Feb

Introduction

Bipolar affective disorder (BPAD) is one of the commonest psychiatric disorders with a lifetime prevalence of about 3% in the general population and is the sixth leading cause of disability worldwide (1,2).This disorder is characterised by repeated episodes in which the patient’s mood and activity levels are significantly disturbed. This disturbance consists on some occasions of an elevation of mood and increased energy and activity (mania or hypomania), and on other occasions of a lowering of mood and decreased energy and activity (depression) (3). As the illness starts early in life, i.e., during teens or early adulthood, persons suffering from BPAD have symptoms of illness for the major part of their life (4, 5).

In India, since professional services, both in public and private sectors are not adequately developed due to shortage of trained human resources and infrastructure, the family support system plays a major role in caring for people with mental illnesses (6). The primary caregiver is identified as an adult relative (a spouse, parent or spouse equivalent) living with a patient, who is involved in the care of the patient on a day-to-day basis, takes the responsibility for bringing the patient to the treatment facility, stays with the patient during the inpatient stay, provides financial support and/or is contacted by the treatment staff in case of emergency (7). Intensive involvement in the care of the patient is often associated with significant caregiver burden.

Caregiver burden can be defined as the presence of problems, difficulties or adverse effects which affect the lives of caregivers of patients with various disorders or illnesses, e.g. members of the household or family (8). Family burden is broadly divided into objective and sub­jective burden. While the notion of the objective family burden relates to measurable problems (e.g. patients’ troublesome behaviours), the idea of subjective family burden is bound to caregivers’ emotions arising in response to the objective difficulties (9). Multiple studies across the world have shown that bipolar disorder is associated with significant caregiver burden (10-31). In view of the high caregiver burden, it is now suggested that the emphasis in psychiatric rehabilitation needs to shift from a patient-focused approach to a combined patient and caregiver-focused approach. Although there are studies from different parts of the country, there is a lack of data on caregiver burden from Kashmir, which is often faced with turmoil, which can influence caregiver burden. The present study is an effort in this direction to assess caregiver burden and its correlates among primary caregivers of patients with bipolar disorder.

Methodology

The present study was conducted on primary caregivers of patients with BPAD. Primary caregivers were defined as those caregivers who were closely involved in the care of the patient during the acute episodes and during the maintenance period in terms of bringing the patient to the hospital, supervising the medications and liaison with the treating team.

The study sample comprised of 100 caregivers of 100 patients diagnosed with BPAD as per the International Classification of Diseases classification of mental and behavioural disorders, 10^th revision (ICD-10) (3), attending either the outpatient or inpatient services at the Department of Psychiatry, SKIMS, Bemina, Srinagar. The study was approved by the Ethics Committee of the institute and all the participants were recruited after obtaining written informed consent.

To be included in the study, the caregivers were required to be involved in the care of patients, aged 18 or above, living with the patient for at least 1 year and were a family member taking care of patients without any wages. Caregivers who were diagnosed with psychiatric illness and staying with the patient for less than 12 months were excluded.

The caregivers were assessed by following scales:

Family Burden Interview Schedule (FBIS) (32):This is a semi-structured interview schedule having 24 items, each of which is scored on a 3-point scale, i.e. 0 indicating no burden, 1 indicating a moderate level of burden and 2 suggesting severe burden. The items of the objective burden of the scale are divided into 6 domains, i.e. financial burden, disruption of routine family activities, disruption of family leisure, disruption of family interaction, physical health and mental health. Subjective burden is evaluated by a single item. This scale has been widely used in previous studies from India (26, 33-35).

DUKE-UNC Functional Social Support Questionnaire (FSSQ) (36):The Duke-UNC Functional Social Support Questionnaire (FSSQ) is an 8-item instrument to measure the strength of the person's social support network (36). Responses to each item were scored as 1 (‘much less than I would like’), 2 (‘less than I would like’), 3 (‘some, but would like more’), 4 (‘almost as much as I would like’) and 5 (‘as much as I would like’). The scores from all eight questions are summed (maximum 40) and then divided by 8 to get an average score. The higher score indicates better perceived social support. Cronbach’s alpha for this scale is 0.84.

Hindi General Health Questionnaire (GHQ-30) (37):The modified version of Goldberg's General Health Questionnaire (GHQ) (38) was used. This is a screening device for identifying minor psychiatric disorders in the general population and within the community or non-psychiatric clinical settings such as primary care or general medical outpatients. The self-administered questionnaire focuses on two major areas: the inability to carry out normal functions and the appearance of new and distressing phenomena. In each question of the 30-item GHQ, the caregivers were asked to choose among: Better than usual or same as usual = 0, less than usual or much less than usual = 1.The results were evaluated by the two-step assessment method (0-0-1-1-method). The minimum GHQ-30 total score was 0 and the maximum total score of GHQ-30 was 30. A cut-off of 6 was used to categorize those with and without psychiatric morbidity. Cronbach’s alpha value of the GHQ-30 was 0.93. The Kappa coefficient was 0.64 (p<0.001).

The recorded data was compiled and entered into a spreadsheet (Microsoft Excel) and then exported to data editor of SPSS Version 16.0 (SPSS Inc., Chicago, Illinois, USA). Continuous variables were summarised in the form of means and standard deviations and categorical variables were summarised as percentages. Student’s independent t-test and Chi-square tests were employed for comparing caregiver burden with different variables. 

Results

Table 1: Description of socio-demographic variables of caregivers

Table 2: Clinical profile of patients.

Table 3: Caregiver burden, social support and psychological morbidity among caregivers

Table 4: Association of caregiver burden with socio-demographic variables of caregivers

Table 5: Clinical Profile of patients with bipolar disorder

Table 6: Clinical Profile of patients with bipolar disorder

The study included nearly equal number of male and female patients. About two-thirds of the patients were married (63%). About one-third of the patients had not received any formal education and another third had completed their secondary education and one-fourth had completed graduation (Table 1).

Description of socio-demographic variables of caregivers

The study included nearly equal numbers of male and female caregivers. The majority (55%) of the caregivers were spouses of the patient. The majority of the caregivers were married (93%). Nearly half of the caregivers had not received any formal education (48%), were homemakers (44%) and three-fifths of them were from low socioeconomic status (60%). The majority of caregivers (77%) had been caring for duration of one to five years (Table 1).

Clinical profile of patients.

In the present study, the majority of patients (77%) had duration of illness in the range of 1-5 years, nearly half of them were never hospitalised, the majority (55%) of patients had one to two manic episodes, most of them (64%) had three to five episodes of depression, and the majority (75%) of them never attempted suicide or homicide. The majority of patients (73%) were compliant with medication. (Table 2)

Caregiver burden, social support and psychological morbidity among caregivers

As is evident from Table 3, the highest burden was reported in the financial domain, followed by disruption in family routine activities, disruption of family leisure, disruption of family interactions, effect on physical health of others and least burden was reported in the form of effect on mental health of others. The mean DUKE UNC FSSQ score was 3.17 (SD=0.84) with range 1.75-4.75.

Mean GHQ-30 score was 13.14(SD=5.65) with a range of 2-25. Of the 100 caregivers, about one-fourth (N=23) had a GHQ-30 score of 6 or more, indicative of psychological morbidity.

Association of caregiver burden with demographic and clinical variables

As is evident from Table 4, higher caregiver burden was associated with higher age, female gender, lack of formal education, being a homemaker, lower socioeconomic status, a nuclear family set-up, being spouse of the patient and longer duration of being in the caregiver role.

Clinical Profile of patients with bipolar disorder

In terms of clinical variables, higher objective caregiver burden was associated with duration of illness more than 10 years, higher number of hospitalisations and higher number of manic and depressive episodes. Caregivers of patients consuming >75% of the prescribed medications reported lower caregiver burden (Table 5).

Advancing age of patient and caregiver, increasing duration of care, prolonged illness, greater number of hospitalisations and higher number of episodes of either polarity were significantly associated with higher caregiver burden. In terms of association of social support and caregiver burden, higher social support was associated with significantly lower caregiver burden, whereas higher caregiver burden was associated with higher psychological morbidity (Table 6).

