Abstract
Background
Suicidality and absconding from psychiatric care are two critical phenomena that complicate mental health care in developing countries. The aim of this study was twofold. First, to determine the prevalence of suicidality among absconders over two decades. Secondly, we set out to determine overall factors that influence the likelihood of having suicidal behaviors among absconders, as well as factors specific to each diagnosis.
Methods
This was a retrospective chart review of files of patients who absconded from inpatient psychiatric care at a tertiary psychiatric facility in southwestern Uganda between 2000 and 2020. A pre-tested electronic questionnaire was used for data abstraction of sociodemographic characteristics, documented suicidality, and other clinical variables. Data cleaning and analysis were conducted using STATA V.17. Logistic regression was performed for factors associated with suicidality.
Results
Among the absconders, 9.5% exhibited suicidality. Factors that heightened the odds of suicidality among absconders included being divorced or separated (adjusted odds ratio [aOR] = 2.00, 95% Confidence Interval [CI]: 1.20–3.31, p = 0.007), having depression (aOR = 5.41, 95% CI: 2.47–11.82, p < 0.001), a history of substance use (aOR = 1.50, 95% CI: 1.01–2.23, p = 0.049), and experiencing violence before hospitalization (aOR = 1.83, 95% CI: 1.14–2.94, p = 0.013). In contrast, substance use disorder (aOR = 0.25, 95% CI: 0.10–0.62, p = 0.003) and having schizophrenic spectrum disorders (aOR = 0.35, 95% CI: 0.18–0.68, p = 0.002) were linked to a decreased likelihood of suicidality among those who absconded.
Conclusion
This study reveals a high burden of suicidality among individuals who abscond, with important risk factors such as marital status, depression, and a history of experiencing violence. It was noted that substance use disorder and schizophrenia spectrum disorders are associated with a reduced suicide risk. This study shows a significant interplay between clinical and demographic factors in predicting suicidality.
Keywords: Suicidality, Absconding, Substance use disorder, Depression, Violent behaviors
Introduction
Suicidality and absconding from psychiatric care are two critical phenomena in mental health care with a plethora of public health consequences [1]. According to the American Psychological Association (APA), “suicidality” is defined as the risk of suicide as indicated through reports of suicidal ideation or intent or with well-established suicidal gestures, plans or attempts [2]. Absconding is when patients leave the medical facility premises illegally without permission from the attending health staff [3]. The global absconding rates from psychiatric facilities ranged between 2.5% and 34%, with sub-Saharan Africa reporting higher absconding rates of 7.83 per 100 patients [4]. In Uganda, 10 to 50 patients are estimated to have absconded from the country’s most secure National Mental Health Referral hospital [5]. Among patients with bipolar affective disorder, 7.8% had absconded from a tertiary mental health care facility in southwestern Uganda [3].
The implications of absconding are significant; it prolongs inpatient hospitalization, interrupts treatment, slows recovery, and may result in loss of lives by suicide [6, 7]. This concerning relationship has been highlighted by studies, which indicate that absconding from care has been associated with high rates of suicides, with estimates suggesting that 20–30% of inpatients who absconded committed suicide [1, 6]. Of the 72% of suicides among in-patients that occurred outside the hospital in the UK, 63% of them had absconded from care [8]. In addition, a study investigating suicide deaths following 12 months of mental health care found that 38% of them had absconded from the mental health facility [9]. A nationwide survey in England and Wales has reported that 1 in every four in-patients who ended up committing suicide had absconded from inpatient care, and 16% of their suicide cases had suicidal intent in the last clinical review before absconding from the ward [6]. Besides, there is a scarcity of studies investigating suicidal behaviors among absconders from Sub-Saharan African mental health settings; thus, the current study aims to address this gap.
Studies in Uganda have explored the prevalence of suicidality among individuals with mental illness; however, there is limited literature about suicidality among those who abscond from inpatient psychiatric care. Many scholars have reported suicidality as a factor increasing the likelihood of absconding from care [1, 6, 10, 11] without examining predictive factors that drive suicidal behavior among at-risk patients before they escape. Such factors may provide opportunities to manage patients better to avoid suicidal behavior and eradicate absconding and its fatal complications. Most factors that independently predict absconding have also been reported to predict suicidal behavior. For example, male gender, history of major mental trauma, impulsivity, family history of suicide, substance misuse, and severe depression symptomatology [3, 12, 13].
