We utilized data from the Neuropsychiatric Genetics of African Populations-Psychosis (NeuroGAP-Psychosis) study in Uganda, Ethiopia, Kenya, and South Africa. NeuroGAP-Psychosis is a case-control study aimed at expanding the understanding of genetic and environmental risk factors for psychotic disorders across different African populations, for the purposes of the current study, we analyzed data from Uganda only.
Participants And Study Procedure
Study participants for the current study consisted of participants without psychosis (i.e., controls) in Uganda who were recruited between February 2018 and March 2020. Participants were individuals seeking outpatient general medical care, caretakers of individuals seeking care, and staff or students working at general medical facilities. Only controls were included in this study because patients with clinical diagnoses of psychosis (i.e., cases) were not administered the PSQ. Participants were recruited from the following medical facilities: Butabika National Mental Health Referral Hospital, Naguru, Arua, Mbarara, and Gulu Regional Referral Hospitals. Inclusion criteria for controls were age 18 years or older and able to provide consent. Individuals were excluded who had acute levels of alcohol or substance use, including being under inpatient hospitalization or acute medical care for one of these conditions. Ethical approval was obtained from all participating sites, including the Makerere University School of Medicine Research and Ethics Committee (SOMREC #REC REF 2016-057), the Uganda National Council for Science and Technology (UNCST #HS14ES), and the Harvard T.H. Chan School of Public Health (#IRB17-0822).
Psychosis Screening Questionnaire
The presence of psychosis was assessed using the Psychosis Screening Questionnaire (PSQ), a self-reported brief screening instrument designed to detect psychotic symptoms. The PSQ has five primary (root) questions that assess the presence of psychotic symptoms: mania, thought-interference, paranoia, strange experiences, and hallucinations. Endorsement of any of the primary questions are followed by one to two secondary questions to further screen for psychotic experiences. The original PSQ assesses symptoms in the past year, but for our purposes we focused on lifetime symptoms “ever” in one’s life. We derived a binary response (0 = negative; 1 = positive) for each of the five psychotic symptoms based on responses to PSQ questions. In addition to the five binary responses, we derived a composite screening measure across the five symptoms. Presence of psychotic experiences was defined if positive on of any individual symptoms on the measure
Data Analysis Plan
Standard sociodemographic variables were collected, including age, sex at birth, level of education, marital status, and current living situation. Participant characteristics were described using means and standard deviation for continuous variables and counts and percentages for categorical variables. Prevalence estimates of psychotic symptoms were also calculated. All study participants from Uganda were included in all analyses, but three individuals from the total sample had missing data on the PSQ and were excluded from the below listed analyses.
Confirmatory Factor Analysis
We examined the construct validity and factor structure of the PSQ by conducting confirmatory factor analysis (CFA) in Mplus 8 v.1.7. We tested a unidimensional factor structure based on past research and theory for the measure [14] including a study that reported one latent factor on a multi-ethnic British sample comparing PSQ’s equivalence across groups [12]. A split sample exploratory factor analysis was not possible due to a floor effect in our sample of controls with a low prevalence of psychotic disorders. Our model fit was calculated with a weighted least square mean and variance adjusted (WLSMV) estimator for categorical data, and measurement error was not assumed to be correlated among items.
CFA model fit was evaluated with the following goodness-of-fit metrics: (1) root mean square error of approximation (RMSEA) of 0.060 or below [15]; (2) comparative fit index (CFI) of 0.95 or above [15, 16]; and (3) Tucker-Lewis index (TLI) of 0.95 or above [15].
Item Response Theory
The study further explored the factorial validity of the PSQ for Ugandan adults with item response theory (IRT) to better understand the relationship between the latent trait of psychosis and items on the PSQ. IRT accounts for how each item measures the latent construct and individual variation across the construct's severity levels. IRT uses two main parameters, item discrimination and item difficulty, to describe the relationship between the participant, the latent construct (psychosis), and each PSQ item. The discrimination (or \({\alpha }\)) parameter describes the ability of each item to distinguish between degrees of psychotic symptom severity. The item difficulty (or \(b\)) indicates the location along the psychosis latent construct at which individuals have a ≥ 50% likelihood of endorsing a particular item.
We examined the assumptions required for an IRT model: unidimensionality, local independence, and monotonicity. The unidimensionality assumption was assessed by examining a one-factor CFA model, while the monotonicity was investigated via Mokken scaling analysis. After checking the assumptions, a 2-Parameter Logistic model was fitted using a unidimensional latent structure. Item information curves (IICs), item characteristic curves (ICCs), and the total information curves were generated using the R statistical program, version 3.6.2, packages Mokken and ltm.