Time to First Optimal Glycemic Control and its Predictors Among Type 1 Diabetic Children<15 Years in Bahir Dar City Public Referral Hospitals, North West Ethiopia: A Retrospective Follow Up Study

Background: Recognizing the level of glycemic control of a client is an important predictor of the development of complication and risk of death from diabetes. However, the other most important predictor which is the time that the patient stayed in that poor glycemic level before reaching optimal glycemic control has not been studied so far. Objective: The aim of this study was to estimate time to rst optimal glycemic control and identify predictors among type 1 diabetic children<15 years in Bahir Dar city public referral hospitals, Northwest, Ethiopia, 2021 Methods: Retrospective cohort study was conducted at Bahir Dar city public referral hospitals among randomly selected sample of 385 patients with type 1 diabetes who were on follow up from January1, 2016 to February30, 2021.Data were collected by using data abstraction tool and then entered into Epi-data version 4.2 and exported into STATA 14.0 statistical software. Descriptive statistics, Kaplan Meier plots and median survival times, Log-rank test and Cox-proportional hazard regression were used for analysis. After performing Cox-proportional hazard regression, model goodness-of-t and assumptions were checked. Finally, association between independent variables and time to rst optimal glycemic control in months were assessed using multivariable Cox Proportional Hazard model and Variables with p-value < 0.05 were considered as statistically signicant. Result: Median survival time to rst optimal glycemic control among type 1 diabetic client was 8 months (95%CI: 6.9-8.9).First optimal glycemic achievement rate was 8.2(95%CI: 7.2-9.2) per 100 person/month observation. Factors that affect time to rst optimal glycemic control were age (AHR=0.32;95%CI=0.19-0.55),weight(AHR=0.96;95%CI=0.94-0.99),primary care giver(AHR=2.09;95%CI=1.39-3.13), insulin dose (AHR=1.05;95%CI=1.03-1.08),duration of diabetes (AHR=0.64;95%CI=0.44-0.94), adherence (AHR=9.72;95%CI=6.09-15.51),carbohydrate counting(AHR=2.43;95%CI=1.12-5.26),and comorbidity (AHR=0.72;95%CI=0.53-0.98). Conclusion and Recommendation: The median survival time to rst optimal glycemic control in this study was long. Age, weight, primary care giver, insulin dose, duration of diabetes, adherence, and carbohydrate counting including history of comorbidity were determinant factors. Therefore, clinicians should advice weight reduction, increase the dose of insulin during initial treatment, counsel their parents about adherence of insulin drug and auditing their children diet as prescription helps to reduce the length of glycemic control.


