Awareness, Knowledge, Perceptions, and Determinants of National Health Insurance Authority Enrolment Among Adult Outpatient Attendees at a Tertiary Hospital in Southwestern Nigeria: A Cross-Sectional Study
Description
This dataset contains primary cross-sectional survey data evaluating the Awareness, Knowledge, Perceptions, and Determinants of National Health Insurance Authority Enrolment Among Adult Outpatient Attendees at a Tertiary Hospital in Southwestern Nigeria The primary dataset comprises N = 431 fully completed, validated patient records collected across outpatient departments (General Outpatient Department and Consultant Outpatient Departments)[cite: 3, 6]. The survey instrument captured 47 discrete variables categorized into seven analytical domains[cite: 6]: 1. Socio-demographic characteristics (age, gender, marital status, education level, employment sector, and monthly income bracket)[cite: 6]. 2. NHIA scheme awareness and primary information dissemination channels (healthcare workers, workplace, digital media, mass media)[cite: 6]. 3. Objective health insurance knowledge, assessed using a 9-item domain-specific scale (Q10–Q18) scored binarily (1 = Correct, 0 = Incorrect/Unsure), cumulative score (0–9), and tiered knowledge categories (Poor: 0–3, Fair: 4–6, Good: 7–9). 4. Perceptions and attitudes toward NHIA financing, risk pooling, operational management, and drug quality, measured on a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree)[cite: 6, 8]. 5. Active health insurance procurement and enrollment status (Registered vs. Unregistered)[cite: 6, 8]. 6. Self-reported barriers and reasons for non-enrollment among uninsured respondents (Question 23; dichotomous multi-response indicators)[cite: 6, 8]. 7. Point-of-care quality ratings, primary provider facility tiers, overall satisfaction levels, and specific operational drivers of dissatisfaction among active enrollees[cite: 6, 8]. The data files include the fully labeled SPSS dataset (.sav), an open-access comma-separated values file (.csv), and the complete SPSS batch processing syntax (.sps) used to generate all descriptive statistics, bivariate Chi-Square cross-tabulations, and multivariate binary logistic regression models[cite: 6, 8].
Files
Steps to reproduce
1. Study Design & Setting: A hospital-based cross-sectional study was conducted at Bowen University Teaching Hospital (BUTH), a tertiary referral and academic medical center in Ogbomoso, Oyo State, Nigeria. 2. Participant Recruitment & Sampling: Adult walk-in patients (aged 18 years and above) attending outpatient clinics (GOPD and COPD) who had resided in Ogbomoso for at least six months were systematically sampled during clinic days[cite: 3]. Patients with severe acute cognitive impairment or altered consciousness were excluded[cite: 3]. Written informed consent was obtained prior to questionnaire administration[cite: 3]. 3. Data Collection Instrument: Trained research assistants administered a semi-structured questionnaire translated into Yoruba and back-translated into English to verify semantic validity[cite: 3]. 4. Data Coding & Cleaning: - Continuous age was recorded in years and collapsed into ordinal age brackets[cite: 6]. - Knowledge questions (Q10 to Q18) were converted into binary numeric variables (1 = Correct, 0 = Incorrect/Don't know)[cite: 6, 8]. - Cumulative knowledge was computed by summing Q10 through Q18 (range: 0–9 points) and categorized into Poor (0–3), Fair (4–6), and Good (7–9) knowledge tiers[cite: 6, 8]. - Perception questions were coded on a 1–5 numerical Likert scale[cite: 6, 8]. - Multiple-response options for non-enrollment reasons (Q23) and service dissatisfaction were coded as individual binary variables (1 = Selected, 0 = Not selected)[cite: 6, 8]. 5. Statistical Analysis Execution: To reproduce all outputs, open the dataset in IBM SPSS Statistics (v25 or higher) and execute the included syntax file ('NHIA_Complete_Syntax.sps')[cite: 3, 8]. This reproduces: - Univariate frequencies, means, and standard deviations. - Pearson Chi-Square tests of independence for procurement determinants[cite: 3, 8]. - Multivariate binary logistic regression predicting health insurance uptake[cite: 3, 8].
Institutions
- Bowen UniversityOsun State, Iwo