Discussion

Families play an important role in care of patients with chronic mental illnesses. In the process of caring for such patients, relatives face a considerable burden.

Findings of the present study suggest that higher burden was seen among the caregivers who were relatively older, of female gender, uneducated or illiterate, homemakers and from nuclear families. Compared to parents and siblings, spouses reported significantly higher levels of caregiver burden. Furthermore, the caregivers involved in the care of the patient for longer durations reported significantly higher levels of caregiver burden.

In terms of clinical variables of patients, higher caregiver burden was associated with longer duration of illness, higher number of lifetime hospitalisations, higher number of manic and depressive episodes and poor medication compliance. Poor social support was associated with a higher level of caregiver burden. Higher caregiver burden was associated with higher psychological morbidity.

Many previous studies from India have evaluated caregiver burden among caregivers of patients with bipolar disorder (10-32). There is a lack of consensus with respect to caregiver variables and their association with caregiver burden (39). Some of the studies suggest that there is no significant difference in the caregiver burden as reported by caregivers of either gender (6), whereas others suggest that females report higher caregiver burden (13, 40). Our findings support the studies which have reported higher caregiver burden among female caregivers. This finding of ours could have been influenced by the relationship of caregivers with patients. In the present study, spouses of patients formed a large proportion of caregivers and they reported significantly higher burden, in contrast to parents and siblings. Cultural issues like restriction of females to household activities with lower opportunities to vent out their distress, inability to spend time on leisure activities, financial dependency and lack of independence could also be responsible for higher perceived burden. It was noticed that caregivers from nuclear families had higher caregiver burden as compared to those from joint families. The joint family system is considered to promote interdependence and possibly is associated with sharing of caregiver burden and this may explain why caregivers from joint families reported lower caregiver burden. Similar findings have been reported in earlier studies from India (41).

Findings of the association of higher caregiver burden with duration of illness are supported by existing literature (14). This finding suggests that possibly with passing time, frequent relapses of illness lead to caregiver burnout, which leads to higher caregiver burden. Previous studies have also noted an association of higher caregiver burden with higher numbers of hospitalisation (30). Findings of the present study too support this association. Higher caregiver burden with greater numbers of hospitalisations possibly indicate more severe episodes and hospitalisation associated with more expenditures and loss of earnings. This suggests that all efforts must be made to pick up relapses at the earliest and manage them effectively to minimise the chances of progression to severe episodes and resultant need for inpatient care. Previous studies have also reported association between higher caregiver burden and higher number of episodes, especially manic episodes (14) and more severe manic episodes (42). Manic episodes of the illness are very disruptive to daily life, work and family relationships. Due to this, these episodes place great demands on family members involved in caregiving. These demands can persist even during remission, where residual symptoms are often still present and lead to caregiver burden. Available data from India suggest that in contrast to patients from the West, patients from India have a higher number of manic episodes (43). Taken together, this finding has important implications as this suggests that efforts must be made to prevent frequent relapses in patients with bipolar disorder, especially in the Indian context to reduce the caregiver burden (44).

In the present study, higher burden was also associated with a higher number of depressive episodes and this finding is supported by existing literature (16).

Long-term management of bipolar disorder requires continuation of medications with good compliance. Poor medication compliance has been shown to be associated with many negative patient-related outcomes like higher risk of relapses, suicidality, poor quality of life, higher residual or sub-syndromal symptoms etc (45, 46). The present study adds to this body of literature and suggests that poor medication compliance in patients is also associated with higher caregiver burden and this finding is supported by the existing literature (11).

Among the demographic variables of caregivers, higher age of caregivers was associated with higher caregiver burden. This finding is also supported by existing literature (6). This association possibly suggests that with increasing age, the caregivers possibly experience more burnout, lose hope and also lose physical vigour to take care of the mentally ill relative.

Accordingly, it is important for the mental health professionals to support the ageing caregivers.

To conclude, the present study suggests that BPAD is associated with higher caregiver burden. Higher caregiver burden is associated with clinical variables of the patients and demographic variables of the caregivers. Among the patient-related variables, longer duration of illness, those with a higher number of lifetime episodes of either polarity and poor medication adherence are associated with higher caregiver burden. Hence, all measures must be taken to minimise relapse in patients with BPAD. Among the demographic variables of caregivers, higher caregiver burden is reported by caregivers who were relatively older, of female gender, uneducated or illiterate, homemakers and from nuclear families.

Our findings highlight the need for additional research on interventions to reduce burden among caregivers of patients with bipolar affective disorder. For better outcomes of disease, more attention needs to be given to the primary caregivers in terms of psycho-education and counselling.

Introduction

India ranks second in world not only in terms of its population but also in disaster proneness.¹ Disasters, whether they are natural or man-made, result in a wide range of mental and physical health consequences.² International public agenda has taken notice of protection and care of children in natural and man-made disasters. This, in large part, is due to observations that those affected and overlooked often include children and adolescents.³ There is continuous controversy about the impact of disasters on victims including children^{4 , 5}and some investigations deny that serious psychological effects occur.^{6, 7, 8}However further researchhas found that the criterion used in these studies were extremely narrow and inadequate and hence more systematic, clinically relevant investigations are required.⁹ For children and adolescents, response to disaster and terrorism involve a complex interplay of pre-existing psychological vulnerabilities, stressors and nature of support in the aftermath. Previous research has shown that direct exposure to differenttypes of mass traumatic events is associated with an increasein post-traumatic stress symptoms, ^{10, 11, 12, 13} anxiety, and depression, ^{11, 14} which are frequently comorbid withpost-traumatic stress reactions among youth.¹⁵ To the best of our knowledge, studies on long term psychological effects of disasters on younger age groups from South Asian countries are only a handful even though the frequency and the extent of natural disasters in this part of the world are considerable. As trauma during childhood and adolescents can etch an indelible signature in the individual's development and may lead to future disorder,¹⁶ it underscores the need for such studies.

A snowstorm followed by an avalanche took over a small mountainous village “Waltengu Nard” in South Kashmir, India on 19th Feb. 2005, about a month after the devastating Indian Ocean Tsunami. Of the total population, 24.77% (n=164) had perished. ¹⁷ As reported, the total population of children and adolescents prior to disaster was 242, of whom 52 died (21.49%).¹⁷ The present study is a discreet one which aims to determine long term psychiatric morbidity among the surviving children and adolescents of this disaster affected region five years after the snowstorm disaster. This is based on the notion that psychiatric disturbances can be present in children and adolescents years after a disaster has occurred.^{18, 19, 20} The socio-demographic variables of the patients are also studied. The results may support the need to apply wide area epidemiological approaches to mental health assessment after any large scale disaster.

Material and Methods

The study was designed as a survey of children attending school. Children from ages 6 years to 17 years from the high school near Waltengu Nard were taken up for the study. Only those children who were present in the area during the disaster were included in the study. Those with presence of any psychiatric disorder prior to the time of disaster, mental retardation, organic brain disorder, serious physical disability prior to disease (e.g. blindness, polio, amputated limbs etc.) or severe medical condition (e.g. congenital or rheumatic heart disease, tuberculosis, malignancy etc.) were excluded from the study. Within the school, an alphabetically ordered list was prepared including all classes of school with children aged 6-17 years 11 months. Every 3^rd student on this list was chosen and subjected to inclusion and exclusion criteria until a sample size of 100 children was complete. Informed consent was obtained both from the child and one of his/her caregivers or parents.

Selected children were subjected to the Mini International Neuropsychiatric interview for children and adolescents (MINI-KID) for evaluation of symptoms and diagnosis which is a DSM-IV based diagnostic interview with high reliability and validity. ^{21, 22} A semi structured proforma was prepared for socio-demographic profile. Kuppuswamy's Socioeconomic Status Scale, 2007 was used for determining socio-economic status. ²³ Oslo-3 Social Support Scale (OSS-3) was used to calculate social support. ²⁴

Interviews were conducted following formal training in instituting MINI-KID by trained psychiatrists of the Department of Psychiatry GMC Srinagar. The data was then subjected to appropriate statistical methods. A p-value less than 0.05 was taken as significant.

