Ghana GH: External Debt: Forgiveness or Reduction data was reported at 0.000 USD mn in 2017. This stayed constant from the previous number of 0.000 USD mn for 2016. Ghana GH: External Debt: Forgiveness or Reduction data is updated yearly, averaging 0.000 USD mn from Dec 1970 (Median) to 2017, with 48 observations. The data reached an all-time high of 0.000 USD mn in 2017 and a record low of -4.647 USD bn in 2006. Ghana GH: External Debt: Forgiveness or Reduction data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ghana – Table GH.World Bank.WDI: External Debt: Arrears and Reschedulings. Debt forgiveness or reduction shows the change in debt stock due to debt forgiveness or reduction. It is derived by subtracting debt forgiven and debt stock reduction from debt buyback. Data are in current U.S. dollars.; ; World Bank, International Debt Statistics.; Sum;

Ghana Exports of ferrous products obtained by direct reduction of iron ore/spongy ferrous products to France was US$215 during 2023, according to the United Nations COMTRADE database on international trade.

Abstract

The efficient development, maintenance and administration of transport infrastructure and services are critical to the socio-economic development of any country. Scarce government resources and support from donor funds are required to provide these essential services to all sectors for the economic development of the country and for attaining equity and the participation of the populace in the creation of wealth and reduction of poverty.

To ascertain the effectiveness of implementation of policies and development programs, for transport related infrastructure and services key performance indicators are required. The data for developing these performance indicators must be collected on a sustainable basis by the various sectors for collation and analysis. Although most of the relevant basic data exist in many establishments, these are often scattered and are not collated nor disseminated in any structured manner. The Transportation sector is no exception. A recent study of the Ghana Road Sub-sector Programme finds that there is an urgent need to reinforce the monitoring system of MRT as performance indicators have only partially been collected and used; the road condition mix is monitored on an annual basis while other basic performance indicators are lacking. A good monitoring system will help improve the policy formulation within the sub-sector while its absence may result in a major fund funding reduction because the contribution to national development objectives, such as poverty alleviation, cannot be substantiated and demonstrated.

Objectives of survey The development objective of the TSPS-II as defined in the Ghana Poverty Reduction Strategy (GPRS), to sustain economic growth through the provision of safe, reliable, efficient and affordable services for all transport users. The focus of the transport sector under the GPRS is to provide access through better distribution of the transport network with special emphasis on high poverty areas in order to reduce transport disparities between the urban and rural communities. The household survey is a component of a bigger programme which will serve as a reliable and sustainable one-stop shop for all the data and performance indicators for the transport sector. The immediate objective of the sub-component is to improve the effectiveness of implementation of policies and development programmes for the transport sector, including related infrastructure and services. The direct aim of the sub-component will be the collection, processing, analysis, documentation and dissemination of transport related data, which will be useful for:

1. Transport planning and policy formulation;
2. Impact assessment, monitoring and evaluation of policies and programmes;
3. Measuring the contribution of the transport to the achievement of MDGs;
4. Impact assessment of the transport sector on poverty alleviation and the general standard of living;
5. Comparisons of performance of the transport sector over time and between countries for the purpose of drawing lessons and giving an indication of where interventions are necessary;
6. Provision of a comprehensive database for justification of programmes and projects under the Multi-Donor Budgetary Support (MDBS).

Geographic coverage

National level Region Level

Analysis unit

Household and Individual

Universe

The survey covered all household members (Usual residents)

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was representative of all households in Ghana. To achieve the study objectives, the sample size chosen was based on the type of variables under consideration, the required precision of the survey estimates and available resources. Taking all of these into consideration, a sample size of 6,000 households was deemed sufficient to achieve the survey objectives. This was enough to yield reliable estimates of all the important survey variables as well as being manageable to control and minimize non-sampling errors.

Stratification and Sample Selection Procedures The total list of the Enumeration Areas (EAs) from the demarcation for the 2010 Population and Housing Census formed the sampling frame for the Phase II of the Transport Indicators Survey. The sampling frame was stratified into urban/rural residence and the 10 administrative regions of the country for the selection of the sample. The sample was selected in two stages.

The first stage selection involved the systematic selection of 400 EAs with probability proportional to size, the measure of size being the number of households in each EA. The second stage selection involved the systematic selection of 15 households from each EA. See Appendix A for more details on the sample design.

Sampling deviation

No deviations

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire had the following sections:

Section A: a household roster which collected basic information on all households members and household characteristics to determine eligible household members

Section B: an education section which was administered to household members aged 3 years and older on the use of transport services to school

Section C: a health section that was used to collect information on all household members on access and the use of transport services to health facilities

Section D: an economic activity section administered to household members 7 years and older to collect information on their economic activities and the use of transport services a market access section administered to household members engaged in agricultural activities to collect information on access to transport services for sale of farm produce

Section E: a general transport services section administered to all household members on the access and use of various modes of transport.

Section F: a general transport services section administered to all households and use of various modes of transport.

