Printer Friendly

Functional status and its associated factors in Nigerian adults with serious mental illnesses.


Functional status is a multidimensional concept that encompasses the subject's ability to perform daily activities and to participate in everyday situations such as working, studying, living independently, having leisure time and keeping relationships (1) while functional impairment refers to limitations due to an illness, as people with a disease may not carry out certain functions in their daily lives. (2)

Mental disorders, generally, create societal problems because they are often associated with impairment in the functional capacity of the patients. (3,4) When disability was added to the public health measures as was the case with the disability adjusted life years (DALYs), mental disorders ranked as high as cardiovascular and respiratory diseases, surpassing all malignancies combined or HIV. (5) The global burden of disease study, using DALYs, thus revealed the true magnitude of mental health problem due to the disability they produce. (6) Larry and Mauksch, (7) in their study among primary care population reported that having psychiatric disorders was associated with lower functional status and more disability days compared with not having mental illness. In particular, major axis 1 psychiatric disorders often cause significant impairment in the functional status of the patients. (8) Depression, bipolar disorders and schizophrenia are major axis 1 disorders (9) and are the most common. (10) The relationship between depression and functional impairment has been demonstrated in some studies. (11) A cross sectional survey by Kessler and his colleagues (3) showed that roughly 60% of depressed people reported severe or very severe impairment among successfully treated respondents. Some patients (20%) indicated substantial daily life functional impairment even after treatment.

Bipolar disorder, a chronic incapacitating condition, is known to be associated with disability and impaired function. (12) It accounts for major functional impairment worldwide. (8) The severity of symptoms and fluctuation of mood in patients with bipolar disorders are known to impact significantly on functioning. (13)

Similarly, major functional deficits are found in the majority of Schizophrenic subjects and they have strong association with low educational levels. (14) Mean functional capacity in patients with schizophrenia is reduced by 20% compared to general population resulting in overall reduction of global functioning. (15)

Because functional impairment is a complex phenomenon, it is likely to be created by many variables, not just by the psychiatric illness. (16,17) Dang and his colleagues, (18) noted that mental health problems are a major but not the sole contributor to functional impairment. This finding seems to be supported by the observation that further deficits in functioning persist even during remission. (11) Thus, although functional limitation may reflect the effects of underlying psychiatric disorder, other variables may compound the effect of the disorder. However, most researchers focus on the impact of specific psychiatric disorders on functional impairment, which is often of primary clinical concern to the physician. (16,17) Studies have shown that psychological variables such as self-esteem provide a significant contribution to the amount of variance in functional impairment (above that explained by demographic and clinical variables). (16,17) Patients with psychiatric disorders often cite decreased self-esteem as significantly impairing their lives. (19) Previous studies reported a relationship between low self-esteem and high functional impairment and that self-esteem is associated with functional impairment even in remitted patients. (20)

Poor medication adherence in psychiatric patients has been reported to be associated with poor psychosocial outcome and poor quality of life. (21,22) Rapoff (23) reported that non-adherence in patients with chronic diseases can adversely impact their social functioning. Zhang and his colleagues (24) similarly reported an association between cost-related non-adherences to medication and limitations in activities of daily living (ADLS) and instrumental activities of daily living (IADLS) and that non-adherence may worsen functional status.

Functional capacity is a domain of everyday functioning and adequate functional capacity is a critical component of recovery from mental illness. It is associated with greater engagement in community responsibility. (25) The ability to perform activities of daily living allows the mentally stable psychiatric patients, including older patients, to live independently in the community. Furthermore, achievement of the typical milestone of adulthood such as keeping a job, raising a family and maintaining a home is strongly dependent on adequate functional ability and performance in the activities of daily living. (26) Therefore, assessing patient's functional status portends important clinical relevance; it helps to determine the level of severity of the diagnosed disorder, (27) track clinical progress, evaluate remission and monitor recovery after treatment. (4) However, because symptoms are the most proximal indicators of a disorder, researchers, after pharmacological treatment, often employ symptoms outcome measures to assess the effectiveness of treatment or recovery from illness; functional outcome measures are often ignored. A meta-analysis of over 90 depression treatment outcomes indicate that less than 5% of the clinical trials measure and report functional outcomes. (4) A literature search revealed that empirical evidence on the comparative influence of the major psychiatric disorders, self-esteem of individual patients, and adherence to medication on functional status are scanty especially in Nigeria and Africa. Therefore, the aims of this study are to:

1. Determine and compare the prevalence of low functional status among outpatients with major axis 1 psychiatric disorders (Schizophrenia, Bipolar affective disorders and Depression).

2. Identify risk factors/independent predictors of low functional status.

3. Assess the proportion of the variance in low functional status explained by self-esteem and adherence to medication.


Study location and design

This study was conducted at a tertiary hospital in Benin City, Nigeria. The hospital serves as a major referral center to many primary and secondary hospitals in the entire state, as well as many neighboring states. The psychiatric unit of the mental health department runs outpatient clinics three times in a week. A cross sectional descriptive design was adopted and data were collected between June and December, 2017.

