Private Doctors' Perspective towards "Patient First" in TB Diagnostic Cascade, Hisar, India.

TB diagnosis has been simplified in India following advances in available diagnostic tools. This facilitates private doctors' "patient first" approach toward early diagnosis; however, costs remain high. India's NTEP established a TB diagnostic network, which is free for patients and incentivizes private doctors to participate. Drawing from this context led to the design and implementation of the One-Stop TB Diagnostic Solution model, which was conducted in the Hisar district, Haryana, allowing specimens from presumptive TB patients from private doctors to be collected and tested as per NTEPs diagnostic algorithm. A subset of data pertaining to private doctors was analyzed for the project period. Qualitative data were also collected by interviewing doctors using a snowball method to capture doctors' perception about the model. Out of 1159 specimens collected from 60 facilities, MTB was detected in 32% and rifampicin resistance was detected in 7% specimens. All specimens went through the diagnostic algorithm. Thirty doctors interviewed were satisfied with the services offered and were appreciative of the program that implements this "patient centric" model. Results from implementation indicate the need to strengthen private diagnostics through a certification process to ensure provision of quality TB diagnostic services.

A simple metric for a complex outcome: proposing a sustainment index for health indicators.

BACKGROUND: Sustainability is, at least in principle, an important criterion for evaluating global health and development programs. The absence of shared metrics for success or achievements in sustainability is however critically lacking. We propose a simple metric, free of causal inference, which can be used to test different empirical models for the sustainment of health outcomes. METHODS: We follow the suggestion of Chambers and use "sustainment" to refer to the verifiable and measured extent to which a health indicator has evolved over time. The sustainment index of a health indicator (Y) advanced by a program is based on a simple-to-calculate approximation of the derivative of Y over time (T0: baseline, T1: endline, and T2: post-project), based on the ratio of the slope of YT1-T2 over YT0-T1. SI(Y) = 1+ (YT1-T2 / YT0-T1). RESULTS: This construct provides three clear benchmarks: SI = 0, when the health indicator returns to baseline value post-project (YT2 = YT0); SI = 1, when the endline-post-project trend is a plateau; and SI = 2, when the progress slope during program is uninterrupted post-program. We find strong correlation (r2 = 0.922) between the SI and independent practitioners' rating of indicator trends. The SI shows different levels of achieved sustainment for a range of indicators in a published ex-post sustainability study. And we find that the SI can be computed for large national datasets for two types of indicators. CONCLUSIONS: The Sustainment Index has limitations and conditions of applicability, but it can be applied to different datasets and studies to provide a reliable dependent measure of the level of sustainment of health outcomes from one period of time to the next. The Index will need additional testing, and future evaluation-research work will need to consider index performance under different situations. The Sustainment Index has the potential to provide a standard metric to build evidence through more systematic research on sustainment and sustainability.

Do Program Implementation Factors or Fidelity Affect Chronic Disease Self-Management Education Programs' Outcomes?

PURPOSE: To evaluate whether implementation factors or fidelity moderate chronic disease self-management education program outcomes. DESIGN: Meta-analysis of 34 Arthritis Self-Management Program and Chronic Disease Self-Management Program studies. SETTING: Community. PARTICIPANTS: N = 10 792. MEASURES: Twelve implementation factors: program delivery fidelity and setting and leader and participant characteristics. Eighteen program outcomes: self-reported health behaviors, physical health status, psychological health status, and health-care utilization. ANALYSIS: Meta-analysis using pooled effect sizes. RESULTS: Modest to moderate statistically significant differences for 4 of 6 implementation factors; these findings were counterintuitive with better outcomes when leaders and participants were unpaid, leaders had less than minimum training, and implementation did not meet fidelity requirements. CONCLUSION: Exploratory study findings suggest that these interventions tolerate some variability in implementation factors. Further work is needed to identify key elements where fidelity is essential for intervention effectiveness.