Geospatial and Logistic Barriers to Healthcare: A Data-Driven Study of OPD Attendance at Hamidu Hosp

This project presents a comprehensive health access audit examining how transportation challenges and road infrastructure affect outpatient department (OPD) attendance at Hamidu Health Centre, a semi-rural facility in Kenya. The study integrates field survey data, KoboToolbox mobile data collection, and R-based statistical analysis, offering actionable insights for health systems strengthening in underserved areas.

Key Objectives:
Evaluate the impact of distance, travel time, and transport mode on OPD use

Assess perceptions of road quality and their link to missed health visits

Identify barriers and community-suggested solutions to improve access

Data and Methodology:
Sample Size: 100 participants from the catchment area

Data Tool: KoboToolbox (structured questionnaire with multi-response support)

Data Analysis: Performed in R using libraries such as dplyr, tidyr, gt, and gtsummary

Variables: Demographics, transport characteristics, road perceptions, access barriers, and service suggestions

Statistical Techniques Used:
Descriptive statistics with tbl_summary() and gt tables

Multi-response reshaping using pivot_longer()

Logistic regression modeling to explore predictors of missed visits

Model diagnostics to detect quasi-complete separation and multicollinearity

Findings:
79% of respondents lived more than 3 km from the facility

77% traveled over 30 minutes to reach the OPD

Motorbike transport was the most common (67%) due to poor road conditions

61% of respondents had missed visits due to road or transport issues

Perceived poor road conditions were strongly associated with low attendance

Community recommendations prioritized improving roads, drug availability, and transport options

Logistic Regression Results:
Although a logistic model was fit to predict missed visits, no predictors (distance, road rating, transport mode) were statistically significant—likely due to:

Small and imbalanced sample categories

High collinearity between predictors (e.g., distance and time)

Overfitting from many categorical variables

Nonetheless, model fit (deviance drop from 113.76 to 49.07) supported a structural relationship between physical access and missed care.

Skills and Value Demonstrated:
Survey design and implementation in a community setting

R-based data cleaning, reshaping, and visualization

Use of logistic regression in public health impact analysis

Translation of raw data into policy-relevant recommendations

Understanding of infrastructure-health systems interplay in LMIC contexts

This project illustrates the power of data science in health equity research, especially in resource-constrained settings where infrastructure and socio-economic factors intersect with access to care. The evidence generated can inform integrated transport–health sector planning at the county level.