Results

Of the 100 children and adolescents studied (41.32% of the affected population of children and adolescents) 41 were noted to have at least one psychiatric diagnosis (patients). The socio-demographic profile of these patients is represented in Table 1. Age and sex distribution of diagnoses is presented in Table 2 and Table 3 respectively.

Table 1: Sociodemographic Profile

Table 2: Age Wise Distribution of Diagnosis

Table 3:Sex Wise Distribution of Diagnosis

A total of 54 diagnoses were observed in these 41 patients (Figure 1), with comorbidities present in 12 patients (29.27%). 11 of these 12 patients were experiencing two psychiatric disorders present concurrently and 1 was enduring three concurrent psychiatric diagnoses. Post-Traumatic Stress Disorder (PTSD) was the commonest comorbidity seen in 6 patients. This comes to 42.86% of total PTSD cases. This was followed by Major Depressive Disorder (MDD), Generalized Anxiety Disorder (GAD), Suicidality, Social Phobia, Panic Disorder, Agoraphobia and Separation Anxiety Disorder (SAD) in 2 each. Attention Deficit/Hyperactivity Disorder (ADHD), Conduct Disorder (CD), Specific Phobia (dark), Substance Abuse and Dysthymia were comorbid in one patient each. Studies have consistently shown presence of psychiatric comorbidities post-disaster.^{48, 49} Of the total 54 diagnoses, the commonest were Anxiety disorders (except PTSD), PTSD and affective disorders (includes MDD, dysthymia and mania) comprising 37.04% (N=20), 25.93% (N=14) and 14.81% (N=8) of total diagnosis respectively.

Figure 1: Diagnostic profile of the patients

Discussion

When children and their families are involved in natural or man-made disasters they may be exposed to diverse stressors which may impact mental health of the survivors, including children.²⁵ Studies have suggested that reliance on parental reports of children’s distress may not be valid as parents typically under-report symptoms compared with child and adolescent self-report in mental health surveys.²⁶ Thus in our study the psychiatric interview of each child was done individually without getting leads from their parents. In the early "heroic" and "honeymoon" phases of disaster relief there is much energy, optimism, and altruism. As fatigue sets in over the time and frustrations and disillusionment accumulate, more stress symptoms may appear.²⁷ Accordingly, the study was carried out five years after disaster to catch this delayed response to disaster in the form of psychiatric morbidity.

Consequences of the extensive amount of stress on our sample population due to the snowstorm resulted in a high prevalence of psychiatric disorders in our sample which was apparently not due to any other psychological stress during this period. Despite the fact that the study was done five years after the disaster, the research generated high psychiatric morbidity. Many young survivors reported restlessness and fear with the return of the season in which snowstorm occurred. All these kept the memories of the disaster and the losses fresh in their mind thus not allowing the wounds to heal. Some said that they couldn't keep their minds off the snowstorm during the weeks approaching the anniversary. This was much like the so called anniversary reactions.²⁸ Even children and adolescents, who have rebuilt their homes or found new dwellings to rent, frequently feel a sense of loss at the anniversary. Though the area was provided with adequate relief in terms of better infrastructure, education, employment and financial help in years post disaster to make their life without psychological distress, but, perhaps four such anniversary reactions and the fact that they are still living in the same geographical area and climate conditions have not allowed them to settle down in a routine since the psychological distress. Of the total sample of 100 patients, 41 % (N=41) reported at least one diagnosis. This is almost similar to a study by Kar and Bastia after a natural disaster in Orissa (cyclone) who found 37.9 % of adolescents with any diagnosis.²⁹ Similarly Margoob et al found that 34.39 % had a psychiatric disorder at the end of one year, after disaster.¹⁷ Other studies yielded results in the range of 12% to 70% in terms of total psychiatric morbidity.^{26, 30-33}

PTSD was the commonest individual diagnosis in our study with 14% (N=14) of the total population. Studies have shown PTSD prevalence after disaster from as high as 72 %³⁴ to as low as 8 %.³⁵ However, these were done immediately or within a few months after the disaster and the longitudinal pattern was not studied. A study conducted by Margoob et al reported a prevalence of 18.51 % in a sample of survivors one year after the same snowstorm on which this present study is based.¹⁷ Similarly, Bockszczanin et al 2.5 years after a flood in Poland reported 18 % of children to be suffering from PTSD.³⁶ Thus our results of 14 % patients suffering from PTSD are also similar to the trend as we are studying them after a period of five years following the disaster. Diagnosis of PTSD in our study was more common among the pre-adolescent age group, 22.58 % (N=7) and adolescents 33.33% (N=2). Similar findings were reported by Hoven et al who found a prevalence of 20.1 % in this age group.³⁰ Also PTSD was more frequent in females in our study. It was observed in 16.98 % females (N=9) as compared to 10.64 % for males. Hoven et al also found high prevalence in girls (13.3 % vs. 7.4 %).³⁰

Anxiety Disorders (excluding PTSD) formed the most common collective diagnostic category in our sample. Anxiety disorders were present in 20 % (N=20) of our sample population which formed about 37.04 % of total diagnosis. These included cases of GAD 5% (N=5), SAD 4% (N=4), Social Phobia 3% (N=3), Agoraphobia 3% (N=3), Panic Disorder 2% (N=2), OCD 2% (N=2) and Specific Phobia 1% (N=1). Similarly Norris et al found anxiety in various forms in 32% of their sample of disaster victims.²⁵ Similar findings were also reported by Reijneveld et al.³⁷ Hoven et al in an important study after 9/11 found prevalence of various anxiety disorders to the magnitude of 28.6%.³⁰ Our study correlated very closely to the above mentioned study. GAD was the commonest anxiety disorder among the above group. A prevalence of 5% (N=5) was found in the study sample. This prevalence was almost half of the earlier studies in children and adolescents after a disaster by Kar and Bastia²⁹ where it was 12% and by Hoven et al ³⁰ where it came out to be 10.3 %. However these studies were conducted within a few months after the disaster and hence came out with a higher prevalence of GAD than ours. It was more common in girls in contrast to boys (7.55 % vs. 2.12%) similar to study by Hoven et al.³⁰ SAD was also seen to predominate in anxiety disorders with 4 % (N=4) of the sample receiving the diagnosis. Some studies like one by Hoven et al found it to be prevalent in 12.3 % of their sample 6 months after 9/11.³⁰ However other studies have found SAD to be comparatively less frequent post disaster in children and adolescents.³⁴ Thus our findings are modest and lie somewhere between the above two studies. Also ours was a long term study hence SAD figures are a bit low. SAD in our study was more prevalent in girls than boys (5.66% vs. 2.12%). Moreover, it was exclusively seen in ages below 10 years. The above findings are in tune with the study by Hoven et al.³⁰ Panic disorder showed a low prevalence in our study and was found in only 2 % (N=2) patients. In both of these patients it was found to be comorbid, with MDD in one and with Agoraphobia in another. Studies immediately post disaster found the prevalence to be around 10.8 % (Math et al)³² and 8.7% (Hoven et al).³⁰ However in the survivors of the same area, in which our study is based, an earlier study one year after the disaster found 3.08 % prevalence of panic disorder which is very similar to our study.¹⁷ It was more prevalent in females and is well correlated to a study by Hoven et al.³⁰ Agoraphobia was present in 3 % (N=3) patients. It was comorbid in two patients with panic disorder and with PTSD, and an individual diagnosis in one. Hoven et al have found high rates of Agoraphobia post disaster i.e., about 14.8%.³⁰ But again this study was done only 6 months after 9/11 hence more morbidity. Female preponderance of the diagnosis was established (3.77 % vs. 2.12 %) as with earlier studies.³⁰ Obsessive traits are known to increase subsequent to disaster in the surviving population.³⁸ Similarly 2 % of cases satisfying the criteria for OCD were seen in our study. The commonest obsessions were recurrent intrusive and annoying themes related to the disaster and ruminations about whether it could have been prevented, followed by worries related to harm befalling themselves, family members, or fear of harming others due to losing control over aggressive impulses. Other obsessive themes were related to scenes of trauma and commonly blood. Obsessions regarding extreme fears of contamination were also present.

