Severe mental Illness
Severe mental illnesses (SMIs) are conditions such as schizophrenia, bipolar disorder and psychotic depression that are debilitating, persistent and associated with serious functional impairment. People with SMI die on average 10–20 years earlier than the general population, and this ‘mortality gap’ is widening.Reference Liu, Daumit, Dua, Aquila, Charlson and Cuijpers1 Although suicide accounts for 15% of deaths, an estimated 80% of the observed premature mortality is attributable to physical disorders (physical multimorbidity), most commonly non-communicable diseases (NCDs).Reference Chan, Wong, Or, Chen and Chang2
Physical health in people with SMI
The excess disease burden from physical multimorbidity in people with SMI may be explained by a combination of factors associated with these mental disorders, including clustering of and predisposition to health risk behaviours (e.g. tobacco and alcohol use, lack of physical activity and poor diet), side-effects of medication, social determinants of poor health (e.g. stigma and poverty) and barriers to accessing healthcare.Reference Fond, Pauly, Leone, Llorca, Orleans and Loundou3 Our current understanding of the distribution and determinants of physical multimorbidity in people with SMI is based mostly on evidence from high-income countries. A few small studies from low- and middle-income countries (LMICs) show similar patterns, but with an even shorter life expectancy and higher mortality for people with SMI.Reference Fekadu, Medhin, Kebede, Alem, Cleare and Prince4,Reference Teferra, Shibre, Fekadu, Medhin, Wakwoya and Alem5 These studies indicate that physical multimorbidity in SMI may be at least as much of a challenge in LMICs as in high-income countries.Reference Liu, Daumit, Dua, Aquila, Charlson and Cuijpers1
In South Asia, the prevalence of both mental disorders and NCDs has been increasing rapidly.Reference Misra, Tandon, Ebrahim, Sattar, Alam and Shrivastava6 This increase is coupled with limited access to essential health services and a widespread neglect of the physical health needs of people with SMI by policy makers and healthcare services.Reference Shavitt, Cho, Johnson, Jiang, Holbrook and Stavrakantonaki7 The overall burden of disease resulting from physical multimorbidity in this population is, therefore, likely to be high and is set to rise further, with a corresponding increase in within-country and global health inequalities. Despite these concerns, there is a lack of empirical studies originating in South Asia on the distribution and determinants of physical multimorbidity in people with SMI.8
Addressing multimorbidity in LMICs is a global priority, recognised in global policies to help achieve the United Nations Sustainable Development Goals.9 A detailed understanding of the prevalence of physical multimorbidity and current access to health advice and treatments for physical disorders in people with SMI in LMICs can inform appropriate service provision and contribute to achieving these goals.
Aims
In the current study, we aim to (a) estimate the prevalence of physical health conditions and health risk behaviours; (b) assess access to physical healthcare and health risk modification advice in people with SMI attending mental health services in Bangladesh, India and Pakistan; and (c) compare the findings with those of the general population.
Method
We conducted a cross-sectional survey of patients with a clinical diagnosis of SMI recruited at three national specialist mental health institutions in South Asia: the National Institute of Mental Health in Dhaka, Bangladesh; the National Institute of Mental Health and Neurosciences in Bangalore, India and the Institute of Psychiatry in Rawalpindi, Pakistan. Further details of the methods are reported in the published protocol,Reference Zavala, Prasad, Aslam, Barua, Hewitt and Huque10 and are summarised below.
Sample size
We aimed to build as large a sample as possible within the resources available over the study period, with an initial target of 1500 participants at each site. As an indicative example of precision to address some of the key research questions, we used the example of diabetes. For investigating the prevalence of type 2 diabetes, assuming a prevalence estimate of 10%, 857 participants per country would provide a precision of ±2% (95% confidence interval).
Eligibility
Consenting adults (aged ≥18 years) with a clinical diagnosis of SMI as defined by the ICD-10 (schizophrenia, schizotypal and delusional disorders (F20–F29); bipolar affective disorder (F30, F31) or severe depression with psychotic symptoms (F32.3, F33.3)), and able to provide informed consent as assessed by the treating clinician, were eligible.
Confirmation of SMI diagnosis
To increase standardisation across sites and alignment with other studies, each SMI diagnosis was confirmed by trained researchers using the Mini-International Neuropsychiatric Interview (MINI) version 6.0.Reference Lecrubier, Sheehan, Weiller, Amorim, Bonora and Harnett Sheehan11 The MINI is a short diagnostic structured interview for mental disorders, designed to allow administration by non-specialists.
Recruitment of participants
We used stratified random sampling to recruit a sample comprising 80% out-patients and 20% in-patients. This reflects the flow of in- and out-patients in the three mental health hospitals on any given day, which was assessed and protocolised in each site before the data collection.