The study explored the prevalence of suicidality among absconders over 2 decades, the overall factors associated with suicidality, and diagnosis-specific factors that predict suicidality among psychiatric patients over 2 decades in a tertiary mental health facility. Findings from this study will not only contribute to the limited literature about suicidality among absconders but also strengthen suicide prevention protocols in psychiatric inpatient care. Patients who escape from inpatient psychiatric care are often at risk of violence, not only to community members/properties but also to themselves. Thus, exploring this aspect will provide baseline data for this problem, offering contextual factors that could be targeted in developing interventions as mitigation plans, ultimately reducing preventable loss of lives.
Methods
Study design and setting
This study was a retrospective chart review of the inpatient records at a tertiary psychiatric unit of Mbarara Regional Referral Hospital (MRRH) in southwestern Uganda. We reviewed records of patients who absconded following admission for a mental health condition between January 2000 and December 2020, and their information as captured by the Ugandan Ministry of Health Management Information System (HMIS) registers and patients’ files. Absconding contextually refers to patients who leave the psychiatry hospital premises illegally (any individual recorded as having escaped, run away, taken without permission, eloped, not found on ward, or not yet returned to the unit) for more than 24 h without permission from the attending mental health staff as recorded in the HMIS form or patient file. The HMIS captures the patient’s identification number, name, address, age, gender, next of kin, use of illicit substances, and diagnosis, among others.
MRRH is about 270 km Southwest of Kampala, the capital city of Uganda. MRRH psychiatric unit is the largest psychiatric facility in southwestern Uganda. It provides both inpatient and outpatient care to patients with psychiatric illnesses confirmed by mental health professionals based on the Diagnostic and Statistical Manual (DSM). Inpatient care is offered to both genders on male and female wards. These are open wards, designed as “dormitories” with no restriction on the movement of patients within them. The facility is enclosed by a boundary perimeter wall, leaving the main gate for entry and exit. However, due to a lack of adequate security personnel, patients frequently abscond from the facility through the unrestricted use of the facility gate [3, 14]. The psychiatry department records office is a well-established section that stores individual patients’ charts in a shelf system following the order of inpatient numbers, which makes medical records easily retrievable at any time. Retrospective chart reviews have been employed to understand and provide insight into the various mental health conditions among patients admitted at MRRH [3, 10, 15, 16].
Eligibility criteria
We reviewed inpatient charts for all patients who absconded following admission at MRRH between 2000 and 2020. We excluded charts without or with unclear documentation of absconding or suicidality (ideation/attempt) and charts with significant missing data. However, all excluded charts were reviewed with the principal investigator (MMK) to ensure that the charts were not unnecessarily included or excluded.
Data collection procedure, management, and quality control
An electronic data abstraction form (Google Form) was designed for data collection. From the patients’ charts, the following information was extracted:
Dependent variable: documented history of suicidality, which was coded as a binary outcome, yes for recorded suicidal behavior, otherwise, no; In the present study, suicidality involved all records about different suicidal behaviors documented during the period of admission before the patient absconded. Due to inconsistency in the recording of the various forms of suicidal behaviors (suicidal ideation/planning/attempt) in the records, all aspects were recorded as suicidality.
Independent variables: age (in completed years), gender (male vs. female), marital status (single, married/cohabiting, separated/divorced, or widowed/widower), number of children, employment status (employed, unemployed), and presence of caregivers at admission (yes vs. no).
As documented in the file, primary psychiatric diagnoses (substance use disorder, depression, bipolar affective disorder, schizophrenia spectrum, among others (anxiety, post-traumatic stress disorder, neurocognitive disorder), previous history of absconding from psychiatric care (yes vs. no), history of extrapyramidal side effects (EPS) in current admission (like acute dystonia, akathisia, pseudo-parkinsonism, clearly recorded in the patients’ file) (yes vs. no), family history of mental illness (yes vs. no), documented history of substance use – including alcohol, tobacco, cannabis, among other (yes vs. no), pre-hospital physical violence to the patient (yes vs. no).
To ensure the rigor of our methods, we followed the methodological guidelines suggested by Matt Vassar et al. (2013) during the planning, conducting, and reviewing stages of the medical records [17].
