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 km2 in 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).
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
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
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.
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 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.
Table 5: Proportion of HbA1c measurement and management (2011-12 & 2013-14) by gender and age group.
(+) 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)
Age adjusted univariate analysis of variance was performed to observe the gender difference of HbA1c measurement rate in 2011-12 and 2013-14 (Table 5). We found that there was no difference in HbA1c measurement rate between females and males in 2011-12 (F = 79.3%, M = 76.1%, P = 0.093) or 2013-14 (F = 89.4%, M = 87.4%, P = 0.122).
It was observed that the measurement rate of HbA1c has been improved in 2013-14 compared with 2011-12 both among females (89.4% vs 79.3%, P < 0.001) and males (87.4% vs 76.1%, P < 0.001).
To observe the differences in HbA1c management between the age groups, a Chi-square test was performed (Table 5). We found that 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 management rate was observed among the age group of 60-69 years in 2011-12 and 20-39 years in 2013-14.
Age adjusted univariate analysis of variance was performed to observe the gender difference in the management of HbA1c in 2011-12 and 2013-14 (Table 5). We found that in 2011-12 females had better management compared with males (F = 73.6%, M = 70.9%, P = 0.008) but in 2013-14 there was no difference in the management of HbA1c between females and males (F = 66.1%, M = 64.4%, P = 0.147).
It was observed that the management of HbA1c in this patient cohort deteriorated in 2013-14 compared with 2011-12 both among females (66.1% vs 73.6%, P = 0.006) and males (64.6% vs 70.9%, P = 0.008) (Table 5).
LDL measurement rate and management among different age groups from the year 2011-12 to 2013-14 is presented in Table 6. Both LDL measurement rate and management appears to have enhanced by the subsequent years (Table 6). A Chi-square test showed that there are statistically significant differences in LDL 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 LDL was seen among the age group of 70-79 years both in 2011-12 and 2013-14.
Table 6: Proportion of LDL measurement and management (2011-12 & 2013-14) by gender and age group.
(+) included participants aged ≥ 20 and whose LDL measured both years. N = 6155 (F = 2890, M = 3265) Univariate analysis of variance:
Age adjusted gender difference in LDL measurement rate in 2011-12 (P = 0.983), and 2013-14 (P = 0.064) Age adjusted gender difference in the management of LDL in 2011-12 (P < 0.001), and 2013-14 (P < 0.001) One sample t-test:
Difference in LDL measurement rate between 2011-12 and 2013-14 (F < 0.001, M < 0.001) Difference in the management of LDL between 2011-12 and 2013-14 (F = 0.409, M < 0.001)
Age adjusted univariate analysis of variance was performed to observe the gender difference of LDL measurement rate in 2011-12 and 2013-14 (Table 6). We found that there was no difference in LDL measurement rate between females and males in 2011-12 (F = 74.3%, M = 73.6%, P = 0.983) or in 2013-14 (M = 84.3%, F = 82.5%, P = 0.064).
It was observed that the measurement rate of LDL has been improved in 2013-14 compared with 2011-12 both among females (82.5% vs 74.3%, P < 0.001) and males (84.3% vs 73.6%, P < 0.001).
To observe the differences in the management of LDL between the age groups, a Chi-square tests was performed (Table 6). We found that 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 LDL management rate was observed among the age group of 70-79 years in 2011-12 and 2013-14.
Age adjusted univariate analysis of variance was performed to observe the gender difference in the management of LDL in 2011-12 and 2013-14. We found that both in 2011-12 (F = 51.3%, M = 57.3%, P < 0.001) and 2013-14 (F = 53.6%, M = 61.9%, P < 0.001) management of LDL was better in males compared with females (Table 6).
It was observed that the management of LDL has improved in 2013-14 compared with 2011-12 among males (61.9% vs 57.2%, P < 0.001), but there was no statistically significant improvement in LDL management among females (53.6% vs 51.3%, P = 0.409) (Table 6).
Mean HbA1c and LDL levels by gender and different age groups in 2011-12 and 2013-14 are presented in Table 7. Analysis of variance (ANOVA) test was performed to observe the differences in mean HbA1c and LDL levels between the age groups and we found that the differences were statistically significant both for females (P = 0.003) and males (P < 0.001).
