died and 9295 patients were available for the follow up. In this study, we included patients who were aged 20 years or more and were alive at the end of 2014. After applying the inclusion criteria, final number of the patients available was 9288, of whom 47.0 % (n = 4366) were female and 53.0 % (n = 4922) were male (Figure 2).

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Figure 2. Flowchart showing the subject selection 4.3 Study variables

4.3.1 Baseline variables

In our study, we investigated age, gender, and socioeconomic status (SES) as demographic variables. Age was continuous variable and gender was dichotomous variable. For descriptive statistics, age was categorized into six groups beginning from age 20 until age 99 years. We aimed to classify age into eight categories according to 10-year age groups, but the youngest and oldest

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groups had very few patients to break-down so they were consolidated. The final age group categories were 20-39, 40-49, 50-59, 60-69, 70-79 and 80-99.

The SES data were available from the Statistic Finland database on the post code area level. Since the data on every patient's place of residence was available, patient’s health and location data were combined with the SES information by the postal codes. We used three variables from the database to depict the socio-economic attributes of the postal code areas. These are 1) proportion of educated citizens in the postal code area (at least high school graduate or vocational training), 2) median income of the citizens of the postal code area and 3) percentage of unemployment in the postal code area. However, these three variables were again transformed into categorical variables.

Proportion educated citizens in the postal code area was classified as < 60%, 60-69.9% and ≥ 70%.

Median income of the citizens on the postal code area was categorized as ≤ 15000 €, 15001-16000

€, 16001-17000 €, 17001-19000 € and ≥ 19001 € and finally percentage of unemployment in the postal code area was categorized as < 6.0 %, 6.0-6.9 %, 7.0-7.9 % and ≥ 8.0 %.

4.3.2 Outcome variables

There were four outcome variables in our study. Process of diabetes care was assessed by % of patients whose HbA1c and LDL measurements were performed during the time periods 2011-2012 and 2013-2014 and the outcomes of care were assessed by % of patient with HbA1c and LDL on target level. According to the Current Care Guidelines (2016), HbA1c is recommended to be measured regularly. We analyzed if HbA1c and LDL was measured during 2011-12 and in 2013- 14. In 2011-2012 those patients whose HbA1c was measured at least 3 months and LDL at least one month after the diagnosis of T2D were included into the treatment outcome analysis to ensure the adequate time for treatment impact. In 2013-2014, the most recent measurement of HbA1c and LDL was taken into account to observe the subsequent follow up status based on the recommendations. Levels of HbA1c and LDL were categorized as HbA1c < 7, 7-8.9 and ≥ 9% and LDL as < 2.5 mmol/l and ≥ 2.5 mmol/l. Accomplishments in glycemic and lipid control was clarified by two dichotomous variables HbA1c < 7% and LDL < 2.5 mmol/l.

4.4 Statistical analyses

For statistical analysis, we used IBM SPSS Statistics for Windows, version 23 (IBM Corp.

Armonk, NY, USA 2013). Descriptive analysis was used to obtain the basic characteristics of the

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patients. Next, measurement and management of HbA1c and LDL was cross tabulated with background variables to observe the proportion of achievement in HbA1c and LDL measurements and management over the years 2011-12 and 2013-14. Chi square test was performed to observe the differences in HbA1c or LDL measurement rate and management by different groups of background variables.

One sample t-test was performed among females and males respectively to observe the difference in measurement rate and management of HbA1c and LDL between the years 2011-12 and 2013- 14. We calculated mean difference in measurement rate and management of HbA1c and LDL between the years 2011-12 and 2013-14 and compared the observed difference to zero. Statistically significant results indicated that the difference deviates from zero. After that, similar test was done to find the difference in mean HbA1c and LDL level between the year 2011-12 and 2013-14 among females and males independently.

Afterwards, an age standardized univariate ANOVA was conducted to investigate the gender difference in measurement rate and management of HbA1c and LDL between the years 2011-12 and 2013-14. Similar test was performed to observe the gender difference in mean HbA1c and LDL level between the years 2011-12 and 2013-14. A one-way analysis of variance (ANOVA) was conducted to observe the significant differences in mean HbA1c and LDL levels within different groups of background variables.

Finally, multivariate logistic regression analysis was performed independently to assess the effect of background variables in the improvement of HbA1c and LDL follow-up and management.

Similar analysis was done to measure the effect of background variables in frequency of measurement rate of HbA1c and LDL. Beta (B) coefficients with 95% confidence intervals (CI) or odds ratios (OR) with 95% CI was used to explain the results of regression analyses. The level of statistical significance was set to P < 0.05 for all statistical analysis.