FOK obtained patient charts. MM and MMK confirmed that the selected files adhered to the study criteria. Three medical officers performed data extraction, all trained in psychiatry and responsible for conducting research. They were blinded to the study hypothesis and worked under MMK’s supervision, meticulously reviewing the clinical notes using a pre-tested data extraction tool. MMK thoroughly trained them in data abstraction and ensured compliance with standard operating procedures. After their training, they entered data from ten randomly selected files, establishing inter-rater and intra-rater reliability with Cohen’s kappa (0.83), a percentage agreement of 90%, and an intra-class correlation coefficient 0.87. Information on the number of patients admitted annually was obtained from the main hospital records.
Ethical considerations
The Research Ethics Committee of Mbarara University of Science and Technology approved the study (Reference number: 17/06–20) and granted a waiver of consent. The hospital administration provided administrative permission to conduct the study and access hospital and patient records. Data collection occurred within the hospital, ensuring no files were removed from the psychiatry unit premises.
Data analysis
An Excel spreadsheet containing the data was downloaded and exported to STATA V.17 [18] for data cleaning and analysis. Demographic and clinical characteristics were presented as percentages for categorical variables and means and standard deviations (SD) for continuous normally distributed variables. Normality was assessed based on the Gaussian assumption and confirmed using Shapiro-Wilks’s test and histograms. Non-normally distributed continuous variables, such as “number of children,” were converted into ordinal categories using quartiles. Differences between absconders with or without suicidality were examined using independent samples t-tests for continuous variables and chi-square tests for categorical variables. The predictors of suicidality were analyzed using logistic regression. In addition, a sensitivity analysis was run to determine the factors associated with suicidality per diagnosis. Likewise deletion was used during logistic regression analysis due to missing variables [19]. All statistical analyses were two-tailed using a 5% alpha, with a 95% level of confidence.
Results
From 2000 to 2020, there were 10,145 admissions, 2,236 absconded from the psychiatry ward, resulting in an absconding rate of 22 patients for every 100 admissions. Of the 2,236 individuals who absconded, 2,046 files were available for detailed review and analysis, reflecting about 91.5% of all absconders’ records considered in this study.
Participants’ characteristics
The average age of absconders was 29.79 (± 11.05) years, with nearly two-thirds (64.6%) males. Almost three-quarters (73.02%) were unemployed, with 36.7% reporting a family history of mental illness. More than half (52.4%) were being managed for schizophrenia spectrum disorders. Almost a third (27.9%) had previously absconded during previous admissions, with about a tenth (9.5%) having a history of suicidal behavior.
The distribution of study variables with suicidality among absconders
The variables that showed a statistical difference with suicidality include gender, primary psychiatric diagnosis, and instances of pre-hospital violence against the patient (refer to Table 1). Male absconders showed higher rates of suicidality than their female counterparts (56.2% vs. 43.8%; χ2 = 6.58, p = 0.010). Furthermore, those absconders receiving treatment for schizophrenia spectrum disorders reported the highest suicidality at a rate of 34.5%, which was significantly higher compared to individuals with other primary psychiatric disorders, with those being managed for other disorders showing the lowest rate of suicidality at 6.7% (χ2 = 138.23; p < 0.001). Additionally, patients who had faced violence or mishandling in the community prior to their admission experienced lower rates of suicidality than those who had not encountered any violence (14.9% vs. 85.1%; χ2 = 4.15; p = 0.042).
Table 1.