The affective disorders have been studied less often than PTSD after disaster. Depression is known to occur with increased frequency subsequent to disaster.²⁵ MDD was present in 4 % (N=4) of the total sample population. Studies conducted immediately after disasters have found higher prevalence. Math et al,³² Kar & Bastia²⁹ and Catani et al³³ found the prevalence of 13.5 %, 17.6 % and 19.6 % in their studies respectively. A study at three months and at one year after disaster on the adults in the same population as our study found the prevalence of MDD as 29.6 % and 14.28 % respectively.¹⁷ This decreasing trend is substantiated by the findings of our study and is in line with it. MDD was more common in females (5.66 % vs. 2.12%) which is similar to the study of Hoven et al.³⁰ Our findings of increased prevalence of MDD in middle adolescents (7.69 %) as compared to other age groups is also comparable to Hoven et al.³⁰ Of the Dysthymia cases, 3 % (N=3) were observed in our studies. Increased prevalence of dysthymia has not been reported post disaster in earlier studies. Our findings could be a part of large affective diaspora with dysthymia resulting from diminished self-esteem and a sense of helplessness subsequent to disaster. In addition to the time period for depression these patients were given the diagnosis of dysthymia because the depressed mood in them was more apparent subjectively than objectively. Finally these patients could have been on a natural course of dysthymia which usually begins in childhood. Combined dysthymia and MDD accounted for 7 % (N=7) of patients which if taken as a collective depression category, the results are slightly more comparable with the above studies. One patient had Mania (past). This patient had a positive family history of Bipolar Affective Disorder. This could be an incidental finding even though psychological stress is known to precipitate mania.³⁹ Also the prevalence is 1 % in our study which is even less than the prevalence in general population thus it could be an artifact.⁴⁰Studies have consistently found increased prevalence of adjustment disorder after disaster.⁴¹ In our study prevalence of adjustment disorder was 3% (N=3, anxiety 2, depression 1). In a study by Math et al 3 months after tsunami it was 13.5%.³² A lower prevalence in our study was again due to the long term nature of study. The role of trauma, stress, and negative life events as risk factors for suicidal ideation and behavior has long been recognized.⁴² A longitudinal investigation looking at the trends in suicidality and mental illness in the aftermath of Hurricane Katrina found significant increases in suicidal ideation and plans in the year after the disaster as a result of unresolved hurricane related stresses.⁴³ The suicidality in our population sample was found to be 2% (N=2) of sample. These results were in tune with that of Kessler et al, although it was immediately after hurricane Katrina and hence a higher prevalence of 6.4%.⁴³

Many symptoms of PTSD overlap with the symptoms of ADHD and CD.⁴⁴ In our study, each of the disorders were present in 2 % of the sample. In one patient, they were comorbid with each other (ADHD with CD). In a study by Hoven et al 6 months after 9/11, the prevalence of CD was found to be as high as 12.8 %.³⁰ This could be because of immediate post disaster nature of the study. Also because of the symptom overlap more weight was given to the PTSD diagnosis.

Three patients had a diagnosis of Substance Abuse, Tic Disorder and PDD, 1 % each. Though substance abuse is known to increase subsequent to disaster in adolescents,³⁰ no evidence was found for relation of tic disorder or PDD to the post-disaster psychiatric stress. The cause of a low prevalence of substance abuse in our sample was because of the fact that the area is inhabited by Muslim population and hence alcohol is not religiously sanctioned, and, harder substances are either not available or they can’t be afforded. The only substance which is available is marijuana or cannabis. However, most used it only recreationally and the criterion for abuse was not met. Even the sole patient of substance abuse was also taking cannabis. Also, it is a well known phenomenon that drug dependent subjects do not reveal the true information and deny any history of abuse at first contact with the investigating team.⁴⁵ Tic disorder and PDD are regarded as biological disorders and their relation to trauma is only incidental.^{46, 47}

Studies have consistently shown presence of psychiatric comorbidities post disaster.^{48, 49} The same was observed in our study where 29.27 % of total patients had comorbid psychiatric diagnosis. Similar results were found by Kar and Bastia who found comorbidities in 39% of adolescents.²⁹ PTSD is the most common comorbid disorder observed during the period post disaster^{48, 49} and the same was observed in our study with PTSD comorbid in 14.63 % (N=6) of cases. However when all the anxiety disorders were combined except PTSD, they were found to exceed the comorbidity of PTSD and they were comorbid in 21.95 % (N=9) patients. There is expanding literature regarding comorbidity of anxiety and depression in children and adolescents.^{50, 51, 52} Similar comorbidity of an anxiety disorder (including PTSD) and depressive disorder (including Dysthymia) were seen in 7.32 % (N=3) of patients in our study. These results show that psychiatric diagnoses are frequently comorbid after the disaster and there is a need to be vigilant about them for a holistic psychiatric assessment, treatment and rehabilitation of the survivors.

Sociodemographic Profile: In our sample the prevalence of psychiatric morbidity was at maximum in pre-adolescents (6-10 years age group) and it was 61.29 % of the sample of pre-adolescents.This is consistent with the research that has suggested that younger children possess fewer strategies for coping with both the immediate disaster impact and its aftermath, and thus may suffer more severe emotional and psychological problems.⁵³ Second commonest group was of 11-13 years with 40 % morbidity in them which was consistent with an earlier study which also found significant morbidity in this age group.⁵⁴ The age characteristics of the total population also closely matched the above findings. More females than males were found to exhibit psychiatric morbidity in our study (47.17 % vs. 34.04 %). Though these findings were in tune with those of Hoven et al,their findings were a little lower than ours (34.7 % vs. 21.8 %).³⁰ Some studies have found that girls express more anxiety-depressive disorders³⁰ and PTSD symptoms^{55, 56} and boys seen to exhibit more behavioral problems.⁵⁷ Similarly in our study rates of anxiety disorders, depressive disorders and PTSD were higher in girls and conduct disorder was exclusively found in boys.

Our study suggested that children up to 5^th standard were (51.02 %) more susceptible than those in higher classes. This was in accordance with an earlier study by Kar et al.⁵⁴ These findings are also in accordance with the findings of a study by Hoven et al. which found maximum morbidity (34.1 %) in preschoolers.³⁰ Thus it could be said that higher educational status was protective, in addition to increasing age. Psychiatric morbidity was highest in children who were from joint family systems (48.15%). This was followed by children from extended nuclear (37.5%) and nuclear (27.27%) families. This pattern is consistent with an earlier study by Margoob et al.⁵⁸ This could be because of the fact that in the sample of joint families there was loss of more family members in the tragedy. There were no cases of upper and upper-middle socio-economic class and lower-middle class was significantly less in our sample. This was because of the demographics per se and was not a sampling error. Consequently, higher morbidity was seen in the upper-lower socio-economic class (49.09%) followed by the lower class (31.71%). All the above findings are in accordance with an earlier study by Margoob et al.⁵⁸

Psychiatric morbidity was not found to be influenced by the source of family income. Same was observed by Kar and Bastia in their study.²⁹ The majority of the patients had poor social support (52.17%, p=0.03). These findings are substantiated by earlier studies.⁵⁹ Loss of a parent was strongly associated with lower social support and high psychiatric morbidity. This was also reported by earlier studies.^{31, 60} Our study reported higher psychiatric morbidity in first-born children (71.43%). This could be due to increased burden of family matters on an eldest child subsequent to a disaster especially when head of family or mother has perished in such a catastrophe. This was in accordance with earlier studies on birth order and psychiatric morbidity.⁶¹ However in our study only childs also documented significant morbidity which is in contrast to earlier studies.⁶¹ This could be due to the fact that an only child had significantly less social support due to fewer siblings and death in the family due to disaster considerably compounding the problem. Also, often the youngest born is more pampered and hence more likely to feel emotionally insecure when attention is shifted from him in the aftermath of a disaster.