Study participants

Participants included consecutive psychiatric patients who presented for appointments at the outpatient clinics of the hospital over a period of six months. Eligibility criteria included are:

* Being an adult aged 18 years and above.

* Being diagnosed for depression, bipolar disorder or schizophrenia based on DSM-1V diagnostic criteria, established over the course of at least three clinic visits.

* Being under regular care at the outpatient clinic and receiving psychopharmacological therapy directed at their clinical condition for a period of, at least, six months.

* Being considered (after relevant mental state examination) mentally stable, enough to participate in the study.

* Expression of willingness to voluntarily participate in the study and giving of consent.


The following instruments were used to collect data from the participants:

The Global Assessment of Functioning (GAF) Scale: The GAF assesses global functioning of adults in terms of a single rating of overall psychological, social, and occupational functions. (28) It is a numeric clinician rated scale used widely and regularly by mental health clinicians and physicians to measure how much a person's illness/symptoms affect his or her day-to-day life on a scale of 1 to 100. Rating ranges from 1-10 (persistent danger of severely hurting self or others, persistent inability to maintain minimal personal hygiene or serious suicidal act with clear expectation of death) to 91-100 (no symptoms, superior functioning in a wide range of activities). Scores within the range of 11 and 90, represent varying levels of symptom severity and functionality, with a lower score indicating low functioning and vice versa. In this study, functional status was dichotomized as 'Low' or 'High' based on the mean score on the GAF scale: patients with scores below the mean, and those with mean and above scores were considered as 'Low' or 'High', respectively. The instrument as well as similar dichotomy/cut off-point has been used by previous authors in Nigeria to assess functioning in adult patients with psychotic disorders. (29)

Morisky Medication Adherence Scale (MMAS-8): An 8-item scale is designed by Morisky et al (30) It was initially developed to evaluate medication adherence in patients with hypertension, but it is now widely used in various other patient populations. Respondents are to answer "Yes" or "No" to each of the first seven items, while the last item is a 5-point Likert scale response. The tool considers three levels of adherence based on the following scores: 0 to <6 (low adherence); 6 to <8 (medium); 8 (high). However, for the purpose of this study the levels of adherence were dichotomized based on the following scores, 0 to <8 (non-adherence); 8 (adherence). Previous authors in Nigeria have used the tool, as well as similar dichotomy/cut off score. (31)

The Rosenberg's Self-esteem Scale: (32) This is a 10-item self-report Likert-type measure that assesses an individual's overall sense of self-worth or self-acceptance. Response options of the scale range from 'strongly disagree' to 'strongly agree'. The sum of the ratings assigned to all the items, after a reverse scoring of the positively worded items gives the global scores which range from 10 to 40 with higher scores indicating higher self-esteem. Rosenberg (32) initially found it to have strong internal consistency reliability of 0.93. (32) In Nigeria, Okwaraji and his colleagues (33) established a Cronback alpha of 0.84 and two week test-retest reliability coefficient of 0.76. In this study, self-esteem was dichotomized as 'Low' or 'High' based on the mean score on the Rosenberg's scale: patients with scores below the mean, and those with mean and above scores were considered as 'Low' or 'High', respectively. Similar dichotomy/cut off-point has been used by previous authors in Nigeria. (34)

A socio-demographic and clinical data collection sheet: This is designed by the authors to collect information from the participants on their socio-demographic characteristics such as age, marital status and so forth, as well as clinical variables such as diagnosis, duration of illness, and so forth. Comorbidity was defined as the presence of another diagnosed chronic illness, like hypertension, diabetes mellitus, arthritis and so forth, in addition to the psychiatric illness in a participant.

Ethical issues/procedure

Prior to the commencement of the study, ethical approval was obtained from the research and ethical committee of the institution. On each clinic day, consecutive potential participants were approached, the nature and purpose of the study was explained to them, and they were informed of their liberty to either participate voluntarily or decline participation. They were also told there would be no penalty for declining participation, nor incentive for participating. Confidentiality was assured and verbal informed consent was obtained from willing subjects. Participants who gave verbal consent and met the eligibility criteria underwent mental state assessment by the authors (consultant psychiatrists) to establish their mental fitness to participate. The questionnaires were self-administered except the GAF, which was administered and rated by the authors (psychiatrists). Information provided by the participants was corroborated or, where necessary, supplemented by clinical details from subjects' case files.

Data analysis

Completed questionnaires were retrieved and coded. SPSS version 21 was used to analyze the data. Categorical ranking of some socio-demographic and clinical variables was done. Frequency distribution and percentages were computed to describe the categorical variables, and Chi-square was performed to test the association of functional status with those socio-demographic variables, and psychiatric diagnoses. Pearson correlation analysis was used to assess the relationship between GAF scores and some other continuous variables. Analysis of variance (ANOVA) with Bonferroni Post-Hoc test was used to determine the effect of diagnosis on functional status by comparing the mean GAF scores of patients with depression, bipolar disorder, and schizophrenic. Variables that had significant association with functional status in the bivariate analyses were simultaneously entered into logistic regression model to determine independent predictors of low functional status, as well as the contribution of low self-esteem and poor medication adherence to the variance in functional status.