Absconders’ characteristics in relationship with their suicidality (N = 2046)
| Variables | n (%) | Suicidality | t2/X2 (p-value) | |
|---|---|---|---|---|
| No, 1852 (90.5%) |
Yes, 194 (9.5%) |
|||
| Age (in years) (µ ± SD) | 29.8 ± 11.1 | 29.8 ± 11.1 | 29.6 ± 10.8 | 0.31 (0.755) |
| Sex | ||||
| Female | 725 (35.4%) | 640 (34.6%) | 85 (43.8%) | 6.58 (0.010) |
| Male | 1321 (64.6%) | 1212 (65.4%) | 109 (56.2%) | |
| Marital status | ||||
| Married/Cohabiting | 634 (31.0%) | 582 (31.4%) | 52 (26.8%) | 6.55 (0.162) |
| Single | 945 (46.2%) | 854 (46.1%) | 91 (46.9%) | |
| Separated/Divorced | 243 11.9%) | 210 (11.3%) | 33 (17.0%) | |
| Widowed/widower | 27 (1.3%) | 25 (1.4%) | 2 (1.0%) | |
| Missing | 197 (9.6%) | 181 (9.7%) | 16 (8.3%) | |
| Number of children | ||||
| None | 1429 (69.8%) | 1302 (70.3%) | 127 (65.5%) | 2.94 (0.401) |
| 1 child | 168 (8.0%) | 147 (7.94%) | 16 (8.3%) | |
| 2 to 3 children | 237 (11.6%) | 213 (11.5%) | 24 (12.4%) | |
| 4 and more children | 217 (10.6%) | 190 (10.3%) | 27 (13.9%) | |
| Employment status | ||||
| Employed | 552 (27.0%) | 499 (26.9%) | 53 (27.3%) | 0.01 (0.911) |
| Unemployed | 1494 (73.0%) | 1353 (73.1%) | 141 (72.7%) | |
| Caregivers present at admission | ||||
| No | 241 (11.8%) | 226 (12.2%) | 15 (7.7%) | 3.38 (0.066) |
| Yes | 1805 (88.2%) | 1626 (87.8%) | 179 (92.3%) | |
| Primary psychiatric diagnoses | ||||
| Othersa | 90 (4.4%) | 77 (4.2%) | 13 (6.7%) | 138.23 (< 0.001) |
| Substance use disorder | 230 (11.2%) | 216 (11.7%) | 14 (7.2%) | |
| Depression | 73 (3.6%) | 39 (2.1%) | 34 (17.5%) | |
| Bipolar disorder | 580 (28.4%) | 514 (27.7%) | 66 (34.0%) | |
| Schizophrenia spectrum disorder | 1073 (52.4) | 1006 (54.3%) | 67 (34.5%) | |
| Previous history of escape | ||||
| No | 1476 (72.1%) | 1344 (72.6%) | 132 (68.0%) | 1.79 (0.181) |
| Yes | 570 (27.9%) | 508 (27.4%) | 62 (32.0%) | |
| History of extrapyramidal syndrome (EPS) in current admission | ||||
| No | 1844 (90.1%) | 1676 (90.5%) | 168 (86.6%) | 3.00 (0.083) |
| Yes | 202 (9.9%) | 176 (9.5%) | 26 (13.4%) | |
| Family history of mental illness | ||||
| No | 1294 (63.3%) | 1179 (63.7%) | 115 (59.3%) | 1.45 (0.228) |
| Yes | 752 (36.7%) | 673 (36.3%) | 79 (40.7%) | |
| History of substance use | ||||
| 1294 (63.3%) | 1241 (60.6%) | 1118 (60.4%) | 123 (63.4%) | 0.68 (0.410) |
| 752 (36.7%) | 805 (39.4%) | 734 (39.6%) | 71 (36.6%) | |
| Pre-hospitalization physical violence towards the patient | ||||
| No | 1828 (89.3%) | 1663 (89.8%) | 165 (85.1%) | 4.15 (0.042) |
| Yes | 218 (10.7%) | 189 (10.2%) | 29 (14.9%) | |
aOthers include anxiety disorder, post-traumatic stress disorder, neurocognitive disorders
Factors associated with suicidality among absconders
All statistically significant variables in Table 1 were examined for multicollinearity via the variance inflation factor (VIF). All variables demonstrated VIFs of less than 2, with a mean VIF of 1.33. Consequently, all variables were included in the final multivariate-adjusted model (Table 2). This model exhibited a sensitivity of 9.55%, a specificity of 98.98%, and a negative predictive value of 91.13%, explaining 10.07% of suicidality among absconders. Moreover, the final model accounted for 20 years of data collection (from 2000 to 2020) due to potential confounding effects. Factors that increased the likelihood of suicidality among absconders included being separated or divorced, which doubled the likelihood (aOR = 2.00, 95% CI: 1.20–3.31, p = 0.007), having depression as the primary psychiatric illness (aOR = 5.41, 95% CI: 2.47–11.82; p < 0.001), and experiencing violence toward the patient pre-hospitalization (aOR = 1.83, 95% CI: 1.14–2.94, p = 0.013). Conversely, a diagnosis of substance use disorder (aOR = 0.25, 95% CI: 0.10–0.62, p = 0.003) or schizophrenia spectrum disorders (aOR = 0.35, 95% CI: 0.18–0.68, p = 0.002) were identified to lower the likelihood.
Table 2.
