Methods
Data
Data for this study were obtained from the Disease Control Unit of the Ministry of Health and Social Welfare, The Gambia. These were collected routinely from peripheral health centres and hospitals (including private facilities) across the country and this system has been in place since 2008, even though there are systemic challenges facing surveillance for NCDs in The Gambia, which is similar to what is obtainable in many African countries [14]. Specific data on health facilities admissions, morbidities (new in and out-patients cases with or without complications) and mortalities were obtained on cardiovascular diseases, hepatic and renal diseases, cancers and asthma for a period between 2008 and 2011. Diagnoses of these health conditions were made in most cases by doctors working at the secondary and tertiary healthcare levels. At the primary health care level, there are algorithms being used for diagnosis by Senior Community Health Nurses for some conditions and referrals were made to higher level in cases where diagnoses cannot be made. The doctors hospitalise patients and ascertain cases of mortality as required.
The Health Management Information System Unit centrally coordinates and manages health data including that of chronic non-communicable diseases in The Gambia. The software application being used for the database is DHIS version2. Data entry by data entry clerks mainly takes place at the Regional Health Level with some contributions at the peripheral levels as some major health facilities now have data entry facilities. Data were verified manually before entry from the source documents and also after data entry. Verifications were done by data entry supervisors. There are restrictions to the database as only data management team has access to it. Each patient has a unique identifier, and other mechanisms are in place to prevent duplication. The database also has protected data field that improves the data quality. There are other consistency checks and validation rules that have been put in place. As much as possible, GCP compliance was observed. The target set for timeliness and completeness for reporting from the peripheral levels was 80% which has been met and surpassed for the period being studied.
Analysis
Proportion were obtained and test of significance were done using Epi 6 statistical package to check whether there are statistically significant differences in mortality, morbidity and hospital admission over the years studied. This study also examined gender differences in the occurrence of NCDs in The Gambia based on the available data. However, other forms of NCDs such as neuropsychiatric conditions, haemoglobinopathies and chronic lung diseases (except asthma) were excluded in the analysis.
These data were reinforced with the World Health Statistics for other useful health, demographic and socio-economic data relevant to this work. The demographic and socio-economic variables are strong determinants of health [15,16]. Furthermore, evidence suggests a correlation between these social factors and the risk factors and prevalence levels of NCDs [2,15].
Methods
Data
Data for this study were obtained from the Disease Control Unit of the Ministry of Health and Social Welfare, The Gambia. These were collected routinely from peripheral health centres and hospitals (including private facilities) across the country and this system has been in place since 2008, even though there are systemic challenges facing surveillance for NCDs in The Gambia, which is similar to what is obtainable in many African countries [14]. Specific data on health facilities admissions, morbidities (new in and out-patients cases with or without complications) and mortalities were obtained on cardiovascular diseases, hepatic and renal diseases, cancers and asthma for a period between 2008 and 2011. Diagnoses of these health conditions were made in most cases by doctors working at the secondary and tertiary healthcare levels. At the primary health care level, there are algorithms being used for diagnosis by Senior Community Health Nurses for some conditions and referrals were made to higher level in cases where diagnoses cannot be made. The doctors hospitalise patients and ascertain cases of mortality as required.
The Health Management Information System Unit centrally coordinates and manages health data including that of chronic non-communicable diseases in The Gambia. The software application being used for the database is DHIS version2. Data entry by data entry clerks mainly takes place at the Regional Health Level with some contributions at the peripheral levels as some major health facilities now have data entry facilities. Data were verified manually before entry from the source documents and also after data entry. Verifications were done by data entry supervisors. There are restrictions to the database as only data management team has access to it. Each patient has a unique identifier, and other mechanisms are in place to prevent duplication. The database also has protected data field that improves the data quality. There are other consistency checks and validation rules that have been put in place. As much as possible, GCP compliance was observed. The target set for timeliness and completeness for reporting from the peripheral levels was 80% which has been met and surpassed for the period being studied.
Analysis
Proportion were obtained and test of significance were done using Epi 6 statistical package to check whether there are statistically significant differences in mortality, morbidity and hospital admission over the years studied. This study also examined gender differences in the occurrence of NCDs in The Gambia based on the available data. However, other forms of NCDs such as neuropsychiatric conditions, haemoglobinopathies and chronic lung diseases (except asthma) were excluded in the analysis.
These data were reinforced with the World Health Statistics for other useful health, demographic and socio-economic data relevant to this work. The demographic and socio-economic variables are strong determinants of health [15,16]. Furthermore, evidence suggests a correlation between these social factors and the risk factors and prevalence levels of NCDs [2,15].
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