Patient and public involvement
A community panel comprising patients, caregivers and advocacy group members ensured community, patient and public involvement. The panel reviewed and piloted the planned survey questionnaire, and advised on its feasibility.
Data collection
We conducted a face-to-face survey with tablets (Qualtrics, Utah, USA; https://www.qualtrics.com/) to collect information about physical disorders, mental health, health risk behaviours, health-related quality of life, health risk behaviour advice and healthcare utilisation, using, wherever available, validated instruments as described below. The survey was translated into Bangla, Hindi, Kannada and Urdu. Interviewers (including males and females, to accommodate participant preference) used regional dialects where required, consistent with usual clinical practice in these settings. Data were collected between July 2019 and December 2021.
STEPwise Approach to Surveillance of NCDs
We used the World Health Organization (WHO) STEPwise Approach to Surveillance of NCDs (STEPS) instrument version 3.2 to collect information about NCDs, associated risk factors and behaviours, access to physical healthcare and health risk modification advice.Reference Bonita, Winkelmann, Douglas, de Courten, McQueen and Puska12 STEPS is an international standardised tool that has already been translated, used and validated in the general population in Bangladesh, India and Pakistan, and therefore allows comparisons with the general population within and between countries.Reference Tripathy, Thakur, Jeet, Chawla and Jain13,Reference Rafique, Saqib, Munir, Qureshi, Rizwanullah and Khan14 The STEPS survey includes the use of show cards with culturally relevant examples used to aid respondents in classifying health risk behaviours. Categorisation of health conditions and risk behaviours followed the WHO guidelines.15
The STEPS module for NCDs was used to ask participants about medically diagnosed type 2 diabetes, raised blood pressure, heart disease and hypercholesterolemia, and treatments advised by a healthcare worker for these conditions (such as medication and dietary, weight management, smoking cessation or physical activity advice). Questions about lung disease, hepatitis B and C, syphilis, tuberculosis and HIV (which are not part of the STEPS survey) were asked in the same format as for the other chronic physical conditions.
Health risk behaviours
Current or past use of smoking or smokeless tobacco was recorded.15 The alcohol module was used to categorise participants into lifetime abstainers, abstainers in the past 12 months and current users of alcohol;15 and the diet module was used to record the number of days that respondents consumed fruit and vegetables in a typical week, the number of servings consumed on average per day, and adherence to the WHO recommendations of at least five fruits and vegetables per day.Reference Bazzano16 The physical activity module was used to record activity for transport purposes (e.g. walking, cycling), vigorous and moderate activity at work, vigorous and moderate activity in leisure time and time spent sitting. In addition, risk behaviours related to sexually transmitted diseases, including multiple sexual partners, unprotected sexual contact and use of injectable drugs, were assessed with three questions adapted from the ten-item HIV Risk Screening Instrument.Reference Gerbert, Bronstone, McPhee, Pantilat and Allerton17
Physical measurements
Blood pressure was taken with an automated blood pressure measuring instrument (OMRON) following instructions in the WHO STEPS surveillance manual; the average of the second and third readings was used for analysis.15 High blood pressure was defined as a measurement of >140/90 mmHg.15
Height, weight and waist circumference were measured for all participants except pregnant women. All measurements were taken in duplicate and the average of the two values was calculated, following the protocols set out in the WHO STEPS surveillance manual.15 We calculated the body mass index (BMI) and classified participants according to the WHO classification: underweight (BMI < 18.49 kg/m2), normal weight (BMI = 18.5–24.9 kg/m2), overweight (BMI = 25–29.9 kg/m2) or obese (BMI ≥ 30 kg/m2). Abdominal obesity was defined as a waist circumference of ≥94 cm for males and ≥80 cm for females.15
Mental health
In addition to administering the MINI, we collected information relevant to the SMI diagnosis, including duration of illness and type and duration of treatments. The Patient Health Questionnaire-9 (PHQ-9) was used to measure the severity of depressive symptoms, and the Generalised Anxiety Disorder-7 (GAD-7) for severity of anxiety symptoms.
Health-related quality of life
The EQ-5D-5L was used to measure health-related quality of life.18 We used the English, Urdu and Bangla validated versions, provided by EuroQol.
Blood tests
A blood sample was taken from consenting participants for haemoglobin, glycated haemoglobin (HbA1c), lipid profile, thyroid function tests, liver function tests and creatinine. The cut-off for high HbA1c was according to the WHO definition of ≥6.5%.Reference Lorenzo, Williams, Hunt and Haffner19 The prevalence of high total triglycerides was defined as ≥180 mg/dL,20 high serum cholesterol was defined as a low-density lipoprotein cholesterol of ≥100 mg/dL.20 Anaemia was defined as haemoglobin ≤13 g/dL for males and ≤12 g/dL for females.21
Statistical analysis
Quantitative data were summarised with descriptive statistics, with mean, s.d. and 95% confidence interval for continuous data and counts, and percentage and 95% confidence interval for categorical data. Overall and by site, we described the prevalence of chronic physical conditions; prevalence of risk factors (obesity, high blood pressure and hypercholesterolemia) and risk behaviours (poor diet, physical inactivity, tobacco and alcohol use); severity of common mental disorder symptoms (anxiety, depression) and health-related quality of life; and access to treatment for physical conditions and health risk modification advice.