Socio-demographic characteristics of the participants

A total of 308 patients, 181 (58.8%) males and 127 (41.2%) females participated in the study. Their mean age was 37.01+12.22 years. One hundred and nineteen (38.6%) were currently married, 163 (53.0%) were never married while 26 (8.4%) were previously married (separated, divorced or widowed). One hundred and ninety-one (62.0%) were currently employed, 117 (38.0%) were currently not employed and the average monthly income was US$172.86 [+ or -] 247.31.

Clinical characteristics

The proportion of participants with schizophrenia, bipolar disorder and depression were 118 (38.31%), 107 (34.74%) and 83 (26.95%), respectively. Ninety-three (30.2%) participants had comorbid illnesses, 141 (46.8%) had low self-esteem, while 216 (70.1%) were non-adherent to their medications. The mean duration of illness was 7.22 [+ or -] 6.34 years and the mean GAF score was 69.13 [+ or -] 10.56.

Prevalence of low functional status

A total of 125 (40.6%) participants had low functional status. Schizophrenic patients had the highest prevalence of low functional status (53.4%), followed by bipolar disorder patients (33.6%) while patients with major depression had the least at 31.3%. The differences were statistically significant (p= 0.061) (Table 1).

ANOVA test revealed that diagnosis had significant effect on functional status (F = 16.251, P<0.01), and the Post-Hoc test showed that the mean GAF score of patients with schizophrenia differed significantly from the mean scores of bipolar and depressive disorder patients; the mean GAF score of schizophrenic patients was reduced by 9.09% and 11.52% compared to that of bipolar and depressive disorder patients, respectively. There was no significant difference between the mean GAF scores of bipolar and depressive disorder patients (Table 2).

Correlates of low functional status

Chi-square test showed that the following social-demographic/clinical variables: age (P = 0.028), educational attainment (P = 0.001), employment (P = 0.012), self-esteem (P = 0.001), medication adherence (P < 0.001), and comorbidity (P < 0.001) had significant association with functional status (Table 3).

Pearson correlation revealed that income (r = .157, P = 0.001), self-esteem scores (r = .146, P = 0.001), and medication adherence scores (r = .413, P< 0.001) had positive significant relationship with GAF scores, while duration of illness (r = -.334, P< 0.001) and number of previous hospitalization (r = .314, P < 0.001) had negative significant relationship with GAF scores (Table 4).

Predictors of low functional status

Multiple binary logistic regressions revealed that low income, presence of comorbid conditions, poor adherence to medication and having schizophrenia significantly predicted low functional status while controlling for the other variables. The tested variables accounted for up to 55.2% of the variation in the outcome variable (Table 5).

Poor medication adherence had the highest relative contribution (35.4%) to the variation in functional status while self-esteem had an insignificant relative contribution of 3.4%.


Prevalence of low functional status

This study examined the prevalence and correlates of low functional status among patients attending the psychiatric outpatient clinic in Benin City. An overall prevalence of 40.6% of low functional status was found among patients with serious mental illness. This rate falls within the range of 37.3%-43.5% previously reported among outpatients with schizophrenia and bipolar disorder in Nigeria. (35) However, higher rates have been reported in the United States by Kessler and his colleagues (3) and Druss et al. (36) among patients with mental illness using Sheehan Disability Scales. The reason for higher rates of low functional status among the USA samples is not clear but, generally, differences in prevalence rates are related to operational definition and measurement of impairment, as well as the targeted populations. The rate of low functional status found in this study is considered high and worrisome considering its implication in limiting activities of daily living and overall treatment outcome. Therefore, clinicians should embrace a more holistic approach in management; beyond pharmacotherapy often aimed at alleviating symptoms, management should include routine assessment of the functional capacity of the patients as well as interventions targeted at enhancing individual functioning in the area of occupation, interpersonal relationships, and psychological well-being.

When the different diagnostic categories (schizophrenia, bipolar disorder, and depression) were compared, patients with schizophrenia reported significantly highest rate (53.4%) of low functional status followed by those with bipolar disorder (33.6%), while the least prevalence was among patients with depression (31.3%). Between bipolar and depressive disorder patients, there was no significant difference found in their functioning status. Numerous studies have reported higher disability and functioning among schizophrenic compared to mood disorder patients, (35,37,38) while only a few found no significant difference between the two groups. (39) A comparable study carried out in Nigeria among 200 dyads of psychiatric outpatients and their care givers revealed a somewhat similar report; higher but non-significant prevalence rate of severe impairment in functioning was found among the schizophrenic compared to the bipolar disorder patients. (35) Notable differences between their survey and the current one are: their study utilized the Social and Occupational Functioning Assessment Scale (a scale derived from the Global Assessment of Functioning Scale) to evaluate the participants' level of social and occupational functioning (40) and a cut off-point of 50 from a range of 1 to 100 was used to determine those with poor functional status.

