Additional files 3: of Introducing payment for performance in the health sector of Tanzania- the policy process

Interview guide for other officials. (DOC 32 kb

Additional files 1: of Introducing payment for performance in the health sector of Tanzania- the policy process

Interview guide for Norwegian Officials. (DOC 30 kb

Additional files 2: of Introducing payment for performance in the health sector of Tanzania- the policy process

Interview Guide for Tanzanian officials. (DOC 29 kb

Prevalence of <i>E. coli</i> virulence genes (ECVG), enteric virus genes, human-specific <i>Bacteroidales</i> genes, and FIB detected in household stored drinking water and hand rinse samples of respondents with at least one child younger than five years old that were either sick with diarrhea (cases) versus matched healthy children under five years of age (controls).

<p>The study consisted of 112 unique case households (containing 113 case children) and 111 unique households with only healthy children (containing 113 matched, control children).</p><p>^a At least one of the seven pathogenic <i>E. coli</i> virulence genes (ECVG) measured present.</p><p>^b At least one of the three enteric viruses measured (rotavirus, adenovirus, enterovirus) present.</p><p>^c CI, confidence interval.</p><p>Presence/Absence of CFU per 2 hands; Presence/Absence or within specified range of CFU/100 mL stored drinking water with 0 CFU/100 mL as the reference group.</p><p>Indicates a median unbiased estimate.</p><p>Statistically significant (p≤0.05).</p

Binary logistic regression model of <i>E. coli</i> virulence genes (ECVG), enteric virus genes, and human-specific <i>Bacteroidales</i> gene presence in household stored water as a function of water management behaviors.

<p>^a Ln-transformed.</p><p>^b Binary variables with values of 0 and 1.</p><p>^c Boiling, chlorinating, filtering, or SODIS (versus no treatment including settling).</p><p>^d Cup, mug, or bowl (versus pouring, long handled dipper, or spigot).</p><p>^e Borewell, rainwater, or tap (versus shallow well, cart/tanker, surface water, or vendor).</p><p>^f TZS Tanzanian Shillings.</p><p>N <306 because sample was lost or survey response not collected.</p><p>Statistically significant (p≤0.05).</p

Binary logistic regression model of <i>E. coli</i> virulence genes (ECVG), enteric virus genes, and human-specific <i>Bacteroidales</i> genes presence in hand rinse samples as a function of hygiene behaviors.

<p>^a Ln-transformed.</p><p>^b Binary variables with values of 0 and 1.</p><p>^c Refers to the reported activity prior to the respondent having their hand rinse sample taken.</p><p>^d TZS Tanzanian Shillings.</p><p>N <306 because sample was lost or survey response not collected.</p><p>Statistically significant (p≤0.05).</p

Time to initiate breastfeeding after delivery, cumulative proportion of respondents.

<p>Time to initiate breastfeeding after delivery, cumulative proportion of respondents.</p

Hygiene practices at delivery for non-facility deliveries.

<p>*More than one answer allowed.</p

Preparations made for delivery for deliveries taking place outside of health facilities.

<p>Preparations made for delivery for deliveries taking place outside of health facilities.</p

Barriers, Facilitators and Priorities for Implementation of WHO Maternal and Perinatal Health Guidelines in Four Lower-Income Countries: A GREAT Network Research Activity

<div><p>Background</p><p>Health systems often fail to use evidence in clinical practice. In maternal and perinatal health, the majority of maternal, fetal and newborn mortality is preventable through implementing effective interventions. To meet this challenge, WHO’s Department of Reproductive Health and Research partnered with the Knowledge Translation Program at St. Michael’s Hospital (SMH), University of Toronto, Canada to establish a collaboration on knowledge translation (KT) in maternal and perinatal health, called the GREAT Network (<u>G</u>uideline-driven, <u>R</u>esearch priorities, <u>E</u>vidence synthesis, <u>A</u>pplication of evidence, and <u>T</u>ransfer of knowledge). We applied a systematic approach incorporating evidence and theory to identifying barriers and facilitators to implementation of WHO maternal heath recommendations in four lower-income countries and to identifying implementation strategies to address these.</p><p>Methods</p><p>We conducted a mixed-methods study in Myanmar, Uganda, Tanzania and Ethiopia. In each country, stakeholder surveys, focus group discussions and prioritization exercises were used, involving multiple groups of health system stakeholders (including administrators, policymakers, NGOs, professional associations, frontline healthcare providers and researchers).</p><p>Results</p><p>Despite differences in guideline priorities and contexts, barriers identified across countries were often similar. Health system level factors, including health workforce shortages, and need for strengthened drug and equipment procurement, distribution and management systems, were consistently highlighted as limiting the capacity of providers to deliver high-quality care. Evidence-based health policies to support implementation, and improve the knowledge and skills of healthcare providers were also identified. Stakeholders identified a range of tailored strategies to address local barriers and leverage facilitators.</p><p>Conclusion</p><p>This approach to identifying barriers, facilitators and potential strategies for improving implementation proved feasible in these four lower-income country settings. Further evaluation of the impact of implementing these strategies is needed.</p></div