To compare our findings with those in the latest STEPS reports from Bangladesh,22 India23 and Pakistan,Reference Rafique, Saqib, Munir, Qureshi, Rizwanullah and Khan14 we calculated weights by comparing the gender and age distribution reported in these STEPS surveys with the distribution in our data. Because of the multiple differences within countries in the operationalisation of socioeconomic status and the definition of rural and urban populations, we did not weight our sample for sociodemographic and geographic variables.
Weights were applied with the complex sample module in SPSS version 26.0 for Windows, and we calculated the odds of people with SMI having an NCD, related risk factors, engaging in health risk behaviours, being screened, being treated and receiving risk modification advice compared with the STEPS survey participants in Bangladesh, India and Pakistan,Reference Rafique, Saqib, Munir, Qureshi, Rizwanullah and Khan14,22,23 using Stata version 17.0 for Windows. Results were presented as odds ratios from cross-tabulations of STEPS and weighted survey data. Significance levels were adjusted via Bonferroni correction for multiple hypothesis testing (adjusted level P < 0.006).
Ethics statement
Trained researchers provided verbal and written study information to patients and their relatives or caregivers, highlighting that participation was voluntary, the decision would not affect care and consent could be withdrawn at any stage without providing a reason. Written consent was obtained (a thumbprint was accepted where a signature could not be provided). No assessments were conducted where the patient appeared reluctant, even if consent had previously been obtained. The study was approved by the ethics committees of the Department of Health Sciences, University of York, UK (HSRGC-3/17); the Centre for Injury Prevention and Research, Bangladesh (CIPRB/ERC/2OI 8/003); the Institute Ethics Committee, National Institute of Mental Health and Neurosciences, India (BEH.SC.DIV 20/19); the Health Ministry Screening Committee, India (HMSC12/18); and the National Bioethics Committee, Pakistan (4-18/NBC-413/19). All study procedures complied with legislation and guidance for good practice governing the participation in research of people who may lack capacity (Mental Capacity Act (UK) 2005). Participants did not receive financial inducements to participate, but results of physical health measurements and blood tests were shared with them and with the treating clinician. This study is registered with the ISRCTN registry under identifier ISRCTN88485933 (https://doi.org/10.1186/ISRCTN88485933). All participants consented and signed an informed consent form.
Results
We approached 5801 people with SMI in the three sites and 3989 (58.8%) participated in the survey (1500 in Bangladesh, 1175 in India and 1314 in Pakistan). Most of the participants in Bangladesh (94%) and Pakistan (70%) were recruited before the COVID-19 pandemic (July 2019 and March 2020), and most of the participants in India (86%) were recruited after the COVID-19 had begun (February 2021–Dec 2021). The details of participants that were not eligible are provided in Figure 1.
Participant flow chart. MINI, Mini-International Neuropsychiatric Interview.
Participant characteristics are shown in Table 1. The proportion of in-patients was 20% for Bangladesh and India, and 10% for Pakistan. In-patients were more likely to be ineligible because of not having the capacity to sign an informed consent or answer the questionnaire. On average, 60.1% of the sample was male, and the mean age was 35.8 years; the Bangladesh cohort was younger than the cohort in India and Pakistan. Almost a third (32.0%) were unemployed, with a higher proportion in Bangladesh (39.7%) than India (19.4%) and Pakistan (22.2%). About half (50.8%) were educated only up to or less than primary school level.
General characteristics of the participants
Confidence intervals were calculated using bootstrap sampling procedure (n = 1000) for binomial and continuous variables and Goodman's method for multinomial proportions. MINI, Mini-International Neuropsychiatric Interview; SMI severe mental illness; PHQ-9, Patient Health Questionnaire-9; GAD-7, Generalised Anxiety Disorder-7.
a. Values presented as mean (s.d.).
b. Data not reported because of low numbers for statistical disclosure control.
SMI, anxiety and depressive symptoms and health-related quality of life
The most common SMI diagnosis was non-affective psychosis (44.7%), followed by bipolar disorder (37.3%) and depression with psychotic symptoms (18.8%). Non-affective psychosis was the most common diagnosis in Bangladesh (62.3%) and India (57.3%), whereas depression with psychotic symptoms was the most common diagnosis in Pakistan (46.0%). Almost 97% of participants were on antipsychotic medication.