It is understandable why persons with schizophrenia will report greater impairment in functioning; elements such as cognitive deficits and negative symptoms which respond poorly to treatment have been strongly linked to functional outcome (41,42) and these factors are worse in persons with schizophrenia. Also, extrapyramidal symptoms, as a fall-out of the pharmacological agents employed in treatment, may contribute to poor functioning. (14) Altogether, the clinical picture and course of an individual with schizophrenia present with a poorer outcome because of the severe disability experienced.

Correlates of low functional status

Low functional status was significantly associated with psychiatric diagnosis (as discussed above), older age, lower educational attainment, lack of employment, and presence of co-morbid medical illnesses. In addition, functioning scores correlated positively with monthly income and medication adherence scores, and negatively with duration of illness, number of hospitalizations, and self-esteem scores. However, following a regression analysis in which significant variables on bivariate analysis were controlled, low functional status was independently predicted by only the presence of medical co-morbidities, low monthly income, poor medication adherence, and having schizophrenia diagnosis. While all the variables investigated contributed jointly up to 55.2% of the variance in low functional status, medication adherence accounted for the highest at 35.4%.

As shown by this study, the presence of physical co-morbidity may influence the outcome of functioning In this regard, reports of previous studies are mixed: while our finding is in agreement with some studies (43) it conflicts with others. (17,44) The influence of comorbid medical condition on functioning may vary with the nature and severity of the medical condition. For example, studies have shown that patients with osteoarthritis reported a significantly more functional disability than other chronic medical illnesses. (45) In this study, various medical co-morbidities reported by the patients include hypertension, diabetes, osteoarthritis, and so forth. Some patients reported having more than one condition so, the presence of co-morbidity in this study is a mixture of conditions. Chronic medical conditions are likely to impair the ability to perform a physical task which may limit home and work functioning, whereas mental disorders are known to impede social functioning and relationships. (36) The debate regarding which of the conditions hamper functioning more may depend on which area of functioning is in question. (46) Nevertheless, the combined effect of the two illnesses would be interactive. It should be mentioned that physical comorbidity is high among people with mental illness but the detection rate is poor. (47) The added effect of physical ailment on an already disabling mental illness will further worsen functioning; therefore, it is advisable to treat physical co-morbidities adequately in mentally ill persons in order to achieve optimal functional outcome.

Lower income level was also found to be an independent risk factor for poor functioning in patients with serious mental illness. The reason for this is not clear, though lower income has been similarly found to predict greater disability in end-stage renal patients. (48) However, when the cross-sectional nature of the study design is considered, low functional status, may be conceived as a consequence, rather than a risk, of poor functioning among the participants. Functioning, expectedly, would impact on employment status, suitability and performance; hence, poor functioning will invariably lead to lack of employment or lower paying job, if the individual is employed.

The result also shows that poor medication adherence predicted low functioning, though the relationship between medication adherence and low functional status could be explained in both directions. It is likely that those with low functioning will be less stable mentally, have poor insight; and consequently comply less with treatment as found in patients with schizophrenia and mood disorders. (49) It could also be surmised that those who comply less with treatment won't make significant clinical improvement resulting ultimately in lower functioning. A longitudinal study, perhaps in future, might help to determine the cause and effect relationship between the two variables.

Self-esteem was a variable of interest investigated in this survey because of previous reports which link it to poor psycho-social functioning, (50) yet, in this study, it fell marginally short of statistical significance as an independent predictor of low functional status. Low self-esteem had a relatively insignificant contribution of 3.4% to the variation in functional status. The initial correlation (r= .146, P= 0.001) that existed with functioning on bivariate analysis ceased following regression analysis, and this suggests the mediation role of a factor. It is probable that one of the independent predictors such as medication adherence, known to be associated with self-esteem (51) contributed to or enhanced the effect of self-esteem initially, but the moment medication adherence was controlled for, the association disappeared. This speculation will be of interest to study in the future.

Limitation of study

The strength of this study is that it compared functioning across three important diagnostic categories; notwithstanding, there are limitations in the study. The method of assessing functioning, which is by self-report, is subjective. Both the mood state and social desirability bias of participants could have influenced rating. An objective means of assessing functioning such as performance-based assessment or third-party rating is advocated in subsequent studies. Similarly, the medical co-morbidities were identified by self-report, however, most of the cases were corroborated by information from patients' case files. The relationship between functional status and specific medical comorbidity will be a topic of interest for future study.


Despite the limitations, the findings reveal that substantial proportion of persons suffering from serious mental illnesses have impaired functional status. Schizophrenic patients have more impairment in functioning compared with those with mood disorders. The contributory effect of multiple factors in determining functioning in persons with serious mental illness was also highlighted. The role of comorbidity and poor medication adherence is of clinical significance. There is a need to strengthen the multidisciplinary approach to the management of mental disorders and regularly assess and encourage adherence to medication in order to improve the level of functioning of the patients.



(1.) Rosa AR, Sanchez-Moreno J, Martinez-Aran A, Salamero M, et al. Validity and reliability of the Functioning Assessment Short Test (FAST) in bipolar disorder. Clin Pract EpidemolMentHealth. 2007;3:5.