4.5 Ethical considerations

The ethics approval for the use of the data in this study was received from the ethics committee of the Northern Savonia Hospital District on 13th November 2012. To preserve the patient safety, individual recognizable data were not uncovered by any means to us. Information was provided only for those variables which were needed for this study rather than access to the entire dataset.

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5 RESULTS

Table 4 represents the general characteristics of the study population. There are total 9288 people aged more than 20, included in our study. Among them, 47% were female and 53% were male.

Age of the population ranged from 21 to 99 years and most of the people were in between the age of 60 to 69 years (33.7 %, n = 3132). The mean age of the population was 67 (F = 69, M = 65) years.

Table 4: General characteristics of the population by gender and age group.

Age group Frequency (%) Total (%)

F M N

20 to 39 76 (1.7) 76 (1.5) 152 (3.2)

40 to 49 201 (4.6) 317 (6.4) 518 (11)

50 to 59 660 (15.1) 975 (19.8) 1635 (34.9)

60 to 69 1211 (27.7) 1921 (39.0) 3132 (33.7)

70 to 79 1229 (28.1) 1165 (23.7) 2394 (25.7)

80 to 99 989 (22.7) 468 (9.5) 1457 (15.2)

Total population 4366 (47) 4922 (53) 9288 (100)

Mean age 69 65 67

Minimum age 21 21 21

Maximum age 99 98 99

HbA1c measurement rate and management of HbA1c by gender between different age groups is presented in Table 5. A Chi-square test showed that there are statistically significant differences in HbA1c measurement rate between the age groups and the differences were statistically significant both for females (P < 0.001 in 2011-12 and P < 0.001 in 2013-14) and males (P < 0.001 in 2011- 12 and P < 0.001 in 2013-14). The best measurement rate of HbA1c was seen among the age group of 70-79 years, both in 2011-12 and 2013-14.

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Table 5: Proportion of HbA1c measurement and management (2011-12 & 2013-14) by gender and age group.

Age group

HbA1c measured (2011-12)^*

(%)

HbA1c measured (2013-14)^*

(%)

HbA1c measured both years ^*

(%)

HbA1c management

(2011-12)⁺ (%)

HbA1c management

(2013-14)⁺ (%)

Female Male Female Male

F M F M F M ˂ 7 7- 8.9 ≥ 9 ˂ 7 7- 8.9 ≥ 9 < 7 7- 8.9 ≥ 9 < 7 7- 8.9 ≥ 9 20 - 39 59.2 67.1 82.9 76.3 55.3 61.8 76.2 14.3 9.5 63.8 21.3 14.9 76.2 19.0 4.8 63.8 25.5 10.6 40 - 49 73.6 67.8 83.1 83.0 65.2 62.5 76.3 16.8 6.9 61.6 24.7 13.6 66.4 25.2 8.4 51 36.4 12.6 50 - 59 68.6 67.4 84.1 78.5 62.9 59.4 73.5 20 6.5 72.7 20.7 6.6 68.2 23.1 8.7 64.8 26.8 8.5 60 - 69 80.2 76.7 91.4 89.4 75.9 71.9 77.3 17.7 5.0 71.2 22.5 6.3 69.6 23.9 6.4 66.2 26.6 7.2 70 - 79 84.0 83.1 92.4 92.6 79.2 79.1 74.5 21.7 3.8 74.0 22.0 4.0 66.7 27.3 6.0 67.9 27.0 5.1 80 - 99 82.2 81.8 88.6 89.3 75.3 75.6 67.1 27.4 5.5 65.3 28.2 6.5 59.1 32.8 8.2 56.8 33.6 9.6 Total 79.3 76.1 89.4 87.4 73.9 70.7 73.6 21.4 5.1 70.9 22.8 6.3 66.1 26.9 7.0 64.6 28.0 7.5 Chi

square P value

< 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.001 < 0.001 (*) Included participants aged ≥ 20, N = 9288 (F = 4366, M = 4922)

(+) Included participants aged ≥ 20 and whose HbA1c measured both years. N = 6707 (F = 3225, M = 3482) Univariate analysis of variance:

Age adjusted gender difference in HbA1c measurement rate in 2011-12 (P = 0.093), and 2013-14 (P = 0.122) Age adjusted gender difference in the management of HbA1c in 2011-12 (P = 0.008), and 2013-14 (P = 0.147) One sample t-test:

Difference in HbA1c measurement rate between 2011-12 and 2013-14 (F < 0.001, M < 0.001) Difference in the management of HbA1c between 2011-12 and 2013-14 (F = 0.006, M = 0.008)

Evaluation of the quality of type 2 diabetes care in North Karelia, Finland