A majority of participants reported having depressive (75.8%) and anxiety (69.7%) symptoms in the ‘moderate or severe’ category. The prevalence of ‘moderate or severe’ depressive symptoms was lower in India (24.3%) than in Bangladesh (59.5%) and Pakistan (65.9%). Similarly, a smaller proportion reported ‘moderate or severe’ anxiety symptoms in India (17.9%) than Bangladesh (34.8%) and Pakistan (51.9%).
The mean EQ-5D-5L (health-related quality of life) visual analogue scale (0–100) score was 66.9 overall, 69.9 for Bangladesh, 76.4 for India and 54.9 for Pakistan. A total of 45% of the participants reported problems in carrying out their usual activities; and around 60% reported pain/discomfort (Table 1).
Physical disorders, risk factors, health risk behaviours and healthcare
As seen in Table 2, 11% of participants had type 2 diabetes (self-report of clinician diagnosis or those with an HBA1c >6.5%), 1% had chronic respiratory disorders, 3.2% had cardiovascular diseases, 2.3% had tuberculosis and 2.0% had chronic hepatitis.
Non-communicable and communicable diseases and health risk behaviours in people with severe mental illness
Confidence intervals were calculated using bootstrap sampling procedure (n = 1000) for binomial and continuous variables and Goodman's method for multinomial proportions. HbA1C, glycated haemoglobin; MET, metabolic equivalents; WHO, World Health Organization.
a. The denominators for HbA1c are 2267 overall, 1370 for Bangladesh and 897 for Pakistan.
b. Data not reported because of low numbers for statistical disclosure control.
c. The denominators for hypertension are 2343 overall, 1422 for Bangladesh and 921 for Pakistan.
d. The denominators for hypercholesterolemia are 2247 overall, 1357 for Bangladesh and 890 for Pakistan.
e. Values presented as mean (s.d.).
Overall, 46.3% of participants were overweight or obese; most women (71.5%) and a high proportion of men (28.7%) had a high waist circumference. Underweight was also prevalent in 7.8% of the participants.
Almost a quarter (23.3%) either reported a diagnosis of hypertension or had high measured blood pressure (>140/90 mmHg): 16.1% in Bangladesh, 20.6% in India and 33.8% in Pakistan. Almost half (49.9%) were found to have hypercholesterolemia based on either previous reported diagnosis or high levels of low-density lipoprotein cholesterol. A total of 35% of participants had anaemia; this was higher in Bangladesh (44.9%, 95% CI 42.3–47.4%) than India (31.6%, 95% CI 28.7–34.5%) and Pakistan (28.3%, 95% CI 25.9–30.8%) (Supplementary Material available at https://doi.org/10.1192/bjo.2023.12). Most people with hypercholesterolemia (94.4%) and almost half with diabetes (49.2%) and with high measured blood pressure (48.5%) were previously unaware of their condition and were detected during the survey through cholesterol, HbA1c and blood pressure measurements, respectively.
Almost half of men (46.8%) consumed either smoking or smokeless tobacco, and 32.8% reported smoking tobacco daily. Smoking rates were 42.8% in Bangladesh, 20.1% in India and 31.7% in Pakistan. A total of 19% of women reported using tobacco in Bangladesh, 10.4% in India and 13.1% in Pakistan. Around half of participants (45.6%) did not meet the WHO recommendations for physical activity (of 600 metabolic equivalents); and 89.1% of the participants reported to not meet the WHO recommended levels of fruit and vegetable intake (at least five servings). Less than 6.2% of males and 0.4% of females reported consuming alcohol in the past month (data not provided in the tables). Less than 9% of the sample reported to have more than two sexual partners in the past 10 years.
As shown in Table 3, only 56.8% of the participants had been previously tested for any NCDs or NCD risk factors: 52.5% for hypertension, 26.7% for type 2 diabetes and 9.0% for hypercholesterolemia. In general, a low proportion of participants received treatment for physical conditions or to address risk factors. Of those with a self-reported NCD or an NCD risk factor, only 48.5% reported receiving related treatment or health risk modification advice. The provision of relevant treatment was highest in those reporting type 2 diabetes (74.5%, 95% CI 68.2–80.0%), followed by hypertension (43.9%, 95% CI 39.5–48.4%) and hypercholesterolaemia (34.6%, 95% CI 25.9–44.5%). Only 42.8% received any type of advice to modify health risk behaviours; the proportion of participants that received any type of health risk modification advice was highest in India (81.7%), followed by Pakistan (54.2%) and Bangladesh (23.8%). Among those who consumed tobacco, only 28.1% had been advised to quit.
