Reflection on modern methods: generalized linear models for prognosis and intervention—theory, practice and implications for machine learning

Prediction and causal explanation are fundamentally distinct tasks of data analysis. In health applications, this difference can be understood in terms of the difference between prognosis (prediction) and prevention/treatment (causal explanation). Nevertheless, these two concepts are often conflated in practice. We use the framework of generalized linear models (GLMs) to illustrate that predictive and causal queries require distinct processes for their application and subsequent interpretation of results. In particular, we identify five primary ways in which GLMs for prediction differ from GLMs for causal inference: (i) the covariates that should be considered for inclusion in (and possibly exclusion from) the model; (ii) how a suitable set of covariates to include in the model is determined; (iii) which covariates are ultimately selected and what functional form (i.e. parameterization) they take; (iv) how the model is evaluated; and (v) how the model is interpreted. We outline some of the potential consequences of failing to acknowledge and respect these differences, and additionally consider the implications for machine learning (ML) methods. We then conclude with three recommendations that we hope will help ensure that both prediction and causal modelling are used appropriately and to greatest effect in health research

Predictors of condom use among unmarried sexually active women of Reproductive age in Tanzania

Background: Condom is one of the methods for prevention against Human Immunodeficiency Virus and other Sexually Transmitted Infections. It is also considered an effective method for preventing unwanted pregnancies. Despite the several interventions that have been put to promote condom use, still a large proportion of women do not use condom during sexual intercourse. Objectives: This study aimed at determining predictors of condom use among unmarried sexually active women of reproductive age in Tanzania. Methods: This study used secondary data from the 2015-16 Tanzania Demographic and Health Survey and Malaria Indicator Survey (2015-16 TDHS-MIS). It involved unmarried sexually active women aged 15-49 years. Multiple binary logistic regression was used to determine predictors for condom use at last sexual intercourse. Results: Overall, lower proportion (31.1%) of unmarried sexually active women used condom at last sexual intercourse. The odds of using condom during last sexual intercourse was lower aOR=0.67 and aOR=0.65 for women aged 20-24 and 25+ years respectively). Women who reported higher age (18+ years) at first sex had higher odds (aOR=1.65) of using condom compared to those started sex before 15 years old. Women owning telephone had higher odds (aOR=1.44) compared to women without telephone. Also, higher odds of using condom were observed for women in the Southern, South West highlands, and Eastern zones compared to the Central zone. Discussion: Age, marital union, parity, wealth, ownership of; mobile phone, television, access to newspapers, and radio significantly predicts condom use among unmarried sexually active women of reproductive age in Tanzania. Conclusion: The level of condom use among unmarried women in Tanzania is very low and varies by age, age at sex intercourse, ownership of phone and zones. Targeted interventions are needed to promote the condom use among unmarried women in order to mitigate the risk of HIV and un-intended pregnancies

Discrepancy between statistical analysis method and study design in medical research: Examples, implications, and potential solutions.

Medical research is the systematic, rigorous investigation of health-related problems in order to generate new knowledge or confirm existing knowledge, with the potential benefit of evidence-based medical practice and policy guidance. The validity of the findings from medical research requires a thorough process from design, to data collection and data analysis.1 However, the methods that researchers use during analyses are often unsuitable for the designs used during study conduct. Researchers are supposed to choose the study designs at the time of protocol development, before any investigation is carried out. Different study designs have different strengths and weaknesses.2 The selected design should be the most appropriate design to answer the objectives of a study. This decision is crucial, as study design reflects directly on the hypothesis of interest. Sample size and other design aspects of the study are aimed at achieving valid conclusions of a trial or study. It is therefore important to strive for compatibility between study design and analysis plan.</p

Discrepancy between statistical analysis method and study design in medical research: Examples, implications, and potential solutions.

Medical research is the systematic, rigorous investigation of health-related problems in order to generate new knowledge or confirm existing knowledge, with the potential benefit of evidence-based medical practice and policy guidance. The validity of the findings from medical research requires a thorough process from design, to data collection and data analysis.1 However, the methods that researchers use during analyses are often unsuitable for the designs used during study conduct. Researchers are supposed to choose the study designs at the time of protocol development, before any investigation is carried out. Different study designs have different strengths and weaknesses.2 The selected design should be the most appropriate design to answer the objectives of a study. This decision is crucial, as study design reflects directly on the hypothesis of interest. Sample size and other design aspects of the study are aimed at achieving valid conclusions of a trial or study. It is therefore important to strive for compatibility between study design and analysis plan.</p

Factors associated with uptake of postpartum family planning services in Dodoma City Council, Tanzania: A cross-section study

