Door – to – door immunization strategy for improving access and utilization of immunization Services in Hard-to-Reach Areas: a case of Migori County, Kenya

Shikuku, Duncan N.; Muganda, Maxwell; Amunga, Soudie O.; Obwanda, Elly O.; Muga, Alice; Matete, Thomas; Kisia, Paul

doi:10.1186/s12889-019-7415-8

Cited by 25 publications

(26 citation statements)

References 23 publications

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“…Amidst the barriers, there are existing opportunities that can be harnessed to improve vaccine utilisation in these settings. These include scheduled vaccination exercises, using VHTs and mobile technology [5,18]. Residents and health managers appreciate the value of these combined strategies.…”

Section: Discussionmentioning

confidence: 99%

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Barriers to vaccine uptake in the fishing and island communities of Uganda: a qualitative study

Bakibinga¹,

Kisia²,

Lodda

et al. 2020

Preprint

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Background: Despite global and local initiatives to increase vaccine uptake, timeliness of vaccinations and vaccine uptake remain low among residents of islands and fishing communities in Uganda. Vaccine coverage (all vaccines) among island communities stands at 37.1% compared to 55%, the national level. This qualitative study was conducted to (i) understand key barriers to vaccine uptake among residents of islands and fishing communities of Lake Victoria, Buganda sub-region, Uganda and (ii) identify specific needs of the communities; ahead of implementation of a mobile health intervention to promote vaccine uptake. Methods: Explorator y qualitative. Data were collected through focus group discussions with women of reproductive age and village health teams (VHT) and key informant interviews with the health coordinators and health service providers. Data were analysed through content analysis, by coding pre-established and recurrent themes.Results: Under the barriers, seven themes were identified: Opportunity cost of seeking vaccines versus making a living, Distance/Cost to health facility, Availability of service provider, Attitudes towards vaccines, Decision making regarding the use of vaccines, Knowledge of availability of services, and Attitudes of service providers. Participants suggested strategies to improve uptake: 1) more health education and regular reminders using VHTs, public address systems and mobile technology; 2) bring services closer to people through scheduled vaccination campaigns to reduce on cost of seeking services; and 3) strengthening public-private partnerships in the health sector for delivery of vaccine services. Conclusion: In light of the current barriers to vaccine uptake, innovations to increase the utilisation of vaccination services in remote underserved settings need to be multi-pronged, responding to user, structural and supply-side factors. The use of mobile technology and public-private partnerships for health have the potential to bridge existing vaccine delivery and uptake gaps.

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Section: Discussionmentioning

confidence: 99%

“…Vaccine coverage (all vaccines) among island communities stands at 37.1% compared to 55%, the national level [4]. Although the importance of vaccines is appreciated, many residents of islands and fishing communities do not access these services [5].…”

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confidence: 99%

Barriers to vaccine uptake in the fishing and island communities of Uganda: a qualitative study

Bakibinga¹,

Kisia²,

Lodda

et al. 2020

Preprint

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“…Some advances have been achieved germane to the coverage of immunization in India under this programme, but it has faced important management challenges and thus, has fallen short of the "for all children" coverage it had intended. Every year immunization saves an estimated 2-3 million lives [5]. In 2015, by focusing on 216 high focus districts across 27 states/UTs, Mission Indradhanush was envisaged to achieve 90% of full immunization coverage by 2020 [6].…”

Section: Introductionmentioning

confidence: 99%

3335 Retrieval Number: D8118118419/2019©BEIESP DOI:10.35940/ijrte.D8118.118419 Journal Website: www.ijrte.org Published By: Blue Eyes Intelligence Engineering & Sciences Publication A Model for Accurate Prediction of Child Immunization Data for Knowledge Discovery using Bayesian TAN and Naive Bayes Classifiers