Logistic regression analysis for factors associated with suicidality among absconders
| Variables | Bivariate analysis | Multivariate analysis | ||
|---|---|---|---|---|
| cOR (95% CI) | p-value | aOR (95% CI) | p-value | |
| Age (in years) | 1.00 (0.98–1.01) | 0.755 | 1.00 (0.98–1.02) | 0.724 |
| Sex | ||||
| Female | 1 | 1 | ||
| Male | 0.68 (0.50–0.91) | 0.011 | 0.75 (0.51–1.10) | 0.136 |
| Marital status | ||||
| Married/Cohabiting | 1 | 1 | ||
| Single | 1.19 (0.83–1.70) | 0.333 | 1.58 (0.92–2.71) | 0.094 |
| Separated/Divorced | 1.76 (1.11–2.80) | 0.017 | 2.00 (1.20–3.31) | 0.007 |
| Widowed/widower | 0.90 (0.21–3.89) | 0.883 | 0.99 (0.21–4.63) | 0.991 |
| Number of children | ||||
| None | 1 | 1 | ||
| 1 child | 1.12 (0.65–1.93) | 0.695 | 0.94 (0.48–1.83) | 0.849 |
| 2 to 3 children | 1.16 (0.73–1.83) | 0.539 | 1.17 (0.64–2.13) | 0.605 |
| 4 and more children | 1.46 (0.94–2.27) | 0.095 | 1.56 (0.85–2.85) | 0.146 |
| Employment status | ||||
| Employed | 1 | 1 | ||
| Unemployed | 0.88 (0.70–1.37) | 0.911 | 0.90 (0.61–1.32) | 0.591 |
| Caregivers present | ||||
| No | 1 | 1 | ||
| Yes | 1.66 (0.96–2.86) | 0.069 | 1.71 (0.92–3.16) | 0.088 |
| Primary psychiatric diagnoses | ||||
| Others | 1 | 1 | ||
| Substance abuse disorder | 0.38 (0.17–0.85) | 0.019 | 0.25 (0.10–0.62) | 0.003 |
| Depression | 5.16 (2.45–10.89) | < 0.001 | 5.41 (2.47–11.82) | < 0.001 |
| Bipolar disorder | 0.76 (0.40–1.44) | 0.403 | 0.58 (0.29–1.16) | 0.124 |
| Psychosis | 0.39 (0.21–0.75) | 0.004 | 0.35 (0.18–0.68) | 0.002 |
| Previous history of escape | ||||
| No | 1 | 1 | ||
| Yes | 1.24 (0.90–1.71) | 0.181 | 1.17 (0.81–1.69) | 0.413 |
| History of extrapyramidal side effects in current admission | ||||
| No | 1 | 1 | ||
| Yes | 1.47 (0.95–2.29) | 0.085 | 1.27 (0.77–2.10) | 0.353 |
| Family history of mental illness | ||||
| No | 1 | 1 | ||
| Yes | 1.20 (0.89–1.63) | 0.229 | 1.23 (0.87–1.73) | 0.233 |
| History of substance use | ||||
| No | 1 | 1 | ||
| Yes | 0.88 (0.65–1.19) | 0.411 | 1.50 (1.01–2.23) | 0.045 |
| Pre-hospitalization physical violence towards the patient | ||||
| No | 1 | 1 | ||
| Yes | 1.55 (1.01–2.36) | 0.043 | 1.83 (1.14–2.94) | 0.013 |
Sensitivity analysis by diagnosis
In an adjusted sensitivity analysis, we identified factors linked to suicidality in patients with certain primary psychiatric diagnoses. No factors correlated with an increased likelihood of suicidality were found in patients suffering from substance use disorder, schizophrenia spectrum disorders, or depression. However, being single (aOR = 3.07, ∂=1.55, p < 0.05), separated/divorced (aOR = 4.59, ∂=2.03, p < 0.05), and having a prior history of substance use (aOR = 1.97, ∂=0.65, p < 0.05) significantly elevated the odds of suicidality among patients diagnosed with BAD who absconded (Table 3).
Table 3.