Introduction
Diabetes mellitus(DM) is a serious, chronic and progressive disease that occurs either when the pancreas does not produce enough insulin or the body can not properly use the insulin it produces (1).There are three classi cation of diabetes mellitus commonly accepted by different scholars (1,2).These are: type one diabetes mellitus( T1DM) ,type two diabetes mellitus( T2DM) and gestational diabetes (3).According to American diabetic association(ADA) type one is the commonest type in pediatrics age categories (2).
Type 1 diabetes also known as insulin dependent, juvenile or child hood onset DM which is characterized by de cient insulin production in the body (1). It encompasses a group of metabolic disease causing in hyperglycemia (2). Juvenile diabetes is currently not preventable but we can control and prevent its complication. Otherwise, uncontrolled diabetes over time may lead to a serious damage to the heart, blood vessels, eyes, kidneys and nerves (1)(2)(3)(4)(5) .
Glycemic control is a level of glucose in diabetic clients(1);Glycemic control followed by the diagnosis was re ected by optimal and poor metabolic control as mean HbA1c <7.5% and >7.5% respectively and /or average FBG level between 80-150mg/dl and either < 80 or >150 mg/dl respectively(6-8, 80) and HbA1c can be calculated from the following formula, if HBA1c is not consistently available for some of the clients; estimated average glucose level in (mg/dl)=28.7*HbA1c-46.7(8).
Although there are a lots of advanced management of T1DM,more than 70% of them were unable to maintain their glycaemia (10,11).
More over noncompliance rate escalating 50% that highlights the need for focusing on timely optimal glycemic control (10). Many children had also suffered from T1DM which is associated with high morbidity, mortality rate and most of the time the poor has been highly affecting by this disease (9,15,16). Both In developed and developing nations the prognosis of children with T1DM is poor (14). As a result, optimal glycemic control were oscillating from 2.6-39.1% (11,15,17). Many are not detected and those diagnosed have dramatically reduced their life expectancy by one year, (17)(18)(19). Poor glycemic control was much higher among type one patients(82.9%) as compared with type two diabetics(57.7%) (14,20,21).
A varieties of factors that predict glucose control in children with T1DM have documented (7,(18)(19)(20)(21)(22).High proportion of patients with uncontrolled glycemic level were due to sociodemographic factors, concomitant disease, personal and other clinical factors (16, 17, 23); health care system with limited resources, lack of trained health personnel and in ability of the patient or family to use and afford treatment expenditures (10,24).
Uncontrolled glycemic situation results complication which can hurt many parts of the body including growth failure later in time (3,(22)(23)(24). As a result, both acute and chronic complications were reported in different studies (24).Adverse effects like lipodystrophy is one of the clinical complication which may occur related to insulin injection and leads to insulin absorption problems, which ultimately can hinder rst optimal glycemic control (25,26).The most common complication prior in three months were hypoglycemia(21-42%) followed by 31.5%-39% of diabetic keto acidosis(DKA),10.5%-32.9% of nephropathy,13.6%of neuropathy,10.5% of convulsion,10.3% of retinopathy (27)(28)(29). Sustained abnormal blood sugar uctuation for periods of greater than two months can also contribute to high burden of the disease, hospitalization and negative consequences of disease out comes (30,32).
Similarly, study in Ethiopia highlights the di culty of achieving glycemic control early in time. As a result, early occurrence of both retinopathy and maculopathy among diabetic children were reported (13). Another study In Ethiopia speci cally in Gojjam, also indicates 58.5% DKA among 354 T1DM children with the incidence rate of 2.27/100 children/month of observation. (31).
However, strict glycemic control minimizes the incidence and progression of such possible complication (14)(15)(16)(17).The Diabetes Control and Complication Trial (DCCT) and the follow-up study Epidemiology of Diabetes Interventions and Complications (EDIC) shows that, good glycemic control with in short duration delays the development of both acute and chronic complication in T1DM patients by 35-76% (9).Novel treatment are emerging to manage T1DM with the ultimate goal being to achieve glycemic control, limit weight gain, reduce comorbidities and improve quality of life (7).T1DM treatment is based on frequent monitoring of blood glucose and administration of insulin, in line with their meal and exercise (33)(34)(35).It was recommended that T1DM children should check their blood glucose at least four times a day(6).And which expected to bring 26.2% satisfactory glycemic control level (7,35).People with diabetes can live longer and have a healthy life if their diabetes is become aware of early and well-managed by multidisciplinary approach with the allocation of accessible resources (10,36,37).Being updated about the recent diabetes care can also help in improving rst glycemic control (15,38).
In Ethiopia a little studies were conducted to recognize level of glycemic control among type one diabetic children(16).However, the other most important parameter, which is the time, in which, the patient stayed on that poor glycemic level before reaching optimal glycemic control has not studied so far. If efforts are not made to recognize the contributing factors for optimal glycemic control with possible time frame, the number of children affected will preserve growing and this in turn lead to an emotional and economical burden on both the clients and the families at large(6).And it will also disturb the sustainability of our health care system which is still over burdened with communicable diseases. Therefore, this study was aimed to estimate time to rst optimal glycemic control among type 1 diabetic children in Bahir Dar city public referral hospitals, Northwest, Ethiopia.

