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1.
Int J Health Policy Manag ; 11(8): 1496-1504, 2022 08 01.
Article in English | MEDLINE | ID: mdl-34273923

ABSTRACT

BACKGROUND: The demand for and use of Traditional and Complementary Medicine (T&CM) has recently increased worldwide drawing a public health attention including malpractice, which puts the health of its clients at risk. Despite efforts made by Tanzania to integrate T&CM in the health system to protect the clients, regulating the subsector has remained a challenge due to lack of information and operational factors facing the regulatory frameworks in Tanzania. The aim of this study was to determine the extent of imperfect information, regulation adherence and challenges among T&CM practitioners and regulators in Tanzania. METHODS: In-depth interviews were carried out with T&CM practitioners in Dar es Salaam Region in Tanzania, and officials from the Ministry of Health and the study municipals. Purposive and snowballing approaches were used to select study participants. Thematic data analysis was done with the help of NVIVO. RESULTS: Awareness of regulations and tools used for regulating the T&CM operations among practitioners was generally very low. There was fragmentation of knowledge on what they were practicing as well as on awareness of the regulations, and what is regulated. Practitioners argued that they cannot be controlled by conventional medical trained personnel. Regulators at municipal level reported to have had no knowledge, interest, and time to work on T&CM. Lack of adequately trained and qualified manpower, lack of financial resources, poor transport and other infrastructure at the municipal regulatory units aggravated non-adherence to regulations, and therefore rendered ineffectiveness to the regulatory framework. CONCLUSION: Existence of imperfect information on T&CM among regulators and practitioners affect effectiveness of T&CM regulatory process. Awareness of regulations among practitioners, presence of knowledgeable regulators, as well as capacity would facilitate adherence to regulations.


Subject(s)
Complementary Therapies , Health Personnel , Humans , Tanzania , Traditional Medicine Practitioners
2.
Pan Afr Med J ; 18: 350, 2014.
Article in English | MEDLINE | ID: mdl-25574326

ABSTRACT

INTRODUCTION: Multiple insurance funds serving different population groups may compromise equity due to differential revenue raising capacity and an unequal distribution of high risk members among the funds. This occurs when the funds exist without mechanisms in place to promote income and risk cross-subsidisation across the funds. This paper analyses whether the risk distribution varies across the Community Health Fund (CHF) and National Health Insurance Fund (NHIF) in two districts in Tanzania. Specifically we aim to 1) identify risk factors associated with increased utilisation of health services and 2) compare the distribution of identified risk factors among the CHF, NHIF and non-member households. METHODS: Data was collected from a survey of 695 households. A multivariate logisitic regression model was used to identify risk factors for increased health care utilisation. Chi-square tests were performed to test whether the distribution of identified risk factors varied across the CHF, NHIF and non-member households. RESULTS: There was a higher concentration of identified risk factors among CHF households compared to those of the NHIF. Non-member households have a similar wealth status to CHF households, but a lower concentration of identified risk factors. CONCLUSION: Mechanisms for broader risk spreading and cross-subsidisation across the funds are necessary for the promotion of equity. These include risk equalisation to adjust for differential risk distribution and revenue raising capacity of the funds. Expansion of CHF coverage is equally important, by addressing non-financial barriers to CHF enrolment to encourage wealthy non-members to join, as well as subsidised membership for the poorest.


Subject(s)
Insurance, Health/economics , National Health Programs/economics , Patient Acceptance of Health Care/statistics & numerical data , Data Collection , Humans , Logistic Models , Multivariate Analysis , Risk , Risk Factors , Rural Population , Socioeconomic Factors , Tanzania
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