There was an unavoidable limitation in the study; the disaster-affected population was not compared with a normal or control population. The difficulty we faced was finding a control population as the area has a racially, geographically and culturally distinct population of Gujjars and all of them were affected. So no appropriate control group could be found. However if we compare it with most of the studies done in populations from the north India, the prevalence in our study is largely greater than those found In those studies.⁶²

Conclusion

This research portrays and scrutinizes the experience of children and adolescents in the aftermath of a snowstorm disaster and supports the idea that children are susceptible to morbid psychological experiences long after the traumatic event has occurred. With that said, we want to stress the decisive role of support agents for children. These agents include the adults and peers who help children and youth recuperate in the long term. Provision of an outreach psychosocial and clinical service long after the disaster when no one is around to help after the initial knee jerk response of relief agencies is also stressed.

Introduction

The American Diabetic Association (ADA) and the American College of Endocrinology (ACE) recommend HbA1c levels as diagnostic criteria for diabetes mellitus. Physicians have adopted HbA1c levels as a convenient way to screen for diabetes, as well as to monitor therapy. There exists concern that because HbA1c is formed from the glycation of the terminal Valine unit of the β-chain of haemoglobin, it may not be an accurate surrogate to ascertain glycemic control in certain conditions that affect the concentration, structure and function of haemoglobin. It makes logical sense to infer that HbA1c levels should at least in part reflect the average haemoglobin concentration ([Hb]). Kim et al (2010) stated that iron deficiency is associated with shifts in HbA1c distribution from <5.0 to ≥5.5% ¹ and significant increases was observed in the patients' absolute HbA1c levels 2 months after treatment of anaemia.² There is a dearth of literature on HbA1c levels in the anaemia population, and a reference range for this unique population does not currently exist. There are a few documented studies on this matter, the findings of which are at best, inconsistent.

It is thought that the various types of haemoglobin found in the myriad of haemoglobinopathies may affect haemoglobin-glucose bonding and/or the lifespan of haemoglobin, and by extrapolation, HbA1c level. Hence, extending target HbA1c values to certain haemoglobinopathaties may be erroneous due to potential differences in glycation rates, analytical methods (HbF interfers with the immunoassay method) and some physiological challenges (markedly decreased red cell survival).³

There is a significant positive correlation between haemoglobin concentration and HbA1c in the patients with haemolytic anaemia.^4,5 Cohen et al (2008) reported that observed variation in red blood cell survival was large enough to cause clinically important differences in HbA1c for a given mean blood glucose,⁶ and haemolytic disorders may cause falsely reassuring HbA1c values.⁷ Jandric et al (2012) inferred that in diabetic population with haemolytic anaemia, HbA1c is a very poor marker of both overall glycemia and haemolysis.⁸ Mongia et al (2008) report that immunoassay methods for measuring HbA1c may exhibit clinically significant differences owing to the presence of HbC and HbS traits.⁹ However, Bleyer et al report that sickle cell trait does not affect the relationship between HbA1c and serum glucose concentration and it does not appear to account for ethnic difference in this relationship in African Americans and Caucasians.¹⁰

Koga & Kasayama (2010) advise that caution should be entertained when diagnosing pre-diabetes and diabetes in people with low or high haemoglobin concentration when the HbA1c level is near 5.7% or 6.5% respectively, citing the implication of changes in erythrocyte turnover. They further assert that the trend for HbA1c to increase with iron deficiency does not appear to necessitate screening for iron deficiency to ascertain the reliability of HbA1c in this population.¹¹

In the light of the uncertainty in the influence of anaemia and haemoglobinopathies on HbA1c, it is imperative that clinicians are aware of the caveats with HbA1c values when they make management decisions in the anaemic population.¹² There is currently a call for the use of other surrogates for ascertaining average glycemic control in pregnancy, elderly, non-Hispanic blacks, alcoholism, in diseases associated with postprandial hyperglycemia, genetic states associated with hyperglycation, iron deficiency anaemia, haemolytic anaemias, variant haemoglobin states, chronic liver disease, and end-stage renal disease (ESRD).^13,14

Study objectives and hypothesis

The study attempts to discern clinical differences in HbA1c levels in patients with anaemia compared to non-anaemic population, as well as to quantify and show the direction of such difference if they indeed exist. We hypothesize that as glucose is covalently bound to haemoglobin in glycosylated haemoglobin, HbA1c levels in non-diabetic anaemic population is significantly lower than in non-diabetic, non-anaemic population.² However, this relationship may not hold true for certain anaemias, haemoglobinopathies and hyperglycation states in some genetic syndromes.

Study design and method

The study is a retrospective chart review of patients with and without anaemia who underwent haemoglobin concentration and HbA1c level testing at The Brooklyn Hospital Center (TBHC) from July, 2009 to June, 2013. Using Cohen (1987) power table, assuming a power of 0.8, alpha level of 0.05, and a small effect size of 0.2 standard deviations (SD), sample size estimation of 461 was computed. A convenient sampling method was used to select patients who meet inclusion criteria, absent exclusionary conditions. In using this sampling method, we queried the electronic medical record at the TBHC using the below-listed inclusion and exclusion criteria. The query generated a list of “potential subjects”. We then reviewed the electronic chart of each patient on this list to confirm that they indeed meet all study criteria (excluding further if any exclusion criteria was identified on “second look”. We continued the selection until the computed minimum sample size of 461 was significantly exceeded. During this process, we had to examine every patient on the “potential subject” list generated by the initial query to achieve this goal. For the purpose of the study, anaemia is defined as haemoglobin concentration <11g/dl.

Inclusion criteria:

• iStudy participant must be at least 21 years of age. We adopted this age criteria because at TBHC, electronic medical records was only available for the non-pediatric population over the study period. Patients below 21 years were managed at the pediatrics department using paper charts until the recent adoption of the EMR system. It would have been difficult conducting the study using paper charts.
• iStudy participant must have at least one documented HbA1c level obtained within a month of a haemoglobin concentration assay. This criterion was adopted to allow for more inclusiveness in the study. It is our experience that haemoglobin assays may not be available on the same day as HbA1C assays considering the retrospective nature of the study.

Exclusion criteria:

• Confirmed cases of diabetes mellitus (using two or more of the following: presence of symptoms related to diabetes, fasting blood glucose, 2 hours post-prandial glucose, and oral glucose tolerance test).
• Documented history of gestational diabetes (GDM)
• Documented history of endocrinopathy with affect for glycemic control
• Current or prior use of medication with potential to increase or decrease HbA1c (includes, but not limited to antidiabetics, corticosteroids, statins, and antipsychotics)
• Pregnancy or pregnancy-related condition within three months of HbA1c assay
• Haemoglobin concentration <6 g/dl or >16g/dl.
• Blood loss or blood transfusion within two months of HbA1c assay

The study assumed a consistent HbA1c assay method at the study center over the study period. 482 (229 anaemic and 253 non-anaemic) were selected. The study reviewed electronic medical records of selected patients, extracting data on HbA1c, fasting blood glucose (FBG), 2-hour post-prandial serum glucose (2HPPG), 2-hour oral glucose tolerance test (OGTT), haemoglobin concentration and electrophoresis, and anaemia work-up results when available. Subsequent measures of HbA1c two months after correction of anaemia was also documented and compared to pre-treatment levels.

Results and Analysis

The mean age of the anaemic and non-anaemic was 51.8 and 64.6 years respectively. Using the student’s t-test and x² analysis respectively, the difference in mean age of both groups (anaemia and non-anaemic) was significant at p0.05 while gender distribution was similar (p>0.05), see table 1. The mean HbA1c for anaemic and non-anaemic groups was 5.35% and 5.74% respectively, amounting to a 0.4 unit difference in (8%) in mean HbA1c. This difference was statistically significant (p0.02). A significantly higher variance was observed in the anaemia group (0.79 vs. 0.64).