Methods And Materials
Study area and period The study was conducted in Bahir Dar city; located 565Km far from Addis Ababa, the capital city of Ethiopia, at Amhara national regional state, North West Ethiopia. In Bahir Dar city there are two public referral hospitals, one primary hospitals, ten health center and four private hospitals. And this study was conducted in the two public referral hospitals, namely: Felege Hiwot comprehensive specialized referral hospital (FHCSH) and Tibebe Ghion specialized teaching hospital (TGSTH). Each of this hospital can be expected to serve for more than 10 million populations coming from Bahir Dar city, west Gojjam zone, east Gojam zone, awi zone, north and south wollo zones, south& north Gondar zones, partial part of Benshangul Gumuz and Oromia region. FHCSH has currently a total of 1431 man power in each discipline with 500 formal beds, 11 wards, 39 clinical and non-clinical departments /service unit / providing Diagnostic, curative, Rehabilitation and preventive service at outpatient &inpatient based. Similarly TGSTH is a teaching hospital under Bahir Dar University College of medicine and health sciences that has 459 bed capacity and with around 14 outpatient departments.
Apart from other services both referral hospitals provide diabetic treatment services by nurse practitioners, pediatrics residents and pediatricians.

Study design
An institution based retrospective follow up study was employed.

Source population
The source population were all type 1 diabetes mellitus children<15 years old who had follow up at diabetes clinic of the two referral hospitals.

Study population
The study population were all type 1 diabetes mellitus children <15 years old who were on follow up during the study period.

Study unit
All type one diabetic children's chart that were selected randomly for investigation.

Inclusion criteria
Children age less than 15 years old and diagnosed with T1DM with regular follow up and had at least one HbA1c and/or a three month consecutive measurements of fasting blood sugar (FBS) with clear date of diagnosis between January 1/2016 to February 30/2021 were included.

Exclusion criteria
Children's medical record/chart with incomplete information (such as HbA1c/average FBG and other relevant predictors like age with date of diagnosis, sex, treatment modality, frequency of follow up visit and last visit health condition of the children), those having less than 3 month follow up during the study period and those cases transferred in with unclear date of diagnosis from other institution were excluded from the study.

Sample size determination
Sample size was determined by double proportion formula after taking of predictors associated to optimal glycemic control from previous study conducted by retrospective cohort design (50)with the help of epi info version 7 by considering the following statistical assumptions: 95% Con dence Interval (CI), power 80%,percent of outcome in unexposed group 8.93%,risk ratio 0.253, marginal error 5% (50) .The calculated total sample size is 378, then by adding 10% for data incompleteness from the client chart, the nal sample size became 416.

Sampling technique and procedure
The study participants were selected from the registration book. The medical records of children who were on follow up with type one diabetes mellitus from January 2016 to February 2021 were selected. A total of 721 children were recorded from the registration book of the two referral hospitals (sampling frame). Of which 416 cards were sampled using a simple random sampling technique by a computer generating method. Finally, cards that ful lled the criteria were reviewed.

Dependent variables
Time to rst optimal glycemic control Independent variables Socio demographic (age, gender, Residence); Institutional related variable (frequency of clinic visit); Diabetic related variables (duration of diabetes, diabetes related complication.); Comorbidities (preceding infections and other pathology) and treatment related variables (insulin therapy and adherence, noncompliance and other self-monitoring practice) Age of the participants, frequency of glycemic control, body mass index and duration of diabetes were categorized in to groups in order to alien with the other literatures(36, 40,50) Operational de nitions Optimal glycemic control: Optimal glycemic control is de ned as the three consecutive month HbA1c <7.5% and/or average FBG of 80-150 mg/dl with more or less stringent glycemic goals for individual clients based on age/life expectancy, comorbid condition, advanced complication, hypoglycemia unawareness and individual patient considerations (6-8,80).
Event: Achieving rst optimal glycemic control during the study period Survival time: The time starting from date of diagnosis to rst optimal glycemic control was determined for each participant Censoring: Patients died, lost to follow up, transferee out, and complete the follow up period without achieving optimal glycemic control Time to event: Time between diagnosis up to achieving rst optimal glycemic control or censoring with measure of interest in month Carbohydrate counting: Practicing healthy diet at home by non-re ned carbohydrate utilization and eating consistent amount of food regularly with application of food pyramid as a meal planning tool to optimize blood sugar level (35).