Proportion of people with severe mental illness screened, diagnosed and treated for non-communicable diseases and their risk factors, including health risk behaviour modification advice
Confidence intervals were calculated using bootstrap sampling procedure (n = 1000) for binomial and continuous variables and Goodman's method for multinomial proportions. NCD, non-communicable disease.
a. People that self-reported not to have type 2 diabetes, hypertension and hypercholesterolemia or had not been tested, but were positive on the test performed during the current survey.
b. Data not reported because of low numbers for statistical disclosure control.
c. Includes type 2 diabetes, hypertension and hypercholesterolemia.
Comparison between people with SMI and the general population (STEPS survey)
The results for the comparisons between our data and country STEPS reports are summarised in Table 4.
Odds of people with severe mental illness having non-communicable diseases, related risk factors and health risk behaviours and receiving healthcare screening and advice compared with the general population (severe mental illness data weighteda)
STEPS, STEPwise Approach to Surveillance of NCDs; SMI, severe mental illness; HbA1C, glycated haemoglobin;, N/A: Not available in the STEPS survey; BMI, body mass index; MET, metabolic equivalents; WHO, World Health Organization.
a. Data from the general population was extracted from the STEPS 2018 survey in Bangladesh and India, and 2014 survey in Pakistan; data from the SMI survey were weighted by age and gender according to the distribution of the STEPS report.
b. After Bonferroni correction for multiple testing, the P < 0.05 significance level was corrected to P < 0.006.
c. Blood glucose ≥126 mg/dL for the STEPS survey and HbA1c ≥6.50% for the SMI survey.
d. Data not reported because of low numbers for statistical disclosure control.
e. People that self-reported not to have the condition or had not been previously tested but tested positive in assessments performed for the current survey or the STEPS survey in Bangladesh.
Prevalence of NCDs and NCD risk factors
People with SMI in Bangladesh (odds ratio 1.7, 95% CI 1.4–2.0, P < 0.001) and India (odds ratio 1.8, 95% CI 1.5–2.2, P < 0.001) were more likely to have type 2 diabetes compared with the general population, and people with SMI in Bangladesh were more likely to have hypercholesterolemia (odds ratio 2.4, 95% CI 2.2–2.7, P < 0.001) compared with the general population. Blood samples were not collected in the STEPS survey in Pakistan and for cholesterol in India, therefore these comparisons are not available.
People with SMI were more likely to be overweight or obese (BMI > 25 kg/m2) compared with the general population (Bangladesh: odds ratio 1.9, 95% CI 1.7–2.2, P < 0.001; India: odds ratio 2.9, 95% CI 2.6–3.3, P < 0.001; Pakistan: odds ratio 1.5, 95% CI 1.3–1.7, P < 0.001). People with SMI in Bangladesh were less likely to be underweight (odds ratio 0.6, 95% CI 0.4–0.7, P < 0.001), but there were no differences in India and Pakistan.
Health risk behaviours
People with SMI were more likely not to meet recommendations for physical activity (Bangladesh: odds ratio 5.4, 95% CI 4.8–6.1, P < 0.001; India: odds ratio 2.8, 95% CI 2.5–3.2, P < 0.001; Pakistan: odds ratio 2.3, 95% CI 2.0–2.6, P < 0.001). However, they were less likely not to meet WHO recommendations for fruit and vegetable intake (Bangladesh: odds ratio 0.6, 95% CI 0.5–0.7, P < 0.001; India: odds ratio 0.3, 95% CI 0.2–0.4, P < 0.001; Pakistan: odds ratio 0.5, 95% CI 0.4–0.6, P < 0.001).
Men with SMI in Bangladesh (odds ratio 0.7, 95% CI 0.6–0.9, P = 0.001) and India (odds ratio 0.4, 95% CI 0.3–0.5, P < 0.001) were less likely to use tobacco products, whereas the opposite was found in Pakistan (odds ratio 2.2, 95% CI 1.8–2.7, P < 0.001).
Screening and diagnosis
Compared with the general population, people with SMI were more likely to be screened for type 2 diabetes in Bangladesh (odds ratio 1.2, 95% CI 1.0–1.3, P = 0.001), India (odds ratio 1.2, 95% CI 1.0–1.4, P = 0.001) and Pakistan (odds ratio 3.0, 95% CI 2.6–3.4, P < 0.001).
People with SMI in Bangladesh (odds ratio 0.6, 95% CI 0.5–0.7, P < 0.001) and India (odds ratio 0.70, 95% CI 0.6–0.7, P < 0.001) were less likely to be screened for hypertension, whereas the opposite was found in Pakistan (odds ratio 1.3, 95% CI 1.2–1.5, P < 0.001). Among those screened, people with SMI in Pakistan were more likely to have hypertension (odds ratio 1.4, 95% CI 1.2–1.7, P < 0.001), whereas no differences were found in Bangladesh and India.