Sensitivity analysis for factors associated with suicidality by diagnosis among absconders
| Variables | Primary psychiatric diagnoses | |||
|---|---|---|---|---|
| Substance abuse disorder | Depression | Bipolar disorder | schizophrenia spectrum disorders | |
| aOR (SE) | aOR (SE) | aOR (SE) | aOR (SE) | |
| Age (in years) (µ ± SD) | 1.05 (0.05) | 0.96 (0.03) | 1.03 (0.02) | 0.97 (0.02) |
| Sex | ||||
| Female | 1 | 1 | 1 | 1 |
| Male | 0.51 (0.07) | 1.25 (0.83) | 0.60 (0.21) | 0.89 (0.28) |
| Marital status | ||||
| Married/Cohabiting | 1 | 1 | 1 | 1 |
| Single | 0.28 (0.31) | 0.52 (0.48) | 3.07 (1.55)* | 1.57 (0.69) |
| Separated/Divorced | 3.20 (3.14) | 0.75 (0.70) | 4.59 (2.03)* | 1.40 (0.61) |
| Widowed/widower | Omitted | Omitted | 1.38 (1.61) | 1.67 (1.84) |
| Number of children | ||||
| None | 1 | 1 | 1 | 1 |
| 1 child | 0.34 (0.43) | 0.59 (0.58) | 1.15 (0.86) | 1.05 (0.59) |
| 2 to 3 children | Omitted | 0.60 (0.65) | 1.35 (0.74) | 1.78 (0.84) |
| 4 and more children | 0.75 (0.78) | 0.51 (0.58) | 1.19 (0.63) | 2.61 (1.39) |
| Employment status | ||||
| Employed | 1 | 1 | 1 | 1 |
| Unemployed | 1.04 (0.74) | 1.10 (1.00) | 1.02 (0.37) | 0.69 (0.20) |
| Caregivers present | ||||
| No | 1 | 1 | 1 | 1 |
| Yes | Omitted | 0.37 (0.38) | 2.80 (2.21) | 2.31 (1.14) |
| Previous history of escape | ||||
| No | 1 | 1 | 1 | 1 |
| Yes | 2.51 (1.97) | 1.62 (1.32) | 1.59 (0.50) | 0.70 (0.23) |
| History of extrapyramidal side effects (EPS) in current admission | ||||
| No | 1 | 1 | 1 | 1 |
| Yes | 2.20 (2.65) | Omitted | 0.79 (0.37) | 1.41 (0.57) |
| Family history of mental illness | ||||
| No | 1 | 1 | 1 | 1 |
| Yes | 1.21 (0.88) | 1.09 (0.71) | 1.72 (0.53) | 1.22 (0.33) |
| History of substance use | ||||
| No | 1 | 1 | 1 | 1 |
| Yes | Omitted | 1.14 (1.33) | 1.97 (0.65)* | 1.18 (0.35) |
| Pre-hospital violence towards the patient | ||||
| No | 1 | 1 | 1 | 1 |
| Yes | 1.75 (1.71) | 0.43 (0.39) | 2.17 (1.05) | 1.91 (0.70) |
SE Standard Error
*p-value < 0.05
Discussion
In this study, we aimed to determine the prevalence of suicidality among absconders from the largest tertiary psychiatric unit in southwestern Uganda between the years 2000 and 2020 and the factors associated with suicidality. The absconding rate in this study was about 22 per 100 patients in the past 2 decades, which means that for every five patients admitted, one or more absconded from inpatient care. This overall absconding rate included patients with all primary psychiatric diagnoses, and it was higher than 7.83%, the absconding rate for various diagnoses in South Africa [4]. Although both rates were not diagnosis-specific, the lower absconding rate from South Africa could be due to the fact that the study reviewed fewer records and for a shorter duration, i.e., one year vs. 20 years. However, the 2-month absconding rate in an Indian open general psychiatric hospital was almost twice as high (45.4%) as in the present study [11]. This stems from the different study designs used. The high absconding rate in the Indian study was a finding from a prospective cohort compared to a retrospective chart review conducted in the current study, which is prone to missing data due to poor documentation. This might have underestimated the absconding rate reported in this study.
The overall prevalence of suicidality was 9.5%. This means that about 1 in every 10 patients who absconded from care were suicidal. Although the study design couldn’t allow for follow-up of these individuals who absconded with suicidal behaviours, such patients bear the highest chances of completing suicide after absconding from care [20]. A recent national clinical survey based on a sample of people who died by suicide from 1997 to 2006 in England and Wales found that 16% of completed suicides had reported suicidal ideation during the last clinical assessment by the mental health team [6]. This proportion of pre-absconding suicidality (suicidal ideation) was almost twice as high as that in the present study. This is because the study participants involved were selected from a sample of patients who had died by suicide, a group with overt suicidality compared to our study population of patients who escaped with and without suicidality.