Age adjusted univariate analysis of variance was performed to observe the gender difference in mean HbA1c and LDL level in 2011-12 and 2013-14 (Table 7). We found that in 2011-12 mean HbA1c was almost similar among females and males (F = 6.60%, M = 6.66%, P = 0.310) and mean LDL level was less satisfactory in females compared with males (F = 2.58 mmol/l, M = 2.41 mmol/l, P < 0.001).
Table 7: Mean HbA1c and LDL level (2011-12 & 2013-14) by gender and age group. Included participants aged ≥ 20 and whose HbA1c or LDL measured both years.
Univariate analysis of variance:
Age adjusted gender difference in mean HbA1c level of 2011-12 (P = 0.310) and 2013-14 (P = 0.104) Age adjusted gender difference in mean LDL level of 2011-12 (P < 0.001) and 2013-14 (P < 0.001) One sample t-test:
Difference in mean HbA1c level between 2011-12 and 2013-14 (F = 0.046, M = 0.016) Difference in mean LDL level between 2011-12 and 2013-14 (F = 0.863, M = 0.009)
Table 8: HbA1c measurement and management (2011-12 & 2013-14) by area level education.
(+) Included participants aged ≥ 20 and whose HbA1c measured both years. N = 6707 (F = 3225, M = 3482) Table 9: LDL measurement and management (2011-12 & 2013-14) by area level education.
Area level
(+) Included participants aged ≥ 20 and whose LDL measured both years. N = 5880 (F = 2778, M = 3102), missing data (n = 275)
In 2013-14 there was no difference in mean HbA1c levels between females and males (F = 6.82%, M = 6.86%, P = 0.104) and LDL level was higher in females compared with males (F = 2.54 mmol/l, M = 2.35 mmol/l, P < 0.001) (Table 7). It was observed that the mean HbA1c level deteriorated in 2013-14 from 2011-12 males (6.86% vs 6.66%, P = 0.016) (Table 7). The mean LDL level improved in 2013-14 from 2011-12 among males (2.35 mmol/l vs 2.4 mmol/l, P = 0.009) but remained almost the same among females (2.54 mmol/l vs 2.58 mmol/l, P = 0.863) (Table 7).
HbA1c measurement rate and achievement in the target level of HbA1c according to the proportion of educated citizens in the postal code area (at least high school graduate or vocational training) is presented in Table 8. A Chi-square test showed that there is statistically significant difference in HbA1c measurement rate between the postal code areas by the proportion of educated citizens and the differences were statistically significant for females (P < 0.001) but non-significant for males (P = 0.106) in 2011-12, in 2013-14 result was significant for both females (P = 0.029) and males (P < 0.001). The best measurement rate of HbA1c was seen in the postal code area where the proportion of educated citizens is 60 - 69.9 % both in 2011-12 and 2013-14.
To observe the differences in the management of HbA1c between the postal code areas by the proportion of educated citizens, a Chi-square tests was performed and we found that the differences were statistically significant for females (P = 0.009 in 2011-12 and P = 0.002 in 2013-14) in both years but for males the differences were significant only in 2011-12 (P = 0.004). The best management rate was observed in the postal code area where the proportion of educated citizens is more than or equals to 70% both in 2011-12 and 2013-14 (Table 8).
LDL measurement rate and management according to the proportion of educated citizens in the postal code area (at least high school graduate or vocational training) from the year 2011-12 to 2013-14 is presented in Table 9. A Chi-square test showed that there are statistically significant differences in LDL measurement rate between the postal code areas by the proportion of educated citizens and the differences were statistically significant for females only in 2011-12 (P = 0.004) but for males these differences were significant in both years (P = 0.013 in 2011-12 and P < 0.001 in 2013-14). The best measurement rate of LDL was seen in the postal code area where the proportion of educated citizens is 60 - 69.9% both in 2011-12 and 2013-14.
Table 10: Mean HbA1c and LDL level (2011-12 & 2013-14) by area level education.