Sourabh*,

Mansotra

2019

IJRTE

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Knowledge Discovery in Databases (KDD) is a splendid methodology of discovering knowledge from gigantic databases by using its various stages viz. Data Selection, Data Preprocessing, Data Transformation, Data Mining and Interpretation/Evaluation. Data Mining is a vital sub-process of KDD methodology that is particularly used to apply the various mining algorithms on the data. In the present research paper, the authors have made an attempt to discover new knowledge by classifying the child immunization data of Jammu and Kashmir State of India. The data for the present work was collected from a web portal named as Health Management Information System (HMIS) facilitated by Ministry of Health and Family Welfare (MoHFW), Government of India. The data consists of diverse health parameters pertaining to the immunization of children and for the present study, the child immunization data of all districts of Jammu and Kashmir State was considered. Two classifiers viz. Bayesian TAN and Naïve Bayes were employed for classifying the districts of Jammu and Kashmir State into High IMR and Low IMR districts based on the available past data from 2014 to 2018. Additionally, various measurement methods have been used to evaluate the performance of the models developed by Bayesian TAN and Naïve Bayes.

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“…Failure to detect data quality issues and to clean these data can lead to inaccurate analyses outcomes [13]. Various studies have extracted data from DHIS2 for analyses [16][17][18][19][20]. Nonetheless, few studies attempt to explicitly disclose the data cleaning strategies used, resulting errors identi ed and the action taken [16][17][18].…”

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confidence: 99%

“…Nonetheless, few studies attempt to explicitly disclose the data cleaning strategies used, resulting errors identi ed and the action taken [16][17][18]. In addition, some of these studies largely fail to exhaustively and systematically describe the steps used in data cleaning of the DHIS2 data before analyses are done [19,20].…”

mentioning

confidence: 99%

Data Cleaning Process for HIV- Indicator Data Extracted from DHIS2 National Reporting System: A Case study of Kenya

Gesicho

Were

Babić

2020

Preprint

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Background: The District Health Information Software-2 (DHIS2) is widely used by countries for national-level aggregate reporting of health-data. To best leverage DHIS2 data for decision-making, countries need to ensure that data within their systems are of the highest quality. Comprehensive, systematic, and transparent data cleaning approaches form a core component of preparing DHIS2 data for analyses. Unfortunately, there is paucity of exhaustive and systematic descriptions of data cleaning processes employed on DHIS2-based data. The aim of this study was to report on methods and results of a systematic and replicable data cleaning approach applied on HIV-data gathered within DHIS2 from 2011 to 2018 in Kenya, for secondary analyses. Methods: Six programmatic area reports containing HIV-indicators were extracted from DHIS2 for all care facilities in all counties in Kenya from 2011 to 2018. Data variables extracted included reporting rate, reporting timeliness, and HIV-indicator data elements per facility per year. 93,179 facility-records from 11,446 health facilities were extracted from year 2011 to 2018. Van den Broeck et al’s framework, involving repeated cycles of a three-phase process (data screening, data diagnosis and data treatment), was employed semi-automatically within a generic five-step data-cleaning sequence, which was developed and applied in cleaning the extracted data. Various quality issues were identified, and Friedman analysis of variance conducted to examine differences in distribution of records with selected issues across eight years. Results: Facility-records with no data accounted for 50.23% and were removed. Of the remaining, 0.03% had over 100% in reporting rates. Of facility-records with reporting data, 0.66% and 0.46% were retained for voluntary medical male circumcision and blood safety programmatic area reports respectively, given that few facilities submitted data or offered these services. Distribution of facility-records with selected quality issues varied significantly by programmatic area (p<0.001). The final clean dataset obtained was suitable to be used for subsequent secondary analyses. Conclusions: Comprehensive, systematic, and transparent reporting of cleaning-process is important for validity of the research studies as well as data utilization. The semi-automatic procedures used resulted in improved data quality for use in secondary analyses, which could not be secured by automated procedures solemnly.

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Door – to – door immunization strategy for improving access and utilization of immunization Services in Hard-to-Reach Areas: a case of Migori County, Kenya

Cited by 25 publications

References 23 publications

Barriers to vaccine uptake in the fishing and island communities of Uganda: a qualitative study

Barriers to vaccine uptake in the fishing and island communities of Uganda: a qualitative study

Data Cleaning Process for HIV- Indicator Data Extracted from DHIS2 National Reporting System: A Case study of Kenya

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