The current study also assessed factors that influence the likelihood of having suicidal behaviours during admission among patients who eventually absconded from care. These factors are meant to increase the index of suspicion of mental health workers by identifying such patients as those who are much more likely to be involved in suicidal behaviour, especially completing suicide and homicide-suicides.
The present study found that being divorced or separated doubles the likelihood of becoming suicidal among individuals who abscond. Many studies have reported divorce or separation from partners as a significant trigger to various mental health problems. Divorce/separation may directly result in suicidality or may indirectly result in suicidality through increasing chances of mental health problems like depression and substance use disorders, which have been associated with suicidality [21–24]. Being divorced or separated from one’s partner results in loss of social support and transfer of unexpected responsibilities with significant emotional exhaustion that may trigger feelings of worthlessness and, ultimately, suicidality [21, 25]. About 7% of adult Ugandans experience divorce annually with significant psychological, socioeconomic, and emotional consequences that may complicate into mental health challenges [25, 26]. Therefore, such risk factors should be explored holistically among patients in order for them to be supported during inpatient care to counter such fatal complications.
Depression, as a psychiatric diagnosis, commonly complicates into suicidality. In this study, absconders who were being managed for depression were more than five times more likely to have suicidality. Various studies show that the psychopathology of depression results in suicidality and absconding to complete suicide or achieve social isolation [3, 14]. Therefore, this is a self-perpetuating feedback mechanism where patients with severe depression become suicidal, which prompts them to abscond to complete suicide or cope with the behaviours. Given that depression increases the likelihood of absconding and also the likelihood of becoming suicidal, individuals diagnosed with depression require effective multidisciplinary support and extract supervision to prevent them from absconding, especially those with suicidal behaviours.
According to this study, having a history of substance use increased the odds of developing suicidality among patients who abscond from the psychiatric unit. Surveillance studies in the United States of America have shown that history (and current use) of illicit substances is associated with a suicide risk that is 10 times greater compared to the general population [27]. Substance misuse increases the suicidal risk by a variety of mechanisms; (a) by decreasing inhibitions and increasing depressed mood, (b) increasing psychological distress and aggressiveness, (c) during withdrawal may induce severe psychosis that may lead individuals to end their lives, and (d) constrict cognition, which impairs the generation and implementation of alternative coping strategies [28, 29]. Such effects may increase the chances of suicidality among patients who abscond from care. However, contrary to the above, absconders who were being managed for substance use disorder had lower odds of suicidality. The majority of studies show that absconders who were being managed for substance use disorders are likely to abscond following intense cravings to use substances rather than suicidality [30]. In addition, absconders diagnosed with schizophrenic spectrum disorders, compared to other conditions, were less likely to have suicidal behaviours during their admission before absconding. Findings from the majority of studies have reported varying reasons for escape (rather than suicidality) among patients experiencing psychosis. Patients with schizophrenic-spectrum disorders may escape due to paranoia, following commanding auditory hallucinations, anosognosia, and (less commonly) antipsychotic side effects like akathisia [31, 32]. In our settings, the cultural perception of psychotic phenomena always attracts interest for spiritual interventions like prayers, which might have contributed to the unauthorized disappearance of patients with carers in order to seek religious intervention [14, 33]. Future studies may need to qualitatively explore the lived experiences of absconders to build a solid understanding of triggers for absconding from care for the different diagnoses in order to improve mental health care provision.
Public stigma and experiences of discrimination present the most complex problem in mental health care in the communities, which potentially leads to self-stigma and further anticipations of discrimination [34]. This not only results in community violence against the victim but also worsens the individualized stigma and feelings of worthlessness and hopelessness, which trigger depressive symptoms, including suicidal ideation and planning [34]. This study had a similar finding where absconders who had experienced community violence before admission were likely to become suicidal. In addition, psychiatric patients react to criticisms from the community through counter-aggression, which may result in destructiveness [35]. This complex interplay of mental health constructs may become self-perpetuating, resulting in suicidality. Further studies may focus on how each of these constructs influences suicidal behaviour in order to prioritize resources and develop specific interventions towards addressing suicidality.
