Nacp Tanzania Database Download =LINK=

A pilot fieldworker visited each participating facility once a month to gather completed referral forms and to review completeness. Data from the forms were entered into an Access database. The database was developed by the research team and included inbuilt validation rules to minimize data entry errors (for example, checking the age against year of birth, and the sex and age against pregnancy status), and to facilitate the matching of corresponding form numbers on the two-parts. Records with missing key variables (name, referral form number, sex, or age) were excluded from data analysis. De-duplication of cases was performed using personal identifying information, either using the three given names, sex, and age of the participant, or by matching one of the names, age, and sex with the same residence for exact matches.

The NACP already has a standard referral form and since the majority of data are already routinely collected, the work effort for facility staff to continue to complete the two-part form would be minimal. The additional cost of data entry of the referral forms could also be seen as minimal when compared to the enhanced availability and accuracy of the data. The pilot utilized field staff to gather forms from the facility on a monthly basis. This was feasible at the scale the pilot was conducted but could prove to be a human resource burden at national scale. Additionally, the pilot utilized an Access database, for implementation at national level, data entry and analysis would need to be incorporated into the existing national system. An assessment should be undertaken on alternate strategies for data collection and entry.

Nacp Tanzania Database Download

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We analyze longitudinal clinical data from the outpatient monitoring system of the National AIDS Control Program (NACP) in Tanzania. The system utilizes sets of recording and reporting tools and is distinctively centralized, with a national database that recognizes each patient by their unique Care and Treatment Clinic (CTC) ID number from all over the country. All individuals enrolled in care are supplied with client held CTC-1 cards. Elementary records of patient encounters are captured within facility-held CTC-2 cards, which are the foundation of both the paper and electronic systems currently in use.

The National AIDS Control Programme Care and Treatment (CTC 2) database was used to obtain information of all children aged 0-14yearsenrolled in the HIV Care and Treatment Program between January 2011 and December 2014. We assessed eligibility for ART, time from enrolment to ART initiation, nutritional status, and mortality using Kaplan-Meier methods.

Primary data generated in the HIV care and treatment clinics (CTC) was captured on facility-held information collection tools designated as CTC-2 card. This was submitted on quarterly basis to national level at NACP in either paper form or electronic system, and subsequently entered in the national care and treatment database designated as CTC-3. The CTC-3 database contains both patient-level section (CTC-3 Macro), exported electronically from clinics using an electronic CTC-2 database and aggregate-level section (CTC-3) that contains data receive quarterly summary reports either paper based or extracted from the CTC-2 data. Data analysis was performed using Stata version 14 [12].

This report has analysed a large amount of data from across Tanzania, but there are several limitations to our findings. One limitation is that the data come from the electronic, CTC database, which is used in the larger and better run clinics. The situation reported here might not be the same in smaller health facilities that have not provided electronic data. Secondly the data are dependent on the self-reporting of conditions (such as opportunistic infections) by patients, and the recording of such data by clinicians and nurses. The amount of missing data has been reduced over the past 5 years but it is still a problem.

Unit cost data collection and validation for the Investment Case 2.0 was supported by concurrent work by authors in this study to develop a Tanzania HIV unit cost database drawing from published literature, grey literature, and program data. Information from the Global Health Cost Consortium Unit Cost Study Repository (GHCC UCSR) online was used when Tanzania-specific data were not available.42

Due to missing HIV status data in several medical records, a separate ORCI-based HIV care and treatment clinic (CTC) database was identified to supplement patient HIV status. This database is supported by the Tanzanian Ministry of Health and housed at ORCI. Linkage between patients contained in the NADC dataset and the HIV database was performed on-site by ORCI staff. This database provided additional HIV-positive data on 20 patients, 17 with previously missing data, 1 recorded as HIV-negative, and 2 previously recorded as HIV-positive.