Background: Postpartum family planning is very essential to mothers’ health. However, its utilization remains low in developing countries. Objective: To determine the proportion and factors associated with uptake of PPFP services in Dodoma Tanzania. Methods: A cross-sectional study employing a quantitative approach was conducted among women who gave birth one year before the study period (June 2020) in Dodoma city council. A two-stage sampling technique was employed to recruit a total number of 209 participants. An interviewer-administered questionnaire was used to collect data. Data were entered and cleaned using Epi Info 7 and later exported to and analyzed using SPSS version 25.0. Bivariate and multiple logistic regression models were employed during data analysis. Odds ratios with 95% confidence intervals were computed to identify factors associated with postpartum family planning. Results: Majority (53.6%) of women used contraceptives within one year after delivery. Three factors were significantly associated with the uptake of postpartum family planning. Lower odds for uptake of PPFP were found among self-employed women (AOR: 0.5, 95% CI 0.25–0.74) and unemployed women (AOR: 0.2, 95% CI 0.05–0.31) when compared with employed women. Using community health fund insurance (AOR: 2.4, 95% CI 1.09–6.42) and National Health Insurance Fund (AOR: 2.7, 95% CI 1.54–5.99) as a mode of payments for health had higher odds for uptake of PPFP compared to cash mode. Women with an adequate number of antenatal care visits had higher odds (AOR: 2.9, 95% CI 1.24–6.89) of uptake of PPFP compared to women with an inadequate number of antenatal care visits. Conclusion: The uptake of PPFP among women was not adequate and was associated with being employed, being covered by health insurance and adequate antenatal care visits. More interventions are needed to enhance PPFP use among women

Predictors of mHealth use in promoting adherence to pre-exposure prophylaxis among female sex workers: an evaluation of the Jichunge intervention in Dar es Salaam, Tanzania

Background There is evidence that pre-exposure prophylaxis (PrEP) is effective in preventing HIV transmission, and PrEP is recommended by the World Health organization (WHO) for use by individuals at high risk of HIV infection. However, low adherence has been reported to hamper its effectiveness. Some evidence indicates that mHealth interventions may be a promising way of promoting PrEP adherence. Nevertheless, evaluations of mHealth interventions in Africa, the region most affected by HIV, are scarce. This study aimed at identifying the extent of and predictors for use of a smartphone based mHealth application among female sex workers in Dar es Salaam, Tanzania. Methods As part of a quasi-experimental study in Tanzania, 470 female sex workers who were eligible for PrEP and who owned a smartphone were recruited using respondent driven sampling. All participants were provided with an mHealth application called Jichunge, a smartphone-based app designed to promote adherence to PrEP by offering users information, advise and support during start-up and use of PrEP. We collected data through structured interviews at baseline and extracted user data from the app for a period of 30 days. Modified Poisson regression model with robust standard errors was used to identify predictors for the optimal use of the Jichunge app. Results Overall, the optimal use of the Jichunge app was 46.4%. Optimal use was significantly higher among women who were older (aPR = 1.3, 95% CI: 1.10-1.65, p = 0.004 for age 25-34 years, and aPR = 1.6, 95% CI: 1.19-2.07, p = 0.001 for age at least 35 years), who had secondary education or higher (aPR = 1.8, 95% CI: 1.08-2.94, p = 0.023), who had suboptimal social support (aPR = 1.2, 95% CI: 1.02-1.48, p = 0.030), who had high awareness of PrEP (aPR = 1.3, 95% CI: 1.08-1.55, p = 0.005), and who had experience using common mainstream social media applications (aPR = 1.4, 95% CI: 1.08-1.71, p = 0.009). Conclusion Optimal use of the Jichunge app was substantially higher among women with higher age, higher education, higher PrEP awareness, less social support, and experience using common social media applications. Individual and interpersonal factors should be considered in planning mHealth interventions. Further studies to determine predictors of longer-term mHealth engagement are needed. Trial registration International Clinical Trials Registry Platform PACTR202003823226570; 04.03.2020

Development of a Mobile Health Application for HIV Prevention Among At-Risk Populations in Urban Settings in East Africa: A Participatory Design Approach

BackgroundThere is limited evidence in Africa on the design and development of mobile health (mHealth) applications to guide best practices and ensure effectiveness. A pragmatic trial for HIV pre-exposure prophylaxis roll-out among key populations in Tanzania is needed. ObjectiveWe present the results of the development of a mobile app (Jichunge) intended to promote adherence to pre-exposure prophylaxis (PrEP) among men who have sex with men (MSM) and female sex workers (FSW) in Tanzania. MethodsA participatory design approach was employed and guided by the information system research framework. MSM and FSW were the target populations. A total of 15 MSM and 15 FSW were engaged in the relevance and design cycles, while the piloting phase included 10 MSM and 20 FSW. ResultsThe relevance cycle enabled the description of the existing problem, provided the compatible app features for the target population, and identified the need to develop an mHealth app that provides health services in a stigmatizing and discriminating environment. User involvement in the app’s design and evaluation provided an opportunity to incorporate social, cultural, and community-specific features that ensured usability. In addition, the participants suggested valuable information to inform the app, text message services, medication registration, and chat platform designs. ConclusionsThe participatory design approach in the development of mHealth apps is useful in identifying and validating population-specific functional features, improve usability, and ensuring future health impacts. Through this participatory process, the Jichunge app took end-user needs, perspectives, and experiences into account, eliciting enthusiasm regarding its potential role in supporting pre-exposure prophylaxis adherence for HIV and related behavioral change promotion. Trial RegistrationInternational Clinical Trials Registry Platform PACTR202003823226570; https://trialsearch.who.int/Trial2.aspx?TrialID=PACTR20200382322657