Data collection procedure
The data were collected from patients chart that visit Felege Hiwot comprehensive specialized referral hospital and Tibebe Ghion specialized teaching hospital. Data that were relevant to measure the association between times to rst optimal glycemic control among diabetic children were collected by two BSc nurses supervised by one senior nurse having second degree in public health.
Patient records were retrieved using their medical registration number identi ed in the total DM case load in the logbook of registration follow up form. Then medical registration number (MRN) of all diabetic pediatric patient were sorted. After that, the sample selection mechanism was simple random sampling technique, in which each of the patients had equal chance of being selected to be part of study.
A structured data extraction tool adapted by considering study variables such as socio demographic, personal and clinical predictors from patients' charts.

Data quality assurance
Training was given for data collectors and supervisors about the objective and process of data collection by the principal investigator. Pretest was done on 5 % of sample size. Then pretested data abstraction tool/check list that comprises of questions to measure the relevant variables were used to collect the necessary data from the patient medical chart by those trained data collectors. Data quality was also assured by designing proper data abstraction tool and through continuous supervision. All collected data were checked for completeness and clarity.

Data processing and statistical analysis
The collected data was coded, enter, cleaned and stored into Epi-data version 3.1 and exported into STATA 14.0 statistical software for analysis. Descriptive statistics were presented with frequency tables, Kaplan Meier (KM) plots and median survival times. Months are used as a time scale to calculate time to rst optimal glycemic control. The outcome of each participant was dichotomized in to censured or event ( rst optimal glycemic control) Kaplan-Meier technique was used to measure survival experience of diverse groups of patients by using survival curves. Log-rank test was used to assess signi cant difference among survival distributions of groups for equality. After performing the Cox-proportional hazard regression, model goodness-of-t was checked by Cox Snell residuals & assumptions was checked by using Shen eld residual test and graphically by using log minus log function survival curves.
Bivariable analysis was performed to calculate crud hazard ratio (CHR) and to screen out potentially signi cant independent variables at p value < 0.25 level of signi cance.
Association between the signi cant independent variables and the time to rst optimal glycemic control was assessed using multivariable Cox Proportional Hazard (PH) model.
Adjusted hazard ratio (AHR) and 95% CI for HR were used to test signi cance and interpretation of results.
Variables with p-value < 0.05 were considered as statistically associated with the time to rst optimal glycemic control in months.

Ethical considerations
Ethical clearance was obtained from the institutional review board (IRB) of Bahir Dar University (IRB number 01-008).Written supportive letter was taken from pediatrics department of the hospitals on behalf of the patients. This study had no any danger or negative consequences for the study participants. Medical record numbers were used for the data collection and personal identi ers of the client were not used in this research report. Access to collected information was limited to the principal investigator and con dentiality had preserved throughout the time.

Socio demographic characteristicswith censuring and event status
Four hundred sixteen (416) medical records were reviewed; off which, thirty one (7.5%) cases were excluded from the study due to pertinent data being missing. As a result, 385 clients were included in the study which is 92.5% in response rate.
Mean age of the study participant was 8.2±4.7 years with 2.4 years mean duration of diabetes.
More than half of the patients were male (53%) and proportion of rst optimal glycemic achievement among male is (72%) which is almost proximal to female (71.3%).
Majority of the patients (64.7%) were from rural area. However, the Proportion of patients who achieved rst optimal glycemic control among rural is (68.7%) which is lower than clients from urban area residents (77.2%).
Those clients having >4 clinical visit for the last year of their follow up had higher proportional glycemic control (82.3%) than clients having clinical visit <=4(663%). (Table 2).
Median survival time to rst optimal glycemic control The estimated median survival time to achieve rst glycemic control was 8 months with inter quartile range of (6.9-8.9).
The median survival time to rst optimal glycemic control among type one diabetic children were varied by various categories of predictors. For example, the median survival time to achieve rst optimal glycemic control among under 5 children was 6.8 where as in above 5-10 and >10-14 years was 8, 8.5 respectively. (Table 5).
Incidence rate of optimal glycemic achievement rate From 385 study participants, 276(71.7%) of the clients have achieved glycemic control with mean value of FBG&HA1c (112±3mg/dl, 5.6%) respectively; whereas 109(28.3%) were censored. The lowest and the highest length of follow up were 2.9 and 36.4 months respectively, and the total person-time risk was 3373 months.