Consistent with methodology detailed in our previous study [11], this study was a secondary retrospective analysis of ORCI medical record data, for which abstraction was limited to the completeness of logbooks and patient files. As logbooks contained medical record numbers associated with cancer diagnoses, identification of medical records corresponding to these malignancies of interest was dependent on the quality of documentation by ORCI staff. Limitations related to abstraction of medical record data included patient history and appointment notes taken by multiple clinicians in differing formats and levels of detail. Due to the lack of routine HIV-screening at ORCI, many patients had missing HIV-statuses even when files indicated that patients had been tested. For this reason, additional HIV CTC clinic data was utilized and linked to this dataset. After HIV database linkage, 24.0% of lung, liver, and head and neck NADC patients had a known HIV-status which compares to the 30.0% of ano-rectal, squamous cell carcinoma of the eye, and Hodgkin lymphoma NADC patients with a supplemented HIV-status found in the previous NADC study at ORCI [11]. Due to the overall lack of consistent HIV status data in ORCI medical records, the slight decrease in HIV-positive NADC cases in this study should be interpreted with caution. Screening for additional cancer risk factors such as HPV is also not routinely completed at ORCI. Given the association between HPV infection and onset of head and neck cancers, more complete data would be informative in this context [31]. Limited available data on HIV and HPV infection in this hospital-based cancer population prevents a clear understanding of the driving force behind incidence of NADCs in Tanzania at this time.

Julee Campbell was supported by the University of Michigan Office of Global Public Health and this work was supported in part by the Cancer Epidemiology Education in Special Populations (CEESP) Program of the University of Nebraska (Grant R25 CA112383). Amr S. Soliman was also supported by the Cancer Research International Training and Intervention Consortium (CRITIC) (Grant U54 CA190155). Statistical methods consultation was provided by the University of Michigan office of Consulting for Statistics, Computing, & Analytics Research (CSCAR). On-site project coordination and database access at the Ocean Road Cancer Institute was facilitated by Dr. Crispin Kahesa, Dr. Julius Mwaiselage, and Dr. Diwani Msemo.

JAC- study conception and design, acquisition of data, data collection and entry, analysis and interpretation of data, drafting of manuscript and critical revision. ASS- study conception and design, interpretation of data, drafting of manuscript and critical revision. CK- study conception and design, logbook retrieval advice, acquisition of data, logistical issue resolution, interpretation of data. SDH- drafting of manuscript and critical revision. DM- study conception and design, advice and assistance to obtain ORCI database records. All authors read and approved the final manuscript.

A cross-section study using secondary analysis of de-identified routinely collected data from PLHIV attending HIV services in 58 care and treatment clinics (CTC) was conducted in Dar es Salaam region. The study retrieved data from the CTC electronic database, which is used to record the clinical management of PLHIV attending CTC.

The study involved analysis of unlinked data; hence, there was no contact with human subjects. Ethics approval for the study was obtained from the Kilimanjaro Christian Medical University college - Research and Ethical Review Committee in 2018 (KCMUCo-RERC approval number 2388). Secondary use of the data from the electronic database was requested and approved by National Aids Control Program (NACP). NACP owns the data on behalf of MoHCDGEC. Patient consent was not required for the analysis of anonymised routine data.

Patient data were retrieved from care and treatment registries. The care register keeps records of basic information on clients who have not yet started ART. Once a patient starts on ART, they are transferred to the ART treatment register. The patient data are kept across registries and longitudinally identified using the 7-digit HIV patient national identifier. The data extraction from the CTC database was done from September 2009 to January 2011. Stata software version 12.0 (Stata Corp, College Station, TX) was used to organize and generate descriptive statistics of patients registered for care and treatment.

Major strengths of this study is the relatively large sample size with inclusion of all the 86 health facilities in the region, providing us substantial power to obtain accurate and reliable estimate over a large number of potential risk factors, adjusted for one another, and the sufficient follow-up time of 24 months. Additionally, the longitudinal nature of this analysis provided an opportunity to assess rate and time of interruption in treatment and associated predictors. The major limitation is that our analysis based on the secondary data from the existing database. It is possible that not all clinical data were correctly captured and that some data were not captured at all, resulting in missing data which however was handled during data analysis but this is a general limitation of using routine data. ff782bc1db