Cleaning operations

Control mechanisms were inbuilt in the data capturing application. Range checks and skip patterns were incorporated into the data capturing application. Partial double entry was done in order to compare and correct errors. After data capture secondary editng was done in the form of consistency checks. CSPro 4.1 was used to capture the data.

Response rate

National: (5996/6000)*100=99.93%

By Regions: Western=99.8% Central= 100.0% Greater Accra= 100.0% Volta = 99.5% Eastern=100.0% Ashanti = 100.0% Brong Ahafo = 100.0% Northern = 100.0% Upper East = 100.0% Upper West = 100.0%

Region Hhs completed Hhs Expected Response rate
Western 569 570 99.8
Central 510 510 100.0
Greater Accra 855 855 100.0
Volta 567 570 99.5
Eastern 705 705 100.0
Ashanti 1,125 1,125 100.0
Brong Ahafo 585 585 100.0
Northern 615 615 100.0
Upper East 285 285 100.0
Upper West 180 180 100.0
Total 5,996 6,000 99.9

Causes of non response Region
Result of Interview Western Volta Total
Refused 1 0 1
No HHold Member at Home 0 2 2
Other 0 1 1
Total 1 3 4

Sampling error estimates

Sample errors was calculated but not in the report.

Data appraisal

No other forms of data appraisal

Ghana Exports of ferrous products obtained by direct reduction of iron ore/spongy ferrous products to Egypt was US$2 during 2023, according to the United Nations COMTRADE database on international trade.

Abstract

The main objective of the GTUS was to measure and analyze the time spent in a 24-hour period by different individuals aged 10 years and over - women, men, girls, and boys - on all activities including paid and unpaid work and leisure activities. solutions that address gender issues in macroeconomics and poverty reduction.

Geographic coverage

National coverage

Analysis unit

Household, individual

Universe

The survey covered all adult household members (usual residents) aged 15 years and older, and all chilrdren aged 3 years and above (usual residents) in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

A representative sample of 4,800 households was drawn randomly from the list of Enumeration Areas (EAs) of the 2008 Ghana Demographic and Health Survey (GDHS), which served as a frame for the GTUS sample. In the selected households all individuals aged 10 years and older were interviewed. The sample frame was first stratified into the 10 administrative regions in the country, then into urban and rural EAs. GTUS used a two-stage stratified sample design. At the first stage of sampling, 300 EAs were selected. These are a sub-sample of the 412 EAs selected from the 2008 GDHS. The second stage involved selection of 16 households from the 2008 GDHS listing in each selected EA.

The Primary Sampling Unit (PSU) was the EA, while the Secondary Sampling Unit (SSU) was the household. In the selected households all individuals aged 10 years and older were interviewed for the 24-hour activity diary. The following factors were considered in the selection of EAs and households:

a) The regional population and average household size in the 2000 Population and Housing Census. The larger the average household size, the smaller the proportion of sampled households in the EA. b) A confidence interval of 95% with an error margin of 0.025. c) The number of EAs for each region in the 2008 GDHS. d) Allowance for a non-response rate of 20 percent for households. The rationale here was to eliminate the need for substitution of unfound or non-responding households during the fieldwork. Giving the option of substituting households to supervisors would have led to a biased sample and therefore field officers were not allowed to substitute. Furthermore, the selection of households considered the average household size of the regions and hence aimed at achieving an adequate sample of individual respondents who were the observation units. e) Increasing the number of selected households to compensate for the exclusion of the population under 10 years old in the households. f) As variations in the variables to be studied in the GTUS are expected to be higher in rural areas, it was decided to draw a larger sample (77% of EAs in GDHS 2008) for these areas than for urban areas (67% of EAs in GDHS).

The regional samples of EAs selected from the 2008 GDHS EAs were done using SPSS syntax that applies a systematic simple random sampling procedure. However, the sampling weights were calculated on the basis of the population size of the EAs and their totals in the region. The households were also selected using a systematic simple random sampling procedure in Microsoft Excel© using the 2008 DHS listing information. A sampling interval and a random starting number were determined. The random starting number served as the first household to be selected. The remaining 15 households were selected by adding multiples of the sampling interval to the random starting number until the desired number was achieved.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were two types of questionnaires that were used in the GTUS: Household Questionnaire and individual Questionnaire. The household questionnaire collected information about demographic and socio-economic characteristics of the members of the household such as age, sex, level of education, household expenditures, housing and living conditions of the households. The household questionnaire permitted the interviewer to identify the eligible household members (10 years and older) for the individual interviews. The individual diary was used to record information on the individual's (10 years and older) activities, and the duration and the location of these activities within one-hour slots for a day (24 hours). All eligible household members were asked about their activities in the 24 hours beginning at 4am on the previous day. Each individual questionnaire was linked to a household questionnaire.

