Describing and Modeling the Burden of Hospitalization of Patients With Neoplasms in Ghana Using Routine Health Data for 2012-2017

Abstract

Purpose: The increasing cancer burden calls for reliable data on current and future associated hospitalizations to enable health care resource planning, especially in low- and middle-income countries. We provide nationwide estimates of the current and future burden of hospitalization because of neoplasms in Ghana.

Methods: We conducted secondary data (2012-2017) analysis using nationwide routine administrative inpatient health data from the Ghana Health Service. Multivariable Poisson regression was used to model spatial and temporal hospitalization trends stratified by sex and 5-year age group. In conjunction with official population projections, the model was used to predict future hospitalization up to 2032.

Results: Out of 2,915,936 hospitalization records extracted for 6 years, 26,627 (1.0%) were for neoplasms, most of them benign (D10-D36, 15,362; 57.7%) and in female patients (20,159; 76%). In total, 9,463 (35.5%) patients with malignancies were mostly female (5,307; 56.1%), had a median age 50 years (interquartile range, 34-66 years) and a median duration of stay of 4 days (interquartile range, 2-8 days). Poisson regression for the malignant cancers revealed an annual increase in hospitalizations with a relative rate of 1.23 (95% CI, 1.19 to 1.27). The estimated hospitalization rate for malignancies of female patients was 1.5 times higher than that of male patients (relative rate, 1.53; 95% CI, 1.00 to 2.34), adjusted for age. We predicted an increase of 67.5% malignant cancer hospitalizations from the empirical years (2012-2017) into the prediction years (2022-2032) in Ghana.

Conclusion: In the absence of a national population-based cancer registry, this nationwide study used secondary health services data on hospitalizations as a proxy for neoplasm morbidity burden. Our results can support planning public health resources and building evidence-based advocacy campaigns for neoplasm-prevention efforts.

FIG 1

Study flowchart of hospitalized patients in the Ghana Health Service DHIMS-2 database, 2012-2017. DHIMS-2, District Health Information Management System 2.

FIG 2

Coefficient and 95% CI plot from the Poisson regression model for patients hospitalized because of neoplasms: (A) neoplasms, (B) C00-C97, (C) D10-D36, and (D) D37-D47.

FIG 3

Yearly number and 95% CI of empirical and predicted neoplastic hospitalizations from Poisson regression model by sex: (A) male and (B) female.

FIG A1

Three main blocks of neoplasms according to the International Classification of Diseases, 10th Revision, classification of neoplasms in the Ghana Health Service District Health Information Management System 2 database, 2012-2017: (A) male and (B) female.

FIG A2

Yearly number and 95% CI of empirical and predicted hospitalizations because of neoplasms of uncertain or unknown behaviors from Poisson regression model by sex: (A) male and (B) female.

FIG A3

Pearson residual plot for (A) malignant cancers, (B) benign tumors, and (C) neoplasms of uncertain or unknown behaviors.