Diabetes related variableswith censuring and event status
Concerning complication, 83.4% of the patients had history of one or more diabetes related complication .Majority of the clients had diabetic keto acidosis (DKA) (81%) including the episodes at the time of diagnosis followed by hypoglycemia (19.7%), other complication (4.9%) and chronic complication (0.8%). The proportion of patients who achieved optimal glycemic control is relatively higher among those with no history of diabetes related complication (76.6%) as compared to those with history of complication (70.7%).Mixed insulin (lent &regular) drugs had given for the majority of the patients (62.9%) during the initiation of treatment as compared to other regimens like NPH with regular and NPH alone (20%, 17.1%) respectively. (Table 3).

Comorbidity related variableswith censuring and event status
In regard to comorbidity, 69.6% of the patients had history of comorbid illness and only 30.4% of them didn't have recognized history of comorbid illness. Majority of the clients had malnutrition (38.7%) followed by pneumonia (16.1%), urinary tract infection (13.8%), acute gastro enteritis (10.1%), fungal infection (7%) and upper respiratory tract infection (6.5%).Nearly half (48%) of the patients had more than one comorbid illness. The proportion of clients who achieved rst optimal glycemic control is higher among those with no history of comorbid illness (74.4%) than those with one or more comorbid illness (70.5%). (Table 4).

Survival estimates for time to rst optimal glycemic control
The survival status of children with type 1 diabetes was estimated by the Kaplan-Meier survival curve.
The curve tends to decrease rapidly with in the rst one year indicating that most children achieved rst optimal glycemic control within this time (Figure 2).
The survival estimates of clients were varied in relation to different predictors. (Figure 3).

Comparison of survival experience
The long rank test was used to assess differences in equality of survival distribution among diverse groups. The median survival time to achieve rst optimal glycemic control among clients in the age groups of <=5 years showed shorter median time to achieve rst optimal glycemic control (6.8 months) as compared with patients whose age group between 6-10 years (8months) and 11-14 years (8.5 months).and the survival time was signi cantly different among the age groups(X 2 (2)) = 6.05, P-value = 0.0486).whereas, the median survival time to achieve rst optimal glycemic control among male participant showed relatively longer time (8.5 months) than females (7.2 months).But the long rank test was not statistically signi cant(X 2 (1))=0.92,p-value=0.3378). (Table 5).
Regarding adherence, those clients who adhere to the management had shorter duration of time (5.7 months) to achieve rst optimal glycemic control than those who didn't adhere towards the management of the disease(14.9 months).The long rank test was statistically signi cant(X 2 (1)) = 131.75, P-value <0.0001). The Kaplan Meier survival function showed that, clients with adherence have satisfactory survival experience by achieving their glycemic targets early in time. The gure also showed that, clients direct chance of achieving rst optimal glycemic control increases for both group as the duration of treatment increases. (Figure 4).
Those patients having comorbid illness appears to extend time to rst optimal glycemic control. The median survival time to achieve optimal glycemic control was shorter among patients with no history comorbid illness (6.3 months) than patients who had comorbid illness (8.9 months) with statistical signi cant difference among the group (X 2 (1)) = 10.85, P-value = 0.0010). (Table 5).
However, no statistically signi cance difference were shown for sex, residence, family history of diabetes militias ,number of clinic visit ,DKA as presentation and being malnourished in determining time to rst optimal glycemic control. (Table 5 & Table 6).

