Additional file 1: Table S1. of An assessment of equity in the distribution of non-financial health care inputs across public primary health care facilities in Tanzania

District Information. Basic information for Ikungi, Kinondoni, Manyoni and Singida district councils: Tanzania. Table S2. Correlation Matrix. Correlation Matrix of health care inputs and wealth/distance of health care facilities from district headquarter in kilometres. Table S3a. Descriptive Statistics by Wealth Quintiles. Descriptive Statistics of health care inputs by Wealth Quintiles. Table S3b. Descriptive Statistics by Distance. Descriptive Statistics of health care inputs by distance of health care facilities from district headquarter in kilometres. Annex 1. Household Ownership of Properties. Information on household ownership of properties which was used to develop wealth index using principal component analysis. Annex 2. Health Facility Survey tool. Health facility survey tool which was used to capture information on health care inputs (availability of staff, drugs, medical supplies and equipment at facilities) Annex 3. Health care reforms. Health care reforms which were being monitored and evaluated under Universal Coverage in Tanzania and South Africa (UNITAS) project. (DOC 312 kb

Study Profile.

<p>Study Profile.</p

Effect of P4P on the use of non-targeted services.

<p>Note to Table: N = facility months;</p><p>*The % D = (beta / baseline mean) × 100, where the baseline mean of the dependent variable is for the intervention group.</p><p>^†The Beta is the estimated intervention effect controlling for a year dummy and facility-fixed effects.</p><p>Effect of P4P on the use of non-targeted services.</p

Direct and indirect effect of P4P on the use of targeted services.

<p>*The % D = (beta / baseline mean) × 100, where the baseline mean of the dependent variable is for the intervention group.</p><p>^†The Beta is the estimated intervention effect controlling for a year dummy, facility-fixed effects, individual-level and household characteristics.</p><p>^{^}Among infants aged 6–11 months.</p><p>Direct and indirect effect of P4P on the use of targeted services.</p

Effect of P4P on quality of care.

<p>Note to Table: Same sizes as indicated at top except where indicated ^.</p><p>^{^} Data from household survey: sample size;</p><p>*The % D = (beta / baseline mean) × 100, where the baseline mean of the dependent variable is for the intervention group.</p><p>^†The Beta is the estimated intervention effect controlling for a year dummy, facility-fixed effects, individual-level and household characteristics</p><p>Effect of P4P on quality of care.</p

Effect of P4P on the cost of services in public facilities.

<p>*The % D = (beta / baseline mean) × 100, where the baseline mean of the dependent variable is for the intervention group.</p><p>^†The Beta is the estimated intervention effect controlling for a year dummy, facility-fixed effects, individual-level and household characteristics</p><p>Effect of P4P on the cost of services in public facilities.</p

Additional file 1: of Subnational variation for care at birth in Tanzania: is this explained by place, people, money or drugs?

Supplementary appendix. (DOCX 13018 kb

Additional file 1: of Countdown to 2015 country case studies: what can analysis of national health financing contribute to understanding MDG 4 and 5 progress?

Health Financing Analysis for Countdown Case Studies: A Guide. (PDF 1459 kb