(2.) Ustun B, Kennedy C. What is "functional impairment"? Disentangling disability from clinical significance. World Psychiatry. 2009;8(2):82-5.

(3.) Kessler RC, Berglund P, Demler O, Jin R, et al. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). JAMA. 2003;289(23):3095-105.

(4.) McKnight PE, Kashdan TB. The importance of functional impairment to mental health outcomes: a case for reassessing our goals in depression treatment research. Clin Psychol Rev. 2009;29(3):243-59.

(5.) Murray CJL, Lopez AD, World Health Organization, World Bank, Harvard School of Public Health. Global health statistics: a compendium of incidence, prevalence and mortality estimates for over 200 conditions: 1996.

(6.) Ustun TB. Global burden of mental disorders. AMJPublic Health. 1999;89:1315-8.

(7.) Larry B, Mauksch MD. Mental Illness, Functional Impairment, and patient preference for collaborative care in an uninsured, primary care population. J Fam Pract. 2001;50(1):41-7.

(8.) Cacilhas AA, Magalhaes PV, Cereser KM, Walz JC, et al. Bipolar disorder and age-related functional impairment. Braz J Psychiatr. 2009;31(4):354-7.

(9.) Semahegn A, Torpey K, Manu A, Assefa N, et al. Psychotropic medication non-adherence and associated factors among adult patients with major psychiatric disorders: a protocol for a systematic review. SystRev. 2018;7(1):10..

(10.) World Health Organization (WHO). Mental disorders fact sheet. 2016. Available from 90/en/ Accessed on February 3, 2018.

(11.) Rosa AR, Reinares M, Michalak EE, Bonnin CM, et al. unctional impairment and disability across mood states in bipolar disorder. Value Health. 2010;13(8):984-8.

(12.) Geller B, Bothofuer K, Craney JL, Williams M, et al. Psychosocial functioning in a prepubertal and early adolescent bipolar disorder phenotype. J Am Acad Child Adolesc Psychiatry. 2000;39(12):1543-8.

(13.) Birmaher B, Axelson D. Course and Outcome of bipolar spectrum disorder in children and adolescents: a review of the existing literature. Dev Psychopathol. 2006;18(4):1023-35.

(14.) Patterson TL, Klapow JC, Eastern JH, Heaton RK, et al. Correlates of functional status in older patients with schizophrenia. Psychiatry Res. 1998;80(1):41-52.

(15.) Araripe-Neto AGA, Bressan RA, Busatto FG. Fisiopatologia da esquizofrenia: aspectos atuais. Rev Psiq CLin. 2007;34:198-203.

(16.) Hollifield M, Vogel AV. The somatizing patient in medicine: A primary care approach. In Rubin RH, Voss C, Derksen DJ, Gateley A, Quenzer RW (eds.) Internal Medicine: A Primary Care Approach. Philadephia; WB Saunders, 1996:389-92.

(17.) Hollified M, Katon W, Skipper B, Chapman T, et al. Chronic disorder and quality of life: variables predictive of functional impairment. Am J Psychiatry. 1997;154(6):766-72.

(18.) Dang HM, Weiss B, Trung LT. Functional Impairment and Mental Health Functioning among Vietnamese Children. Soc Psychiatry Psychiatr Epidemiol. 2016;51(1):39-47.

(19.) Blanco RM. Mindfulness as a moderator of self-esteem, functional impairment, and psychological flexibility in adult ADHD population. M.Sc in clinical psychology, thesis; 2012. Accessed on February 10, 2018

(20.) Zortea K, Belmonte-de-Abreu PS. Schizophrenia and functional status. Trends Psychiatric Psychother. 2012;34(1):42-3.

(21.) Al-Qasem A, Smith F, Clifford S.. Adherence to medication among chronic patients in Middle Eastern countries: review of studies. EastMediterr Healh J. 2011;17(4):356-63.

(22.) Farooq S, Naeem F. Tackling nonadherence in psychiatric disorders: current opinion. Dorepress. Neuropsychiatr Dis Treat. 2014;10:1069-77.

(23.) Rapoff MA. Issues in clinical child psychology. Adherence to pediatric medical regimens (2nd ed.). New York, NY, US: Springer Publishing Co. 2010.

(24.) Zhang Jx, Lee JU, Metzer DO. Risk factors for cost-related medication non-adherence among older patients with diabetes. World J Diabetes. 2014;5(6):945-95.

(25.) Mausbach BT, Depp CA, Cardenas Jeste DV, et al. Relationship between functional capacity and community responsibility in patients with schizophrenia: Differences between independent and assisted living settings. Community Ment Health J. 2008;44:385-91.

(26.) Leung WW, Boure CR, Harvey PD. Functional Implications of neuropsychological normality and symptom remission in older outpatients diagnosed with schizophrenia: a cross sectional study. J Int Neuropsychol Soc. 2008;14:479-88.

(27.) American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). Arlington: American Psychiatric Publishing, 2013.