Included participants aged ≥ 20 and whose HbA1c or LDL measured both years.
Table 11: HbA1c measurement and management (2011-12 & 2013-14) by area level unemployment.
Area level
(+) Included participants aged ≥ 20 and whose HbA1c measured both years. N = 6623 (F = 3193, M = 3430), missing data (n = 84)
To observe the differences in the management of LDL between the postal code areas by the proportion of educated citizen’s, a Chi-square tests was performed and we found that the differences were statistically significant only for males (P = 0.020) in 2011-12 and in 2013-14 there was no significant differences among females (P = 0.881) or males (P = 0.681) (Table 9).
The mean HbA1c and LDL level in postal code areas by the proportion of educated citizen’s in 2011-12 and 2013-14 are presented in Table 10. Analysis of variance (ANOVA) test was performed to observe the differences in mean HbA1c and LDL levels and we found that there are statistically significant differences in mean HbA1c in 2011-12 between the postal code areas with different education level for females (P = 0.001) and males (P < 0.001). Similar result was observed in mean HbA1c in 2013-14 (F < 0.001, M = 0.002). For the mean LDL, the differences between the areas were not statistically significant in 2011-12 or in 2013-14 neither among females (P = 0.511 in 2011-12 and P = 0.917 in 2013-14) nor males (P = 0.135 in 2011-12 and P = 0.864 in 2013-14).
HbA1c measurement rate and achievement in the target level of HbA1c according to the percentage of unemployed citizens in the postal code area is presented in Table 11. A Chi-square test showed that there is statistically significant difference in HbA1c measurement rate between the postal code areas by the percentage of unemployed and the differences were statistically significant for both females (P = 0.005) and males (P < 0.001) in 2011-12, but in 2013-14 significant differences was seen only among males (P = 0.004). The best measurement rate of HbA1c was seen in the postal code areas where the proportion of unemployed citizens is < 6.0 % in 2011-12 and in 2013-14 measurement rate was the highest among those whose postal code area level unemployment rate is 6.0% - 6.9%.
To observe the differences in the management of HbA1c between the postal code areas by the percentage of unemployed, a Chi-square tests was performed and we found that the differences were not statistically significant among females (P = 0.121 in 2011-12 and P = 0.153 in 2013-14) or males (P = 0.526 in 2011-12 and P = 0.576 in 2013-14) (Table 11).
Table 12: LDL measurement and management (2011-12 & 2013-14) by area level unemployment.
(+) Included participants aged ≥ 20 and whose LDL measured both years. N = 6073 (F = 2860, M = 3213), missing data (n = 82)
Table 13: Mean HbA1c and LDL level (2011-12 & 2013-14) by area level unemployment.
Included participants aged ≥ 20 and whose HbA1c & LDL measured both years.
Table 14: HbA1c measurement and management (2011-12 & 2013-14) by area level median income of the citizens.
Area level median income (€) of citizens
HbA1c Measured (2011-12) *
(%)
HbA1c Measured (2013-14) *
(%)
HbA1c Measured both year *
(%)
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
≤ 15000 75.9 73.4 87.2 88.1 68.8 68.3 72.2 22.1 5.8 68.4 23.9 7.8 64.7 28.6 6.7 67.2 25.5 7.3 15001 - 16000 81.4 79.6 91.5 89.0 76.8 75.0 72.9 22.2 4.9 71.4 22.3 6.3 65.9 26.1 8.0 64.7 27.8 7.5 16001 - 17000 80.8 73.5 88.8 87.5 74.9 68.4 72.7 22.2 5.1 69.4 24.8 5.8 63.5 28.6 8.0 60.6 31.2 8.3 17001 - 19000 78.1 69.2 89.3 82.3 72.1 61.8 77.2 19.0 3.8 73.5 21.6 4.9 70.6 24.1 5.4 66.8 26.5 6.7
