Understanding the link between trafficking in persons and HIV and AIDS risk in Tanzania

The magnitude of trafficking in persons in Tanzania is unknown. Consequently, available information on health risks of persons trafficked for different forms of exploitation is extremely scanty. We conducted a baseline study in eight administrative regions of Tanzania using both qualitative and quantitative methods to generate data on the health conditions of trafficked persons to inform trafficking in persons control measures through HIV and AIDS interventions. Study participants included the national, regional and district community development officers, district medical officers, local government leaders, managers or representatives of non-governmental organizations involved in anti-trafficking in persons activities, members of the community and victims. Findings indicated that common forms of labour into which persons are trafficked include domestic services, agriculture (farming), construction, mining/quarrying, fishing, lumbering and manufacturing. Trafficked persons are reported to be exposed to risks like overcrowding, long working hours, psychological problems, physical injuries, impotence, breathing problems and sexually transmitted infections including HIV. It is concluded that the reported occupational hazards in industries where trafficked persons are forced into are not specific to trafficked persons as they affect all labourers. However, the underground nature of the trafficking in persons process increases health problems and risks, including the vulnerability to HIV infection. More tailored research is needed, especially to find means of how to reach out and provide services to this particular vulnerable population, validate labour forms of exploitation into which persons are trafficked to enable the integration or mainstreaming of HIV and AIDS and trafficking in persons at the policy and programmatic levels. In addition, findings would facilitate the understanding of the link between increased risk of HIV and trafficking in persons

Predictors of condom use among unmarried sexually active women of Reproductive age in Tanzania

Background: Condom is one of the methods for prevention against Human Immunodeficiency Virus and other Sexually Transmitted Infections. It is also considered an effective method for preventing unwanted pregnancies. Despite the several interventions that have been put to promote condom use, still a large proportion of women do not use condom during sexual intercourse. Objectives: This study aimed at determining predictors of condom use among unmarried sexually active women of reproductive age in Tanzania. Methods: This study used secondary data from the 2015-16 Tanzania Demographic and Health Survey and Malaria Indicator Survey (2015-16 TDHS-MIS). It involved unmarried sexually active women aged 15-49 years. Multiple binary logistic regression was used to determine predictors for condom use at last sexual intercourse. Results: Overall, lower proportion (31.1%) of unmarried sexually active women used condom at last sexual intercourse. The odds of using condom during last sexual intercourse was lower aOR=0.67 and aOR=0.65 for women aged 20-24 and 25+ years respectively). Women who reported higher age (18+ years) at first sex had higher odds (aOR=1.65) of using condom compared to those started sex before 15 years old. Women owning telephone had higher odds (aOR=1.44) compared to women without telephone. Also, higher odds of using condom were observed for women in the Southern, South West highlands, and Eastern zones compared to the Central zone. Discussion: Age, marital union, parity, wealth, ownership of; mobile phone, television, access to newspapers, and radio significantly predicts condom use among unmarried sexually active women of reproductive age in Tanzania. Conclusion: The level of condom use among unmarried women in Tanzania is very low and varies by age, age at sex intercourse, ownership of phone and zones. Targeted interventions are needed to promote the condom use among unmarried women in order to mitigate the risk of HIV and un-intended pregnancies

Factors associated with child sexual abuse in Tanzania: a qualitative study

Background: Child sexual abuse (CSA) is one of the most pervasive occurrences which are reported all over the world. It often goes unnoticed and undocumented due to surrounding taboos; its sensitivity in nature and affects the less powerful population. Anecdote information is available on the nature and extent of sexual abuse among children in Tanzania. The aim of this study was to explore factors, forms, context of abuse and perpetrators of child sex abuse in selected regions of Tanzania.Methods: Key informant interviews were conducted among adults including parents of the victims to explore factors associated with sexual abuse of children under 10 years old in Tanzania. The interview guide centred on factors for child sexual abuse, the type of perpetrators and the context into which these abuses take place.Results: There were incidences of child sexual abuse in Tanzania and the major forms were anal and vaginal penetration, and the most affected were girls. The abuses were rarely reported due to shame and embarrassment faced by the affected children and parents. The causes of child sexual abuse were poverty, ambitions and moral degradation, myths and beliefs, urbanization, foreign culture and poor parental care. Incidents of CSA were reported to occur in perpetrators’ homes and in semi-finished housing structures, madrassa and recreational venues where children can freely access entertainment by watching movies. These acts were committed by people in position of power, close relationship and trusted by the children. Contexts where child sexual abuses occur included overcrowded living spaces and social activities that go on late into the night.Conclusion: We recommend for strengthened interventions at different levels within the society to address the root causes and different contexts in which child sex abuse occurs. Increased awareness of the root causes should go hand in hand with measures to encourage parents and survivors to report incidents to relevant authorities timely as they occur

