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http://doi.org/10.25358/openscience-5059Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Narh, Clement Tetteh | - |
| dc.date.accessioned | 2020-05-27T13:30:18Z | - |
| dc.date.available | 2020-05-27T15:30:18Z | - |
| dc.date.issued | 2020 | - |
| dc.identifier.uri | https://openscience.ub.uni-mainz.de/handle/20.500.12030/5062 | - |
| dc.description.abstract | Background The World Health Organization (WHO) reported that Noncommunicable Diseases (NCDs) were responsible for 41 million (71%) of the world’s 57 million deaths in 2016. More than 36% of them (15 million) were premature deaths (30–70 years); again, 78% of all NCD deaths and 85% of the premature deaths occur in low- and middle-income countries. This study quantified and predicted the future (2018–2032) burden due to hospitalization and mortality of the five major NCDs in Ghana: cardiovascular diseases (CVDs), diabetes, cancers, chronic respiratory diseases (CRDs) and sickle cell disease (SCD). The spatial and temporal trends, and explanatory factors associated with hospitalization and mortality were also identified for these NCDs. Materials and Methods Nearly 3 million records (comprising 13 variables) on hospitalizations in Ghana from 2012 – 2017 were extracted from the District Health Information Management System (DHIMS II) database of the Ghana Health Service (GHS). The population data and standard population for this study were extracted from the Ghana Statistical Service. These datasets were extracted after the Ghana Health Service Ethics Review Committee approved the study. Three main statistical methods were used in this study: • Age-standardized rates (ASR) and 95% confidence intervals (95% CI) were computed for each of the five NCD per region and stratified by sex. • The Fine-Gray competing-risks regression model was used in estimating the subdistribution hazard ratio (SHR) for the failure of an event of interest (hospital discharge) also known as the subhazard and the associated Cumulative Incidence Function (CIF). The model fit was using the Schoenfeld residuals. • A Poisson regression model was fitted for each of the five NCDs with sex, age, calendar-year and region as covariates using the empirical from 2012 – 2017. The model assumed a log linearity and the population was used as the offset. We then predicted the future burden of these NCDs among hospitalized patients in Ghana from 2018 – 2032. The model fit was using the Pearson residuals. The 95% confidence intervals (95% CI) and p-values were reported for the Fine-Gray and Poisson models. Statistical significance was set at a p-value <0.05. Results We analysed a complete dataset of 265,490 with the following breakdown: CVDs 151,272; diabetes 40,202; cancer 26,627; CRDs 24,709 and SCD 22,680 records. There were more females than males in each disease category but higher among the CVDs, diabetes and cancer patients. In terms of age, the patients in the CRDs and SCD were much younger compared to the remaining three NCDs. The median with 25th and 75th percentiles for hospitalization durations were 3 days (1,5) for CVDs, 4 days (2,6) for diabetes, 4 days (2,6) for cancer, 2 days (1,3) for CRDs and 3 days (2,4) for SCD. The estimated ASR per 100,000 population for each of five NCDs were 93.7 for CVDs, 24 for diabetes, 15.2 for CRDs, and 14 for SCD. Sex was one of the several covariates associated with hospitalization discharge. Males vs. females SHR for CVD (SHR:1.13; 95% CI: 1.07,1.20; p-value <0.001); diabetes (SHR:1.09; 95% CI: 0.98,1.21; p-value =0.102); cancer (1.53; 95% CI: 1.34,1.75; p-value <0.001); and CRD (SHR:1.07; 95% CI: 0.96,1.19; p-value =0.196). The relative rate for all the NCDs increased as age increased. Similar trends for sex was seen for all the NCDs where females had a higher RR compared with males (CVD [RR: 3.43; 95%CI:2.55,4.62; P<0.001]; diabetes [RR: 2.18; 95%CI:1.56,3.05; P<0.001]; cancer [RR: 5.15; 95%CI:3.53,7.51; P<0.001]; CRD [RR: 3.08; 95%CI:2.34,4.05; P<0.001]; SCD [RR: 1.57; 95%CI:1.15,2.14; P=0.004]). Based on a Poisson model for each of the NCDs, we made the following predictions; CVDs would increase from 92,810 in 2022 to 208,969 in 2027, diabetes would increase from 25,399 in 2022 to 58,860 in 2027, cancer would increase from 7,609 in 2022 to 8,424 in 2027, CRDs would increase from 10,224 in 2022 to 18,269 in 2027, and SCD would increase from 13,835 in 2022 to 31,352 in 2027. Discussion and conclusion Overall, we noticed a higher disease burden and risk factors among females compared to males. Although, this could be explained by the higher female to male ratio reported in the 2010 Ghana census, this proportion was more pronounced. The burden of CVDs, diabetes and cancers were more tailored towards adults, while CRDs and SCD were higher in the young population. Furthermore, we predicted the future burden of CVDs, diabetes, cancers, CRDs and SCD from 2018 – 2032. These forecasts should be interpreted with caution as they are based on extrapolation of population counts and hospitalisation rates. We have demonstrated the usefulness of the GHS database in estimating the national burden of diseases. This understanding could be used to inform service planning, or to direct public health interventions in Ghana. | en_GB |
| dc.language.iso | eng | - |
| dc.rights | InCopyright | de_DE |
| dc.rights.uri | https://rightsstatements.org/vocab/InC/1.0/ | - |
| dc.subject.ddc | 610 Medizin | de_DE |
| dc.subject.ddc | 610 Medical sciences | en_GB |
| dc.title | Modelling the Burden of Noncommunicable Diseases among Hospitalized Patients in Ghana | en_GB |
| dc.title | Modellierung der Krankheitslast nicht übertragbarer Krankheiten bei stationären Patienten in Ghana | de_DE |
| dc.type | Dissertation | de_DE |
| dc.identifier.urn | urn:nbn:de:hebis:77-diss-1000035365 | - |
| dc.identifier.doi | http://doi.org/10.25358/openscience-5059 | - |
| jgu.type.dinitype | doctoralThesis | - |
| jgu.type.version | Original work | en_GB |
| jgu.type.resource | Text | - |
| jgu.description.extent | XI, 131 Seiten | - |
| jgu.organisation.department | FB 04 Medizin | - |
| jgu.organisation.year | 2020 | - |
| jgu.organisation.number | 2700 | - |
| jgu.organisation.name | Johannes Gutenberg-Universität Mainz | - |
| jgu.rights.accessrights | openAccess | - |
| jgu.organisation.place | Mainz | - |
| jgu.subject.ddccode | 610 | - |
| opus.date.accessioned | 2020-05-27T13:30:18Z | - |
| opus.date.modified | 2020-08-05T12:30:11Z | - |
| opus.date.available | 2020-05-27T15:30:18 | - |
| opus.subject.dfgcode | 00-000 | - |
| opus.organisation.string | FB 04: Medizin: Institut für Med. Biometrie, Epidemologie und Informatik | de_DE |
| opus.identifier.opusid | 100003536 | - |
| opus.institute.number | 0424 | - |
| opus.metadataonly | false | - |
| opus.type.contenttype | Dissertation | de_DE |
| opus.type.contenttype | Dissertation | en_GB |
| jgu.organisation.ror | https://ror.org/023b0x485 | - |
| Appears in collections: | JGU-Publikationen | |
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| File | Description | Size | Format | ||
|---|---|---|---|---|---|
 | 100003536.pdf | 2.75 MB | Adobe PDF | View/Open |