≥19001 77.5 78.3 88.3 86.1 72.8 72.3 76.0 18.7 5.3 73.3 21.0 5.7 69.0 25.9 5.1 65.1 28.3 6.7 Total 79.3 76.1 89.5 87.4 73.9 70.8 73.7 21.3 5.0 71.0 22.7 6.3 66.2 26.7 7.0 64.7 27.9 7.4 Chi square
P value 0.013 < 0.001 0.014 0.002 0.001 < 0.001 0.614 0.451 0.188 0.504 (*) Included participants aged ≥ 20. N = 9165 (F = 4321, M = 4844)
(+) Included participants aged ≥ 20 and whose HbA1c measured both years. N = 6623 (F = 3193, M = 3430), missing data (n = 84)
The measurement rate and management of LDL according to the percentage of unemployed in the postal code area from the year 2011-12 to 2013-14 is presented in Table 12. A Chi-square test showed that there are statistically significant differences in LDL measurement rate between the postal code areas by the percentage of unemployed and the differences were statistically significant both for females (P = 0.040 in 12 and P = 0.001 in 2013-14) and males (P = 0.006 in 2011-12 and P = 0.008 in 2013-14) (Table 2011-12). To observe the differences in management of LDL between the postal code areas by the percentage of unemployed, a Chi-square tests was performed and we found that the differences were statistically significant for females (P = 0.009 in 2011-12 and P = 0.040 in 2013-14), and males (P = 0.008 in 2011-12 and P = 0.049 in 2013-14). The best LDL management rate was observed among the citizens of the postal code area where unemployment rate is between 6.0-6.9% both in 2011-12 and 2013-14 (Table 12).
The mean HbA1c and LDL levels of postal code areas by unemployment in 2011-12 and 2013-14 is presented in Table 13. Analysis of variance (ANOVA) test was performed to observe the differences in mean HbA1c and LDL level between the postal code areas by unemployment and we found that there are statistically significant differences in mean LDL levels only among males in 2011-12 (P = 0.005) and 2013-14 (P = 0.035).
HbA1c measurement rate and achievement in target level of HbA1c according to the median income of the citizens of the postal code area is presented in Table 14. A Chi-square test showed that there are statistically significant differences in HbA1c measurement rate by the different levels of median income of the citizens and the differences were statistically significant both for females (P = 0.013 in 2011-12 and P = 0.014 in 2013-14) and males (P < 0.001 in 2011-12 and P = 0.002 in 2013-14). The best measurement rate of HbA1c was seen in the postal code areas with citizens whose median income is within 15001 – 16000 euros both in 2011-12 and 2013-14.
To observe the differences in the management of HbA1c between the postal code areas by the level of median income of the citizens, a Chi-square tests was performed and we found that the differences were statistically non-significant both for females (P = 0.61 in 2011-12 and P = 0.188 in 2013-14) and males (P = 0.450 in 2011-12 and P = 0.504 in 2013-14). The best management rate was observed among the citizens whose median income of the postal code area is within 15001 – 16000 euros both in 2011-12 and 2013-14 (Table 14).
Table 15: LDL Measurement and management (2011-12 & 2013-14) by area level median income of the citizens.
(+) Included participants aged ≥ 20 and whose LDL measured both years. N = 6073 (F = 2860, M = 3213), missing data (n = 82)
Table 16: Mean HbA1c and LDL level (2011-12 & 2013-14) by area level median income of the citizen.
Included participants aged ≥ 20 and whose HbA1c or LDL measured both years.
Table 17: HbA1c measurement and management (2011-12 & 2013-14) by municipality.