Madang Province: Text summaries, maps, code lists and village identification

The major purpose of the Papua New Guinea Agricultural Systems Project is to produce information on small holder (subsistence) agriculture at provincial and national levels (Allen et al 1995). Information was collected by field observation, interviews with villagers and reference to published and unpublished documents. Methods are described by Bourke et al. (1993). This Working Paper contains a written summary of the information on the Agricultural Systems in this Province, maps of the location of agriculture systems, a complete listing of all information in the database in coded form, and lists of villages with National Population Census codes, indexed by agricultural systems. This information is available as a map-linked database (GIS) suitable for use on a personal computer in ESRI and MapInfo formats. An Agricultural System is identified when a set of similar agricultural crops and practices occur within a defined area. Six criteria are used to distinguish one system from another: 1. Fallow type (the vegetation which is cleared from a garden site before cultivation). 2. Fallow period (the length of time a garden site is left unused between cultivations). 3. Cultivation intensity (the number of consecutive crops planted before fallow). 4. The staple, or most important, crops. 5. Garden and crop segregation (the extent to which crops are planted in separate gardens; in separate areas within a garden; or are planted sequentially). 6. Soil fertility maintenance techniques (other than natural regrowth fallows). Where one or more of these factors differs significantly and the differences can be mapped, then a separate system is distinguished. Where variation occurs, but is not able to be mapped at 1:500 000 scale because the areas in which the variation occurs are too small or are widely dispersed within the larger system, a subsystem is identified. Subsystems within an Agricultural System are allocated a separate record in the database, identified by the Agricultural System number and a subsystem number. Sago is a widespread staple food in lowland Papua New Guinea. Sago is produced from palms which are not grown in gardens. Most of the criteria above cannot be applied. In this case, systems are differentiated on the basis of the staple crops only. The Papua New Guinea Resource Information System (PNGRIS) is a GIS which contains information on the natural resources of PNG (Bellamy 1986). PNGRIS contains no information on agricultural practices, other than an assessment of land use intensity based on air photograph interpretation by Saunders (1993. The Agricultural Systems Project is designed to provide detailed information on agricultural practices and cropping patterns as part of an upgraded PNGRIS geographical information system. For this reason the Agricultural Systems database contains almost no information on the environmental settings of the systems, except for altitude and slope. The layout of the text descriptions, the database code files and the village lists are similar to PNGRIS formats (Cuddy 1987). The mapping of Agricultural Systems has been carried out on the same map base and scale as PNGRIS (Tactical Pilotage Charts, 1:500 000). Agricultural Systems were mapped within the areas of agricultural land use established by Saunders (1993) from aerial photography. Except where specifically noted, Agricultural Systems boundaries have been mapped without reference to PNGRIS Resource Mapping Unit (RMU) boundaries. Agricultural Systems are defined at the level of the Province (following PNGRIS) but their wider distribution is recognised in the database by cross-referencing systems which cross provincial borders. A preliminary view of the relationships between PNGRIS RMUs and the Agricultural Systems in this Province can be obtained from the listing of villages by Agricultural System, where RMU numbers are appended. Allen, B. J., R. M. Bourke and R. L. Hide 1995. The sustainability of Papua New Guinea agricultural systems: the conceptual background. Global Environmental Change 5(4): 297-312. Bourke, R. M., R. L. Hide, B. J. Allen, R. Grau, G. S. Humphreys and H. C. Brookfield 1993. Mapping agricultural systems in Papua New Guinea. Population Family Health and Development. T. Taufa and C. Bass. University of Papua New Guinea Press, Port Moresby: 205-224. Bellamy, J. A. and J. R. McAlpine 1995. Papua New Guinea Inventory of Natural Resources, Population Distribution and Land Use Handbook. Commonwealth Scientific and Industrial Research Organisation for the Australian Agency for International Development. PNGRIS Publication No. 6, Canberra. Cuddy, S. M. 1987. Papua New Guinea Inventory of Natural Resources, Population Distribution and Land Use: Code Files Part 1 Natural Resources. Division of Water and Land Resources, Commonwealth Scientific and Industrial Research Organisation and Land Utilization Section, Department of Primary Industry, Papua New Guinea, Canberra