(28.) 28. Bodlundo O, Kullgren G, Ekselius L, et al. Axis- V Global Assessment of functioning scale: Evaluation of a self-report Version. Acta Psychiatr Scand 1994; 90(5):342-347.

(29.) Olotu SO, Abgonile IO, Omoaregba JO, et al. Efficacy and tolerability of Aripiprazole in a Nigerian cohort with first episode psychosis: a post marketing survey. Int Neuropsychiatr Dis J. 2017;10(1):1-10.

(30.) Morisky DE, Ang A, Krousel-Wood M, Ward HJ. Predictive validity of a medication adherence measure in an outpatient setting. J Clin Hypertens. 2008;10(5):348-54.

(31.) Ibrahim AW, Yahya S, Pinder SK, Wakil MA, et al. Prevalence and predictors of sub-optimal medication adherence among patients with severe mental illnesses in a tertiary psychiatric facility in Maiduguri, North-eastern Nigeria. Pan Afr Med J. 2015;21:39.

(32.) Rosenberg KP, Bleiborg KL, Koscis J, Gross C. A survey of sexual side effects among severely mentally ill patients taking psychotropic medications: impact on compliance: J Sex Marital Ther. 2003;29(4):289-96.

(33.) Okwaraji FE, Aguwa EN, Shiweobi C. Life satisfaction, self-esteem and depression in a sample of Nigerian adolescents. Int Neuropsychr Dis J. 2015;5(3):1-8.

(34.) Koleoso AN, Osasona SO. Linking personality dimension, imprisonment status and type of crime to anxiety and depression among prison inmates. Malaysian J Psychiatry. 2014;23(2):19-30.

(35.) Osundina AF, Akanni OO, Ayilara OO, et al. A comparative study of psychiatric morbidity and associated factors in carers of patients with schizophrenia and bipolar affective disorder in Nigeria. Sri Lanka J Psychiatry. 2017,8(2):14-19.

(36.) Druss BG, Hwang I, Petukhova M, Sampson NA, et al. Impairment in role functioning in mental and chronic medical disorders in the United States: results from the National Comorbidity Survey Replication. Mol Psychiatry. 2009;14(7):728-37.

(37.) Bowie CR, Depp C, McGrath JA, Wolyniec P, et al. Prediction of real-world functional disability in chronic mental disorders: a comparison of schizophrenia and bipolar disorder. Am J Psychiatry. 2010;167(9):1116-24.

(38.) Shihabuddeen ITM, Mehar H, Pinto DA. Disability in schizophrenia and bipolar mood disorder at General Hospital Psychiatry Unit, Delhi Psychiatry J. 2011;14:(2):258-61.

(39.) Rahman MBA, Indran SK. Disability in schizophrenia and mood disorders in a developing country. Soc Psychiatry Psychiatr Epidemiol. 1997;32:387-90.

(40.) Goldman H, Skodol A, Lave T. Revising axis V for DSM IV. A review of measures of social functioning. Am J Psychiatry. 1992;149(9): 1148-56.

(41.) Green MF, Kern RS, Braff DW, et al. Neurocogniture deficits and functional outcome in schizophrenia: are we measuring the "right stuff". Schizophr Bull. 2000;26:119-36.

(42.) Bowie CR, Reichenberg A, Patterson TL, Heaton RK, et al. Determinants of real-world functional performance in schizophrenia subjects: correlations with cognition, functional capacity, and symptoms. Am J Psychiatry. 2006;163:418-25.

(43.) Chwastiak LA, Rosenheck RA, McEvoy JP, Keefe RS, et al. Interrelationships of psychiatric symptom severity, medical comorbidity, and functioning in schizophrenia. Psychiatr Serv. 2006;57:1102-9.

(44.) Nishanth KN, Chadda RK, Sood M, Biswas A, et al. Physical comorbidity in schizophrenia & its correlates. Indian J Med Res. 2017;146(2):281-4.

(45.) Valderrama-Gama E, Damian J, Ruigomez A, Martm-Moreno JM. Chronic disease, functional status, and self-ascribed causes of disabilities among noninstitutionalized older people in Spain. J Gerontol A Biol Sci Med Sci. 2002;57(11):M716-21.

(46.) Baune BT, McAfoose J, Leach G, Quirk F, et al. Impact of psychiatric and medical comorbidity on cognitive function in depression. Psychiatry Clin Neurosci. 2009;63(3):392-400.

(47.) 47. Lambert TJR, Dennis Velakoulis D, Pantelis C. Medical comorbidity in schizophrenia. Schizophren MJA 2003; 178: S67-S70

(48.) Harris LE, Luft FC, Rudy DW, Tierney WM. Clinical correlates of functional status in patients with chronic renal insufficiency. Am J Kidney Dis. 1993;21:161-6.

(49.) Novick D, Montgomery W, Treuer T, Aguodo J, et al. Relationship of insight with medication adherence and the impact on outcomes in patients with schizophrenia and bipolar disorder: results from a 1-year European outpatient observational study. BMC Psychiatry. 2015;15:189.

(50.) Roe D. A prospective study on the relationship between self-esteem and functioning during the first year after being hospitalized for psychosis. J Nerv Ment Dis. 2003;191(1):45-9.