Table 1: Gender and age distribution and statistics

p-Values: Age=0.023, Gender=0.061

Assuming that 95% of the population is normal, computation of HA1c reference range (mean ±1.96SD) for the anaemia and non-anaemic group yielded 3.8-6.9 and 4.5-7.0 respectively. There was a significantly positive spearman correlation between [Hb] and HbA1C (r=0.28, p0.00). The mean HbA1c level and proposed reference ranges for the five anaemia subgroups (anaemia of chronic disease [ACD], iron deficiency anaemia [IDA], mixed anaemia, macrocytic anaemia and sickle-cell disease) are shown in table 2. Using one-way ANOVA analysis, the difference in the mean [Hb] and HbA1c across anaemia subtypes was not statistically significant (p0.08 and p0.36 respectively), see table 2.

Table 2: Anaemia subtypes with HbA1c statistics

p-values: [Hb] for anaemia subtypes=0.08, HbA1C for anaemia subtypes=0.36, HbA1C anaemia vs. non-anaemia=0.02. ACD: anaemia of chronic disease, IDA: iron deficiency anaemia, SCD: sickle cell disease.

The study also examined the anaemia group to document the effect of anaemia correction on HbA1c levels. Only 62 of the 229 anaemic participants had documented [Hb] and HbA1c after interventions to correct anaemia, see table 3 and 4.

Table 3: Trend in [Hb] and HbA1c

[Hb]₁ and [Hb]₂: haemoglobin concentration pre- and post- treatment for anaemia. HbA1c₁ and HbA1c₂: HbA1c pre- and post-treatment for anaemia

Table 4: Trend in [Hb] and HbA1c for anaemia subtypes

ΔHb: change in haemoglobin concentration ([Hb]), ΔA1c: change in HbA1c

Using the student’s t-test, analysis, a 0.9g/dl mean improvement in [Hb] in the anaemia group (significant at p0.00) did not result in a statistically significant change in HbA1c (-0.02 units, p0.78). Similar results were obtained with anaemia of chronic disease and iron deficiency anaemia (ICD: change [Hb] =+0.6g/dl, change HbA1c=0.09, p0.31; IDA: change [Hb]=+1.3g/dl, change HbA1c=0.03, p0.79).

Discussion

There was an over-representation of the elderly in the anaemia group (58.1% vs. 21.7%). This is not unexpected as nutritional anaemia and anaemia of chronic disease increase in prevalence with the increasing co-morbidities associated with increasing age. The linear relationship between [Hb] and HbA1c holds true for anaemic and non-anaemia populations. There is a statistically significant difference of 0.4units (8%) in the mean HbA1c between the anaemic and the non-anaemic population. This difference is even more marked when the lower limit of the range is compared (3.8 vs. 4.5, difference of 0.7unit, 18%), the significance of which is not as clinically impacting as the upper limit of the range (diabetes mellitus diagnostic criteria). However, the relatively lower limit of normal for HbA1c in anaemic subgroups (especially of anaemia of chronic disease) may make low values of HbA1c in these patients less indicative of over-enthusiastic glycemic control, as well as less predictive of the increase in mortality associated with such tight control.

The upper range of normal for HbA1c for the anaemia and the non-anaemic groups and by extrapolation the proposed diagnostic criteria for diabetes, is however more similar (6.9 vs. 7.0%). This result appear consistent with Koga and Kasavama (2010) assertion that the trend in HbA1c does not appear to necessitate screening for iron deficiency to ascertain the reliability of HbA1c in this population.¹¹ Our observation is explained by the greater variance associated within the anaemia group. The significantly higher variance observed in the anaemia may be explained by the convenient homogenization of clinically heterogeneous anaemia entities in the anaemia group. Perhaps a prospective study that avoids this may report differently.

The significantly higher variance (23%) in the anaemia is explained by the heterogeneity of the subtypes within the anaemia group. The myriad of pathophysiologies (from variant haemoglobin affecting structure and function, and perhaps glycation rates of haemoglobin, to shortened erythrocyte lifespan due to intravascular and extravascular haemolysis) accounts for a less precise HbA1c reference range for the anaemia group. Separating the anaemia group into unique anaemia subtypes created less heterogeneity, reduced some within group variance and yielded a more precise references range for some anaemia subtypes.

The widened 95% CI of mean and reference ranges observed with mixed and sickle cell anaemia (95% CI of mean =4.82-5.59 and 4.84-6.26 respectively) may be attributable in part to the small number of participants in these subgroups (11 and 5 respectively, the normal curve is less robust in these circumstances [when n<30])). Furthermore, the marked variability in the type, severity, and the number of chronic morbidities and deficiencies causing mixed anaemia may be contributing. The imprecision of HbA1c observed with the sickle cell may be compounded by the unstable clinical course of sickle disease, marked by periodic crises with fluctuating [Hb] associated with intermittent or chronic haemolysis. These observations make the case for defining HbA1c reference ranges for each anaemia type.

A modest correction of anaemia (Δ [Hb] of +0.9g/dl, i.e <1g/dl) did not appear to cause a significant change in HbA1c levels. It is possible that higher increments in [Hb] may produce significant change in HbA1c (we predict in the direction of increment). A similar pattern was observed with anaemia of chronic disease and iron deficiency anaemia subtypes, where improvements in [Hb] of 0.6 and 1.3g/dl respectively did not cause a significant change in HbA1c. We propose that with anaemia of chronic disease, the change in [Hb] concentration was too modest to cause a significant change in HbA1c. The relative small size of participants (33) examined also makes type II statistical errors highly likely. We further propose that with anaemia of chronic disease, the myriad of functional cellular and system abnormalities (many, potentially affecting cellular homeostasis, especially acid-base balance and haemoglobin molecule covalent binding) associated with the primary disorder may impact on the potential for increase in HbA1c with increasing [Hb]. In view of the retrospective nature of the study, we could not ascertain the timelines of certain interventions and hence accurately determine the persistence of anaemia correction. Theoretically, a recent correction in [Hb] is less likely to impact on HbA1c. As alluded to above Kim et al (2010) evaluated for changes in HbA1c two months after correction of anaemia. Similar explanations are offered for the observation with iron deficiency anaemia. There were only 21 participants in the iron anaemia subgroup (i.e. <30, probable violation of a rule for use of parametric tests), making the parametric statistical tests less robust for the analysis. We did not study patterns with mixed, macrocytic and SCD, as each subtype had <7 (1,6,1) participants.

The study examined a large volume of data, eliminating as much as possible, potential extraneous factors in the relationship between [Hb] and HbA1c levels. However, the retrospective nature of the study made the control of other extraneous variables and certain patient attributes infeasible. It was also difficult to discern critical timelines and hence eliminate the potential impact of certain therapeutic interventions. Also, our exclusion of the younger population of patients (i.e. 16-20 years) does not necessarily indicate the result of the study may not be extended to this population of anaemia patients. In fact the similar human haemoglobin physiology in this group advises that the results may be extended to this younger population without concern. Due to the retrospective nature of the study, and in our attempt to increase inclusiveness, we allowed haemoglobin concentration and HbA1c assays done within a month of each other. In reality though, the majority (57%) had same day assays and even a greater majority (79%) had within same week assays. We recommend a larger scale prospective study with participants representative of all anaemia subtypes and ages so that the results can be extrapolated to the general population of anaemia patients.

Conclusion

The study emphasizes the need to exercise caution when applying HbA1c reference ranges to anaemic populations. It makes the case for defining HbA1c reference ranges and thus, therapeutic goals for each anaemia subtype. Redefining such reference ranges may increase the sensitivity of HbA1c in diagnosing diabetes in anaemic population if indeed the lower mean HbA1c (observed in this study) translates into significantly lower upper limits of references ranges (not observed in this study). Also, the realized reduced lower limits of reference range in this population will lead to appropriate clinical tolerance for lower HbA1c levels, with avoidance of inappropriate intervention for erroneous perception of over-enthusiastic control of diabetic hyperglycemia. We recommend that, absent risks factors for and symptoms relatable to diabetes, marginal elevations in HbA1c levels (i.e. HbA1c >6%) in anaemic patients should warrant confirmation of diagnosis using fasting blood glucose and 2HPPG or OGTT. The use of other surrogates of glycemic control, immune to the blur associated with haemoglobin type and concentration, may circumvent the problem associated with use of HbA1c in this special population. To this end, fructosamine and glycated albumin assays are currently being examined. ^1,15

Introduction

Non-attendance in outpatient clinics accounts for a significant wastage of health service resources. Psychiatric clinics have high non-attendance rates and failure to attend may be a sign of deteriorating mental health. Those who miss psychiatric follow-up outpatient appointments are more ill with poor social functioning than those who attend (1). They have a greater chance of drop out from clinic contact and subsequent admission (1). Non-attendance and subsequent loss to follow up indicate possible risk of harm to the patient or to others (2).

