Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 4

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

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

Narh, Clement T., Der, Joyce B., Ofosu, Anthony, Blettner, Maria, Wollschlaeger, Daniel·2022·JCO Global Oncology
DOI 10.1200/go.21.00416PMC PMC9470136
Open this paper in CoChat — free account

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1200/go.21.00416

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Figures

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 1 Study flowchart of hospitalized patients in the Ghana Health Service DHIMS-2 database, 2012-2017. DHIMS-2, District Health Information Management System 2.CC BY-NC-ND · PubMed Central
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 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.CC BY-NC-ND · PubMed Central
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 3 Yearly number and 95% CI of empirical and predicted neoplastic hospitalizations from Poisson regression model by sex: (A) male and (B) female.CC BY-NC-ND · PubMed Central
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 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.CC BY-NC-ND · PubMed Central
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 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.CC BY-NC-ND · PubMed Central
FIG A3 Pearson residual plot for (A) malignant cancers, (B) benign tumors, and (C) neoplasms of uncertain or unknown behaviors.
FIG A3 Pearson residual plot for (A) malignant cancers, (B) benign tumors, and (C) neoplasms of uncertain or unknown behaviors.CC BY-NC-ND · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 1 references

Research works printed but not linked: 33

  1. 10.1016/j.gheart.2016.10.024 · DOI
  2. Institute for Health Metrics and Evaluation (IHME), 2018, Findings From the Global Burden of Disease Study 2017
  3. 10.1016/S2214-109X(19)30374-2 · DOI
  4. 10.1093/ije/dyr050 · DOI
  5. World Health Organization, 2018, Noncommunicable Diseases Country Profiles 2018
  6. 10.1002/ijc.31937 · DOI · Paper page
  7. 10.1371/journal.pmed.1001885 · DOI · Paper page
  8. 10.1002/sim.4780100407 · DOI
  9. Spasoff RA, 1999, Epidemiologic Methods for Health Policy
  10. 10.1136/bmj.l2239 · DOI
  11. 10.1093/pubmed/fdm065 · DOI
  12. 10.1186/1471-2407-14-362 · DOI
  13. Ministry of Health, 2011, National Strategy for Cancer Control in Ghana 2012-2016
  14. 10.1200/JGO.2016.005744 · DOI
  15. Ohene-Yeboah M, 2012, Ghana Med J, 46, 8
  16. 10.4314/jmbs.v3i3.4 · DOI
  17. Ghana Statistical Service, 2014, 2010 Population and Housing Census Report
  18. WHO, International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) Version for 2010
  19. 10.1186/s12885-019-5480-0 · DOI
  20. 10.1200/JGO.2016.006098 · DOI
  21. 10.1017/CBO9781139013567 · DOI · Paper page
  22. Hilbe J, 1999, Stata Tech Bull, 8
  23. StataCorp LLP, 2015, Stata Statistical Software (Version Release 14)
  24. Adaletey DL, 2014, J Health Inform Afr, 2
  25. International Agency for Research on Cancer, 2019, The Global Cancer Observatory. Globocan, 2018
  26. 10.1016/j.ejca.2007.11.014 · DOI
  27. 10.1002/(SICI)1097-0258(19971030)16:20<2297::AID-SIM668>3.0.CO;2-F · DOI
  28. Godlewski D, 2012, Contemp Oncol (Pozn), 16, 38
  29. Comber H, 2006, Trends in Irish Cancer Incidence 1994-2002 With Projections to 2020
  30. 10.1002/1097-0142(19860615)57:12<2454::AID-CNCR2820571235>3.0.CO;2-D · DOI
  31. French D, 2006, Ulster Med J, 75, 120
  32. 10.1038/sj.bjc.6603746 · DOI
  33. 10.1038/nrc1781 · DOI

Cited by

See all 4

Metadata

Authors
Narh, C. T., Der, J. B., Ofosu, A., Blettner, M., & Wollschlaeger, D.
Venue
JCO Global Oncology
Publisher
American Society of Clinical Oncology (ASCO)
Published
2022-08
Issue
8
ISSN
2687-8941
DOI
10.1200/go.21.00416
PMC
PMC9470136
Cited by
4
References
1
Language
en

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Describing and Modeling the Burden of Hospitalization of Patients With Neoplasms in Ghana Using Routine Health Data for 2012-2017. https://app.cochat.ai/papers/10.1200/go.21.00416
The paper
Narh, C. T., Der, J. B., Ofosu, A., Blettner, M., & Wollschlaeger, D. (2022). Describing and Modeling the Burden of Hospitalization of Patients With Neoplasms in Ghana Using Routine Health Data for 2012-2017. JCO Global Oncology. https://doi.org/10.1200/go.21.00416

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.