Municipality HbA1c Measured (2011-12) *
(%)
HbA1c Measured (2013-14) *
(%)
HbA1c Measured both year *
(%)
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 Ilomantsi 77.2 74.7 92.6 87.5 73.5 69.0 73.8 21.1 5.1 72.6 22.3 5.1 64.0 27.3 8.6 56.2 29.4 14.4 Joensuu 77.2 73.0 86.9 83.8 70.5 66.3 67.3 24.5 8.2 70.5 22.3 7.3 66.8 27.4 5.7 64.1 31.1 4.8 Juuka 84.6 87.1 91.0 92.7 79.1 82.8 72.2 22.2 5.6 65.2 28.1 6.7 62.9 29.6 7.5 61.7 29.0 9.3 Kitee 89.0 82.5 93.1 91.0 85.2 77.8 69.9 25.3 4.8 77.2 18.8 4.1 65.3 25.0 9.7 61.8 30.0 8.2 Kontiolahti 79.5 79.6 91.8 85.0 75.8 71.9 77.5 18.5 4.0 78.8 15.3 5.8 62.7 29.5 7.8 64.5 26.4 9.1 Outokumpu 89.5 83.8 91.9 90.8 84.1 79.2 69.2 24.4 6.3 68.6 24.0 7.4 71.9 22.9 5.2 72.3 21.5 6.2 Lieksa 74.0 70.1 91.3 87.2 70.0 66.5 79.1 16.0 4.9 71.6 21.9 6.5 59.7 28.9 11.4 64.7 25.6 9.6 Liperi 87.0 81.4 89.8 90.7 81.4 77.9 74.7 20.4 4.9 72.2 19.8 8.0 65.8 28.9 5.3 63.2 28.1 8.7 Nurmes 67.7 69.9 89.4 90.3 61.6 66.5 83.2 13.9 3.0 68.5 28.3 3.3 70.4 22.2 7.4 71.2 22.2 6.6 Polvijärvi 80.3 70.1 87.6 86.4 73.7 62.6 64.3 27.1 8.6 60.2 24.1 15.7 73.3 21.8 5.0 65.2 28.3 6.5 Rääkkylä 82.2 81.7 90.0 95.2 77.8 79.8 78.5 19.8 1.7 73.9 17.0 9.1 54.3 37.1 8.6 60.2 28.9 10.8 Tohmajärvi 82.4 84.5 90.2 90.8 79.1 80.1 78.7 21.3 0.0 57.4 34.0 8.5 68.6 23.1 8.3 67.9 23.6 8.5
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 Ilomantsi 77.2 74.7 92.6 87.5 73.5 69.0 73.8 21.1 5.1 72.6 22.3 5.1 64.0 27.3 8.6 56.2 29.4 14.4 Joensuu 77.2 73.0 86.9 83.8 70.5 66.3 67.3 24.5 8.2 70.5 22.3 7.3 66.8 27.4 5.7 64.1 31.1 4.8 Juuka 84.6 87.1 91.0 92.7 79.1 82.8 72.2 22.2 5.6 65.2 28.1 6.7 62.9 29.6 7.5 61.7 29.0 9.3 Kitee 89.0 82.5 93.1 91.0 85.2 77.8 69.9 25.3 4.8 77.2 18.8 4.1 65.3 25.0 9.7 61.8 30.0 8.2 Kontiolahti 79.5 79.6 91.8 85.0 75.8 71.9 77.5 18.5 4.0 78.8 15.3 5.8 62.7 29.5 7.8 64.5 26.4 9.1 Outokumpu 89.5 83.8 91.9 90.8 84.1 79.2 69.2 24.4 6.3 68.6 24.0 7.4 71.9 22.9 5.2 72.3 21.5 6.2 Lieksa 74.0 70.1 91.3 87.2 70.0 66.5 79.1 16.0 4.9 71.6 21.9 6.5 59.7 28.9 11.4 64.7 25.6 9.6 Liperi 87.0 81.4 89.8 90.7 81.4 77.9 74.7 20.4 4.9 72.2 19.8 8.0 65.8 28.9 5.3 63.2 28.1 8.7 Nurmes 67.7 69.9 89.4 90.3 61.6 66.5 83.2 13.9 3.0 68.5 28.3 3.3 70.4 22.2 7.4 71.2 22.2 6.6 Polvijärvi 80.3 70.1 87.6 86.4 73.7 62.6 64.3 27.1 8.6 60.2 24.1 15.7 73.3 21.8 5.0 65.2 28.3 6.5 Rääkkylä 82.2 81.7 90.0 95.2 77.8 79.8 78.5 19.8 1.7 73.9 17.0 9.1 54.3 37.1 8.6 60.2 28.9 10.8 Tohmajärvi 82.4 84.5 90.2 90.8 79.1 80.1 78.7 21.3 0.0 57.4 34.0 8.5 68.6 23.1 8.3 67.9 23.6 8.5