(51.) Fung KM, Tsang HW, Corrigan PW. Self-stigma of people with schizophrenia as predictor of their adherence to psychosocial treatment. Psychiatr Rehabil J. 2008; 32(2):95-104.

Samuel Obateru Osasona [1], Oluyemi Oluwatosin Akanni [[PSI] 2]

[1] Department of Mental Health, University of Benin Teaching Hospital, Benin City, Nigeria

[2] Department of Clinical Services, Federal Neuropsychiatric Hospital, Benin City, Nigeria

(Received 22 September 2018 and accepted 01 November 2018)

[[PSI]] Correspondence at: Department of Clinical Services, Federal Neuropsychiatric Hospital, Benin City, Nigeria. Email:
Table 1: Prevalence of low functional status and its association
with psychiatric diagnosis

                                 Functional status
Psychiatric Diagnosis
                                 High         Low

Schizophrenia                    55(46.6)     63(53.4) *
Bipolar affective disorder       71(66.4)     36(33.6) *
Major depression                 57(68.7)     26(31.3) *
Total                            183(59.4)    125(40.6) **

Psychiatric Diagnosis

Schizophrenia                    118(100.0)
Bipolar affective disorder       107(100.0)
Major depression                 83(100.0)
Total                            308(100.0)

[chi]2 = 13.112 df = 2 P = 0.001

* Specific (diagnosis related) prevalence of low functional status.

** Overall prevalence of low functional status.

Table 2: Comparison of the mean GAF scores of patients with
schizophrenia, bipolar and depressive disorders (ANOVA)

GAF scores

                   Sum of Squares    Df    Mean Square    F

Between Groups     3296.010          2     1648.005       16.251
Within Groups      30930.051         305   101.410
Total              34226.062         307

GAF scores


Between Groups     0.000
Within Groups

Bonferroni Post Hoc Tests

Dependent Variable: GAF scores

(I) Psychiatric       (J) Psychiatric        Mean           Std.
diagnosis             diagnosis              Difference     Error

Schizophrenia         Bipolar affective      -5.912 *       1.344

                      Major depression       -7.503 *       1.443

Bipolar affective     Schizophrenia          5.912 *        1.344
                      Major depression       -1.592         1.473

Major depression      Schizophrenia          7.503 *        1.443

                      Bipolar affective      1.592          1.473

                                                     95% Confidence
(I) Psychiatric       (J) Psychiatric        Sig.    Interval
diagnosis             diagnosis
                                                     Lower    Upper
                                                     Bound    Bound

Schizophrenia         Bipolar affective      .000    -9.15    -2.68

                      Major depression       .000    -10.98   -4.03

Bipolar affective     Schizophrenia          .000    2.68     9.15
                      Major depression       .842    -5.14    1.95

Major depression      Schizophrenia          .000    4.03     10.98

                      Bipolar affective      .842    -1.95    5.14

The mean GAF score in schizophrenic patients is reduced by 9.09%
and 11.52% in bipolar and depressive disorder patients,

* The mean difference is significant at the 0.05 level.

Table 3: Association between social demographic/clinical variables
and functional status

                            Functional Status
                                                       [chi square]
Variables                   High          Low
                            n=183(%)      n=125(%)

Age (years)
<20                         13(76.5)      4(23.5)      10.836
20-39                       109(61.9)     67(38.1)
40-59                       51(58.6)      36(41.4)
60 & above                  10(33.3)      18(66.7)

Male                        105(58.0)     76(42.0)     0.359
Female                      78(61.4)      49(38.6)

Marital Status
Currently married           75(63.0)      44(37.0)     2.558
Never married               96(58.9)      67(41.1)
Previously married          12(46.2)      14(53.8)

Level of Education
No formal education         3(21.4)       11(78.6)     17.519
Primary education           38(49.4)      39(50.6)
Secondary education         74(60.7)      48(39.3)
Tertiary education          68(71.6)      27(28.4)

Level of Support
Good support                123(62.8)     73(37.2)     2.493
Poor support                60(53.6)      52(46.4)

Employment status
Currently employed          124(64.9)     67(35.1)     6.321
Not currently employed      59(50.4)      58(49.6)

High                        112(68.3)     52(31.7)     11.463
Low                         71(49.3)      73(50.7)

Any comorbid illness?
No                          151(70.2)     64(29.8)     34.551
Yes                         32(34.4)      61(65.6)

Medication adherence
Non-adherence               107(49.5)     109(50.5)    29.265
Adherence                   76(82.6)      16(17.4)

Variables                   P-value

Age (years)
<20                         0.028
60 & above

Male                        0.549

Marital Status
Currently married           0.278
Never married
Previously married

Level of Education
No formal education         0.001
Primary education
Secondary education
Tertiary education

Level of Support
Good support                0.114
Poor support

Employment status
Currently employed          0.012
Not currently employed

High                        0.001

Any comorbid illness?
No                          <0.001

Medication adherence
Non-adherence               <0.001

Table 4: Correlation matrix of GAF scores with other continuous

                                          GAF      Monthly
                                          scores   income

GAF scores          Pearson               1        .157 **
                    Correlation                    .010
                    Sig. (2-tailed)