The Teleform automated data capturing software was used to design the questionnaires. They were then printed and tested to ensure that all the variables in the questionnaires were in the database. English language was used in published the questionnaires

Cleaning operations

Capturing of the data was automated through scanning to speed up data processing. A scanning technology called the Automated Teleform System was used to capture the data collected. This system combined Optical Mark Reader (OMR), Optical Character Reader (OCR) and Intelligent Character Recognition (ICR) for the processing. Before scanning, manual edits were performed on the questionnaires received from the field to check for completeness and accuracy of the questionnaires. After the scanning exercise, structural edits were done followed by consistency checks to further reduce errors.

Data were captured, cleaned and edited in Microsoft Access© format and transferred to SPSS. Further cleaning and imputations were done during analysis where the information was found to be inconsistent or incomplete. On the whole, scanning of the questionnaires, data cleaning and data validation were carried out from June 29 to July 31, 2009.

Response rate

The response rate for the 2009 GTUS was 99.5 percent at the household level and 86.5 percent at the individual level. As can be seen, the response rate at the individual level was higher in rural areas (87.2%) compared with urban areas (85.5%). It was also higher overall for females compared with males (88.1% against 84.8%). This can be explained by the fact that individuals are more likely to be absent from home in urban areas than in rural areas and females are more likely than males to be present in the household premises at the time of the interviewer's visit. It should also be noted that diary questionnaires that could not be linked to a fully completed household questionnaire have not been maintained in the sample for analyses.

Ghana GH: External Debt: Stock: Reduction data was reported at 0.000 USD mn in 2016. This stayed constant from the previous number of 0.000 USD mn for 2015. Ghana GH: External Debt: Stock: Reduction data is updated yearly, averaging 0.000 USD mn from Dec 1970 (Median) to 2016, with 47 observations. The data reached an all-time high of 4.621 USD bn in 2006 and a record low of 0.000 USD mn in 2016. Ghana GH: External Debt: Stock: Reduction data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ghana – Table GH.World Bank: External Debt: Arrears and Reschedulings. Debt stock reductions show the amount that has been netted out of the stock of debt using debt conversion schemes such as buybacks and equity swaps or the discounted value of long-term bonds that were issued in exchange for outstanding debt. It includes the effect of any financial operation that will reduce the debt stock other than debt stock restructuring, repayment of principal and debt forgiven. In particular, debt stock reduction will include the face value of debt bought back, the face value of debt swapped for equity (or 'nature' or 'development'), any face value reduction that might result as the consequence of a bond exchange, and any face value reduction resulting from an exchange of debt for discount bonds. Data are in current U.S. dollars.; ; World Bank, International Debt Statistics.; Sum;

Ghana Exports of ferrous products obtained by direct reduction of iron ore/spongy ferrous products to United Kingdom was US$23 during 2022, according to the United Nations COMTRADE database on international trade.

Ghana Imports from United Kingdom of Ferrous Products Obtained By Direct Reduction of Iron Ore/Spongy Ferrous Products was US$6.37 Thousand during 2023, according to the United Nations COMTRADE database on international trade.

Ghana Imports from India of Ferrous Products Obtained By Direct Reduction of Iron Ore/Spongy Ferrous Products was US$92 during 2022, according to the United Nations COMTRADE database on international trade.

Mali Exports of ferrous products obtained by direct reduction of iron ore/spongy ferrous products to Ghana was US$422 during 2016, according to the United Nations COMTRADE database on international trade.

European Union Exports of ferrous products obtained by direct reduction of iron ore/spongy ferrous products to Ghana was US$324 during 2024, according to the United Nations COMTRADE database on international trade.

IntroductionPaediatric nephropathy, a condition associated with significant morbidity and mortality, is increasing in developing countries. Data on children with renal diseases are insufficient in Ghana despite the risk it poses. This study assessed the pattern, spectrum, outcome, and predictors of dialysis and death among children with renal disease at Korle Bu Teaching Hospital (KBTH), Ghana.Materials and methodsA cross-sectional retrospective review was conducted among children aged 0–17 years. Demographic characteristics, clinical and laboratory data, information on dialysis and treatment outcomes were obtained from their medical records and analyzed accordingly using STATA software version 15.1.ResultsA total of 332 children with renal diseases were seen (median age of 6 years), with 200(60.98%) being males. The most common renal diseases were nephrotic syndrome 141(42.47%) and acute kidney injury (AKI) 87(26.20%). Idiopathic (unknown) causes (82.98%) and intravascular hemolysis secondary to malaria (41.38%) were the major causes of nephrotic syndrome and acute kidney injury respectively. A death rate of 15.06% resulting mostly from AKI (6.33%) was observed whilst 7.83% underwent dialysis. Predictors of dialysis among those who had dialysis included being a female and having acute on chronic kidney disease whilst having high white blood cell count and acute on chronic kidney disease were significant predictors of death among children with renal diseases.ConclusionPaediatric renal diseases at KBTH were dominated by nephrotic syndrome and AKI. Timely treatment and prevention of common infectious agents and conditions causing intravascular hemolysis, which can contribute to paediatric renal diseases in Ghana, is needed to help reduce their progression to various forms of kidney disease.