Prompts to encourage attendance at clinics are often used and may take the form of reminder letters (3), telephone prompting(4) and financial incentives (5). Issuing a copy of the referral letter to the appointee may prompt attendance for the initial appointment (6). Contacting patients by reminder letters prior to their appointments has been effective in improving attendance rates in a number of settings, including psychiatric outpatient clinics and community mental health centres (3).

Studies investigating the efficacy of prompting for improving attendance have generated contrasting findings and non-attendance remains common in clinical practice. We, therefore, carried out a naturalistic, prospective controlled study to investigate whether reminder letters would improve the rate of attendance in a community-based mental health outpatient clinic.

Design and Methods

The study was carried out at the Community Mental Health Centres based in Runcorn and Widnes in Cheshire, UK. The community mental health team (CMHT) provides specialist mental health services for adults of working age. Both CMHTs are similar in demographics, socio-economic need and, have relatively higher non-attendance rates in the clinic. In the week prior to the appointment, clerical staff from community mental health team sent a standard letter to some patients reminding the date and time of the appointment and name of the consulting doctor.  They recorded whether patient attended, failed to attend or cancelled the appointment irrespective of whether they had received a reminder letter or not.

We compared the attendance rates between experimental group (those who had received the reminder letters) and the control group ( those who had not received the reminder letters) over a period of 18 months. Throughout the study period, the same medical team held the clinics and there had been no major change in the outpatients’ clinic setting or administrative and procedural changes influencing outpatients’ attendance. Care Planning Approach (CPA) was implemented and in operation even before the introduction of reminding letters at both the sites.

Attendance rates for all the clinics held during the study period were obtained from medical records.  For all subjects who failed to attend, age and gender, was obtained from patients’ database. Patients whose appointments were cancelled were also included in the study.

Statistics and Data analysis

The data was analysed using SISA - Simple Interactive Statistical Analysis (7). Chi -squared tests were used to investigate the attendance rates between the groups, new patients and follow-ups, with the P value for statistical significance set at 0.05. Odds ratios were calculated to measure the size of the effect. In addition, we examined how age and gender may have influenced the effect of the text based prompting on attendance.

Results

In the experimental group a total of 114 clinics were booked, with clinic lists totalling 843 patients. Of these, 88 were new referrals and 755 were follow-up appointments.  65 of 114 clinics had full attendance.  A total of 228 patients failed to attend the clinic. Of those who failed to attend, 25 patients were new referrals and 203 were follow-up patients. 28 follow up patients and 2 patients newly referred to the team called to cancel their appointments.

In the control group, a total of 71 clinics were booked amounting to a total of 623 patients. Of these, 86 were new referrals and 537 were for follow-up patients. Only 25 out of 71 clinics had full attendance.  A total of 211 patients failed to attend. Of those who failed to attend, 32 were new referrals and 179 were follow-up patients. 55 follow up patients and 13 patients newly referred to the team called to cancel their appointments.                                        

Of those who failed to attend in the experimental group, 98 (43%) were women. The mean age of non-attendees was 38 years; with a range of 18-76 yrs .Of those who failed to attend in the control group110 (52%) were women. The mean age of non-attendees was 32 years; with a range of 19-70 yrs. 

In our study, failure to attend was not distributed evenly but had seasonal peaks at Christmas and during the summer vacation period.

The outcome from prompting in the experimental group is compared with the control group and displayed in Table 1.

χ^{2 =} Chi square, df = degree of freedom, OR= Odds Ratio, CI= Confidence Interval

The attendance rate in the experimental group was 71.95% (585/813) as opposed to 56.57% (344/555) in the control group (OR=1.57; p=0.0001).

The attendance rate for new patients in the experimental group was 70.9%( 61/88) as opposed to 56.16 %( 41/ 86) in the control group (OR=1.9; p=0.053).

The attendance rate for follow up patients in the experimental group was 72.0%( 524/727) and 62.8% (303/482) in the control group (0R=1.52; p=0.0007).

In addition, there were significantly more (by 22%) number of clinics with full attendance in the experimental group (OR= 2.44, P=0.003).

The observed difference was not influenced by patient’s age or gender.

Discussion

The results from this study confirm previous findings that reminder letters within a week before the appointment can improve attendance rates in community mental health clinics. Our results are similar to those of the Cochrane systematic review, which has suggested that a simple prompt in the days just before the appointment could indeed encourage attendance (8).

Although it has been reported elsewhere(8) that text based prompting increases the rate at which patients keep their initial appointments, our study did not show a similar result for new patients.

It is already demonstrated that new patients and follow-up patients in psychiatric clinics are distinct groups with different diagnostic profiles, degrees of mental illness and with different reasons for non-attendance. Follow-up patients are severely ill, socially impaired and isolated than new patients. (1). Forgetting the appointment and being too unwell are the most common reasons given for non-attendance by follow-up patients, while being unhappy with the referral, clinical error and being too unwell are the most common reasons in the new patient groups (1).  In addition, it has also been observed that increased rate at which patients keep their first appointments is more likely related to factors other than simple prompting (4) This explains our finding that prompting was more beneficial for follow-up patients as opposed to new referrals to the Community Mental Health Team.

We also identified several patients with severe mental illness who ‘did not attend’ for three successive outpatient appointments. Their care plans were reviewed and arrangements made to follow up with their community psychiatric nurses as domiciliary visits at regular intervals.  Such measures should reduce duplication of the services and shorten the waiting times for psychiatric consultation, which are well-recognised factors associated with non-attendance (9).

Non-attendance is an index of severity of mental illness and a predictor of risk (1). In addition to reminder letters, telephone prompts are also known to improve attendance (4). Successful interventions to improve attendance may be labour intensive but they can be automated and, ultimately, prove cost effective (8)

We noticed that there is limited research and lack of quality randomised controlled trials in the area of non-attendance and the effectiveness of intervention to improve attendance in mental health setting. More large, well-designed randomised studies are desirable. We also recommend periodic evaluation of outpatient non-attendance in order to identify high-risk individuals and implement suitable measures to keep such severely mentally ill patients engaged with the services.

There was no randomisation in this study and we relied on medical records.  We have not directly compared the characteristics of non-attendees with those patients who did attend the clinics. We did not evaluate other clinical and socio-demographic factors (e.g. travelling distance, financial circumstances, etc) that are known to influence the attendance rates in mental health setting. Hence, there may be limitations in generalising the results beyond similar populations with similar models of service provision.

Introduction       

Urinary tract infection (UTI) is the second most common infectious complaint in geriatric clinics overall, and the most common outpatient complaint caused by bacteria.¹ The diagnosis and treatment of UTI in the elderly is not the same as treating UTI in adults. In frail elderly patients with age-associated multiple severe underlying disorders and cognitive impairment, early recognition of bacteraemic UTI and prompt, appropriate treatment are critical in reducing the mortality.²Also, the extensive and inappropriate use of antimicrobial agents has invariably resulted in the development of antibiotic resistance which, in recent years, has become a major problem worldwide.³ The diagnosis and empirical treatment of UTI in the elderly is challenging and a sound knowledge of the prevalent epidemiology of bacteria and their resistance pattern is necessary for the same. However, there is not much information on the aetiology and resistance pattern of UTI in the elderly in India. This study was done to find out the present uropathogen profile causing UTI in our centre and their antibiotic resistance patterns.