Regarding hypercholesterolemia, people with SMI in India (odds ratio 2.5, 95% CI 2.1–3.0, P < 0.001) and Pakistan were more likely to be screened (odds ratio 1.5, 95% CI 1.2–1.8, P < 0.001), and those that were screened in Pakistan were more likely to have hypercholesterolemia (odds ratio 2.1, 95% CI 1.3–3.3, P < 0.001) than people in the general population. In Bangladesh, there was no difference in screening; however, those that were screened were less likely (odds ratio 0.4, 95% CI 0.2–0.7, P = 0.001) to have hypercholesterolemia than the general population.
Health risk modification advice
People with SMI were less likely to receive advice to quit or not take up tobacco in Bangladesh (odds ratio 0.3, 95% CI 0.2–0.4, P < 0.001) and Pakistan (odds ratio 0.3, 95% CI 0.2–0.4, P < 0.001), whereas no differences were found in India. A similar pattern was observed for receiving advice on maintaining healthy body weight (Bangladesh: odds ratio 0.4, 95% CI 0.3–0.5, P < 0.001; Pakistan: odds ratio 0.4, 95% CI 0.3–0.5, P < 0.001, Pakistan); this indicator was not available for the STEPS report in India.
Discussion
This is the first multi-country study from South Asia to report on physical multimorbidity, health risk behaviours and access to related healthcare in people with SMI. We found a high prevalence of physical health conditions, primarily NCDs and related risk factors. We also found that people with SMI were more likely to have NCDs and NCD risk factors (overweight/obesity, hypertension, hypercholesterolemia) and engage in some health risk behaviours (tobacco use), but were less likely to receive risk modification advice than the general population. Many people with SMI in our sample reported that they had never been tested or screened for NCDs or NCD risk factors despite the well-established link between SMI and cardiometabolic conditions.Reference Fekadu, Medhin, Kebede, Alem, Cleare and Prince4,Reference Teferra, Shibre, Fekadu, Medhin, Wakwoya and Alem5 Moreover a large proportion of people with type 2 diabetes, hypertension and hypercholesterolaemia had not been previously diagnosed, and these conditions were only detected on testing during the survey. Most had not received appropriate treatment and risk modification advice for their physical health. Therefore, even in the two major specialist mental health institutes included in our survey, most people with SMI failed to receive adequate screening, prevention and management of NCDs and NCD risk factors.
At the time of the survey, there were no policies or recommendations for people with SMI to attend or visit primary care. The difference within countries in terms of healthcare utilisation (visited a doctor or other healthcare worker in the past 12 months) is most likely a result of the differences in the timing of data collection: most of the sample in India was recruited during the COVID-19 pandemic, whereas most of the sample in Bangladesh and Pakistan was recruited before the COVID-19 pandemic. This finding is supported by a multicentre cross-sectional study in India reporting that people had 2.5 higher odds of not being able to access healthcare services during the COVID-19 pandemic compared with before the COVID-19 pandemic.Reference Raman, Rajalakshmi, Surya, Ramakrishnan, Sivaprasad and Conroy24
The finding that people with SMI are more likely to have NCD risk factors compared with the general population extends previous findings in high-income countries and LMICs for risk factors such as obesity, hypercholesterolemia and decreased physical activity.Reference Holt, Afzal, Ahmad, Afsheen, Aslam and Ali25,Reference Soundy, Wampers, Probst, De Hert, Stubbs and Vancampfort26 Importantly, it should be noted that psychotropic medication might contribute to some of these adverse risks.Reference Correll, Detraux and De Lepeleire27 Almost all survey participants were prescribed antipsychotics, which are associated with tiredness and sedation, an increased risk of obesity and adverse effects on glucose and lipid metabolism. The high prevalence of anaemia among participants is consistent with findings in people with SMI in other LMICs,Reference Korkmaz, Yıldız, Korucu, Gundogan, Sunbul and Korkmaz28 and this has been associated with poor diet and side-effects of mood stabilisers.Reference Korkmaz, Yıldız, Korucu, Gundogan, Sunbul and Korkmaz28
In Pakistan, we found a higher prevalence of tobacco use in people with SMI compared with the general population. This is consistent with other studies in people with SMI,Reference Evins, Cather and Laffer29 where tobacco use has been associated with a greater susceptibility to addiction because of a higher subjective experience of reward and an attempt to self-medicate to mitigate anxiety and depressive symptoms.Reference Peckham, Bradshaw, Brabyn, Knowles and Gilbody30 Unexpectedly, the opposite was found in Bangladesh and India. This may be because the STEPS survey for Bangladesh and India reported an unusually high estimate of the prevalence of tobacco use (in men, 70% for Bangladesh and 52% for India). The more reliable Global Adult Tobacco Survey31 for the same period reported a prevalence of 58% in Bangladesh and 43% in India in the same group, which is closer to the figures reported in our study.