Results of multivariable cox proportional hazard model
Goodness of t checked by cox Snell residuals by plotting cox Snell residual against the cumulative hazard function. As a residuals follow unit of exponential distribution or a linear line through the origin with a unit gradient, which indicates a well tted model to the observed data point and expected value. (Figure 5).
Proportional assumption of cox proportional hazard model was tested by using Schoen eld residual test and graphically by using log minus log function on Stata version 14.2 (Table 6& Fig 6).The survival curve looks like parallel throughout the study time; which shows equitable tting to the proportional hazard assumption. (Figure 6).
The independent variables such as age client educational status, primary care giver, dose of insulin at initiating of treatment, duration of diabetes, rst insulin regimen, current insulin regimen, frequency of glycemic control, carbohydrate count, exercise, noncompliance, adherence, diabetes related acute complication, having history of comorbidity were signi cantly associated with time to rst optimal glycemic control at the point less than 0.25 level of signi cance from bivariable analysis. However, only age, duration of DM, dose of insulin at initiating of treatment, weight, primary care giver, adherence to DM care, carbohydrate counting and history of comorbidity were found to be signi cantly associated with time to rst optimal glycemic control in the multivariable cox regression hazard model less than 5% level of signi cance.
The presence of interaction among independent variables were checked by multicollinearity test but there was no signi cant interaction as it was con rmed by the value of variance in ation factor (VIF) which is less than ten.. Consequently, after adjusting other predictor, the hazard of achieving optimal glycemic control among the age groups >10-14 years were lower by 67.6% as compared with the age groups of the client<=5 years(AHR=0.324,95%CI=0.192-0.546).
Likewise, the hazard of achieving optimal glycemic control among clients with history of comorbid illness was lower by 24.3% compared to clients with no history of comorbid illness (AHR= 0.722, 95%CI=0.530-0.981).this means, the time needed to reach optimal glycemic control among clients with history of comorbid illness was signi cantly longer compared with clients with no history of comorbid illness.
However, the rate of achieving rst optimal glycemic control among clients who adhered to diabetic care had 9.7 times increment than clients who didn't adhered to diabetic management (AHR=9.723, 95%CI=6.094-15.513). (Table 6).

Discussion
The aim of this study was to identify predictors of time to optimal glycemic control in Ethiopia. The estimated median survival time to achieve rst glycemic control was 8 months with inter quartile range of (6.9-8.9).The nding in this study is in line with another study conducted among type 1 diabetic children in united states (38) but a little bit shorter than previous study conducted in Ethiopia(9.5months) (3).This could be due to differences in age pattern, type of diabetes and comorbidity among study participants(28, 31, 47, 49, 50, 55, 57-60).
In regard to predictors, the age of the participant was found to be signi cantly associated variables that determine time to rst optimal glycemic control. The study showed that, the time needed to reach rst optimal glycemic control is longer among clients of age group >10-14 years followed by the age group 6-10 years compared to clients in the age group<=5 years(AHR=0.324,95%CI=0.192-0.546), indicating that for children older than 10 years, the rate of achieving optimal glycemic control decreases as age increases which is in line with study done in Tanzania ,Bulgaria, Iraq, Taiwan and Jordan (26,46,47,49,50). This can be due to the fact that As a child develops, he/she under goes a varieties of physical and life style changes (24). In addition to this, it can be also due to hormonal effect at pubertal age of the child and decline in parental supervision over different clinical aspects of diabetic care in the adolescents(46, 50).
Weight of the client also signi cantly associated with time to rst optimal glycemic control. Rate of glycemic achievement decreases by 3.6% as weight increase by one unit which means the weight of the client is 0.964 times less likely associated with optimal glycemic achievement rate. This could be due to, weight gain may contribute to increased insulin resistance and cardio metabolic risk such as increased dyslipidemia and blood pressure(62).It is in line with another controlled study among T1DM patients which stated previously as "normal weight preschool children have better glycemic control than age matched overweight children (63, 64).''It can signi cantly implies that, body weight status may impede achievement of glycemic targets with in the expected time in this group of patients. Therefore, having regular exercise which is non-strenuous can be encouraged. The recommendation is supported by the study conducted in United Kingdom and the authors of International society of pediatrics and adolescents diabetes (ISPAD) guide line revised since 2018 GC (6, 34).
Dose of insulin at initiation of treatment increases rst optimal glycemic achievement rate by 1.053 times as dose of insulin increases by one unit. This nding is supported by the study done in many countries such as India, china, Germany, Austria, and Luxembourg (66-70).
This study also showed that, primary care giver during the follow up period was signi cantly associated with optimal glycemic control. Especially those clients whose care giver mother and father was two times more likely associated with rst optimal glycemic control as compared with clients supported by their mothers alone. The nding was supported by the study conducted in Tanzania and middle east Jordan (32,50).
In regard to adherence to diabetic care, those clients with adherence had 9.7 fold of instantaneous chance of increasing their glycemic achievement rate as compared with those clients with no adherence to wards their diabetic management. Which is in line with the study conducted in Ethiopia entitled with incidence of diabetic keto acidosis and its predictors among type one diabetic children (31).Correspondingly, those clients well adhered to Diet counseling speci cally on food pyramid and non-re ned carbohydrate utilization were found to have increasing their glycemic achievement rate by 2.4 folds as compared with those clients with no habit of practicing healthy diet at home and the nding is in line with the study conducted in Uganda (35,54,64).
Duration of diabetes was also signi cantly associated with time to rst optimal glycemic control in this study. Those clients living with diabetes for more than or equal to four years were 35.8% times less likely to achieve optimal glycemic control as compared with clients who were living with diabetes less than two years. This could be due to age maturation with advancement of the disease following to diabetic duration as it was explained above (24,46,50).This nding is similar with the study done in Tanzania(31) but different with study done in cameron (75).
In addition to the above factors, having comorbid illness is another important predictors that can affect time to optimal glycemic control. The rate of achieving optimal glycemic control among clients with history of comorbid illness were 27.8% times less likely as compared with clients with no comorbid illness. This is because having comorbid illness has an in uence on diabetes disease progress with impairment of glucose metabolism possibly lead to deterioration of glycemic control. Comorbid illness such as infection might also cause high level of counteracting hormones which triggering an episode of hyperglycemia and could also be due to the effect of taking many drugs which can lead to drug interaction and also can decrease drug adherence which interferes with drug effectiveness. This nding is in line with the studies conducted in Saudi Arabia, Brazil and university of California, San Francisco(18, 57-60).