                    Pearson                        1
Monthly             Correlation
income ($)          Sig. (2-tailed)

Duration of         Pearson
illness             Correlation
                    Sig. (2-tailed)

Number of           Pearson
Hospitalization     Correlation
                    Sig. (2-tailed)

Self- esteem        Pearson
scores              Correlation
                    Sig. (2-tailed)

Medication          Pearson
adherence scores    Correlation
                    Sig. (2-tailed)

                                          of illness

GAF scores          Pearson               -.334 **
                    Correlation           .000
                    Sig. (2-tailed)

                    Pearson               .057
Monthly             Correlation
income ($)          Sig. (2-tailed)       .351

Duration of         Pearson               1
illness             Correlation
                    Sig. (2-tailed)

Number of           Pearson
Hospitalization     Correlation
                    Sig. (2-tailed)

Self- esteem        Pearson
scores              Correlation
                    Sig. (2-tailed)

Medication          Pearson
adherence scores    Correlation
                    Sig. (2-tailed)

                                          Number of

GAF scores          Pearson               -.314 **
                    Correlation           .000
                    Sig. (2-tailed)

                    Pearson               .047
Monthly             Correlation
income ($)          Sig. (2-tailed)       .446

Duration of         Pearson               .914 **
illness             Correlation           .000
                    Sig. (2-tailed)

Number of           Pearson               1
Hospitalization     Correlation
                    Sig. (2-tailed)

Self- esteem        Pearson
scores              Correlation
                    Sig. (2-tailed)

Medication          Pearson
adherence scores    Correlation
                    Sig. (2-tailed)

                                          Self-esteem    Medication
                                          scores         adherence

GAF scores          Pearson               .146 *         .413 **
                    Correlation           .010           .000
                    Sig. (2-tailed)

                    Pearson               .106           .096
Monthly             Correlation
income ($)          Sig. (2-tailed)       .082           .118

Duration of         Pearson               -.081          -.218 **
illness             Correlation           .155           .000
                    Sig. (2-tailed)
                                                         -.211 **
Number of           Pearson               -.067          .000
Hospitalization     Correlation           .243           .160 **
                    Sig. (2-tailed)

Self- esteem        Pearson               1
scores              Correlation                          .005
                    Sig. (2-tailed)

Medication          Pearson                              1
adherence scores    Correlation
                    Sig. (2-tailed)

** Correlation is significant at the 0.01 level (2-tailed)

* Correlation is significant at the 0.05 level (2-tailed)

Table 5: Predictors of functional status (Multiple logistic

Predictors                      B        Wald     Exp(B)

Age                             .010     0.242    1.010
Level of Education *                     1.788
No formal education             .294     1.112    1.342
Primary education               .163     .099     1.177
Secondary education             -.367    .546     .693
Monthly Income                  -.006    9.418    .994
Currently unemployed **         .665     2.239    1.945
Duration of illness             .039     .268     1.040
Hospitalization                 -.025    .004     .976
No comorbidity ***              -1.897   16.647   .150
Medication Adherence score      -.785    36.387   .456
Self Esteem Scores              -.064    3.340    .938
Diagnosis ****                           10.437
Schizophrenia                   1.878    9.674    6.539
Bipolar disorder                1.070    3.014    2.915
Constant                        6.341    12.720   567.583

                                95% C.I. for EXP(B)  Sig.

                                Lower      Upper

Age                             .970       1.053      .623
Level of Education *                                  .618
No formal education             .239       7.547      .738
Primary education               .427       3.244      .753
Secondary education             .262       1.833      .460
Monthly Income                  .990       .998       .002
Currently unemployed **         .814       4.648      .135
Duration of illness             .896       1.207      .605
Hospitalization                 .471       2.019      .947
No comorbidity ***              .060       .373       .000
Medication Adherence score      .354       .589       .000
Self Esteem Scores              .877       1.005      .068
Diagnosis ****                                        .005
Schizophrenia                   2.003      21.352     .002
Bipolar disorder                .871       9.753      .083
Constant                                              .000

Reference category for categorical variables: * Tertiary education,
** currently employed, *** presence of comorbidity, **** Depression

[R.sup.2]: Coefficient of determination of the logistic model;
[R.sup.2] = 40.2% to 55.2%
COPYRIGHT 2018 Dr. Arun Kumar Agnihotri
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2018 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:Original Work
Author:Osasona, Samuel Obateru; Akanni, Oluyemi Oluwatosin
Publication:Internet Journal of Medical Update
Date:Jul 1, 2018
Previous Article:Selfmonitoring of blood glucose practices by people living with diabetes who use their personal glucometers in Port Harcourt, Niger Delta Region,...
Next Article:Neuroendocrine tumor of the hepatic flexure: a rare colonic tumor.

Terms of use | Privacy policy | Copyright © 2022 Farlex, Inc. | Feedback | For webmasters |