The low observed prevalence of alcohol use in both men and women is similar to the STEPS survey reports,Reference Rafique, Saqib, Munir, Qureshi, Rizwanullah and Khan14,22,23 and is likely to be explained by religious proscription.
Despite the high prevalence of overweight/obesity, hypercholesterolemia, hypertension and tobacco use, health risk modification advice was provided to less than a quarter of people with SMI, and we found that the odds of receiving such advice was lower in people with SMI than in the general population in Bangladesh and Pakistan. Similar treatment gaps have been reported in high-income countries.Reference Holt, Afzal, Ahmad, Afsheen, Aslam and Ali25 Although psychiatrists are trained in motivational interviewing, there are attitudinal barriers that make mental health professionals reluctant to engage with patients about their tobacco use.Reference Rogers, Gillespie, Smelson and Sherman32 Moreover, misconceptions about potential side-effects of tobacco cessation medication, unfounded fears of exacerbating depressive symptoms following quitting and low expectations of patients’ motivation or ability to stop smoking are additional barriers.Reference Kerr, Woods, Knussen, Watson and Hunter33 On the other hand, there is high-quality evidence from high-income countries about both the effectiveness and cost benefits of smoking cessation interventions in people with SMI.Reference Gilbody, Peckham, Bailey, Arundel, Heron and Crosland34 Such approaches need to be adopted in South Asia, where tobacco use is common. Similarly, lifestyle interventions have shown promise to reduce weight and improve metabolic risk factors, and are recommended as an essential part of the management of SMI in these countries.Reference Sheals, Tombor, McNeill and Shahab35
An important study finding is the differences in the proportion of participants with moderate or severe depressive and anxiety symptoms within the countries (with the highest in Pakistan and lowest in India). This may be explained by the type of patient flow in each hospital: the National Institute of Mental Health and Neurosciences is a tertiary care, exclusive neuropsychiatric setting, and the proportion of patients with schizophrenia and bipolar disorders is likely to be higher compared with a general hospital psychiatry unit, whereas the proportion of patients with depression is higher in the Institute of Psychiatry in Rawalpindi, Pakistan, as compared with private mental health facilities. These differences may also be related to the higher proportion of participants in the depression with psychosis category in Pakistan.
The prevalence of tuberculosis was three times higher than in the general population. This is consistent with previous findings in LMICs and the clustering of tuberculosis risk factors reported in people with SMI.Reference Doherty, Kelly, McDonald, O'Dywer, Keane and Cooney36 In contrast, the prevalence of HIVReference Khan and Khan37 and hepatitis B and CReference Ali, Donahue, Qureshi and Vermund38 were similar to those reported in the general population – a surprising finding considering the several risk factors for blood-borne viruses that have been reported to cluster in people with SMI.
Although most of the comparisons between people with SMI and the general population are in line with clinical expectations and previous findings,Reference Soundy, Wampers, Probst, De Hert, Stubbs and Vancampfort26 there were some anomalous results. These include the lower odds of people with SMI with a self-reported clinical diagnosis of type 2 diabetes, hypertension and hypercholesterolemia in Bangladesh. This may be because of ‘diagnostic overshadowing’, where the presence of a mental disorder means clinicians do not look for physical health problems, or failure to recall such diagnosis by patients. The lower education and socioeconomic levels for participants from Bangladesh (compared with India and Pakistan) may have contributed to the latter.Reference Vellakkal, Subramanian, Millett, Basu, Stuckler and Ebrahim39
We report findings from the first large-scale effort to document physical multimorbidity in people with SMI attending specialist services in three South Asian countries. We used standardised tools for data collection (i.e. STEPS, EQ-5D-5L, PHQ-9, GAD-7) that allowed us to compare our findings with those in the general population. Data were collected by trained researchers having experience of working with this population. Finally, we gathered objective data on physical conditions (including blood tests), and reported on both previously diagnosed and undiagnosed conditions.
Of the several limitations that need to be mentioned, the first is although we have used findings from studies in the general population to compare and discuss our findings, caution needs to be exercised in such comparisons, since our sample was collected from mental health hospitals and the analyses were only adjusted by gender and age and other. Moreover, we need to be mindful of the time lag between these studies, during which a number of parameters of interest might have changed.