Strength and Limitation of the Study
Strength of the study Since the data were collected from two referral hospitals, the nding can have more power in regard to generalizability.

Limitation of the study
Since the data were collected from medical records, variables like parental socio economic factors cannot be addressed through card review which may affect the outcome of the study.
Fasting blood glucose level (FBG) measurements obtained from medical records might be subjected to measurement errors that lead to underestimated or overestimated of the result. However, effort was made to overcome this issues by taking the mean value of three month consecutive value of FBG measurements.

Conclusion And Recommendation
The median survival time to rst optimal glycemic control in this study was long compared to other studies. Age, weight, primary care giver, insulin dose, duration of diabetes, adherence, and carbohydrate counting including history of comorbidity were determinant factors. Therefore, clinicians should advice weight reduction, increase the dose of insulin during initial treatment, counsel their parents about adherence of insulin drug and auditing their children diet as prescription helps to reduce the length of glycemic control. Ethical clearance and approval were obtained from the institutional review board (IRB) of Bahir Dar University (IRB number 01-008).Written supportive letter was taken from pediatrics department of the hospitals on behalf of the patients. This study had no any danger or negative consequences for the study participants. Medical record numbers were used for the data collection and personal identi ers of the client were not used in this research report. Access to collected information was limited to the principal investigator and con dentiality had preserved throughout the time.

Consent for publication
Not applicable Availability of data and materials Data will be available upon consortium approval.

Competing interests
All authors declared that they have no competing interests.

Funding
For this research the principal investigator (PI) receive grants from Haramaya University as funding agency.

Authors' contribution
Fentahun Meseret had a substantial contribution from conception to the acquisition of the data. All the authors had a great contribution to the study design, analysis, and interpretation of the ndings. Fentahun Meseret drafted the manuscript. All authors revised the drafted manuscript carefully for important intellectual contents. All authors read and approved the nal manuscript.