In a sensitivity analysis, only patients with bipolar affective disorder had specific risks; patients with other illnesses had no specific suicidality-associated factors. Patients with bipolar disorder who are single or divorced/separated, and those with substance use, had higher chances of becoming suicidal. In a cohort study of 12,850 patients with bipolar affective disorder, living alone increases the likelihood of suicide by more than twice [36]. Being single, divorced/separated is associated with poor social support. Social support reduced the lifetime risk of suicide attempts by more than 30% among a sample of adult Americans [37]. The risk of substance use, in increasing the likelihood of suicide among bipolar patients, is still matched with the risk among the general population, as described above.
Strengths and limitations of the study
This retrospective chart review utilized a larger sample size, which increases the statistical power of the study and improves the precision of the estimates, thus increasing the reliability and generalizability of study findings. However, there are limitations to be noted. First, this was a retrospective study that relied on the efficiency of record reporting and the quality of clinical notes of mental health workers in the facility. There might have been variations in the quality of clinical notes since the facility has grown from a small facility run by non-specialists to a psychiatry department offering specialized care. Despite this, DSM has consistently been used in the diagnosis of patients. Secondly, data was collected over 20 years, a period of political, economic, and socio-cultural transition, which may confound the study’s findings. However, this time duration was adjusted for in the final logistic regression model. Thirdly, in the chart review, there was a lot of missing data whose variables therein might have confounded the study outcomes. However, we employed the listwise deletion method of handling missing data during analysis since we had a big sample size, and the effect on statistical power was minimal [19, 38]. Fourth, this was a one-centred study, limiting our findings’ generalizability throughout the country. Fifth, we assessed for “suicidality” among inpatients who had absconded from care without comparing the outcome with those who remained in care, which could increase risk for confounding. Lastly, we did not use a standardized scale to assess for suicidality, which may limit the precision of the study estimates. In addition, suicidality was evaluated as a single construct, yet it embeds a severity spectrum of suicidal behaviour from suicidal ideation to suicidal attempts. This study didn’t give an account of the different suicidality forms, although any suicidal behaviour, be it ideation or attempt, warrants immediate and effective management to halt progress to completing suicide among absconders [30].
Conclusions
This study recognizes a high prevalence of suicidality among mental health patients who absconded from psychiatric inpatient care. In addition, the study characterizes individuals with suicidality who abscond to identify patients requiring more rigorous care. The study findings indicate that being divorced or separated, having a depressive disorder, having a history of substance use, and having pre-hospital violence against patients increase the likelihood of becoming suicidal. On the contrary, substance use disorder and schizophrenia spectrum disorders lowered the likelihood of having suicidal behaviors during admission before absconding. All these factors should be considered while developing multi-level interventions aimed at both prevention and appropriate holistic management of suicidal behavior. More so, prospective cohort studies may be important to determine how inpatient suicidality and other clinical and sociocultural factors influence the possibilities of absconding from care and worsening suicidal behavior.
Acknowledgements
The team appreciates the efforts of Dr. Innocent Arinaitwe and Dr. Elicana Nduhuura, who assisted in data collection. We also acknowledge the efforts of the late Mr. Felix Onyango Kijoki; may his soul rest in peace. The Department of Psychiatry MRRH also provided extra-support to make this work possible – we are very grateful for your incredibly supportive and research-driven leadership.
Authors’ contributions
MMK played a key role in developing the research concept. MM was essential in collecting data. Both MMK and MM contributed to the data analysis. MM prepared the first draft of the manuscript, while MMK offered significant intellectual input during the revisions. Both authors approved the final published version and agreed to take responsibility for every aspect of the work.
Funding
Not applicable.
Data availability
Due to the sensitivity of the population being explored, the datasets will be made available to appropriate academic parties on request from the corresponding author.
Declarations
Ethics approval and consent to participate
The present study was conducted in accordance to the Declaration of Helsinki 2024 [39], and Mbarara University of Science and Technology-Research Ethics Committee approved the study (Reference number: 17/06–20) and granted a waiver of consent. The hospital administration provided administrative permission to conduct the study and access hospital and patient records. Data collection took place within the hospital, ensuring that no files were removed from the psychiatry unit premises. Being a medical records review, we did not interact with human participants, thus not requiring us to consent participants.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Due to the sensitivity of the population being explored, the datasets will be made available to appropriate academic parties on request from the corresponding author.