Subjects and methods

This prospective study was done at our tertiary care centre from January to December 2008. The study included all patients who were admitted or visited the outpatient departments in the hospital with symptoms of UTI during the study period and had UTI confirmed by positive urine culture reports. Only one sample from each subject was considered. Subjects with clinical symptoms of UTI but no growth on culture were excluded from final analysis. Subjects who were treated with another antimicrobial within the previous 48 hours, or within 24 hours if only a single dose and in the presence of an appropriate positive culture and ileal loops or vesicoureteral reflux were also excluded from the study. Complete data regarding demography, sex preponderance, associated symptoms, pathogenic organisms causing UTI and their antibiotic resistance were collected.

Overall, 194 subjects were included in the study (male: 116, female: 78). The mean age of the study population was 73.54±7.19 years, ranging between the ages of 65 and 96. The distribution of patients according to gender across various age groups is given in table 1. A general trend of more male subject enrolment was seen across all the age groups. 

Isolation and identification of uropathogens

A clean catch midstream specimen, or suprapubic aspirate in subjects who were unable to give the former, was collected in a sterile, wide-mouth, leak-proof container to hold approximately 50ml from these subjects. Using a calibrated loop method of loop diameter 4 mm, 10 µL of the uncentrifuged specimen was transferred onto the agar plate and streak, using the modified Mayo’s technique without flaming the loop for isolation, and incubated at 35- 37⁰C for 24 hours. A specimen was considered positive for UTI if a single organism was cultured at a concentration of >10⁵ Colony Forming Units/ml. The Gram-positive and Gram-negative organisms culture isolates were further identified by using various biochemical reactions up to genus/species level wherever applicable.

Antibiotic sensitivity testing

In the presence of any potential growth, antibiotic sensitivity testing was done by the Modified Kirby-Bauer disc diffusion method according to the Clinical and Laboratory Standards Institute (CLSI) guidelines.⁴ The antibiotics tested were Imepenem, Meropenem, Ciprofloxacin, Ofloxacin, Norfloxacin, Amikacin, Gentamicin, Nitrofurantoin and Cotrimoxazole (Pathoteq Labs, India).

Extended Spectrum Beta-Lactamase (ESBL) detection

The screening for ESBL was done using Cefpodoxime (<17mm), Ceftazidime (<22mm), Aztreonam (<27mm), Cefotaxime (<27mm) and Ceftriaxone (<25mm). If the organisms showed the zone of inhibition lower than the minimum for any antibiotic disc, ESBL positivity was suspected. The phenotypic confirmation was done by testing the strain against Ceftazidime (Ca) and Ceftazidime/Clavulanic Acid. A > 5mm diameter of the zone of inhibition for Ceftazidime/Clavulanic Acid in comparison to Ceftazidime was considered indicative of ESBL production. Escherichia coli (E. coli) ATCC 25922 was used as ESBL negative and Klebsiella pneumoniae (K. pneumoniae) 700603 was used as ESBL positive reference strain.⁴

Statistical analysis

Descriptive statistics (totals, means, percentages, and standard deviations) were conducted using the statistical software package - SPSS Version 16.0 (SPSS Inc., Chicago, USA). Age, gender, organisms causing UTI, their antibiotic sensitivity and resistance, symptomatology of these subjects, and risk factors for UTI were included in the analysis and the results presented in tables and figures.

Results

Fever (57/194 - 29.4%) and dysuria (52/194 - 26.8%) were common symptoms of most UTI patients (Fig. 1). Diabetes mellitus (DM) and recent uro-genital instrumentation were the most common risk factors associated with UTI in the present study (Table 2). The organism profile and their antibiotic resistance profile were similar in patients with or without DM.

Figure 1. Various symptomatologies seen in patients with urinary tract infection during the initial presentation

Table 2. Frequency of various risk factors in subjects with urinary tract infection.

E. coli (138/194 - 71.1%) was the most commonly isolated pathogen responsible for UTI in the present study (Figure 2). 56.2% of the total infection was caused by ESBL positive organisms. The antimicrobial potency and spectrum for nine selected antimicrobial agents (Imepenem, Meropenem, Ciprofloxacin, Norfloxacin, Ofloxacin, Gentamicin, Amikacin, Nitrofurantoin and Cotrimoxazole) against the uropathogens were studied. The highest and least antibiotic resistance was noted against fluoroquinolones (79.9%) and carbapenems (3.61%) respectively (Fig. 3).

Figure 2. Frequency and distribution pattern of urinary tract infection pathogens and percentage Extended Spectrum Beta-Lactamase (ESBL) production.

Figure 3. Resistance pattern to various antibiotics of the uropathogens

Discussion

While increased frequency and dysuria are usual symptoms of UTI, uncertainty looms around the same as these symptoms can be masked by catheterisation, or be common and chronic in the elderly even in the absence of UTI.^5-10Fever was the most common symptom of UTI in the present study as with similar studies worldwide.^11-13 Studies have found that the elderly do not lack a febrile response; that an elevated temperature was the most common initial symptom, a marker for a serious infection, and the most important clinical indicator for antibiotic treatment.^14-16 Whitelaw et al¹⁷ reported that a delay in interpreting fever as a symptom of UTI led to a high mortality rate in the elderly within 24 hours of admission.

Diabetes isconsidered as an important risk factor for UTI with manyauthors having defined UTI in patients with DM as complicated when the UTI is symptomatic.^18-19 However, the authors did not find that DM influenced the organism profile and their antibiotic resistance in the present study. Bonadio et al²⁰ studied the influence of DM on the spectrum of uropathogens and antimicrobial resistance in elderly adult patients with asymptomatic UTI (mostly hospital-acquired). They found that DM per se did not seem to influence the isolation rate of different uropathogens and their susceptibility patterns to antimicrobials. These findings indicate that, although DM is a known immunomodulator, the role played by the same in altering the antibiotic resistance is minimal compared to recent invasive procedures.

Although the uropathogen profile of the present study resembles similar studies worldwide, the antibiotic resistance of these organisms was unusually high.^{2, 21} Cotrimoxazole is the recommended drug for treating UTI. However, more than one third of the study subjects were resistant to the first-line drug. 79.9% of the uropathogens were resistant to fluoroquinolones, which are considered as the second-line drug.  As prior fluoroquinolone use is a known risk factor for fluoroquinolone-resistantE. coli infection, it is plausible that frequent fluoroquinolone prescriptions may be contributing to the observed resistance.^22-23 Aypak et al ²⁴ found that treatment durations were statistically longer than the recommended three-day course when patients were empirically treated with fluoroquinolones due to increased resistance rates, and suggested to discourage the empirical use of fluoroquinolones in UTI.

The most troublesome finding of the present study is that ESBL-positive organisms accounted for 56.2% of the total infection. Not much information on ESBL-producing organisms causing UTI is available from India and most of these reports are from the younger population. The prevalence of ESBL-positive UTI in these studies varied between 26.6% and 48.3%.^25-26 To the best of our knowledge, this is the highest ever reported prevalence of ESBL-positive UTI in the elderly worldwide. ESBL-producing organisms are frequently resistant to many of the antimicrobial agents usually recommended for the treatment. As lesser new antibiotics are available for their management, we need to be concerned of this issue in years to come especially in tertiary care centres.  A unified antibiotic protocol is necessary to limit the morbidity and mortality associated with inappropriate and under-treatment of UTI.

The limitations of the present study were that altered mental status was not considered as one of the clinical manifestations of UTI in the elderly, which could have mitigated the total number of study subjects included in the study.  In addition, the phenotypic confirmation of ESBL-positive organisms was done using only Ceftazidime/Clavulanic Acid and not Cefotaxime/Clavulanic Acid as per the latest CLSI guidelines. As a result, there may be under-reporting of the incidence of ESBL organisms in the present study.

In conclusion, we report a significantly high resistance to common antibiotics among the uropathogens in the present study. Furthermore, the very high rate of ESBL-positive UTI is of concern, and monitoring for the same is necessary to prevent treatment failure and increased morbidity and mortality with UTI.

INTRODUCTION