Experience and Lessons Learnt on Health Care Personnel Participating In the Implementation of TB Operational Research at (EAPHLNP -OR) Malindi, Kenya

No Abstract

Morobe Province: Text summaries, maps, code lists and village identification

The major purpose of the Papua New Guinea Agricultural Systems Project is to produce information on small holder (subsistence) agriculture at provincial and national levels (Allen et al 1995). Information was collected by field observation, interviews with villagers and reference to published and unpublished documents. Methods are described by Bourke et al. (1993). This Working Paper contains a written summary of the information on the Agricultural Systems in this Province, maps of the location of agriculture systems, a complete listing of all information in the database in coded form, and lists of villages with National Population Census codes, indexed by agricultural systems. This information is available as a map-linked database (GIS) suitable for use on a personal computer in ESRI and MapInfo formats. An Agricultural System is identified when a set of similar agricultural crops and practices occur within a defined area. Six criteria are used to distinguish one system from another: 1. Fallow type (the vegetation which is cleared from a garden site before cultivation). 2. Fallow period (the length of time a garden site is left unused between cultivations). 3. Cultivation intensity (the number of consecutive crops planted before fallow). 4. The staple, or most important, crops. 5. Garden and crop segregation (the extent to which crops are planted in separate gardens; in separate areas within a garden; or are planted sequentially). 6. Soil fertility maintenance techniques (other than natural regrowth fallows). Where one or more of these factors differs significantly and the differences can be mapped, then a separate system is distinguished. Where variation occurs, but is not able to be mapped at 1:500 000 scale because the areas in which the variation occurs are too small or are widely dispersed within the larger system, a subsystem is identified. Subsystems within an Agricultural System are allocated a separate record in the database, identified by the Agricultural System number and a subsystem number. Sago is a widespread staple food in lowland Papua New Guinea. Sago is produced from palms which are not grown in gardens. Most of the criteria above cannot be applied. In this case, systems are differentiated on the basis of the staple crops only. The Papua New Guinea Resource Information System (PNGRIS) is a GIS which contains information on the natural resources of PNG (Bellamy 1986). PNGRIS contains no information on agricultural practices, other than an assessment of land use intensity based on air photograph interpretation by Saunders (1993. The Agricultural Systems Project is designed to provide detailed information on agricultural practices and cropping patterns as part of an upgraded PNGRIS geographical information system. For this reason the Agricultural Systems database contains almost no information on the environmental settings of the systems, except for altitude and slope. The layout of the text descriptions, the database code files and the village lists are similar to PNGRIS formats (Cuddy 1987). The mapping of Agricultural Systems has been carried out on the same map base and scale as PNGRIS (Tactical Pilotage Charts, 1:500 000). Agricultural Systems were mapped within the areas of agricultural land use established by Saunders (1993) from aerial photography. Except where specifically noted, Agricultural Systems boundaries have been mapped without reference to PNGRIS Resource Mapping Unit (RMU) boundaries. Agricultural Systems are defined at the level of the Province (following PNGRIS) but their wider distribution is recognised in the database by cross-referencing systems which cross provincial borders. A preliminary view of the relationships between PNGRIS RMUs and the Agricultural Systems in this Province can be obtained from the listing of villages by Agricultural System, where RMU numbers are appended. Allen, B. J., R. M. Bourke and R. L. Hide 1995. The sustainability of Papua New Guinea agricultural systems: the conceptual background. Global Environmental Change 5(4): 297-312. Bourke, R. M., R. L. Hide, B. J. Allen, R. Grau, G. S. Humphreys and H. C. Brookfield 1993. Mapping agricultural systems in Papua New Guinea. Population Family Health and Development. T. Taufa and C. Bass. University of Papua New Guinea Press, Port Moresby: 205-224. Bellamy, J. A. and J. R. McAlpine 1995. Papua New Guinea Inventory of Natural Resources, Population Distribution and Land Use Handbook. Commonwealth Scientific and Industrial Research Organisation for the Australian Agency for International Development. PNGRIS Publication No. 6, Canberra. Cuddy, S. M. 1987. Papua New Guinea Inventory of Natural Resources, Population Distribution and Land Use: Code Files Part 1 Natural Resources. Division of Water and Land Resources, Commonwealth Scientific and Industrial Research Organisation and Land Utilization Section, Department of Primary Industry, Papua New Guinea, Canberra