Second, we relied on blood results from each mental health institution's laboratory, but we did not standardise these tests between laboratories. Third, there are methodological considerations that should be considered when making comparisons between the countries: recruitment in India was done during the COVID-19 pandemic, which needs to be considered when comparing the country estimates, since the pandemic might have affected the physical and mental health outcomes as well as healthcare utilisation of the SMI population in India; in-patients were more likely to be excluded because a ‘lack of capacity to answer’, and these patients with more severe SMI symptoms have also shown to have more physical health problems,Reference Kasckow, Twamley, Mulchahey, Carroll, Sabai and Strakowski40 which may be associated with an underestimation of physical ill conditions in our sample; there was a lower proportion of in-patients in Pakistan than in Bangladesh and India, and in-patients are known to have more severe mental health symptoms and are more likely to have physical health conditions,Reference Kasckow, Twamley, Mulchahey, Carroll, Sabai and Strakowski40 which might lead to an underestimation of the prevalence of mental and physical health conditions in Pakistan. Fourth, the information from some of the questions from the STEPS survey are easy to recall (e.g. diabetes diagnosis), whereas some other may be more difficult (e.g. time performing physical activity or receipt of health risk modification advice) and prone to recall bias. To the best of our knowledge, there is no information on the performance of the access to healthcare and health risk modification advice questions in the STEPS survey that could provide further information about the risk of bias. Fifth, since the sample was drawn from tertiary care, the findings may not be representative of the total SMI population in each country. However, unlike mental health services in high-income countries, tertiary care services in South Asian countries accept self-referral without the need for primary or secondary care referral, and often function as ‘the first port of call’ for people with SMI. They also attract patients from both urban and rural areas. Therefore, the study population is likely to be similar to the overall population of people with SMI in these countries.
The high prevalence of physical health conditions and health risk behaviours in SMI compared with the general population, and their underdetection even in specialist centres, merits attention to improve early identification, prevention and management, in line with international recommendations and guidance. Given many of these physical health monitoring and management guidelines for SMI are based on evidence from (and developed in) high-income countries, they may not necessarily be applicable to low-resource settings in LMICs. Our findings can help to identify and contextualise the priority areas for LMICs, and to develop more appropriate guidance for such settings. In view of challenging resource limitations, interventions to address health risk behaviours that are brief and delivered by non-specialist personnel need to be tested in these settings. Integration of physical healthcare with mental healthcare that has been envisioned at all levels of mental healthcare delivery needs to be actioned and scaled up.8 Representative community-based studies may further answer questions related to regional differences in physical health conditions and health risk behaviours.
In conclusion, people with SMI in South Asia have a high prevalence of NCDs, which may be attributable to the associated clustering of several health risk factors and behaviours in this population. There is an unmet need to address physical multimorbidity in people with SMI in South Asia. Policy makers and healthcare professionals working with people with SMI need to recognise the extent and importance of physical multimorbidity in this vulnerable group, and prioritise the prevention, screening and treatment of NCDs in people with SMI.
Supplementary material
Supplementary material is available online at https://doi.org/10.1192/bjo.2023.12
Data availability
The data-sets used and/or analysed during the current study are available from the corresponding author, G.A.Z., on reasonable request.
Acknowledgements
The authors would like to acknowledge Abu Musa Robin, Anjuman Jum Tithi, Tanvir Arafat, Lipon Saha, Tasmia Rahman, Dr Md. Helaluddin, Dr Khaleda Islam, Dr Mamun and Dr Bappi from the team in Bangladesh; Archith Krishna, Sathish Kumar, Neeta P.S., Venkatalakshmi C., Bhuvneshwari L., Manjunatha S., Sobin George and Krishna Jayanthi from the team in India; and Rubab Ayesha, Nida Afsheen, Najma Hayat, Zaheen Amin and Aniqa Maryam from the team in Pakistan, for conducting all the interviews and physical measurements, and providing technical and managerial support. The authors also would like to thank all the participants who consented and provided their time to complete the interview.
Author contributions
N.S. conceived the study. A.H.-C., A.T.N., F.A., H.K., K.P.-M., K.S., P.M., R.H. and S.R. provided important contextual information. A.H.-C., F.S., F.A., J.R.B., K.P.-M., K.S. and N.S. developed the methodology and selected the outcomes. A.H.-C., G.A.Z. and J.R.B. developed the analysis plan. A.H.-C., G.A.Z., H.K., J.R.B. and S.R. revised the data and conducted the statistical analysis. A.H.-C., D.S., G.A.Z., H.K., J.R.B., K.A., K.S. and N.S. wrote the manuscript. All authors revised and approved the manuscript.
Funding
This research was funded by the National Institute for Health Research (NIHR) (grant number GHRG 17/63/130; awarded to N.S.), using UK aid from the UK Government to support global health research. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK Department of Health and Social Care.
Declaration of interest
The authors declare that they have no competing interests. D.S. is an expert advisor to the UK National Institute for Health and Care Excellence (NICE) centre for guidelines; the views expressed are the authors’ and not those of the NICE.
Open access
eLetters
No eLetters have been published for this article.